LloydAmanda.RevisedDraft

Implications of Legalized Abortion on Female Labor Force
Participation
Prepared for:
Dr. William B. Vogt, Faculty Advisor
Econ 5900
Spring 2015
Prepared by:
Amanda Lloyd
alloyd1@uga.edu
April 13, 2015

Female Labor Force Participation 2
Abstract
This thesis analyzes various factors that affect female labor force participation
rates at the national level while primarily focusing on legalization of abortion. Using a
fertility control model as the basis for a proposed multivariate regression model, this
thesis attempts to estimate the true effect of legalizing abortion on a country’s female
labor force participation rate using data from the Worldbank and UN. Analysis of the
regression results suggest that legalization of abortion on request is the most significant
factor in determining female labor force participation and results in a 5.2% increase in
female labor force participation rates holding constant the effects of contraceptive
prevalence, fertility rates, total labor force participation, and education.

Amanda Lloyd
Dr. William B. Vogt
ECON 5900
February 18, 2015
Implications of Legalized Abortion on Female Labor Force Participation
In the labor market, an ongoing debate surrounds the equality and treatment of
men and women. In specific, there is a noticeable difference in the labor supply curves of
men and women. One significant difference between men and women’s ability to supply
labor results from greater opportunity costs for women due to child bearing. Studies have
shown that on average, a woman’s labor supply decreases by two years for every live
birth (Bailey et al. 2001).
This difference between men and women’s labor supply raises the question of
whether inefficiencies in the labor market are remedied by government intervention.
Through statistical analysis of data collected from the UN and the World Bank it is
possible to analyze the statistical significance of legalization of abortion on a country’s
level of female labor force participation.
In order to properly examine this research question, the Introduction section
presents the necessary economic theory regarding the household fertility control problem
and labor supply. The Literature Review section will then discuss similar studies that
analyze varying aspects of this research question in addition to several critiques. The

Data section then outlines the data methods and design for the regression model in order
to establish legitimacy. After outlining the regression model, the Analysis section
provides a thorough explanation and analysis of the ANOVA results along with data
limitations and possible areas for concern. Finally, the Conclusion briefly summarizes
and interprets the statistical findings of the estimated regression model and discusses the
final implications of this paper.
Introduction
Fertility control plays a critical role in the household utility maximization
problem. The household fertility control decision can be thought of in terms of a
household production model where the household decides whether to have an additional
child by comparing the costs and benefits of an additional child over time. When the cost
of an additional child exceeds the benefit, a household will practice fertility control
through the strategic purchase and consumption of specific goods and through the
allocation of time in a way that reduces the probability of pregnancy (Michael, 1973).
Despite the effectiveness of current contraceptive methods, there is only one good that
effectively reduces the probability of conception to zero: abortion. (Medoff, 1973).
However, abortion differs from most modern contraceptive methods in that it
serves as a posterior method of fertility control while other methods serve as preventative
methods. Additionally, women who chose preventative fertility control measures may not
choose abortion as a posterior fertility control measure in the event of contraceptive
failure.

Studies have shown an inverse relationship between female labor force
participation and the number of children in a household. The explanation for this
occurrence is due to the idea that fertility control methods such as contraceptives and
birth control lower fertility, which then increases female labor force participation rates.
On the other hand, a positive relationship should exist between access to abortion and
female human capital investment due to a lower probability of females exiting the labor
force due to an unintended pregnancy. Overall, access to abortion as a method of fertility
control should lead to an increase in the rate of return to human capital investment and
market participation should also increase. (Kalist, 1996). Employers who demand
supplied labor would benefit from increased market participation due to better matching
of applicants to job vacancies with more applicants to choose from.
Currently, 57 out of 195 member states recognized by the UN allow abortion
upon request of the mother while other countries may allow abortion for various other
reasons including rape, incest, and endangerment of the mother’s life (UN News Center
2013). For the results of this research question to be consistent with prior academic
research and economic theory, countries that permit abortion procedures on request
should have higher female labor force participation rates and the estimated regression
coefficient for abortion should be positively related to female labor force participation
and statistically significant at the 5% level.
Literature Review
A majority of studies on female labor force participation coincide with the idea
that fertility control methods have a positive relationship with female labor force

