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less than 7 pages need a proofreading. improving the structure of the sentences as well as grammar needed.

1
Data and methodology description
Through the study of a sensitive subject like teenage pregnancy and the extent of the
impact of this problem on education. Our study focused on three countries that have a high rate
teenage pregnancy in Sub-Sharan African countries (Congo, Mali, and Uganda) in a period of time
from 1968 to 2017. Data used for the study were downloaded from the World Bank dataset. We
have two models that show the impact of this issue on the school enrollment as well as economic
growth. Ordinary Least Squares regression (OLS) method used to predict outputs' values for
sample. The first model:
π πβπππ ππππππππππ‘
= π½0 + π½1 π‘ππππππ πππππππππ¦ + π½2 πΊπ·π ππππ€π‘β πππ πππππ‘π
+ π½3 πππππ π π‘π πππππ‘πππππ‘π¦ + ππ‘
Where the dependent variable Y is the rate of students who are in secondary education fulfills
the provision of basic education that started at the primary level, π½1 teenage pregnancy rate is
our first independent variables in order to measure the impact on school enrollment, π½2 GDP
growth per capita (proxy for poverty), π½3 access to electricity is a good indicator in order to
capture the life standard.
2
The second model:
GDP per capita
= π½0 + π€ ππ(π‘ππβππππππ¦) +
π½1 School enrollment + π½2
Foreign
investment + π½3 Inflation + π½5 urban population growth + π½6 Exports +
π½7 Dum_Mali + π½8 π·π’πππ¦1 + π½9 π·π’πππ¦_2 + ππ‘
Through this model is related the first in terms of capture the impact of school enrollment on
GDP per capita which we assume that is a good representative economic growth. based on the
Solow model, A is the level of technology in each country and w is the weight. Through level of
technology, we found that each country has a different top trade partner. The table below show
the top trade partner in each country.
Country
Top Partner Trade
Mali
Switzerland 30.4%
Congo
China 39.8%
Uganda
Kenya 20.9%
We the dummy variables in order to measure a significant shift on GDP per capita in each
country. The inflation (proxy), Foreign investment, and Exports variables have a significant on
GDP; thus, we should take it into count. AR process was applied in the regressions.
3
Results and Discussion Model 2
GDP_PER_CAPITA_GROWTH SCHOOL_ENROLLMENT...FOREIGN_IN... INFLATION_... URBAN_POP...
GDP_PER_C...
SCHOOL_EN...
FOREIGN_IN...
INFLATION_...
URBAN_POP...
EXPORTS
LN_W_TECH
1.000000
0.347325
0.283058
-0.024676
0.301388
-0.194417
-0.037812
0.347325
1.000000
0.303234
0.036623
0.417346
-0.246429
-0.052706
0.283058
0.303234
1.000000
-0.132993
-0.181205
0.414076
-0.085165
-0.024676
0.036623
-0.132993
1.000000
0.355232
-0.136952
0.047343
0.301388
0.417346
-0.181205
0.355232
1.000000
-0.573731
-0.162126
EXPORTS
LN_W_TECH
-0.194417
-0.246429
0.414076
-0.136952
-0.573731
1.000000
0.211789
-0.037812
-0.052706
-0.085165
0.047343
-0.162126
0.211789
1.000000
The table above indicates the correlation among the variable that we use on our
regression in Model 2. It shows that there is a positive correlation between school enrolment and
GDP Per Capita Growth in Congo, Mali, and Uganda which does not mean causality. However,
our dependent variables are not highly correlated which mean they would capture the actual
result
Dependent Variable: GDP_PER_CAPITA_GROWTH
Method: Panel Least Squares
Sample (adjusted): 1981 2016
Periods included: 36
Cross-sections included: 3
Total panel (unbalanced) observations: 91
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
SCHOOL_ENROLLMENT
FOREIGN_INVESTMENT
INFLATION_GDP
D(URBAN_POP_GROWTH)
D(EXPORTS)
DUM_MALI
DUMMY_1
DUMMY_2
LN_W_TECH
GDP_PER_CAPITA_GROWTH(-1)
GDP_PER_CAPITA_GROWTH(-2)
GDP_PER_CAPITA_GROWTH(-3)
-4.790522
6.108952
6.55E-10
-0.001203
0.189994
-0.091714
0.439315
11.12914
-11.05418
1.788783
0.064732
0.184693
0.128594
4.568553
2.953326
4.48E-10
0.018531
1.057464
0.082307
0.847922
2.163801
3.118664
1.633551
0.096184
0.075229
0.078786
-1.048586
2.068499
1.462009
-0.064902
0.179670
-1.114287
0.518108
5.143329
-3.544525
1.095028
0.673004
2.455061
1.632190
0.2976
0.0419
0.1478
0.9484
0.8579
0.2686
0.6058
0.0000
0.0007
0.2769
0.5029
0.0163
0.1067
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.635048
0.578902
3.013547
708.3542
-222.4933
11.31058
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.487480
4.643937
5.175676
5.534370
5.320387
2.141569
4
Dependent Variable: GDP Per Capita Growth
Independent Variables:
1- School Enrolment growth.
2- Foreign Investment % of GDP
3- Inflation % of GDP.
4- Urban population Growth.
5- Export % of GDP.
6- Dummy for Mali.
7- Dummy 2 is for the shift on GDP in 1990 for 1993 Congo
8- Dummy 1 is for the shift on GDP in 1987 for Mali
9- Level of technology weighed by top partner
10- lag 1,2,3 for GDP Per Capita Growth
In this model, we have attempting to see the impact of school enrollment on GDP per
capita which we assume that is a good representative economic growth. As school enrollment is
affected negatively by teenager pregnancy in the first model because teenager pregnancy
statically significant impact school enrolment. As consequences, the outcomes of education as
whole in these countries would be affected negatively due increase drop-out rate. However, in
term of measure the impact of these circumstances on economic growth and development, we
considered school enrollment on our measurement of economic growth to see the exactly
impact.
After testing the model from any violation, we found that some variables have problem
with unit root. Urban population Growth has problem with unit root in level, so we generate first
different for that variable. Also, we found problem on variable of Export and we generate first
different too.
5
In addition, we consider in this model autocorrelation which corrected by conducting tree
AR processes. Also, Multicollinearity have checked by using VIF of all the variables and the result
shows no Multicollinearity because the value of VIF were under 10.
In addition, the sample on this model after adjustment is consider the period between
1981 and 2016 which is good enough to see the long run impact on economic growth (GDP Per
Capita Growth). Also, the total observations are in total 91 which also is effective to see the actual
effect because it is more than 45 observations, and we will be able to find appropriate result.
However, the dependent variables mean is 0.487a and the stander deviation of dependent
variables is 4.643.
Table
The regression result Variable
Independent Variable
Constant
School Enrolment
Foreign Investment
Inflation
Urban Population Growth
Export
Dummy (Mali country)
Dummy 1 (Mali shift in 1987)
Dummy 2 (Congo shift in 1990 to 1993)
Level of Technology
Equation (2)
Ξ GDP Per capita
-4.79
(4.56)
6.1**
(2.95)
6.5
(4.48)
-0.001
(0.018)
.0189
(1.057)
-0.091
(0.082)
0.439
(0.847)
11.12***
(2.163)
β-11.05***
(3.11)
1.788
(1.633)
6
0.0647
(0.096)
0.18***
AR (2)
(0.09)
0.128*
AR (3)
(0.075)
R-squared
0.635
Durbin-Watson statistic
2.14
* indicates significance at 1% level, ** significance at 5% level, *** significance at 10% level
AR (1)
After running the regression above, the result illustrates almost all the variables are
statistically insignificant except school enrolment, the dummy 1 variable for Mali shift in 1987,
and dummy 2, as well as the three AR processes which would capture the effect on GDP in
previous years on current GDP. In addition, the value of the Durbin-Watson statistic is 2.14 which
seem good and R-squared value is 0.63, which indicates that the variables could be a good fit
more than 0.5. However, the school enrolment is statistically significant at the level of 5% level
and have positive influence on GDP per capita growth, in other word a one percent change on
school enrolment is associated with an increase on GDP per capita by 6.1.
The dummy variable of the shift on GDP per capita growth of Mali in 1987 which has
positive impact on economic growth in these countries and the its probability is statically
significant. As well as, the dummy 2 which indicate shift on GDP Per Capita of Congo between
1990 and 1993 is statically significant. These two dummies created to see the real impact of
school enrolment on GDP Per Capita Growth by ignoring these years that may give us different
result. Moreover, the dummy that generated to capture the impact of Mali country on GDP Per
Captiva of these country is statically insignificant with positive sign. Which mean, there are no
impact of this dummy of GDP.
7
Moreover, when we look to the three AR processes that added to the model to solve the
problem of autocorrelation, they indicate different result. The first AR (1) is insignificant with
positive sing of coefficient. The second AR (2) processes has a statically significant impact on our
independent variable (GDP Per Capita Growth) at 1% level. Also, the last AR (3) processes is
statically significant at 10% level.
The regression result gives an evidence that as the school enrolment increase the GDP
Per Capita would increase too. Which mean, the educational outcomes in these countries are a
fundamental factor that will enhance economic development in long run. Investing in education
will result a benefit on economic growth. However, it is essential that these countries should
overcoming the violations that may impact education negatively such as teenage pregnancy and
poverty as well as access electricity and transportation.
...

