This document is an econometric analysis of factors influencing state abortion rates in the United States from 2010-2011. It finds that the variables with the biggest impact on abortion rates are disposable personal income per capita and female labor force participation rate. Higher income is correlated with higher abortion rates, while higher female labor participation is correlated with lower abortion rates. The analysis uses regression analysis to examine the relationship between abortion rates and various economic and demographic variables in each state.

1. 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 “leadingresearchandpolicyorganizationcommittedto
advancingsexual andreproductivehealthandrightsinthe UnitedStatesandglobally”.The Guttmacher
Institutealsoprovidedalarge part of the data that wasdeemedinsignificantinregressions;such as
differentcontraceptiveuse ratesandmore specificabortion andpregnancy rates.The variable CHRIS

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was extractedfromthe PewResearch Center; whichis describesitself asa“nonpartisanfacttank that
informsthe publicaboutthe issues,attitudes, andtrendsshapingAmericaandthe world”.DPINCwas
gatheredfromthe Stateof NewJersey Departmentof Laborand WorkforceDevelopment. Moreover,
RACE wasassimilatedfromthe Henry J.KaiserFamily Foundation, whichis“A leaderinhealthpolicy
analysisandhealthjournalism…dedicatedtofillingthe needfortrustedinformationonnational health
issues”.The variable URBAN wastakenfrom Iowa StateUniversity’s“IowaCommunityIndicators
Program”; which obtained itsdatafromthe Decennial Census,U.S.CensusBureau. Finally,FLFPwas
gatheredfromthe Bureau of LaborStatistics 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 FLFP
Mean 13.97 37761.86 0.71 1.39 0.22 74.11 60.00
Median 12.60 37436.00 0.72 1.00 0.18 74.20 59.60
Standard
Deviation 6.25 5887.48 0.07 2.15 0.14 14.89 4.45
Minimum 5.30 29571.00 0.54 -2.00 0.02 38.70 48.20
Maximum 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:
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
40 50 60 70 80 90 100 110
ARATE (Y) and URBAN (X)

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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.
0.0
10.0
20.0
30.0
40.0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
ARATE (Y) And RACE (X)

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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
Trends:
VIF, 3.031
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
AxisTitle
VIF

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0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
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 0.694316
Adjusted R Square 0.652631
Standard Error 3.683508
Observations 51
ANOVA
df SS MS F
Significanc
e F
Regression 6
1355.99
9
225.999
9
16.6565
5 6.56E-10
Residual 44
597.002
1
13.5682
3
Total 50
1953.00
2
Coefficient
s
Standar
d Error t Stat P-value
Intercept 47.05555
15.2517
1
3.08526
3 0.00351
DPINC 0.000532
0.00013
4
3.96171
4
0.00026
9
CHRIS -30.549
8.32628
6 -3.66898
0.00065
4
percentage of women per state needing
contraceptive services -1.74247
20.1510
7 -0.08647
0.93148
5
RACE 9.363488
5.97712
6
1.56655
4
0.12438
4
URBAN 0.090108
0.04955
7
1.81826
6
0.07583
5
Female LF participation rate -0.6527
0.18170
3 -3.59212
0.00082
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 Statistics
Multiple R 0.886451
R Square 0.785795
AdjustedR
Square 0.755906
Standard
Error 3.085287
Observation
s 50
ANOVA
df SS MS F
Significan
ce F
Regression 6 1501.546
250.257
7
26.2903
5 6.86E-13
Residual 43 409.3168
9.51899
5
Total 49 1910.863
Coefficien
ts
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 33.93273 10.26078
3.30703
2
0.00191
1 13.23989
54.6255
6
13.2398
9
54.6255
6
DPINC 0.00071 0.000124
5.71719
6
9.41E-
07 0.000459 0.00096
0.00045
9 0.00096
CHRIS -27.3564 7.204646
-
3.79706
0.00045
5 -41.886
-
12.8269 -41.886
-
12.8269
CONTRA -0.49595 0.225831
-
2.19609
0.03352
7 -0.95138
-
0.04051
-
0.95138
-
0.04051
RACE 16.48586 5.103213
3.23048
7
0.00237
1 6.194252
26.7774
7
6.19425
2
26.7774
7
URBAN 0.071397 0.042173
1.69294
1
0.09770
1 -0.01365
0.15644
7
-
0.01365
0.15644
7
FLFP -0.58882 0.152089
-
3.87154
0.00036
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 Statistics
Multiple R 0.878361
R Square 0.771518
AdjustedR
Square 0.745554
Standard
Error 3.150031
Observation
s 50
ANOVA
df SS MS F
Significan
ce F
Regression 5 1474.265
294.852
9
29.7149
9 4.57E-13
Residual 44 436.5987
9.92269
7
Total 49 1910.863
Coefficien
ts
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 38.51612 10.10485
3.81164
6
0.00042
6 18.15113
58.8811
2
18.1511
3
58.8811
2
DPINC 0.00077 0.000121
6.33887
9
1.07E-
07 0.000525
0.00101
4
0.00052
5
0.00101
4
CHRIS -31.1828 6.984502
-
4.46457
5.52E-
05 -45.2591
-
17.1064
-
45.2591
-
17.1064
CONTRA -0.43697 0.22781
-
1.91813
0.06159
8 -0.89609
0.02215
1
-
0.89609
0.02215
1
RACE 21.39992 4.285372
4.99371
3
9.84E-
06 12.76332
30.0365
2
12.7633
2
30.0365
2
FLFP -0.58865 0.155281
-
3.79085
0.00045
3 -0.90159 -0.2757
-
0.90159 -0.2757