Social economic factors of marriage market in Bulgaria

SOCIAL-ECONOMIC FACTORS OF MARRIAGE MARKET IN BULGARIA 1

Social-Economic factors of marriage market in Bulgaria

A marriage market analysis, based on macro social-economic factors and secondary

information from other researches on the same topic

Hristo B. Kolev1

AUBG2, EMBA3 program

1 Corresponding Author – contact: [email protected] 2 American University in Bulgaria 3 Executive Master of Business Administration


Abstract

Purpose:

Investigate and analyze the marriages in Bulgaria, through a regression. The main

purpose is to investigate how independent macro variables (social and economic)

influence the number of marriages in the country through the last years.

Methodology/approach:

The paper and the final results are based on the regression made in this

investigation. The independent variables chosen for the regression are based on other

investigation made over the last years on the same/similar topics. Data is extracted

from the Bulgarian National Statistic Institute4, European Union official statistics

(Eurostats)5, and The World Bank6. There are 3 different set of variables, depending

on existing data (1960-2010; 1990-2010; and 1998-2010). 3 regressions have been

run including all relevant set of variables (see table further down).

4 http://www.nsi.bg 5 http://epp.eurostat.ec.europa.eu/portal/page/portal/eurostat/home/ 6 www.worldbank.org


Variable (independent) Set Regression Divorces 1 1 Population 1 1 Live births

• BIM7 • BOM8

1 1

Previous marriages 2 2 GDP per habitant 2 2 Total GDP 2 2 Inflation 2 2 Unemployment

• Male • Female

3 3

Education • Male • Female

3 3

Findings:

Marriages in Bulgaria depend more on social than on economic variables. Number

of marriages doesn’t depend on punctual economic crashes like the 1996-1997 strong

inflation. Women participation in education and labor market is different from other

societies and independent variables related to this issues are not related to marriages

in Bulgaria, which is the main difference, compared with the 5 consulted researches

on the same topic.

7 BIM: births in marriage 8 BOM: births outside marriage


Introduction

Society is changing. The Marriage is a historical framework of society over the

world, no matter religion or political views. As a consequence of the change in

society a change in the framework is observed. In Bulgaria marriages have been

decreasing in a constant rate and now days represent 35% of marriages 50 years ago.

Today births outside marriage represent 54% of all births, and in 1960 were only 8%.

These changes occurred little by little in the society, there was not a drastic inversion.

Is there a logical explanation about this change? What happened in the society that led

to these changes? This paper is analyzing some social and economic variables that

explain part of what is occurring.

This paper is organized as follows. Part one is reviewing 5 previous researches

bout same/close topic. Part two presents a brief explication of the variables analyzed

in this research. Part three provides several regressions of the 3 SETs of variables.

Part four is a concluding section.

1. Theory of the Marriage Market:

This first part of the paper is a brief review of the recent works over Marriage

Market and its dependence of the social-economic factors. These works are the bases

of the independent variables used further down in this paper to explain the dependent

variable (Number of Marriages in Bulgaria).

Gary Becker (1971) in his work “The Economics of Discrimination”, models the

marriage market as a system, in which agents (men and women) have to maximize


their utility. They have to compare their utility functions based on two states: the first,

in which utility depends on the consumption and personal income of a single agent;

and, second, of marriage, where the utility depends on the sum of the man and

woman’s income. The agent will marry only if the marriage case utility is higher

compared on the single case utility. Becker’s model concludes that, because of this

direct connection of utility and income, couples may tend to be formed by agents with

similar income levels. Becker includes in his analysis the possibility of divorce. The

reason is that when an agent is searching for his/her best mate he/she collects

information. As this information is costly to find, the agent will marry when the

marginal utility from additional information equals the marginal cost of obtaining it.

The possibility of divorce is given in the long run, when married agents get more

information, which leads them to re-evaluate their marriage decision.

Blau, Kahn, Waldfogel, (2002) in their work “Understanding Young Woman’s

Marriage Decisions”, analyzed the labor market impact on the decisions of young

women. Their methodology consisted of two steps, the first is a regression where

Marriage is the dependent variable, and independent variables are personal

characteristics (ethnic of the agent and education level); and the second step is the

relation between results from the regression and the labor conditions. The final result

are: a) When the female labor market conditions are favorable (easy for wemen to

enter in the labor market, good salaries, etc.), then the marriages are decreasing; and

b) In contrast, when conditions of men's labor market are favorable, the marriage rate

is increasing.

