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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‘ π‘β„Žπ‘œπ‘œπ‘™ π‘’π‘›π‘Ÿπ‘œπ‘™π‘™π‘šπ‘’π‘›π‘‘ = 𝛽0 + 𝛽1 π‘‘π‘’π‘’π‘›π‘Žπ‘”π‘’ π‘π‘Ÿπ‘’π‘”π‘›π‘Žπ‘›π‘π‘¦ + 𝛽2 𝐺𝐷𝑃 π‘”π‘Ÿπ‘œπ‘€π‘‘β„Ž π‘π‘’π‘Ÿ π‘π‘Žπ‘π‘–π‘‘π‘Ž + 𝛽3 π‘Žπ‘π‘π‘’π‘ π‘  π‘‘π‘œ π‘’π‘™π‘’π‘π‘‘π‘Ÿπ‘–π‘π‘–π‘‘π‘¦ + πœ€π‘‘ 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, 𝛽1 teenage pregnancy rate is our first independent variables in order to measure the impact on school enrollment, 𝛽2 GDP growth per capita (proxy for poverty), 𝛽3 access to electricity is a good indicator in order to capture the life standard. 2 The second model: GDP per capita = 𝛽0 + 𝑀 𝑙𝑛(π‘‘π‘’π‘β„Žπ‘›π‘œπ‘™π‘œπ‘”π‘¦) + 𝛽1 School enrollment + 𝛽2 Foreign investment + 𝛽3 Inflation + 𝛽5 urban population growth + 𝛽6 Exports + 𝛽7 Dum_Mali + 𝛽8 π·π‘’π‘šπ‘šπ‘¦1 + 𝛽9 π·π‘’π‘šπ‘šπ‘¦_2 + πœ€π‘‘ 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) –-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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nkostas
School: Purdue University

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Running Head: DATA METHODOLOGY AND DESCRIPTION

Data Methodology description
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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‘ π‘β„Žπ‘œπ‘œπ‘™ π‘’π‘›π‘Ÿπ‘œπ‘™π‘™π‘šπ‘’π‘›π‘‘
= 𝛽0 + 𝛽1 π‘‘π‘’π‘’π‘›π‘Žπ‘”π‘’ π‘π‘Ÿπ‘’π‘”π‘›π‘Žπ‘›π‘π‘¦ + 𝛽2 𝐺𝐷𝑃 π‘”π‘Ÿπ‘œπ‘€π‘‘β„Ž π‘π‘’π‘Ÿ π‘π‘Žπ‘π‘–π‘‘π‘Ž
+ 𝛽3 π‘Žπ‘π‘π‘’π‘ π‘  π‘‘π‘œ π‘’π‘™π‘’π‘π‘‘π‘Ÿπ‘–π‘π‘–π‘‘π‘¦ + πœ€π‘‘

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, 𝛽1 is our first independent variable which represents teenage pregnancy rate in the
measure of its impact on school enrollment, 𝛽2 represents the GDP growth per capita (proxy for
poverty), 𝛽3 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
=𝛽0 + 𝑀 1𝑛(π‘‘π‘’π‘β„Žπ‘›π‘œπ‘™π‘œπ‘”π‘¦) +

𝛽1School enrollment + 𝛽2 Foreign

investment + 𝛽3Inflation + 𝛽5urban population growth + 𝛽6Exports +
𝛽7Dum_Mali + 𝛽8 π·π‘’π‘šπ‘šπ‘¦1 + 𝛽9 π·π‘’π‘šπ‘šπ‘¦_2 + πœ€π‘‘
This model is r...

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Anonymous
awesome work thanks

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