The Change in the Abortion Rate Per State

An econometric analysis

Feras Zarea

Professor Granitz

5/3/2016

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Abstract

The legalization and ethical implications of abortion are heavily debated in the United

States. This paper uses cross-sectional of the years 2010-2011 data to examine leading variables

that can explain the abortion rate in the United States. The data proposes that the per-state

variables Disposable Personal Per Capita Income, the Percentage of Adult Christians Per State,

the Percentage Change in Women Needing Contraceptives, the Percentage of Each State’s

Population That is Black or Hispanic, the Percentage of the Population in Each State That

Resides in urban areas, the Female Labor Force Participation Rate help determine the abortion

rate. A key finding is that the two variables that have the biggest influence on the abortion rate

are the Disposable Personal Income Per Capita and the Female Labor Force Participation Rate.

I. Introduction

In 1973, the Supreme Court decision of Roe V. Wade placed abortion in the forefront of

debates in the United States. The Supreme Court ruled that a woman has the “right to terminate a

pregnancy”. However, in 1992, the Supreme Court reversed a decision that allowed states to

regulate abortions more freely. Moreover, moral and religious views led to some states

restricting abortions and led to a divide in the services provided by the different states. In 2010,

there have been 13.9 abortions per state per 1000 women in the United States. And, regardless of

people’s views on abortion, such a large rate makes it important to understand the reasons why

women contemplate an abortion. An economic model can help address why the abortion rate

varies in different states.

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II. Literature Review

Christopher Garbacz, published “Abortion Demand” in 1990, in which he provides “an

economic model of abortion demand”. Garbacz concludes that the independent variables that are

significant in formulating the abortion demand model are price and income, which is the

disposable personal income per capita. Garbacz also found that the percentage of the population

in each state that lives in cities (URBAN) and the number of abortion sites located in rural areas

of each state are both statistically significant. Garbacz was limited because of “the limited data

set and the aggregate nature of the data”. But he did conclude that Medicaid, education, and

religious views are not significant factors in the overall abortion demand (although Medicaid is

significant if only teenage abortions constituted the dependent variable. two variables from

Garbacz’ paper proved significant in explaining the abortion rate, URBAN and INCOME.

Professor Donna Rothstein’s “An Economic Approach to Abortion Demand”, published

in 1992, offers multiple independent variables in her cross-sectional analysis that she notes have

provided an R2 of 87.5%. Variables she included were the price of abortion, the disposable

personal income per capita (DPINC), abortions funded by Medicaid per state, the percentage of

unmarried women aged 15 and older, the unemployment rate, the high school graduation rate for

women older than 15, the divorce and annulment rate, and a dummy variable of countries that are

in the west. Price, Medicaid, the high school graduation rate, and the divorce and marriage

annulment rate all proved to be insignificant in the abortion rate equation. DPINC, as Garbacz

also suggested, proved to be significant and replaced median annual household income in this

paper’s equation.

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In 1995, Three years after Rothstein published her paper, Sun Wei published a response

on “The American Economist” titled “A note on ‘An Economic Approach to Abortion Demand’”.

Rothstein used the national price of abortion ($213 in 1985), and used it as a dummy variable.

According to Wei, Rothstein put states that performed abortions as 1 and states that did not as 0.

Professor Wei reestimated Rothstein’s variables by using a “continuous abortion price variable”

instead of making the price a dummy variable, as Rothstein had done and found it to be more

significant. However Wei’s data suggested that the high school graduation rate is insignificant

and a hypothesis made suggested that price and Medicaid-funded abortions were both

insignificant variables in relations to abortion demand. Wei concluded that it is unsurprising that

abortion price is insignificant as the “expenditures of childbirth and child rearing” is much higher

and also because there are few substitutes to abortion. Moreover, the analysis concerning this

paper regarding both price and Medicaid backs Wei’s conclusion that they are insignificant

variables. Finally, the “continuous abortion price variable” proved difficult to calculate in

accordance to the resources available to write this paper; and the emergence of the abortion pill

makes it superfluous.