participation. In specific, Bailey (2011) found that teenage girls in Northeast Brazil who
received abortions were the most likely cohort to be working or attending school one year
after pregnancy. This study implies that the effect of induced abortion is positively
related to female labor force participation and human capital investment in Northeast
Brazil.
It is important to differentiate between the effects of preventative contraceptive
methods and abortion procedures. Preventative contraceptive methods and abortion are
substitutes in reaching a constant level of fertility in a population. However, despite the
fundamental theory of substitute goods in economics, contraceptive use and abortion
rates do not always demonstrate a negative relationship. Marston (2003) showed evidence
of countries that experienced a parallel rise in contraceptive use and abortion rates as a
result of the need for additional fertility control measures beyond the effect of
contraceptive use for a rapidly growing population. For countries with stable birthrates, a
decline in abortion rates implies substitution of preventative contraceptive methods for
abortion (Bailey, 2011).
Studies also differ in regards to the measuring the effects of abortion on female
labor force participation rates. While some studies use abortion rates as an explanatory
variable in the regression model, other studies use a dummy variable to denote
legalization of abortion. For studies that use abortion rates in the regression model,
fertility and abortion rates were found to have varying effects that depend on multiple
factors. Levine et al. (1999) suggested that legalization of abortion in the U.S. reduced
the abortion rate for pregnancies out of wedlock by twice as much as pregnancies
conceived in wedlock. Kalist (2004) suggested that abortion has varying effects on

fertility rates, depending on race and age. For this reason, measuring the effect of an
abortion rate is less precise than using a dummy variable due to a higher likelihood of
endogeneity (Medoff, 1988).
Although most studies examine the supply side effects of legalization of abortion,
it is important to note the demand side as well. In Medoff’s study of the demand for
abortion, the price of abortion is statistically significant at the 5% level and demonstrates
an inverse relationship with the quantity demanded. This demonstrates that the
fundamental law of demand holds for abortion. Additionally, Medoff (1973) finds
income to be positive and statistically significant, which confirms abortion as a normal
good. Furthermore, Medoff (1973) found that both working and unmarried women have a
greater demand for abortion as a result of higher costs of childbearing for both cohorts.
Data
In order to estimate the true effect of abortion on female labor force participation,
the initial proposed regression model analyzes data for 38 countries using 6 variables.
Data was collected for each country and combined from two data sets. The UN World
Population Policy data set includes data for member and non-member states of the United
Nations on information pertaining to abortion policies. Information obtained from this
data set includes legalization of abortion on request, government support for family
planning, prevalence of contraceptive methods, and fertility rates. The Worldbank World
Development Indicators data set includes time series data for officially recognized
international sources on a number of topics pertaining to economic development of
countries. Information obtained from this data set includes observations for each country
on female labor force participation rates and the percentage of the female labor force with

secondary education. The proposed regression model to examine female labor force
participation rates across countries is listed below:
𝐹𝑒𝑚𝑎𝑙𝑒 𝐿𝐹̂ = 𝛽̂0 + 𝛽̂1( 𝑂𝑛 𝑅𝑒𝑞𝑢𝑒𝑠𝑡) + 𝛽̂2( 𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑒𝑝𝑡𝑖𝑣𝑒𝑠) + 𝛽̂3( 𝐹𝑒𝑟𝑡𝑖𝑙𝑖𝑡𝑦)
+ 𝛽̂4( 𝐺𝑜𝑣′
𝑡 𝑆𝑢𝑝𝑝𝑜𝑟𝑡) + 𝛽̂5 ( 𝑇𝑜𝑡𝑎𝑙 𝐿𝐹)+ 𝛽̂6 ( 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛)
Female labor force participation rate (“Female LF”), the dependent variable of the
regression equation, represents the percentage of females that are active in the total labor
force of each country. The total labor force is classified as individuals aged 15 years and
older who fit the criteria of the economically active population as defined by the
International Labor Organization.
Legalization of on request abortion procedures (“On Request”) is represented in
the regression equation by a dummy variable where a value of “1” denotes a country that
permits abortion procedures on request and a value of “0” denotes a country that does
not. Of the seven legal grounds for which a country may permit abortion procedures, on
request abortion procedures does not require a woman to request permission for an
abortion procedure. While it is possible for a woman to obtain a legal abortion in a
country for varying reasons, the on request condition is examined in order to measure the
effect of a women’s unrestricted ability to choose abortion as a method of fertility
control. In accordance with economic theory and previous studies on female labor force
participation rates, legalization of abortion on request provides women with a posterior
method of fertility control in accordance with their human capital investment and
preference for labor supply.