Caucutt, Guner and Knowles (2001) in their analysis “Time of Births: A Marriage

Market Analysis”, state that one of the main independent variables when analyzing

the Marriage Market is the decision to have a babe. This is because while agents are


single and don’t have children, they have more possibilities to enter in the labor

market, and in contrast, having children leads to higher direct costs and opportunity

costs. The authors developed an equilibrium model based on marriage, divorces, and

human capital accumulation, which allows them to find different moments in time life

to have children. The model is analyzing the effects of a relative increase in wages of

women to wages of men and the increased the return rate on women's experience. The

study concludes that the decision of having children has an important role in

determining Marriages and Income differences.

Ermisch and Francesconi (2002) in their work “Intergenerational Social Mobility

and Assortative mating in Britain” investigated the relationship between

socioeconomic status of parents and children. The model defends that all agents have

a utility-maximizing behavior that requires the existence of two sub-problems: the

problem of the children, that is to select optimal mate in optimal time; an, the problem

of parents, which is to determine the optimal level of investment in education of their

children, to ensure its economic future. The results are: low intergenerational

elasticity; human capital (intergenerational), rather than the marriage market,

determines the social status that an agent can achieve; and finally, their results support

the idea that there are strong intergenerational rigidities because of the quality of

capital that children could inherit by reach parents.

Mercado, Leitón, Ríos (2004), in their research “The Marriage Market: a link

between Social Mobility and Labor Market” analyzed the elements that are behind the

low social mobility in Bolivia. The binding hypothesis follows that the marriage

market could be acting as a mechanism that reinforces the low social mobility.

Moreover, the marriage market is considered as an important link between social

mobility and the labor market. The result show an evident strong marriage market


segmentation that would slowdown the social mobility and, is a factor that leads to

misallocations in the labor market.

2. Analyzed variables

The preliminary independent variables are base on the works cited in the previous

part of this paper. Other macroeconomic variables are included, as GDP and Inflation.

The independent variables are classified in 3 sets (because of different available data).

Set 1 of variables includes those with most information from 1960/1961 to 2010. Set

2 of variables includes those with information from 1990 to 2010. And set 3 of

variables including time series from 1998 to 2010. The present paper will analyze 3

regressions in order to include all a priori important information.

Variables:

• Marriages: this is the dependent variable, expected to be explained.

The graph further down represents the marriages in Bulgaria since 1960 until

2010 (51 periods). Marriages have fallen from 69.000 in 1960 to 24.300 in 2010.

The regressions run in the next part of the paper will try to explain this decrease

with social-economic macro variables. The biggest decrease is from period 29

(1989) when the number of marriages was 63,263 to period 34 (1994) with 37,910

marriages. This period has a historical meaning, 1989 is a year of big social and

political changes in Bulgaria (change in the politic situation from Communism to

Democracy) and in the region (Union of the 2 German countries). Unfortunately

these changes won’t be captured by the regression.

• Divorces: independent variable (set 1 of independent variables)


There is data for this variable since 1961 to 2010. The variable was previously

analyzed as related to marriages by Gary Becker (1971) as possibility on the long run

to get more information about the other partner in the marriage and to reconsider the

utility function of the marriage. The graph show us that divorces increase form 8,549

in period 2 (1961) to 11,012 in period 51 (2010), but the relative weight increased

much more, from 13% of all marriages in 1961 to 45% in 2010.

• Population: independent variable (set 1 of independent variables)

This variable hasn’t been analyzed by previous researches, we will see if it is

relevant or not in the case of Bulgarian marriages. The population has stayed stable

over the years, as it only decreased by 300,000 people in 70 years (7,829,246 in

1960 to 7,563,710 in 2010).

• Live births in and outside marriage: independent variable (set 1 of

independent variables)

This variable is interesting because it is very close to the decision of having a

babe. The variable decision of having a babe was already examined by Caucutt,

Guner and Knowles (2001) with the conclusion that the kids suppose direct costs

and opportunity costs for their parents. Actually there are 2 variables: births in

marriage (BIM); and births outside marriage (BOM). On one hand the BIM have

been decreasing: 128,883 in 1960, representing 1,87 births per marriage to 34,663

or 1,43 births per marriage in 2010. On the other hand BOM have been increasing

from 11,199, representing 16% births per marriage in 1960 to 40,850 or 1,68 births

per marriage in 2010. Please note that both type of births (BIM AND BOM) are

related to the total number of marriages.


• Previous marriages or second time marriages (STM): independent variable

(set 2 of independent variables)

This variable collects the agents that got married more that once. The variable

hasn’t been analyzed in previous studies, but is an interesting social variable to

determine if it is relevant or not to marriages. In 1990 the number of second or

more time marriages were 7,181 or 12% of all marriages and in 2010 were 3,388

or 14%.