In 1997, Marshall H. Medoff published “A Pooled Time Series Analysis of Abortion

Demand”; in which he concluded that the education level and a state’s welfare payment were

statistically insignificant in relation to abortion demand and that the business cycle and female

labor force participation rate are. In this paper’s regressions, the business cycle cannot be used to

explain abortion, as it is cross-sectional; however, the female labor force participation rate

proved significant and was titled (FLFP). Moreover, in order to estimate abortion demand,

Medoff uses the independent variables: price of abortions, the “average income of women aged

over 15”, the percentage of unmarried women, “the percent of a state’s population which is

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Catholic”, and a dummy variable of states that are in the west. Medoff’s different regressions

provided coefficients of determination (R2) ranging from 0.65 to 0.70. The percent of a state that

is catholic proved insignificant in this paper’s regressions. This might be as a result of states

having a low catholic percentage and a high percentage of another form of Christianity being

high. Therefore, in this paper, the percentage of Christians per state was used instead

III. Model

A model for the abortion rate has been developed in this paper that is based on the

previous literature concerning the abortion rate and personal assumptions over which variables

might be applied to suggest an appropriate model for abortion rates. The following equation will

estimate abortion rate:

ARATE = β0 + β1 DPINC + β2 CHRIS+ β3 CONTRA + β 4 RACE + β5 URBAN + β6 FLFP

ARATE is the Abortion Rate, the dependent variable in the equation. The abortion rate is

the rate of pregnancies that end in abortion per 1000 women in each state and Washington DC.

DPINC is the Disposable Income, an independent variable, is the disposable personal per

capita income for each state and Washington DC. My previous variable, the annual household

median income, helped explain R2 within a 95% confidence level. However, disposable income

explained a higher R2 rate and therefore replaces median income as a better estimator of the

abortion rate.

CHRIS is the percentage of adult Christians per state. A more specific gauge of only

Catholic and Evangelical Christians would likely be more explanative. However, the catholic

rate proved insignificant on its own and a search for a combination of Christianity subsections

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whose followers vocally condemn abortions proved fruitless. Therefore, a broader, but likely less

explanative, percentage of all Christians per state is the second variable used to explain the

ARATE.

CONTRA is the percentage change in women needing contraceptives between the years

2010 and 2013. The percentage of total women in need of contraceptives was not provided by

the Guttmacher Institute. However, they do provide the total women in need of contraceptives

per state and the population of women per state, and a simple excel equation provided the

percentage of women per state and Washington DC that need contraceptive services. However,

the variable turned to be insignificant in the regression estimating the abortion rate (added in the

appendix). Therefore, the percentage change in women needing contraceptives, which

Guttmacher provided and will be discussed later, was used instead and proved to be a significant

indicator of the ARATE.

RACE is the percentage of each state’s population that is Black or Hispanic. URBAN is

the Percentage of the total population in urban areas. FLFP is the Female Labor Force

Participation Rate

IV. Data Gathering

The ARATE and CONTRA variables were both extracted from the Guttmacher Institute, which,

according to its mission statement, is a “leading research and policy organization committed to

advancing sexual and reproductive health and rights in the United States and globally”. The Guttmacher

Institute also provided a large part of the data that was deemed insignificant in regressions; such as

different contraceptive use rates and more specific abortion and pregnancy rates. The variable CHRIS

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was extracted from the Pew Research Center; which is describes itself as a “nonpartisan fact tank that

informs the public about the issues, attitudes, and trends shaping America and the world”. DPINC was

gathered from the State of New Jersey Department of Labor and Workforce Development. Moreover,

RACE was assimilated from the Henry J. Kaiser Family Foundation, which is “A leader in health policy

analysis and health journalism…dedicated to filling the need for trusted information on national health

issues”. The variable URBAN was taken from Iowa State University’s “Iowa Community Indicators

Program”; which obtained its data from the Decennial Census, U.S. Census Bureau. Finally, FLFP was

gathered from the Bureau of Labor Statistics database.

V. Empirical Results

Table one displays the mean, the median, the standard deviation, the minimum, and the

maximum of each variable; which will help us understand if the variables exhibit any

abnormalities.

Table 1. Summary Statistics

ARATE DPINC CHRIS CONTRA RACE URBAN FLFPMean 13.97 37761.86 0.71 1.39 0.22 74.11 60.00Median 12.60 37436.00 0.72 1.00 0.18 74.20 59.60Standard Deviation 6.25 5887.48 0.07 2.15 0.14 14.89 4.45Minimum 5.30 29571.00 0.54 -2.00 0.02 38.70 48.20Maximum 33.70 58454.00 0.86 71.00 0.58 100.00 68.40

The difference between the minimum and maximum in each variable helps show the

outliers in the data and the differences between the mean and median show if such outliers are

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worrisome. Furthermore, trend lines demonstrated where the outliers are most removed from the

means. In most variables, the outliers and their trend lines (included in the appendix), are not

significant. However, the following trend lines warrant an examination:

