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Does Being in a Relationship Increase Mens Earnings? A
Cross-sectional Study on DU Male Students
1. Introduction:
This study was motivated by the concept of male marriage premium, a long-standing
question of interest in labor economics. It has been seen in many empirical studies that
married men, on average, earn more than their single counterparts holding all else equal.
More interestingly, cohabiting men have also been found to have more income than their
single counterparts. In this path we found it the obvious next question: Does being in a
relationship increase mens earnings?
This question did not get attention in the existing literature yet and probably will not get
attention ever. But it does not mean that it is an unimportant question, particularly in the
context of Bangladesh. Traditionally Bangladesh is a conservative country in the case of free
mixing between men and women; cohabiting is not allowed and being in a relationship is
usually a much more a serious matter, in responsibility and commitment, in comparison with
countries with advanced economy where almost all the marriage premium related studies
took place. Unlike those countries, in a relationship is not typically a light relationship; it
involves a greater amount of sharing and caring; it is usually associated with commitment or
at least willingness to take the relationship to marriage. So, Bangladeshi men who are in a
relationship are likely to have some unobserved characteristics that are considered as
potential reasons behind marriage premium. For example, they are likely to be more stable,
matured and forward looking; they are likely to be interested in maintaining a regular stream
of income and thus be more tolerable to unfavorable working conditions; they may get career
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advice and moral support. However, some other potential reasons behind marriage premium,
such as greater opportunity for married men to concentrate more on outside home earning
activities due to less tasks at home, employers discrimination etc. are not applicable in the
case of relationship. Again, relationship is comparatively short-lived than marriage and
thus probably has less strong effect, if any. So, whether being in a relationship helps men to
earn more than their similar single counterparts is not clear in advance and can be a question
of empirical interest. Where formal studies relevant with marriage premium has stopped in
cohabiting men, we think we should extend it to men who are in a relationship at least in the
context of Bangladesh.
In this study, using primary data of 286 male students of University of Dhaka, first we
assessed whether students who are in a relationship have higher tendency to participate in
income earning activities. Then we focused on only those students who have own sources of
earning and sought if being in a relationship has any positive impact on their income.
We chose University of Dhaka for three reasons. Firstly, it is a large university with students
from different socioeconomic backgrounds. So we are likely to get enough variability in the
data. Secondly, this university has students from every corners of the country and thus this
population represents the university level students of Bangladesh better than any other
institution. The third reason was the convenience of data collection.
Rest of the paper is organized in the following manner: section 2 deals with the conceptual
framework of this study, section 3 provides a brief literature review, section 4 discusses the
empirical methods used, section 5 discusses the data, section 6 analyses the results, section 7
admits some limitations of this study and provides some guidance for further research and
finally, section 8 draws conclusions.
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2. Conceptual Framework:
In this study we applied the concept of marriage premium in the pursuit of our objective.
There are two broad explanations behind marriage premium. One explanation says this
premium is a causal effect of marriage. The other explanation deals with the reverse causality
and attributes this premium to the issues of selectivity in marriage.
Casual effect of marriage on income works through at least five channels. Firstly, Married
men have to perform less on household works, as traditionally their wives do most of them,
and thus can concentrate on earning activities. This specialization allows them to earn more.
Since being in relationship does not involve such division of labor, this process is not
applicable in our context. Secondly, married men may get career advice and moral support
from their spouses which may increase their earning. Thirdly, married men feel greater
financial responsibility and this feeling induces them to earn more. In our context this process
is applicable since most of the common expenditures in a relationship are carried by men.
Fourthly, marriage develops regularity in lifestyle that induces regular working habit and
increases productivity. Whether this process is applicable in our case is uncertain because
relationships sometimes become volatile and usually less smooth than marriage. Fifthly,
marriage makes men more tolerant, stable, matured and regular. Thus they might get higher
wages as a result of employers discrimination for a given level of productivity. As
employers do not care for whether a man is in a relationship status, such employers
discrimination is not applicable in our case.
On the other hand selectivity may cause marriage premium in two ways. Firstly, men who
have high unobserved ability exhibit some characteristics like stability, patience, enthusiasm,
industriousness etc. which are attractive to both employers and potential spouses. So, men
with high wages or wage growth may have greater chances to get married. Secondly, same
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married men may prefer jobs that have higher wages and less non-monetary benefits. Both of
these processes may hold in case of relationship.
