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 pattern of LMICs financial debt over a period of 20 years.

The specific objectives are as follows:

  • To determine if there exists trend or seasonal variation in borrowing behaviours in LMICs

  • Predict the amount of funds to be borrowed in the next 5 years

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 such as time series plots.
  • Simple exponential smoothing,Holt winters,Double exponential methods shall be used appropriately to predict the funds to be borrowed.

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.