In this unit, issues of global importance are examined through the lens of data. Sustainability and climate change, inequality and global public health are discussed through two student-led projects. The second project involved assessing the causes, consequences, and factors of one aspect of global health. Our group chose teenage pregnancy. This topic is nuanced and relevant: the reversal of Roe vs. Wade and the reduction of British sex education funding mean data-driven insights on the topic are necessary.
Our group analysed the relationship between literacy rates and teenage pregnancy, as well as the rates between teenage pregnancy and GDP. We analysed data using Excel: scatter graphs helped us to understand the relationship between two variables while line graphs illustrated global trends over time. We used regression analyses to mathematically sort out which variables, or factors, had an impact on other variables. It answered the questions: Which factors matted most? Which can we ignore? How did these factors interact with each other? And, perhaps most importantly, how certain are we about the relationship between these factors? We used correlation coefficients to determine the strength of the relationship between the relative movements of two variables. The Gini coefficient, although imperfect, was used to generate a number representing inequality in a country. These coefficients can be compared with each other.
We conducted a study on the relationship between literacy rates and births from teen mothers. On face value these factors would seem to be connected, however our study proved otherwise. The reality is more complex. To analyse these variables, we conducted a regression analysis. We concluded that there was no statistically significant relationship between the literacy rate and teenage childbearing. This means that there is insufficient evidence to conclude that lower literacy rates contribute to higher rates of teenage childbearing. The complex relationship between literacy rate and teen pregnancy becomes clearer when the factor of race is considered. This has been shown by academic LeVine, who found that the factor of race was more telling than literacy levels in predicting teenage childbearing. This relationship is also nuanced due to religious and cultural factors. Despite a high literacy rate of 99%, Irish women could not access abortion until 2018 due to religious reasons. Other factors influence teenage pregnancy such as access to contraceptives and abortion; sexual literacy is redundant if access to contraceptive measures is not available.
Our investigation on the relationship between GDP and adolescent fertility rate resulted in a significant negative correlation between the two variables. Gross Domestic Product (GDP) is a measure of the size and health of a country’s economy over a period of time. A negative correlation occurs when one factor increases as another decreases; as GDP decreases, adolescent fertility rate increases. This can be viewed on the scatter graph: as GDP per capita increases, the adolescent fertility rate falls to under 20 births per 1,000. Countries with the highest GDP per capita do not have adolescent fertility rates above 10 births per 1,000.
Allen Franca has written what GDP is an indirect factor to teenage childbearing, it is a useful indicator for several direct factors such as access to contraception, education, and health clinics. This conclusion corroborates our findings. While direct factors contributing to teenage fertility rate are numerous, GDP is a useful single factor to indicate the rate.
We concluded that factors affecting teenage childbearing are nuanced, numerous and complex. What may seem an ‘obvious’ factor may not be statistically significant in effecting change. Variables such as literacy rate are affected by racial and religious forces impossible to quantify. Factors may be many but held together by a general measure of economic health, GDP. Researching a dynamic topic such as this yields surprising, fascinating results.