3. Literature Review
Existing literature does not have any study on exactly the issue this paper deals with.
However, there are plenty of relevant works on male marriage premium and some works on
cohabiting men which are closely related to our context.
Riber (2004) described the causes behind marriage premium and summarized different
quantitative methodologies to empirically work with this issue. He also pointed out potential
obstacles that are likely to occur in empirical studies in marriage premium.
The works on marriage premium mainly follow on two empirical approaches: study of cross-
sectional data and study of panel data. In forming empirical models in both types of studies,
income or log of income is taken as the dependent variable, one or more than one binary
variables like married, cohabiting divorced etc. are used as key explanatory variable and
demographic variables like age, race etc. indicators of ability like education, IQ score etc are
controlled.
Among cross-sectional studies Bellas (1992), Blau and Beller (1988), Blackburn and
Korenman (1994), Chun and Lee (2001), Hill (1979) and Krashinsky (2004) are worth
mentioning. These cross-sectional studies typically have estimated a marriage premium
between 6% and 35% (Cornaglia and Feldman 2010) However, the effect is usually
overstated due to selection into marriage.
To correct this bias of selection into marriage, panel data methods have increasingly been
used in modern days. Among panel studies Bardasi and Taylor (2005), Cornwell and Rupert
(1995), Korenman and Neumark (1991), Krashinsky (2004), Richardson (2000), Stratton
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(2002), Loughran and Zissimopoulos (2009), Neumark (1988), Rogers and Stratton (2005)
are mentionable. These studies usually involve the use of fixed effect models. Most of the
studies with panel data found a positive correlation between wage and marital status while
some have found the effect to be indistinguishable from zero (e.g. Gray 1997). Panel studies
generally conclude that there is some causal effect of marriage on wage but whether this
effect is due to increased productivity or merely employers discrimination remains
unresolved.
Selectivity is a major concern in the study of marriage premium which has been admitted in
numerous works. After controlling for selectivity, the remaining associations are possible
causal effects. Waite and Gallagher (2000) cited many studies that indicate that marriage
premium reflect more than just pre-existing differences in individuals economic abilities.
These studies support causal effects.
Some papers extended the study range from marriage to cohabitation (e.g. light 2004) and
found the existence of similar premium among cohabiting men. However cohabiting mens
premium is usually found smaller than that of married men.
4. Econometric Models and Estimation Methods
There are a good number of cross-sectional studies on marriage premium that used Ordinary
Least Squares (OLS) method. Again, Two Stage Least Squares (TSLS) approaches, use of
instrumental variables etc. are common. Some researchers have worked with longitudinal
data and used Fixed Effects Model, Random Effects Models. While some of these advanced
methods can reduce, if not eliminate, the problem of selectivity and reverse causality
discussed in section 2, they involves computational complexity. Again, these methods differ
in underlying assumptions and data requirements. There exist no cross-sectional or panel data
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that could serve our purpose of this study. So, a cross-sectional study with primary data
became an automatic choice.
In this study we dealt with two models. Firstly, we constructed a Linear Probability Model to
roughly estimate if being in a relationship increases students chances to have own sources
of earning. Then we used the concept of marriage premium and the insights from the existing
literature to construct a Multiple Linear Regression Model to capture the ceteris paribus
effect of being in a relationship, after controlling for controlling for family background
variables (fathers education, mothers education, number of siblings, family income etc.),
education related variables (SSC GPA, HSC GPA, university CGPA, year and a broad
division of the subject currently studying, i.e. Science, Business), job related variables
(experience and hours of work per week) etc. In estimating both models we used Ordinary
Least Squares (OLS) method.
For the first model, the population is all undergraduate and masters level male students of
university of Dhaka. In the second model the population is only those undergraduate and
masters level male students of university of Dhaka who have own sources of income. We
excluded PhD and M.Phil. students since most of them are professional people, married and
less available for surveying.
The Linear Probability Model that we to estimate the effect of being in a relationship on the
likeliness of having an own income source was as follows:
ownsource = 0+ 1 relationship + 2 breakup + 3fathereduc + 4 mothereduc + 5siblings
+6lfamilyinc + 7netallowance + 8 dhaka + 9sscgpa + 10 hscgpa +11 cgpa
+ 12year +13science + 14business + . (1)
Here ownsource is a binary variable that takes value 1 if a student has own source of earnings
and 0 if does not have any own earning source. As students can be stratified into always
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single, presently in a relationship and previously in a relationship, we introduced two
relationship status related dummy variables. One is relationship whichtakes 1 if the student
is presently in a relationship and 0 if otherwise. Another is breakup which takes 1 if the
student was previously in a relationship that broke up. Other variables in this model were
used to control for family background, whether student spent most of his life in Dhaka City,
students education indicators etc. is the error term that represents unobserved
characteristics that affects having own sources of earnings. (A complete description of all
variables used in this model is available in Table-1 in the appendix.)
