Activity report of visit to InGRID
research infrastructures
Sean de Hoon
The male marriage wage premium from a cross-national comparative
perspective
Abstract (max 300-500 words)
Introduction and motivation of visit
Numerous studies show that marriage is positively associated with men’s wages (for a
review see Ribar, 2004; Waite & Gallagher, 2000). Part of the positive association is
attributable to selection (e.g. Ginther & Zavodny, 2001). Nevertheless, a positive
relation typically remains after controlling for differences in the characteristics of the
married and unmarried, indicating that being married actually contributes to men’s
higher wages. In spite of general agreement on the existence of at least some marriage
wage benefits, scholars have been unable to explain why the magnitude of these benefits
varies substantially across countries (Rodgers & Stratton, 2009). To illustrate, the male
marriage wage premium varies from 1 percent in Poland to 25 percent in Germany
(Schoeni, 1995).
Whereas previous studies have generally examined the male marriage wage
premium within a single country, the proposed project recognizes that individual
behaviour is embedded in dynamic, interdependent contexts (Mayer, 2009; Moen,
Elder, & Lüscher, 1995). Therefore, rather than being a universal phenomenon, the
premium is likely to vary in relation to contextual characteristics.
In the project we specifically focus on the specialization hypothesis (Becker,
1981). According to this perspective, having a wife, who presumably takes care of
household labor, allows men to spend more time and-or energy on market activities,
increasing their productivity and thereby their wages. Single men lack a partner to take
care of the household, therefore they cannot specialize in market activities. Country
level conditions can be expected to shape the extent of married men’s specialization in
market activities, leading to variations in the male marriage wage premium. We focus
on gender differences in labor market opportunities, norms about acceptable behavior
for men and women, divorce rates and public care provisions, as country level
conditions that we expect shape the specialization in market activities among married
men.
The main goal of the visit to the LIS cross-national data center is to make use of
LIS’s unique comparative database for conducting analyses. The fact that the LIS
database “is the largest available income database of harmonised microdata collected
from multiple countries over a period of decades” (LIS website, 2014), means that it
provides the best possible opportunity to examine income inequality of any sort from a
comparative perspective. Furthermore, the Bayesian modelling techniques we intend to
use to analyse the data require direct access to the data, as these techniques cannot be
used with statistical packages that are available for analysing these data with remoteaccess.
A secondary goal of the visit to the LIS cross-national data center is to benefit
from the knowledge of the data experts that are present there. Being able to physically
meet people who are experts on the data will be invaluable in dealing with problems and
answering the questions that might arise when analysing the data. In addition, these
experts will be able to point out important strengths and possible weaknesses in the
data, which will contribute to a successful stay at the LIS data center and a great
scientific paper as end product.
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Scientific objectives of visit
The project is expected to contribute to the scientific literature on the male marriage
premium in three ways.
First, the project will add to the cross-national comparative literature on the male
marriage wage premium, which has been limited to only a handful of studies (Jakobsson
& Kotsadam, 2013; Schoeni, 1995). It will do so by using the best available
comparative data source, the LIS database.
Second, the project will include explicit measures of country-level
characteristics (e.g. the position of women on the labor market, gender norms, divorce
rates, and childcare availability) that might explain why the premium is greater in some
countries than in others. This will be an improvement upon earlier work that has used
rather crude country level measures (Jakobsson & Kotsadam, 2013).
Third, the use of Bayesian analytical methods will provide the opportunity to
examine country-level estimates of the male marriage wage premium. Again, this will
be an improvement on recent work, which focused on the average European marriage
premium among men (Jakobsson & Kotsadam, 2013).
Reasons for choosing research infrastructure
As noted above, the reasons for choosing the research infrastructure and the datasets is
that the LIS data are the largest available collection of harmonised micro-level datasets
on income. This makes it the most suitable datasource for our project, which examines
cross-country differences in income inequality at the micro-level.
Activities during your visit
The activities during the visit were all research-related. They mostly consisted of
analysing the available datasets. In addition, meetings were held with the data experts in
order to solve issues that arose during the analysis of the data. Halfway through the
visit, the progress of the project was presented to the staff of the research infrastructure.
At this occasion valuable feedback was provided, which was taken into account in the
second half of the visit.
Method and set-up of research
For the current project we used data from Wave VII of the LIS data, which were
collected in or around 2007. These data were supplemented with data from waves V
(1990s and 2000), VI (2004) and VIII (2010) in order to be able to examine as many
countries as possible. The results are based on data from 29 countries1. These are
countries for which information on hourly wages was available directly, or where it
could be calculated based on income combined with hours worked.
1
Austria, Belgium, Canada, Colombia, the Czech Republic, Estonia, Finland, France, Germany, Greece,
Guatemala, Hungary, Iceland, India, Ireland, Israel, Italy, Japan, Luxembourg, Mexico, the
Netherlands, Russia, Slovakia, South Africa, Spain, Sweden, the United Kingdom, the United States
and Uruguay
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Before moving on to multilevel models, we estimated models on individual
countries, to get a first look at the cross-national differences in the male marriage wage
premium. After this, multilevel regression models were employed to analyse first to
what extent married men earn higher hourly wages across countries than unmarried
men. Second, we examined how much of the difference in hourly wages can be
accounted for by controlling for selection into marriage. We did this by including a
number of human capital variables, such as education and age. Third, we assessed the
cross-country variation in the premium by including a random slope for the effect of
marital status on wages. Finally, we attempted to explain differences in this effect of
marital status across countries by including several cross-level interactions with
country-level characteristics.
