Married men earn significantly more than unmarried men, around 20-40% more according to studies. This paper analyzes whether this "marital premium" differs between the public and private sectors using data on over 40,000 men. The results show a 20.6% marital premium overall but the premium is smaller, 10.6% less, for married men working in government compared to the private sector. The authors conclude that while marital status benefits men's earnings, the effect is less pronounced in government work.

1. Marriage and Wages
Final Paper Presentation
Online Micro-econometrics: Fall 2015
Xuefeng Xing (xxing@jhu.edu)
Assel Dykanbayeva (adykanb2@jhu.edu)

2. The Effect of Marriage on Earnings of men
• It is well-known fact in the labor economics that married men on
average earn more than unmarried men.
• The difference between wages of men, which can be related to the
marital status, is called the marital (marriage) premium.
• Most studies find that the marriage premium is between ten and
forty percent.
• Although the existence of the marital premium is almost universally
accepted, there is much less agreement on its’ source.

3. What is the source of the Marital Premium?
• The productivity approach explains that the marital premium exists
because the marriage makes men more productive.
• The selection approach states that the marital premium is paid
because married men have some unobservable characteristics, which
made them more attractive both for marriage and labor market.
• The specialization approach implies that due to the marriage men
have opportunity to concentrate their efforts on career and devote
less time to housework. This results in more earnings of married men.

4. Purpose of the study
• The main purpose of our study is to analyze the effect of the marital
status of men on their earnings in government and private sector.
• We consider that there can be a significant difference in the effect of
marital status on earnings in the private versus governmental sector.
• This hypothesis is based on the assumption that civil service requires
special characteristics of an employee and a higher appreciation of
traditional family values.
• To some extent the hypothesis supports the selection approach in
explaining marital premium for men (existence of unobservable
characteristics of men, which made them more attractive both for
employee and for marriage).

5. Data and Empirical Specifications
• We utilized 2011 data from the Current Population Survey (CPS).
• The dataset includes working men in the age range between 17 and 66,
excluding those who are either unemployed or employed in the military.
Married
68%
Divorced/
Widowed
9%
Not
Married
23%
Married Divorced/Widowed Not Married
We also exclude from the
dataset men whose
information on wage was
missing or with wages less than
the minimal wage.
There are 40,509 men in the
sample.

6. Mean Wages of Men of different age groups, $
0
10000
20000
30000
40000
50000
60000
70000
80000
18-24 25-34 35-44 45-54 55-65 Total
1.Married 2.Divorced/Widowed 3.Not Married
We observe significantly higher wages of married men – on average, it is 62%
or $25,970 higher than the average wage of single men.

7. Mean Wages of Men occupied in different
industries, $
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
100000
Married Divorced/Widowed Not Married

8. Mean Wages of Men by Races and Marital
Status, $
0
10000
20000
30000
40000
50000
60000
70000
80000
White Black Hispanic Others
Married Divorced/Widowed Unmarried

9. The Model for estimating marital premium
• In the analysis of the relationship between marital status and wages
the dependent variable is wages (P_EARN_VAL). Independent
variables are an education, an experience and its square (EXSQ),
dummies for marital status, dummy on type of occupation (1 –
government, 0 – private), dummies for industry (variables
AGRICULTURE, CONSTRUCTION, MANUFACTURING,
TRANSPORTATION, INFORMATION, FINANCE, PROFESSIONAL
SERVICES AND SERVICE), dummies on race.
• The base group is white single men, working in a private company in
the trade industry.

10. Results
• The coefficient of interest on the dummy Married is equal 0.206 with
SE 0.008 and p-value 26.56.
• It implies that holding other variables constant, the predicted wage of
married men is 20.6% higher than never-married men.
• The F-test value are high (1059.8), which means that coefficients in
the model are jointly statistically significant.
• R-squared is 0.3079
• The estimates of other variables have the expected signs, and all of
the estimates are statistically significant at the 1% level.

11. Does the Government pay more marital
premium?
• We run the second regression, which additionally includes interaction
term between dummies MARRIED and GOVERNMENT.
• As a result, we obtain small changes in the coefficients’ estimates on
MARRIED and GOVERNMENT, and both statistically significant and
significant in magnitude estimated coefficient on the interaction term
with negative sign (β=- 0.1061, SE=0.0182).
• These results imply that a marriage premium, paid by governmental
employer is less than those, obtained by the private-sector employee.

12. Conclusion
• In our research, based on the CPS 2011 data, we discovered a marital
premium for men 20.6%. The hypothesis of bigger marital premium
for men from governmental employers is rejected at 1% confidence
level.
• More than likely that the male marital wage premium is a result of
multiple factors. We suppose that a considerable part of the marriage
premium stems from ability bias. Men who marry are just more
conscientious, ambitious, and cooperative, and the CPS data lacks
good measures of these traits.

13. Variables Model 1 Std. Err. Model 2 Std. Err.
education 0.101 0.001 0.101 0.001
experience 0.037 0.001 0.037 0.001
experience squared -0.001 0.000 -0.001 0.000
married 0.206 0.008 0.219 0.008
divorced 0.048 0.012 0.045 0.012
government 0.021 0.009 0.097 0.016
agriculture 0.152 0.017 0.151 0.017
construction 0.035 0.011 0.034 0.011
manufacturing 0.124 0.011 0.123 0.011
transportation 0.102 0.013 0.102 0.013
information 0.219 0.019 0.219 0.019
finance 0.253 0.014 0.252 0.014
professional_services 0.076 0.010 0.076 0.010
service -0.022 0.011 -0.021 0.011
black -0.200 0.011 -0.201 0.011
hispanic -0.131 0.008 -0.131 0.008
others -0.095 0.010 -0.096 0.010
gov_mar - - -0.106 0.018
_cons 8.730 0.021 8.724 0.021
Appendix. Estimated coefficients