Trend Lines 1, 2, and 3; explaining DPINC, URBAN, and RACE respectively:

40 50 60 70 80 90 100 1100.05.0

10.015.020.025.030.035.040.0

ARATE (Y) and URBAN (X)

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.70.05.0

10.015.020.025.030.035.040.0

ARATE (Y) And RACE (X)

The trend lines concerning the DPINC, URBAN, and RACE variables (shaped as [ ] in the

graphs for distinction from other plot points) include the same outlier, Washington D.C., at the

points $58,454, 100%, and 0.58 respectively. Since Washington D.C. is not a state and is instead

a federal district, it frequently appears as an outlier. However, without Washington D.C. the

URBAN variable is not significant at the 95 percent level and that in turn leads to CONTRA also

being insignificant at the 95 percent level. Therefore, even though it will marginally skew the

data, keeping Washington D.C. as an independent variable provides a more wide-ranging.

Finally, the regression results for Washington D.C. are included in the appendix.

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Trend line 4: Explaining CONTRA

CONTRA has one outlier that is far from the mean. Between the years 2010-2013, North Dakota

increased their contraceptive use by 10%. According to Ilyce Glink’s article “Top 10 Fastest-

Growing States”, published by CBS News, North Dakota has the highest the population growth

rate in the United States between the years 2010-2015. The article simply states “oil has meant

growth for North Dakota.” The increase job opportunities as a result of oil drilling also meant

more male workers travel to North Dakota to drill the oil. Moreover, that means females follow

the males to North Dakota. That, in turn, leads to the influx of females requiring more

contraceptive use in that state. Finally, that sudden influx led to North Dakota becoming the

outlier.

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Table 2 provides the coefficients for each variable; which shows if they are directly or inversely

related to the ARATE. The table also shows the significance level and standard error of each

variable.

Table 2. Final Regression: The Abortion Rate

Independent Variable Coefficients standard errors P-Value

Constant 36.09358 11.23013 0.002452

DPINC ** 0.000543 0.000123 6.64E-05

CHRIS** -25.1616 7.865951 0.002559

CONTRA** -0.68191 0.239121 0.006598

RACE* 10.8403 5.241977 0.044552

URBAN* 0.103417 0.044899 0.026046

FLFP** -0.56428 0.166611 0.001499

R2 = 0.742

95% *

99% **

It can be concluded that, at the 95 percent confidence level, the six variables in table 2

explain the abortion rate. Table 3, titled “Impact Table”, shows the impact of each variable on

the Abortion Rate. RACE and URBAN proved to be significant at the 95% level. Moreover,

DPINC, CHRIS, CONTRA, and FLFP all proved to be significant at the 99% level.

Furthermore, CHRIS, CONTRA, and FLFP proved to be inversely related to the abortion rate;

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while DPINC, RACE, and Urban were directly related to it. These six independent variables

provided an R2 of 0.742.

Table 3 helps us understand the impact of each independent variable on the dependent

variable (ARATE). It displays the mean of each variable, and how an increase or decrease of the

mean by half its standard deviation alters the ARATE. Moreover, it also shows the percentage

change of the ARATE.

Table 3: Impact Table

Average Half a

Standard

Deviation

Y if each

independent

variable separately

decreases by half a

standard deviation

Y if each

independent variable

separately increases

by half a standard

deviation

Percent

change of

ARATE

DPINC 37761.86 2943.74 15.570 12.375 11.4%

FLFP 59.99804 2.23 12.72 15.23 -9.0%

CHRIS 0.711569 0.04 13.04 14.91 -6.7%

URBAN 74.10784 7.44 14.74 13.20 5.5%

RACE 0.224118 0.07 14.72 13.22 5.4%

CONTRA 1.392157 1.08 13.24 14.71 -5.3%

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The mean abortion rate per state is 13.97 abortions per 1000 women. in the following

paragraphs, the effect of each independent variable on the abortion rate will be discussed,

ordered from the most impactful of the variables (DPINC) to the least impactful (CONTRA).

The most influential variable on the abortion rate is the disposable personal per capita

income, DPINC. As the disposable per capita income changes by half a standard deviation

($2943.74), the rate of abortions increases by 11.4%. In her “An economic approach to abortion

demand”, Rothstein explained that income effect is the first reason for DPINC being an

impactful variable, as when “income rises, the woman’s time is more valuable…the demand for

quantity of children falls” (Rothstein, P59).

