In this article, I developed a linear regression model to help us understand how different public expenditure programs implemented by the government affect labor market dynamics, particularly the unemployment rate. The model was built using Python, primarily with the statsmodels library, along with other supporting libraries such as pandas and numpy, which were essential for cleaning and manipulating the data to prepare it for modeling.
Moreover, the model was corrected for heteroscedasticity and autocorrelation, ensuring that all other assumptions for this type of model were satisfied. This approach allowed me to draw insightful conclusions about how government spending influences labor market behavior and its components. Consequently, I was able to recommend specific actions that policymakers can take to minimize these distortions while maximizing household welfare.
Read the Full Article in PDF
Review the Code (Available on my GitHub Profile)
Download the dataframes here: DataFrames – How Public Expenditure Affects Unemployment