Our parameter of key interest in this model is 1 whichrepresents the ceteris paribus effect of
being in a relationship on the probability of having an own source of earnings. We
estimated it and tested its significance against one-sided alternative using t-test:
H0: 1 = 0
H1: 1 > 0
We chose one sided alternative because it is very much unlikely that being in a relationship
will reduce students chances of having own sources of earnings.
Then we concentrated on our main model:
linc = 0 + 1 relationship + 2 breakup + 3fathereduc + 4 mothereduc + 5siblings
+ 6lfamilyinc + 7netallowance + 8 dhaka + 9sscgpa + 10 hscgpa + 11 cgpa
+ 12year + 13science + 14 business + 15 exper + 16weekly_hrs + u . (2a)
Here we took linc as the dependent variable which is log of students self-earned monthly
income. We took logarithmic form due to three reasons. Firstly, most of the papers in existing
literature took income in logarithmic form. Secondly, taking logarithm allows us to capture
percentage change in income which is convenient while judging the effects of explanatory
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variables. Thirdly, we experimented with level form which did not pass in functional form
misspecification test. Again, since zero cannot enter into logarithmic function, this model
naturally considers only those students who have own income as its population.
Like the first model, we introduced two relationship status dummy variables here. One is
relationship which takes 1 if the student is presently in a relationship and 0 if otherwise.
Another is breakup which takes 1 if the student was previously in a relationship that broke
up. In this model we used several control variables because existing literature indicates that
there are many determinants of income and many of them are correlated with relationship
status. We controlled for family background variables (fathers education, mothers
education, number of siblings, family income etc.), education related variables (SSC GPA,
HSC GPA, university CGPA, year and a broad division of the subject currently studying, i.e.
Science, Business), job related variables (experience and hours of work per week) etc. and
whether student spent most of his life in Dhaka City. u is the error term that represents
unobserved characteristics that affects having own sources of earnings. (A complete
description of all variables used in this model is available in Table-1 in the appendix.)
In choosing the functional form, we chose linear model for its simplicity. In this model key
explanatory variable relationship does not interact with other explanatory variables. So, it
assumes that the effect of being in a relationship is uniform across all levels of other
explanatory variable. We kept fathers education, mothers education, number of siblings,
SSC GPA, HSC GPA, university CGPA and year in linear and level form because we
assumed that marginal effects of these variables do not depend on their values. We also kept
experience and weekly working hours in linear form. However, we tried with their quadratic
terms to allow diminishing returns. But these quadratic terms did pass in F-tests and we
became convinced not to include them because it might well happen that students experience
and work hours are too small to have diminishing returns. We entered netallowance in level
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form since it can take both positive and negative values. Beside relationship and breakup we
used three other dummy variables. We divide the subjects studied by students of University
of Dhaka into three broad arbitrary categories: Science, Social Sciences & Humanities and
Business. To capture the effects of these differences, we assigned two dummies: science and
business. The rest dummy variable is dhaka which takes value 1 if the student spent major
part of his life in Dhaka. We took family income in log form to reduce the variability. The
coefficient of lfaminc thus represents constant elasticity while coefficients of the other
regressors represent semi-elasticity.
In this model, our parameter of key interest is 1, the coefficient of relationship, which
represents the ceteris paribus effect of being in a relationship on linc. Its sample
counterpart will give an unbiased estimator of 1 if error term u is uncorrelated with all
explanatory variables. Unfortunately, this model lacks this property because of the omission
of ability, aptitude, presentation skill, appearance etc. variables in this model. These variables
have impact on students income and are likely to be correlated with relationship status.
Ability and aptitude cannot be observed and measured. So, their obvious omission causes
bias. One way to reduce the bias is to assign proxy variables like Intelligent Quotient (IQ),
results of standardized tests etc. But in our case even these data were not possible to acquire.