The second variable that is proven at the 95% confidence level to affect the abortion rate

is the female labor force participation rate, FLFP. The FLFP is inversely related to the abortion

rate, and when it increases by half a standard deviation, (2.23%), the abortion rate decreases by

an average of 9%. An explanation for states were there was a higher FLFP committing fewer

abortions is that those females have less leisure time; which leads to overall fewer pregnancies.

The rate of Christianity in each state (CHRIS) is another variable that is inversely related

to the abortion rate. When the percentage of Christians in a state is half a standard deviation (4%)

higher than the mean Christian rate (71.2%), there are, on average, 6.7% fewer abortions. Such a

conclusion is a logical one given that most of the orthodox subsections of the Christian religion,

(such as Catholicism and Evangelism) tend to condemn abortions.

The percentage of the population in each state that lives in cities (URBAN) proved to be

directly related to the abortion rate. This means that, when a higher percentage of people leave in

cities, the rate of abortions increases by 5.5%. Christopher Garbacz connected this with the fact

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that there are fewer abortion centers in rural areas than in urban ones. However, the data for the

rate of abortion centers in rural areas could not be obtained in the research for this paper.

The percentage of a population that is Black or Hispanic (RACE) also proved to be

directly related to the rate of abortions. As a state has more Black and Hispanic people, the rate

of abortion tends to increase by 5.4%.

The final variable that proved to be related to the abortion rate is the percent change in

women needing contraceptive services and supplies, dating the years 2010-2013 (CONTRA).

Regressions including the actual rate of contraceptive use per state proved it to be insignificant in

explaining the abortion rate; yielding a P-value of 0.72, a t-statistic of 0.54, and an R2 lower of

0.69. However, when the percent change of women needing contraceptives was examined

instead, the regression proved more significant. A reason the percent change is more accurate

than the actual rate of contraceptive use is likely as a result of the women in certain states being

encouraged to use more contraceptives, which leads to a lower abortion rate.

VI. Conclusion

Since the six independent variables change it, a state wishing to influence the abortion

rate must change one or more of the six variables. Variables that do not change directly as a

result of government decisions are CHRIS, RACE, and possibly URBAN. The rate of Christians

cannot change as a result of state decisions as a result of the separation of church from state.

Also, a person’s race is biological and a state cannot influence it. The percent of a population

that lives in urban areas is difficult for a state to change, but not impossible. States can

incentivize or corporations by offering lower taxes and/or by subsidizing them, which would lead

to corporations urbanizing rural areas and influence the URBAN variable. Additionally, a tax

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increase on households would decrease DPINC. The state can also teach females more skills and

provide them incentives to join the labor force, which would increase FLFP and thus decrease

the abortion rate. Finally, female contraceptive use can change by a multitude of actions, such as

the taxation, funding, and sexual education of contraceptives and their use.

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References:

Literature:

Garbacz, Christopher. “Abortion Demand”. Population Research and Policy Review 9.2 (1990): 151–160. Web...www.jstor.org/stable/40229889. Accessed 17-04-2016.

Rothstein, Donna S. “An Economic Approach to Abortion Demand”. The American Economist 36.1 (1992): 53–64. Web... www.jstor.org/stable/25603912. Accessed 16-04-2016.

Sun, Wei. “A Note on "an Economic Approach to Abortion Demand"”. The American Economist 39.2 (1995): 90–91. Web... www.jstor.org/stable/25604048. Accessed 14-04-2016.

Medoff, Marshall H.. “A Pooled Time-series Analysis of Abortion Demand”. Population Research and Policy Review 16.6 (1997): 597–605. Web...http://www.jstor.org/stable/40230168. Accessed 17-04-2016

Medoff, Marshall H.. “The Response of Abortion Demand to Changes in Abortion Costs”. Social Indicators Research 87.2 (2008): 329–346. Web... http://www.jstor.org/stable/27734665. Accessed 17-04-2016. Accessed 17-04-2016

Glink, Ilyce, “Top 10 Fastest-Growing States.” CBS News,

http://www.cbsnews.com/media/top-10-fastest-growing-states/11/. Accessed 29-04-2016.

Data:

The Guttmacher Institute (ARATE, CONTRA), https://data.guttmacher.org/states . Accessed 16-03-2016.

The Pew Research Center (CHRIS), http://www.pewforum.org/religious-landscape-study/christians/christian/ Accessed 04-27-2016.

