Mario Arreola Proposal EC 5552 Econometrics I Spring 2020

Women’s Development in Latin America

My research would analyse Gross Domestic Product per Capita against six variables representing women’s well being. For example one of the variables is the percentage of women in government. These six variables would try to measure women’s well being and compare it against gross domestic product per capita. This idea goes hand in hand with gender equality and is an important topic because it would find a correlation between women well being and GDP per capita. The six explanatory variables would only measure female data. The responsive variable is Gross Domestic Product per capita and it would represent the standard of living. This research would assume a higher gross domestic product per capita leads to a higher quality of living. The analysis would cover four Latin American countries. The countries are Chile, Colombia, Mexico, Peru. The reason these four countries is because some of these countries suffer from gender inequality. For example, Peru has a rank of 32.0 percent in the Violence Against Women indicator. Developed countries like the United States have a rank of 11.0 percent in the Violence Against Women indicator. Canada has a rank of 8.0 percent.

Finding out whether there is a correlation between the six datasets and GDP per capita is important because it would try to find a correlation with the reduction of violence against women against a higher GDP per capita. This research paper would also provide a correlation on whether higher percentages of women in politics benefit GDP per capita. The third explanatory variable would be teenage pregnancies and how it affects GDP per capita. For example, when a woman has a child at a younger age, the woman would most likely lose in getting a higher education as in college degree because they will most likely be taken care of the child. Thus, making the output of the women less than if the woman have had the opportunity to get a college degree. This goes hand and hand with the idea of the female labor force participation increasing GDP per capita. More educated female labor would lead to a higher female labor force participation and will increase GDP per capita.

There has been no research on the six indicators combined like in this analysis. There has been research on each of the six explanatory variables. For example, Sandra L. Hofferth wrote about the dangers of teen childbearres. Sandra wrote her paper for the National Center for Biology Technology Information (NCBI). She talked about how having children at a younger age can lead to less lifetime income, because of the lower probability of the teen childbearrer being able to complete her education. She also talked about how this pattern may continue with the teen childbeares children. The child has a higher probability of having a child at teenage years. Another example is how violence against women leads to a negative ripple effect in the individual micro-level and the macroeconomy level. Also Walby and Olive wrote about the costof violence against women can have. For example the cost of damage to property, enhancing phone security, moving expenses, home repossession and personal legal costs. Economic Gains From Gender Inclusion: New Mechanisms, New Evidence published in 2018. The UN has publications about the same topic of gender equality. For example, Women in Politics: 2020 which is a report on how the political system in a country is diversified with men and women politicians. This goes hand and hand with this analysis explanatory variable of Proportion of seats held by women in national parliament.

I am going to use panel data analysis for the research paper . The reason I am using panel data is because it would analyse the data in cross sectional and time series form. The cross sectional would be the four countries (Chile, Colombia, Mexico, & Peru) and the time series would be between 2000 to 2015. Panel data would also lower the probability of having less multicollinearity because of the large number of data points. This would improve the efficiency of the model. Another benefit is that panel data gives more information about the data. It gives out more virbality and more degree of freedom which can lead to a more efficient model. The data is going to come from the World Bank. Most of the organizations dealing with the wellbeing of women’s being are the international organizations like the World Bank (WB), International Monetary Fund (IMF), and The Organisation for Economic Co-operation and Development.