On the other hand appearance, presentation skill and smartness can be observed but are
largely subjective and tough to measure. So, we had to omit them too. All of these five
omitted variables have positive impact on earning and likely to be positively related with
being in a relationship. So, they are likely to cause upward bias in the estimated impact of
relationship.
Checking for heteroskedasticty requires having a proper functional form. Our model
specification passed Ramseys Regression Specification Error Test (RESET). After the
RESET test, we inquired for heteroskedasticity using the White Test and found no strong
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evidence of heteroskedasticty. We are not sure if the Normality Assumption holds but it
should not be a problem as our sample size is large.
After estimating 1, we tested its significance using t-test against one-sided alternative
hypothesis:
H0 :1 = 0
H1 :1 > 0
We selected one sided alternative because it is very much unlikely that being in a
relationship will reduce students income.
Finally we used a slightly different specification of (2a):
linc = 0 + 1 relationship + 2 breakup + 3fathereduc + 4 mothereduc + 5siblings
+ 6lfamilyinc + 7netallowance + 8 dhaka + 9sscgpa + 10 hscgpa + 11 cgpa
+ 12year + 13science + 14 business + 15 exper + u . (2b)
The only difference between these two specifications is that we dropped hours of work per
week in (2b). So,1 in model (2b) captures that effect of being in a relationship on income
that comes through the channel of increased work hour.
5. The Data
All the data used in this paper is taken from a primary survey that we conducted on 1st
and 2nd
November 2011 at University of Dhaka. We requested students to participate in a survey of
Economics Department and did not tell them the topic or showed the questionnaire in
advance. We didnt pressurize anyone and only the willing students were given a
questionnaire that did not ask any identification information. The questionnaire sought
information about their income, relationship status, family income, parents education,
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number of siblings, background, present and previous academic records, whether they receive
allowance or have to support their family and if, then by how much amount etc. (The survey
questionnaire is available in the appendix.)
We surveyed undergrad and masters level male students. PhD and M.Phil. students were
excluded from the survey since they are mostly professional and married people. The survey
took place at all major academic buildings, some randomly chosen student halls, TSC,
playgrounds, Central Library, and university bus stoppages. In case of student dormitories,
we first randomly picked 4 halls from all 13 male dormitories, then collected associated room
numbers and picked some of the rooms randomly. We surveyed after 11 p.m. so that most of
the resident students are available in their rooms. We attempted to survey all students who
were present at the chosen rooms and around 80% of them agreed to participate in the survey
and the rest rejected. Unfortunately, we could not conduct the survey in a purely random
manner in other places. While surveying in academic buildings, we went to some random
stories and targeted students standing in the corridor or waiting for the class to start. In this
case response rate was around 60%. While surveying at the Central Library, we went to each
floor, chose closest and furthest tables from the main doors and then approached to the people
sitting at the corners of the tables. Here we got almost 90% responses. In case of TSC,
playgrounds and bus stoppages we targeted people sitting alone or in groups. Around 80%
students participated in this case. Finally total response reached to 287 but we found one
questionnaire completely blank. So, our data set includes response of 286 students. However,
not everyone answered all questions. Again, as the actual proportions in population of three
stratums: always single, previously in a relationship and presently in a relationship are
unknown, we could not maintain them in the sample.
In the data, around 56% students have own source of earning. Length of having own source
of earning, denoted by exper, has a mean of approximately 13 months while the maximum
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length is as high as 84 months. Students average income per month is Tk. 3364 while the
maximum income is Tk. 35000. Data shows the students on average works for 8 hours per
week. However, this average includes people who do not have own source of earning. If we
consider only those who has own source of earning then the average working hour per week
reaches to 15. Among 286 students, 278 revealed their relationship status. Around 60%
reported that they are single and were never in relationship, 14% said they are presently in
relationship and the rest said they are now single but previously were in relationship. Only
around 50% students who are presently in relationship reported their spouses income which
is only 10% of the total sample size. So, we had to exclude this variable from our models
even though it was important. Around 92% students whom we surveyed reported their family
income the average family income was around Tk. 23,000 per month while the minimum and
the maximum are Tk. 0 and Tk. 150,000 per month respectively. Around 90% students
reported their parents education. In case of fathers education the average was 12 years and
in case of mothers education the average was 9 years. In these cases standard deviations
were around 4 years. Around 32% students in the sample spent major portion of their life up
to HSC in Dhaka city. The sample consists of 23% science students, 32% business students
and the rest from Social Sciences or Humanities. Students in the sample on average get
allowance of Tk. 2,350 per month. A complete summary of the data is given in Table-2 in the
appendix.
