Introduction

The level of financial debt in LMICs (Low and medium income countries) has been increasing over the years (World Bank, 2021). Amid this increase, issues such as youth unemployment, crime, and poor maternal and child morbidity have also been on the rise (United Nations, 2020). Concerns have therefore been raised as to whether bilateral or multilateral lending or borrowing influences social and economic outcomes as purported or not (Smith & Jones, 2019).

This paper therefore aims to address the above concern by addressing the following general objective:

  • To establish the influence of a country’s financial debt on maternal and child health outcomes, as well as on youth unemployment and suicide rates.

The specific objectives are as follows:

  • To determine the relationship between a country’s financial debt and maternal health.
  • To investigate the relationship between a country’s financial debt and child mortality and morbidity.
  • To examine the relationship between a country’s financial debt and suicide rates.
  • To explore the relationship between a country’s financial debt and unemployment rates.

Research Methods

Data Collection:

Data will be extracted from the World Bank and WHO using Python’s World Bank API (Python Software Foundation, 2023). Additionally, Power BI’s OData feed will be utilized to extract data from WHO API endpoints (Microsoft, 2022).

Data Analysis:

A mixed methods approach will be employed to:

  • Identify patterns between the predictor (financial debt) and explanatory variables (health outcomes, unemployment, and suicide rates) using EDA techniques.
  • Establish the distribution of financial debt in relation to maternal and child health outcomes, youth unemployment, and suicide rates using time series methods and multiple regression methods (Hastie et al., 2009).

Study Shortcomings

The study results will be cross-sectional and therefore caution has to be used to generalize them over time as most variables are influenced by changes in political regimes and natural factors such as floods, drought, and invasion due to pests (Brown, 2018).

Discussion of Results

References

  • World Bank. (2021). World Development Indicators. Retrieved from worldbank.org
  • United Nations. (2020). World Economic Situation and Prospects. United Nations Publications.
  • Smith, J., & Jones, A. (2019). Economic and Social Impact of Public Debt. Journal of Economic Perspectives, 33(2), 47-70.
  • Python Software Foundation. (2023). Python World Bank API. Retrieved from python.org
  • Microsoft. (2022). Power BI OData Feed. Retrieved from powerbi.microsoft.com
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer.
  • Brown, K. (2018). Environmental Impact on Economic and Social Development. Journal of Environmental Economics and Management, 12(1), 85-101.