The Henry Kaiser Family Foundation (RACE), http://kff.org/other/state-indicator/distribution-by-raceethnicity/#. Accessed 04-27-2016

Iowa State University’s Iowa Community Indicators Program, extracted from the U.S. Census Bureau (URBAN), http://lwd.dol.state.nj.us/labor/lpa/industry/incpov/dpci.htm . Accessed 04-29-2016

The Bureau of Labor Statistics (FLFP) http://www.bls.gov/lau/lastrk10.htm. Accessed 04-27-2016

New Jersey Department of Labor and Workforce Development, Bureau of Economic Analysis http://lwd.dol.state.nj.us/labor/lpa/industry/incpov/dpci.htm. Accessed 04-29-2016

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Appendix:

A Variance Inflation Test proved that there is no multicollinearity

00.5

11.5

22.5

33.5

44.5

VIF; 3.031

VIF

Axis Title

Trends:

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.70.05.0

10.015.020.025.030.035.040.0

ARATE (Y) And RACE (X)

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The regression that includes the percentage of women per state who need contraceptives. This proved

tremendously insignificant and the variable has been replaced by the more specific CONTRA.

Regression Statistics

R Square 0.694316Adjusted R Square 0.652631Standard Error 3.683508Observations 51

ANOVA

df SS MS FSignificanc

e F

Regression 61355.99

9225.999

916.6565

5 6.56E-10

Residual 44597.002

113.5682

3

Total 501953.00

2

Coefficients

Standard Error t Stat P-value

Intercept 47.0555515.2517

13.08526

3 0.00351

DPINC 0.0005320.00013

43.96171

40.00026

9

CHRIS -30.5498.32628

6 -3.668980.00065

4

percentage of women per state needing contraceptive services -1.74247

20.15107 -0.08647

0.931485

RACE 9.3634885.97712

61.56655

40.12438

4

URBAN 0.0901080.04955

71.81826

60.07583

5

Female LF participation rate -0.65270.18170

3 -3.592120.00082

2

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The first of the regressions concerning DC. While accurate at the 90th percent level, URBAN proved insignificant at the 95th percent level, which is the aim of this paper.

Regression StatisticsMultiple R 0.886451R Square 0.785795Adjusted R Square 0.755906Standard Error 3.085287Observations 50

ANOVA

df SS MS FSignifican

ce F

Regression 6 1501.546250.257

726.2903

5 6.86E-13

Residual 43 409.31689.51899

5Total 49 1910.863

Coefficients

Standard Error t Stat P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept 33.93273 10.260783.30703

20.00191

1 13.2398954.6255

613.2398

954.6255

6

DPINC 0.00071 0.0001245.71719

69.41E-

07 0.000459 0.000960.00045

9 0.00096

CHRIS -27.3564 7.204646-

3.797060.00045

5 -41.886-

12.8269 -41.886-

12.8269

CONTRA -0.49595 0.225831-

2.196090.03352

7 -0.95138-

0.04051-

0.95138-

0.04051

RACE 16.48586 5.1032133.23048

70.00237

1 6.19425226.7774

76.19425

226.7774

7

URBAN 0.071397 0.0421731.69294

10.09770

1 -0.013650.15644

7-

0.013650.15644

7

FLFP -0.58882 0.152089-

3.871540.00036

3 -0.89554 -0.2821-

0.89554 -0.2821

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The second regression, which excludes URBAN, has CONTRA as insignificant at the 95 percent

level. Therefore, without Washington D.C., only four variables are significant at the 95 percent

level and including D.C. means more variables can explain abortion.

Regression StatisticsMultiple R 0.878361R Square 0.771518Adjusted R Square 0.745554Standard Error 3.150031Observations 50

ANOVA

df SS MS FSignifican

ce F

Regression 5 1474.265294.852

929.7149

9 4.57E-13

Residual 44 436.59879.92269

7Total 49 1910.863

Coefficients

Standard Error t Stat P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept 38.51612 10.104853.81164

60.00042

6 18.1511358.8811

218.1511

358.8811

2

DPINC 0.00077 0.0001216.33887

91.07E-

07 0.0005250.00101

40.00052

50.00101

4

CHRIS -31.1828 6.984502-

4.464575.52E-

05 -45.2591-

17.1064-

45.2591-

17.1064

CONTRA -0.43697 0.22781-

1.918130.06159

8 -0.896090.02215

1-

0.896090.02215

1

RACE 21.39992 4.2853724.99371

39.84E-

06 12.7633230.0365

212.7633

230.0365

2

FLFP -0.58865 0.155281-

3.790850.00045

3 -0.90159 -0.2757-

0.90159 -0.2757