Our inspections found no evidence of endogenous sample selection while there was a little
exogenous sample selection in case of parents education: those around 10% students who
did not report fathers education and those around 10% students who did not report mothers
education are likely to have parents with less education. However, these endogenous sample
selections are harmless and do not cause any harm in regression results.
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6. Results
Regression results are given in Table-3 in the appendix. Since we did not find evidence of
heteroskedasticty in White Test, actual standard errors have been reported. As we did not find
exclusion of any apparently influential observation to alter regression results significantly, we
did not drop any observation as outlier. Please note that number of observations in models
(2a) and (2b) is remarkably fewer than that of model (1) because the previous two models
treat only those students who have own income as their populations as described in section 3.
In model (1), estimated coefficient ofrelationship is 0.208. It means that students who are in
a relationship, have higher probability of around 0.21 of having own sources of earning than
their always single counterparts. This estimator is statistically significant at 1 percent level of
significance against both one-sided and two-sided alternatives. This result definitely has
practical significance because having own sources of earning depends on many factors and as
a marginal effect of 0.21 on its probability is large.
Regression results also say that students who were previously in a relationship that broke up
have 0.14 higher probability, which is practically significant, to have own sources of earning
than their similar always single counterparts. However, the coefficient of breakup is not
statistically significant which implies that this effect is statistically not different from zero in
population.
The results say students likelihood of having own earning source is negatively related with
fathers education and positively with mothers education after controlling for other factors. A
4 years increase in fathers education reduces probability of having own source of earning by
approximately 0.11 while a 4 years increase in mothers education increases the probability of
having own source of earning by approximately 0.09. Though the effects are not very large,
they are not too small to neglect. Both of the coefficients are statistically significant.
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Estimated coefficient of linc is -0.09 which means one percent increase in monthly income
reduces the likelihood of having own sources of earning by 0.0009 which is practically very
small, though the coefficient is statistically significant at 5 percent level. The estimated
coefficient ofnetallowance is-0.050 which says an additional one thousand Tk. netallowance
decreases the probability of having own earning source by 0.05. Though this effect is small
the coefficient is statistically very significant. The estimated coefficient of dhaka is 0.005
which means students from Dhaka background are less than one percent more likely to have
own earning sources. However this coefficient is not statistically significant that is in the
population there is no difference, in terms of probability of having own source of income,
between students from Dhaka background and students from outside Dhaka. The coefficients
of SSC and HSC GPAs say that these variables have very little positive impact on the
likelihood of having own source of earning. These coefficients are statistically insignificant
too. Coefficient of university CGPA is -0.126 which says a one point increase in university
CGPA (Which is a very large change) reduces students likelihood of having own sources of
earning by 0.126 which is a moderate decrease. Results show that Business students have
0.108 higher probability and Science students have 0.04 lower probability of having own
source of earning than similar Social-sciences & Humanities students.
In case of model (2a), estimated coefficient of relationship is 0.184. It means that students
who are in a relationship have around 18 percent higher income than their similar single
counterparts. The estimatorrelationship is found statistically significant at 5 percent level of
significance against one-sided alternative and at 10 percent level against two-sided
alternative. This result definitely has economic significance because 18 percent increase in
income is large.
Interestingly, estimated coefficient of breakup is 0.316 that means students who were
previously in a relationship that broke up have 32 percent higher income than their always
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single counterparts. So, effect ofbreakup is higher than the effect of relationship! And the
coefficient ofbreakup is statistically significant at 1 percent level. How can we explain this
result? Can we still consider the coefficient ofrelationship as a causal effect on income?
We propose two plausible explanations. First explanation: people have a natural tendency not
to reduce their income level. They either maintain a level of income or try to increase it if
possible. It might happen that students who were previously in a relationship that broke up
reached to a higher income stream than their always single counterparts while they were in a
relationship. After the breakup, due to the natural tendency just stated, they might be
unwilling to reduce their income. Rather, they might increase income using their more
flexible time. (As we have controlled for weekly working hour in (2a), we must not attribute
their higher income to increase working hour due to their additional free time.) Second
explanation: People with higher level of ability, aptitude, appearance, presentation skill and
smartness are more likely earn more. But people who have these attributes in greater extent
are higher valued in the market for spouses. As getting a spouse is easier for them, their cost
of breakup is relatively low. So, they are less tolerable to the frictions of a relationship and
more likely to break up. Thus it may happen that the effect of breakup on income is not
causal rather the breakup group simply represents people with higher degree of ability,
aptitude, appearance, presentation skill and smartness which brings them higher income.