Government support for family planning (“Gov’t Support”) is represented in the
regression using a dummy variable where the value of “1” denotes a country in which
the government provides support for family planning services and a value of “0” for a
country that does not. Because some countries provide differing amounts of support for
family planning services, countries providing direct and indirect support are classified as
providing government support for family planning services. Governments that provide
financial support decrease the cost of fertility control for women, which could potentially
affect the amount of fertility control and the amount of abortions demanded in a country.
Data obtained for contraceptive prevalence (“Contraceptives”) is measured by the
percentage of women aged 15 to 49 who are either classified as married or in a union
using any contraceptive as a method of fertility control. The definition of a union is a
man and a woman “regularly cohabiting in a marriage-like relationship”. (UN News
Center, 2013) The prevalence and successful use of contraceptives should have a direct
effect on the number of women participating in a country’s labor force by allowing
women to choose to delay childbearing in order to accumulate human capital or to
participate in the labor force. In addition, a larger number of available contraceptive
methods could lead to more women choosing fertility control due to a better fit with
lifestyle choices and personal preferences.
Fertility (”Fertility”) represents the average number of births that would be
expected of a woman during the fertile years of her lifetime using age-specific fertility
rates. Because the number of children per woman is negatively related to female labor
force participation, countries with lower fertility rates should have higher female force
participation rates.

Education (“Education”) measures the number of females in the labor force with
secondary education as a percentage of the total female labor force. Human capital
accumulation and investment should be positively related to female labor force
participation due to an increased number of skilled workers in the labor market.
Analysis
The ANOVA results of the estimated multivariate regression model without
countries that are missing data observations are shown in Table 1 below.
Table 1-A
Means of observations for variables segmented by legalization of On Request
Table 1-B
Regression Statistics for Regression Model with All Variables

Table 1-C
Coefficients Standard Error t Stat P-value
Intercept -37.7060 9.0959 -4.1454 0.0002
On Request 2.0190 1.9429 1.0392 0.3068
Contraceptives 0.0157 0.0678 0.2316 0.8183
Fertility -0.8097 2.0666 -0.3918 0.6979
Govt Support 2.7632 2.0360 1.3572 0.1845
Total LF 1.3458 0.0866 15.5356 0.0000
Education 0.0706 0.0605 1.1677 0.2518
ANOVA Regression Output Including Countries with Data for all variables
The regression output from Table 1 shows that On Request, along with each
explanatory variable in the regression aside from Total LF, is not statistically significant
at the 5% level. However, because only 38 countries have observations for all six
variables in the regression model, the small sample size raises concerns for bias. As a
result, a separate regression was run for all countries containing observations for each
variable in the model with the exception of education in order to obtain a larger sample
size.
Table 2 shows the results of the regression model that includes all countries with
data observations for all variables with the exception of education.
Table 2-A
Regression Statistics for Regression Model Excluding Education

Table 2-B
ANOVA Regression Output Excluding Education
In the second regression, 3 variables are now statistically significant: On Request,
Gov’t Support, and Total LF. Fertility is somewhat statistically significant at the 10%
level.
The difference in the significance of the variable for On Request could be due to
the increased sample size or due to the exclusion of the education variable. In order to
further investigate the varying significance of the explanatory variables in each model
given the limited data available for education levels in different countries, a third
regression was completed. This regression included only the countries from the first
regression (countries with data observations for education), however, it does not include
the observation for Education. The results of this regression are shown in Table 3 below.