Comparing the results from analyses of single countries to those of the
multilevel models, we assessed whether it was necessary to estimate multilevel models
in a Bayesian framework. These comparisons revealed that the use of Bayesian
modelling techniques was not necessary.
Project achievements during visit (and possible difficulties encountered)
During the visit, the main objectives were all achieved. No difficulties were encountered
other that the normal issues that arise during data management and analysis. We were
able do all the analyses that we wanted to do and, in line with the feedback from the
staff, we included several additional controls and checks.
Preliminary project results and conclusions
The results of the analyses on single countries revealed that for the most part and across
countries, much of the wage difference between married and unmarried men can be
explained by differences in human capital. Figure 1 illustrates this. It plots the estimates
of the marriage premium without additional controls against the estimates of the
premium with the controls. The diagonal line represents the situation where there is no
difference between two estimates. We see that in the large majority of countries,
controlling for human capital decreases the estimate of the premium. Notably, in seven
countries the premium actually seems to increase with human capital controls,
suggesting a negative selection on these characteristics into marriage. Figure 2 further
illustrates that out of the 29 countries we examine, in 11 countries the 95% confidence
intervals around the effect of marriage on wages overlap 0. This means that in those
countries there seems to be no significant effect of marriage on wages and in some
countries there is even the indication of a negative effects. Furthermore, there is overall
quite some variation as the estimate of the premium ranges from around -10% to around
+22%. Whether the variation is related to country-level variables is examined in
multilevel models.
4
.6
.4
.2
0
-.2
-.2
0
.2
.4
Marriage premium (crude)
.6
-.4
-.2
0
.2
.4
.6
Figure 1. Crude marriage premium plotted against premium controlled for human capital.
Figure 2. Marriage premium across 29 countries, controlled for human capital.
For brevity, we do not to include a table of the multilevel models here. Of the four
contextual factors that we expected would influence the marriage premium, three do
seem to matter. However, the relations are not all as expected. We expected that the
premium would be greater in countries where women’s labor market position is weaker.
This because married men’s specialization in market activities will be greater there.
However, we found evidence for the opposite relationship, in countries where women’s
labor market position is weaker, the premium is smaller. Similarly, we found that that
the premium is greater in countries where more childcare is provided, while we
expected the opposite. The normative climate did have the expected effect on the
marriage premium. In countries with a more traditional normative climate, we found a
greater premium. The divorce rate was found to be unrelated to the magnitude of the
premium.
Our preliminary conclusion has to be that the context affects the marriage
premium mostly in ways that we did not anticipate. Aside from the normative climate,
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there seem to be different dynamics at work in shaping the magnitude of the marriage
premium across countries. These findings may result from selection processes into and
out of marriage. This remains as an interesting and necessary avenue for future research.
Outcomes and future studies
As noted above, the outcomes of the present study were not as we expected and in
future studies we aim to explain why the contextual effects seem to run in the direction
opposite to that expected. For now however, we may draw the tentative conclusion that,
barring selection effects, a strong position of women on the labor market and more
childcare provision actually lead to greater inequality between married and unmarried
men. If it turns out that these dynamics are in fact not the result of selection processes,
than the results may signal that government policies are unintendedly increasing
inequality among male citizens.
References
Becker, G. S. (1981). A treatise on the family. Cambridge, MA: Harvard University
Press.
Cohen, P. N. (2002). Cohabitation and the declining marriage premium for men. Work
and Occupations, 29(3), 346–363. doi:10.1177/0730888402029003004
Ginther, D. K., & Zavodny, M. (2001). Is the male marriage premium due to selection?
The effect of shotgun weddings on the return to marriage. Journal of Population
Economics, 14, 313–328.
Jakobsson, N., & Kotsadam, A. (2013). Does marriage affect men’s labor market
outcomes? A European perspective. Review of Economics of the Household.
doi:10.1007/s11150-013-9224-7
Mamun, A. (2012). Cohabitation premium in men’s earnings: Testing the joint human
capital hypothesis. Journal of Family and Economic Issues, 33(1), 53–68.
doi:10.1007/s10834-011-9252-5
Mayer, K. U. (2009). New directions in life course research. Annual Review of
Sociology, 35(1), 413–433. doi:10.1146/annurev.soc.34.040507.134619
Moen, P., Elder, G. H., & Lüscher, K. (Eds.). (1995). Examining lives in context:
perspectives on the ecology of human development. Washington, D.C.: APA Press.
Ribar, D. C. (2004). What do social scientists know about the benefits of marriage? A
review
of
quantitative
methodologies.
Retrieved
from
http://papers.ssrn.com/abstract=500887
Rodgers, W. M., & Stratton, L. S. (2009). Male marital wage differentials: training,
personal characteristics and fixed effects. Economic Inquiry, 48(3), 722–742.
Schoeni, R. (1995). Marital status and earnings in developed countries. Journal of
Population Economics, 8(4), 351–359.
Waite, L. J., & Gallagher, M. (2000). The case for marriage: why married people are
happier, healthier and better off financially. New York: Broadway Books.
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