• GDP per habitant (GDPH): independent variable (set 2 of independent

variables)

This variable was previously analyzed and considered as relatively important by

Becker (1971); Caucutt, Guner and Knowles (2001); and Ermisch and Francesconi

(2002). The data available for Bulgaria is from 1990 to 2010. The expected (from the

cited previous researches) relation of this variable to the dependent one is as women

income increases marriages would decrease. In the present paper aggregate data will

be used (men + women). Separate data analysis have been discard due to few

available data (only 5 years series from 2006 to 2010). Income per habitant in euros in

Bulgaria has increased from 1,200 in 1990 to 4,800 in 2010.

• Total GDP in millions (TGDP): independent variable (set 2 of independent

variables)

This variable is another way to measure the income of the habitants. It is

supposed that if GDP of one country is getting higher, the social status of its

population is becoming better (the income per habitant is rising). If we suppose

that higher GDP means high income for women in the Bulgarian society, we could


expect fewer marriages. The regression will prove or reject this hypothesis. There

is available data for this variable from 1990 to 2010.

• Inflation, consumer prices (ICO): independent variable (set 2 of


This variable hasn’t been analyzed in the previous researches, mentioned in the

first part of the paper. Still the variable is close related to the consume power of the

agents as high inflation could mean that that income increase could be not

sufficient to increase consume power and increase poverty of population.

Information for this variable is available from 1990 to 2010.

• Unemployment male and female (UM and UF): independent variable (set 3

of independent variables)

This variable was deeply analyzed in Blau (2002) with the conclusion that the

most favorable are the conditions of the labor market for women less marriages we

would have and the most favorable labor conditions for men, more marriages we

would have. Available data for this variable is from 1998 to 2010. In the regression

2 variables are used: female unemployment, which is decreasing from189 in 1998

to 156.2 in 2010, with some fluctuations; and male unemployment, which is

relatively stable - 212.9 in 1998 and 211.3 in 2010, but with serious fluctuations

over the years.

• Education male and female (EM and EF): independent variable (set 3 of


This variable was previously analyzed by Blau (2002). The results were already

mentioned. It is supposed that higher rate of education for women means better labor


conditions and entry facilities in the labor market and as a consequence lower rate of

marriages. The variable education is composed of 2 variables: EF; and EM. For both

the data is 1998-2010 time series where only the highest level of education has been

considered: first and second stage of tertiary education (levels 5 and 6). The data

shows increase in male education from 101,784 in 1998 to 127,709 in 2010 and stable

rates for female education 158,703 in 1998 and 159,337 in 2010. The regression will

give us details if fluctuations in male and female education are relevant to marriages

in Bulgaria.

3. Regressions9

The first regression to be made is with the variables from SET_1 (1961-2010) as

follows:

• Dependent variable: Nº of Marriages in Bulgaria from 1961 to 2010

• Independent variables, all data from 1961 to 2010 for Bulgaria:

o Nº of Divorces

o Population

o Births in Marriages (BIM)

o Births outside of Marriages (BOM)

9 Please check regression matrix in the appendix


First analysis of the summary output indicates that the variable Population in not

statistically significant as it doesn´t pass the T test (0.49) and have too high P-value.

On the other hand, there is possibility of multicollinearity problem as the F test shows

that the regression is statistically significant. Let’s drop out the problematic

independent variable and run the regression again. The results are as follow:

In this regression we have correct the previous problem and now all the

independent variables pass the T test: Divorces 5.44; BIM 24.99; BOM -3.6. The T

test shows that the variables are significant at 5% level. The P-value, that gives us the


significant value in also ok, all variables are below 1%, that means that there is only

1% chance that the variables are not related to Marriages. The regression equation is

Marriages=10,904+0.916Divorces+0.43BIM-0.26BOM. The adjusted R2 tells us that

these variables explain 98% of the dependent variable. F test is also significant

1,2477E-42.

The regression with data variables from SET_1 mixes the findings from 2 different

researches: Gary Becker (1971) who states that the divorce is a consequence on the

long run of gathering more information about the maximizing of the utility function of

marriage; and Caucutt, Guner and Knowles (2001) who related the decision of having

babes to the marriages. The present regression goes further and separates the effect of

pregnancy of married and not married women.

The second regression is to be made with the significant independent variables

from SET_1 and variables from SET_2. For this regression the data series is from

(1990-2010). The independent variables are as follows:

o Nº of Divorces SET_1

o Births in Marriages (BIM) SET_1

o Births outside of Marriages (BOM) SET_1

o Second or more times Marriages (STM) SET_2

o Total GDP (TGDP) SET_2

o Inflation, consumer prices (ICP) SET_2

o GDP per habitant (GDPH) SET_2


From the summary output we can see that there are several variables that doesn’t

pass the T-test are not significant with the P-value and can present correlation

problems. As the idea is to add more significant variables to the previous regression

the only variable that is statistically significant from this SET_2 is STM. Let’s drop

out the other 3 variables TGDP, ICP and GDPH. The results of the adjusted

regression are as follows:


All independent variables pass the T-test, P-value, and the regression is significant

as F-test is 7.7E-12. The only variable that is a little bit less significant is Divorces as

there is 5% of probability that Divorces is not related to Marriages. The equation is

Marriages=12,810+0.63Divorces+0.3BIM-0.27BOM+1,799STM. These variables

explain 96% of the dependent variable as shows the R2.