Table 3-A
Regression Statistics for Regression Model Excluding Education for Countries Included
in Table 1 Regression
Table 3
ANOVA Regression Output For Countries with Education Data Observations, Not
Including Education
Comparing the levels of significance of the values from Table 1 and Table 3, the
p-value for On Request is less significant when Education is included. However,
Contraceptives and Government Support become relatively more significant than in the
original regression from Table 1. Even though education is not found to be significant in
the results from Table 1, the overall idea is consistent with Kalist (2004) that women with
higher education are more likely to be working than women who do not have higher
education. Additionally, the coefficient estimates remain relatively stable despite the
removal of the Education variable in the second and third regression equation, which

suggests that the second regression model is valid in measuring female labor force
participation and does not have omitted variable bias.
One way to solve the issue with the original regression would be to use another
measure of education that would be more widely available at the national level in order to
account for Education without sacrificing sample size. It is likely that a large number of
the countries without data observations for Education also have certain characteristics in
common, leading to correlation among countries excluded from the regression and raises
concerns of bias. It is possible that countries with better record keeping are more
developed countries with higher incomes, which would lead to better education outcomes
for individuals living in these high-income countries.
Another likely explanation for the variability in the significance of On Request on
Female LF could be related to the explanation offered by Kalist (2004) that legalized
abortion on request affects female labor force participation rates for the women who are
most likely to seek a termination of their pregnancy. This subset of women includes
working women who may terminate their unintended pregnancy in order to avoid
dropping out of the labor force along with women who terminate their pregnancy in order
to lower the opportunity cost of human capital investment. This primarily includes
teenage women, unmarried women, and women who are economically disadvantaged
(Levine et al. 1996).
Finally, internal validity may be a concern in the model due to cultural differences
among men and women in different countries regarding the value of participation of
women in the labor force, expected returns to female human capital investment, and
utilization of abortion for reasons such as son preference and gender discrimination rather

than fertility control. In addition to issues of third factor causality within the model,
reverse causality could also influence the results of the regression coefficient estimates
due to situations such as working women who may have better resources to advocate for
legalization of abortion.
Conclusion
Overall, the regression results imply that excluding Education does not result in
instability of the regression model, and therefore the second regression is valid and useful
in estimating the effects of abortion legalization on female labor force participation at the
national level. Overall, the data doesn’t show that legalization of abortion on request
changes women’s views on abortion or create a higher reliance on it as a method of
fertility control, but instead allows women who would otherwise choose abortion as a
method of fertility control the ability to choose seek an abortion to reduce the risk of
exiting the labor force unexpectedly due to an unintended pregnancy and for women not
yet in the work force to reduce the risk of investing in human capital to later participate in
the labor force.
Despite its absolute insignificance, the variable for legalization of abortion
remains relatively more significant than contraceptives in each regression and only less
significant than total labor force participation. This implies that, holding constant all
other effects on female labor force participation, legalization of abortion on request
increases female labor force participation in a way that benefits some women by allowing
the to continue participate in the labor force and achieve better life outcomes for
themselves and their households.

Bibliography
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Oliveira, and Mario Chen-Mok. (2001) "Adolescent Pregnancy 1 Year Later: The
Effects of Abortion vs. Motherhood in Northeast Brazil." Journal of Adolescent
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Bloom, David, David Canning, Gunther Fink, and Jocelyn Finlay. (2009) "Fertility,
Female Labor Force Participation, and the Demographic Divid." Journal of
Economic Growth 14, no. 2: 79-101. Accessed January 21, 2015.
http://link.springer.com/article/10.1007/s10887-009-9039-9.
Donohue, John J., and Steven D. Levitt. (2011) "The Impact of Legalized Abortion on
Crime." The Quarterly Journal of Economics 116, no. 2: 379-405.
Henshaw, Stanley, Susheela Singh, and Taylor Haas. (1999) “Recent Trends in Abortion
Worldwide.” International Family Planning Perspectives 25, no. 1: 44-48.
Accessed February 7, 2015.
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Kalist, David. (2004) "Abortion and Female Labor Force Participation: Evidence Prior
to Roe v. Wade." Journal of Labor Research 25, no. 3: 503-14. Accessed January
1, 2015. http://link.springer.com/article/10.1007/s12122-004-1028-3.

Marston, Cicely and Cleland, John. (2003) “Relationship between Contraception and
Abortion: A Review of the Evidence.” International Family Planning
Perspectives 29, no. 1: 6-13. Accessed March 1, 2015.
http://www.jstor.org/stable/3180995
Medoff, Marshall. (2007) “An Economic Analysis of the Demand for Abortions.”
Economic Inquiry 26, no. 2: 353-359. Accessed February 1, 2015.
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"World Population Policy 2009 Publication." UN News Center. UN, n.d. Web. 02 Feb.
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