While the first explanation acknowledges the causal effect of being in a relationship on
income, the second explanation attributes the result to selectivity.
Note that the estimated coefficient of relationship is likely to overstate the actual partial
effect as we could not control for ability, aptitude, appearance, presentation skill and
smartness (discussion in section 4)
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Let us now see the effects of the control variables. Estimated coefficient of lfaminc is 0.145
which says a one percent increase in family income increases students income, on average,
only by 0.15 percent. Though this coefficient is statistically significant at 5 percent level,
practically this effect is too small to recognize. So, we can say, income of DU students is
almost perfectly inelastic to their family income.Net allowance is found statistically very
significant. Its estimated coefficient -0.064 says a one thousand taka increase in net
allowance decreases income, on average, by 6.4 percent which is a moderate influence.
Coefficient of mothers income is 0.030 and it says that a 4 years increase in mothers
education raises students income, on average, by 12 percent after controlling for other
factors. This coefficient is statistically significant at 10 percent level. Estimated coefficient of
HSC GPA is 0.36 which says that a one point increase in HSC GPA (which is a large
increase) raises income, on average, by 36 percent holding all other constant. This coefficient
is found statistically significant in 5 percent level. On the other hand, estimated coefficient of
SSC GPA produces a counterintuitive negative sign. The estimated coefficients say a one
point increase in SSC GPA (which is a large increase) reduces income by a moderate 10
percent but this estimator is found statistically insignificant. Estimated coefficient of
university CGPA is 0.013 which says that a one point increase in CGPA (which is a very
large increase) raises income, on average, only by 1.3 percent holding all other constant. So,
it says university CGAP has almost no practical significance on university students earning.
Again, this coefficient is found statistically insignificant. The reason behind this result lies in
the sources of students earnings. As most of the students depend on tuition that does not
count performance at university and thus we get such a result. Regression results say Science
students and Business students, on average, earns 7.6 percent and 16.7 percent respectively
more than similar Social-sciences & Humanities students. However, statistical significances
of relevant estimators say this is only a sample phenomenon and there is no such evidence in
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the population. According to the regression results, a one month increase in experience
increase income by 0.2 percent. This effect is practically and statistically insignificant.
Estimated coefficient of weekly hours of work is 0.007 which says an additional hours of
work only brings a 0.7 percent increase in income which is little. This estimator is also
statistically insignificant.
In specification (2b) we dropped hours of work per week to allow include impact of
relationship through increased working hour. In this specification, estimated coefficient of
relationship is 0.192. It means that students who are in a relationship, on average, have
around 19 percent higher income than their similar single counterparts. Like specification
(2a) the estimator of relationship is found statistically significant at 5 percent level of
significance against one-sided alternative and at 10 percent level against two-sided
alternative. So, from this specification we understand that only a very little portion of the
effect ofrelationship comes through the channel of increased working hour.
In case of other coefficients, estimated values and statistical significance of the estimators are
almost same as specification (2a).
We see we have some important variables statistically insignificant. Our calculation of
Variance Inflating Factor says this phenomenon did not happen because of multicollinearity
problem. Probably we have experienced such result because OLS might not be in appropriate
in this problem. Use of Two Stages Least Squares (2SLS) approach or Panel data methods
might solve this problem.
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7. Some Limitations of the Study and Guidance for Future Work:
OLS is not a strong enough model to deal with our objective. 2SLS or fixed effect models
would have worked better to deal with the selectivity problem. We lack data on some
important variables (discussed in section 4). Getting data on these variables or at least there
proxies would make results better. Besides, we admit that the data set we used lacks in
random sampling properties to some extent (discussed in section 5).
Further works on this topic should be caring to eliminate these drawbacks. In addition, using
duration of relationship, duration of break period and interactions of explanatory variables
would be interesting.
8. Conclusion:
This study finds that students in a relationship are around 21 percent more likely to have
own sources of earnings than their always single counterparts. In case of earning itself,
students in a relationship have around 18 percent higher income than their always single
counter parts if we control for working hours per week, and 19 percent if we do not control it.
