Gor Varosyan | American University of Armenia (original) (raw)
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Papers by Gor Varosyan
This paper examines factors that are affecting abortion rates in 66 developing and developed coun... more This paper examines factors that are affecting abortion rates in 66 developing and developed countries. The variables examined are abortion rate, religion, contraceptive prevalence rate, maternal mortality rate, male to female birth ratio, GDP per capita, health expenditure, and Human Development Index. In order to understand the relationship and the weight of the factors affecting abortion rates, the paper uses the population, multiple linear regression model, on the cross-sectional data which is valid when several assumptions are met. The results show that the most statistically significant variable is religion. After removing the Human Development Index from the model, the significance level of the GDP per capita increased. A rule that was used to determine the optimal model was to not include any covariate that had a p-value of 10% or more, omitting variables having a higher Variance Inflation Factor than 10. In addition to this, covariates that did not contribute to a higher R-squared were excluded from the model.
This paper examines factors that are affecting abortion rates in 66 developing and developed coun... more This paper examines factors that are affecting abortion rates in 66 developing and developed countries. The variables examined are abortion rate, religion, contraceptive prevalence rate, maternal mortality rate, male to female birth ratio, GDP per capita, health expenditure, and Human Development Index. In order to understand the relationship and the weight of the factors affecting abortion rates, the paper uses the population, multiple linear regression model, on the cross-sectional data which is valid when several assumptions are met. The results show that the most statistically significant variable is religion. After removing the Human Development Index from the model, the significance level of the GDP per capita increased. A rule that was used to determine the optimal model was to not include any covariate that had a p-value of 10% or more, omitting variables having a higher Variance Inflation Factor than 10. In addition to this, covariates that did not contribute to a higher R-squared were excluded from the model.