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Running Head: DATA METHODOLOGY AND DESCRIPTION

Data Methodology description

Name

Institution Affiliation

12/14/2017

1

DATA METHODOLOGY AND DESCRIPTION

Data and methodology description

This study is a sensitive subject on teenage pregnancy and the impact of this problem on

education. Our study focused on three countries that have a high rate of cases of teenage

pregnancy in the Sub-Saharan African, these countries are; Congo, Mali, and Uganda. The study

focused on the period from 1968 to 2017. The data used for the study was downloaded from the

World Bank database. We came up with two models that show the impact of this issue on school

enrollment as well as economic growth. Ordinary Least Squares regression (OLS) method was

used to predict outputs' values for the sample. The first model:

π πβπππ ππππππππππ‘

= π½0 + π½1 π‘ππππππ πππππππππ¦ + π½2 πΊπ·π ππππ€π‘β πππ πππππ‘π

+ π½3 πππππ π π‘π πππππ‘πππππ‘π¦ + ππ‘

In this model, the dependent variable Y is the rate of students who are in secondary

education who started at the primary level, it represents the fulfillment of provision of basic

education, π½1 is our first independent variable which represents teenage pregnancy rate in the

measure of its impact on school enrollment, π½2 represents the GDP growth per capita (proxy for

poverty), π½3 represents access to electricity, which is a good indicator in order to capture the life

standard.

2

DATA METHODOLOGY AND DESCRIPTION

3

The second model:

GDP per capita

=π½0 + π€ 1π(π‘ππβππππππ¦) +

π½1School enrollment + π½2 Foreign

investment + π½3Inflation + π½5urban population growth + π½6Exports +

π½7Dum_Mali + π½8 π·π’πππ¦1 + π½9 π·π’πππ¦_2 + ππ‘

This model is r...

Review

Anonymous

awesome work thanks

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