Due to some omitted variables that were not possible to incorporate in this study (discussed
in section 4), these results are likely to be overstated. All these results are both statistically
and practically significant. However, due to the evidence of highest premium among students
who were previously in a relationship that broke up, we are not clear whether the effect of
being in a relationship on income is a causal effect or an outcome of selectivity.
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References:
Blau, Francine and Andrea Beller. 1988. Trends in Earnings Differentials by Gender,
1971-1981.Industrial and Labor Relations Review 41 (4), 513-529.
Becker, Gary S. 1981. Treatise on the family. Cambridge, MA: Harvard University Press.
Bellas, Marcia. 1992. The Effect of Marital Status and Wivess Employment on the Salaries
of Faculty Men: The (House) Wife Bonus. Gender and Society 6 (December), 609-622.
Blackburn, McKinley and Sanders Korenman. 1994. The Declining Marital-Status Earnings
Differential.Journal of Population Economics 7 (July), 247-270.
Chun, Hyunbae and Injae Lee. 2001. Why Do Married Men Earn More: Productivity or
Marriage Selection.Economic Inquiry 39 (April), 307-319.
Cornaglia, F. & Feldman, N.E., 2010. The Marriage Premium Revisited: The Case of
Professional Baseball, The London School of Economics.Cornwell, Christopher and Peter Rupert. 1995. Marriage and Earnings.Economic Review,
Federal Reserve Bank of Cleveland, Q (IV), 10-20.
Daniel, Kermit. The Marriage Premium, In The New Economics of Human Behavior,
Mariano Tommasi and Kathryn Ierulli (eds.). New York: Cambridge University Press, 1995.
Hill, Martha. 1979. The Wage Effects of Marital Status and Children. The Journal of
Human Resources 14(4), 579-594.
Krashinsky, Harry A. 2004. Do Marital Status and Computer Usage Really Change the
Wage Structure?Journal of Human Resources 39 (3), 774-791.
Light, Audrey 2004. Gender Differences in the Marriage and Cohabitation Income
PremiumDemography41 (May), 263-284
Neumark, David. 1988. Employers Discriminatory Behavior and the Estimation of Wage
Discrimination.Journal of Human Resources 23 (3), 279-295.
Ribar, David C. 2004. What Do Social Scientists Know about the Benefits of Marriage? A
Review of Quantitative Methodologies. IZA DP No. 998.
Rogers, William M. III and Leslie S. Stratton. 2005. The Male Marital Wage Differential:Race, Training, and Fixed Effects. IZA DP No. 1747.
Waite, Linda J., and Maggie Gallagher. 2000 The Case for Marriage: Why People Are
Happier, Healthier, and Better Off Financially. New York: Broadway Books.
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APPENDIX
Survey Questionnaire
This questionnaire does not ask for any identification information. So, please feel free.
1. Do you have your own source of earning?a) Yes b) No 11. Fathers educational qualification:..
*** If your answer in No, Please skip
question 2 to 5 and start answering again
from question 6***
12. Mothers educational qualification
..
2. For how many months you have your own
source of income earnining?
..
13. Number of brothers and sisters
..
3. What is your average monthly income?
..
14. Up to HSC, where have you spent the
major portion of your life?
a) Dhaka b) Outside Dhaka
4. What is/are your source(s) of earning?
a)Tuition b) Others
15. SSC GPA (without fourth subject)
.
5. How many hours you usually work in a
week for that earning?
..
16. HSC GPA (without fourth subject)
..
6. What is your relationship status?
a. Single & never in a relationship
b. Single but previously in relationship that
broke up
c. In a relationship / engaged
17. Present Field of Study:
a) Social Sciences and Humanities
b) Business
b) Science
7. (If you are presently in a relationship or
engaged) what is your partners/ girlfriendsincome?
..
18. Currently studying in which year?
....................................................
8. What is your family income per month
(excluding your own income)?
..
19. Current CGPA (In case of masters
students use Last Honors CGPA)
..
9. Do you live with your own family?
a) Yes b) No
20. Do you receive any regular amount from
your family?
a) Yes b) No.
10. What is rent of the house where your
family live? (Please write 0 in case of own
house)..
21. (If yes) what is the average amount that
you received from family per month?
..
23. (If yes) what is the average amount that
you send to your family per month?
..