This regression adds more information to the Marriages Market, as one additional

no previously studied variable gives us more information about the market. No one of

the 5 previously consulted studies had in mind this variable - the previous experience

of agents in this market (Marriage Market)

The third regression is to be made with the significant independent variables from

SET_1, SET_2 and variables from SET_3. For this regression the data series is from

(1998-2010). The independent variables are as follows:

o Nº of Divorces SET_1

o Births in Marriages (BIM) SET_1

o Births outside of Marriages (BOM) SET_1


o Second or more times Marriages (STM) SET_2

o Unemployment male (UM) SET_3

o Unemployment female (UF) SET_3

o Education male (EM) SET_3

o Education female (EF) SET_3

After the summary output analysis we see several problems with T-test and P-

value. To adjust the regression let’s drop out problematic variables from SET_3 EM

and EF:


In this regression there are still problems with independent variables Divorces and

BIM. Let’s adjust the regression dropping out these variables:

All independent variables pass the T-test and the P-value. When analyzing the

variable BOM, there is 10% possibility that this variable is not related to Marriages,

in the variable STM this probability is 5%. The equation is Marriages= 26,976 -

0.15BOM+1.12STM-154.85UM+211UF. The regression is significant as the F-test is

3,8E-5.


This regression gives us some more detail about how labor market is related to

Marriages and supports Blau, Kahn, Waldfogel, (2002) findings in “Understanding

Young Woman’s Marriage Decisions”.

4. Conclusions

Factors that intervene on Marriage Market are more social than economic. In the

present paper have been analyzed several macro variables and their relation with the

number of marriages in Bulgaria. The results are obvious: 6 out of 12 analyzed

variables are statistically significant to explain the number of marriages in Bulgaria:

Divorces; BIM; BOM; STM; UM; and UF. Only 2 from these 6 could be considered

economic variables: UM; and UF. Other 6 variable were dropped out from the

regression as not relevant: Population, GDPH; TGDP; ICP; EM; and EF. 3 of the

dropped out variables are macroeconomic: GDPH; TGDP; ICP.

After the present regression analysis it could be concluded that the high decrease in

marriages in Bulgaria from 1960 to 2010 is due on social changes in the society,

rather than in economic changes. Bulgaria’s GDP has been growing ever since, as

well as habitant’s income, but marriages kept falling down in a compound annual rate

of about 2%. This rate remained even in the years of strong inflation of 121% in 1996

and 1,058% in 1997. One difference is observed, compared with the previously

examined researches about the topic: in most researches the women emancipation of

men in strongly related to marriages, not in the Bulgaria’s case. This is because, on

one hand, in Bulgaria women has historically received good education, education’s

levels of women, opposite to other countries, are greater that men’s education level,

and on other hand, women has historically participated very actively in the labor

market – housewife rate is very low compared with other countries.


Interesting variables that haven’t been considered, because of lack of information

about them, are economic variables as women income and access to higher job

position. Probably this is the variable that would explain the very high rate of BOM in

the Bulgarian society (BOM in 2010 are 40,850 when BIM in 2010 are 34,663).


References:

Becker, G., 1971 “The Economics of Discrimination”. The university of Chicago Press, Chicago, IL

Blau F., Kahn L., Waldfogel J., 2002 “Understanding Young Women’s Marriage Decisions: The Role of Labor and marriage Market Conditions” National Bureau of Economic Research. Working paper 7510

Caucutt E., Guner N., Knowles J., 2001 “Time of Births: A Marriage Market Analysis” University of Pennsylvania

Ehrenberg, R. & Smith, 1991 “Modern Labor Economics. Theory and Public Policy”. Harper Collins Publishers Inc., Fourth Edition

Mercado, Leitón, Ríos, 2004 “The Marriage Market: a link between Social Mobility and Labor Market”, Instituto de Investigaciones Socio-Económicas, Universidad Católica Boliviana

Bulgarian National Statistic Institute: http://www.nsi.bg

European Union official statistics (Eurostats): http://epp.eurostat.ec.europa.eu/portal/page/portal/eurostat/home/

The World Bank : www.worldbank.org


APPENDIX

Regression Matrix including all data sets:

Documents

Social economic factors of marriage market in Bulgaria