Thank You
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Table 1: Definition of the Variables
Variable Description
ownsource = 1 if student has own source of earning, = 0 if otherwise
exper = for how long the student has own source of earning, in months
inc = students average self-earned income (Tk.) per monthlinc = log(inc)
weekly_hrs = hours of work per week for that earning
weekly_hrs_sq = (weekly_hrs)^2
always_single = 1 if single and never in a relationship,
= 0 if otherwise
breakup = 1 if presently single but previously in a relationship that broke
up, = 0 if otherwise
relationship = 1 if presently in a relationship,
= 0 if otherwisespouseinc = spouses income (Tk.), if presently in a relationship
familyinc = family income excluding students own income (Tk.)
fathereduc = fathers education in years
mothereduc = mothers education in years
siblings = number of siblings
dhaka = 1 if up to HSC major portion of life spent in Dhaka,
= 0 if otherwise
sscgpa = SSC grade point average (in 5.0 scale)
hscgpa = HSC grade point average (in 5.0 scale)business = 1 if presently studying business,
= 0 if otherwise
science = 1 if presently studying science,
= 0 if otherwise
scosci_humnts = 1 if presently studying social science or humanities,
= 0 if otherwise
year = years spent in university
cgpa = university cumulative grade point average (in 4.0 scale)
allowance = amount of allowance received (Thousand Tk. per month)support = amount of money sent to family (Thousand Tk. per month)
netallowance = allowance support(Thousand Tk. per month)
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Table 2: Summary Statistics
Variable Obs Mean Std. Dev. Min Max
ownsource 285 0.554 0.498 0 1
exper 275 13.87 18.75 0 84
inc 283 3364.31 4622.31 0 35000
weekly_hrs 276 8.00 11.87 0 100
always_single 278 0.594 0.492 0 1
breakup 278 0.137 0.344 0 1
relationship 278 0.270 0.445 0 1
spouseinc 38 3881.58 5729.76 0 25000
familyinc 263 22742.54 20249.15 0 150000
fathereduc 273 12.11 4.86 0 21
mothereduc 269 9.14 4.39 0 18
siblings 283 3.28 2.05 0 11
dhaka 284 0.317 0.466 0 1
sscgpa 279 4.432 0.482 3.13 5
hscgpa 279 4.394 0.438 2.4 5
business 283 0.318 0.467 0 1
science 283 0.233 0.424 0 1
scosci_humnts 283 0.452 0.497 0 1
year 282 3.429 1.281 1 5
cgpa 240 3.287 0.303 2.18 3.9
allowance 279 2.353 3.679 0 50.000support 281 0.570 2.406 0 31.500
netallowance 277 1.629 3.629 -31.500 12.000
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Table 3: Regression Results
Standard errorsin parentheses,
+p < 0.10, *p < 0.05, **p < 0.01
(d) for discrete change of dummy variable from 0 to 1.
Dependent Variable:
(1)ownsource
(2a)
linc
(2b)
linc
relationship (d) 0.208**(0.073)
0.184+(0.104)
0.192+(0.100)
breakup (d) 0.138
(0.087)
0.316*
(0.129)
0.338**
(0.127)
fathereduc -0.027**
(0.010)
-0.022
(0.016)
-0.014
(0.015)
mothereduc 0.022*
(0.011)
0.030+
(0.017)
0.027+
(0.016)
siblings 0.005
(0.016)
-0.004
(0.024)
-0.015
(0.023)
lfamilyinc -0.090*(0.043)
0.145*(0.065)
0.118+(0.064)
netallowance -0.050**
(0.000)
-0.064**
(0.000)
-0.076**
(0.000)dhaka (d) 0.005
(0.074)
-0.065
(0.111)
-0.086
(0.109)
sscgpa 0.048
(0.087)
-0.102
(0.134)
-0.170
(0.130)
hscgpa 0.029
(0.098)
0.360*
(0.159)
0.316*
(0.155)
cgpa -0.126
(0.109)
0.013
(0.154)
0.041
(0.151)
year -0.009
(0.030)
0.032
(0.049)
0.049
(0.047)
science -0.040(0.084)
0.076(0.132)
0.101(0.129)
business 0.108
(0.082)
0.167
(0.124)
0.180
(0.122)
exper 0.002
(0.003)
0.001
(0.003)
weekly_hrs 0.007(0.004)
intercept 1.662**
(0.612)
5.515**
(0.965)
6.189**
(0.900)
Number of Obs. 197 110 114R2 0.325 0.487 0.477
Adj. R2 0.274 0.399 0.397
Root MSE 0.416 0.447 0.448