joshcoronel / discretionary-spending

Visualizing discretionary spending reported by the US Bureau of Labor Statistics by analyzing demographic factors using pandas and matplotlib.

US Spending Behaviors of Discretionary Income

Project Description

Our goal is to identify insights into consumer discretionary spending habits by different demographic factors using pandas and matplotlib. All our data is from the Consumer Expenditure Surveys published by the U.S. Bureau of Labor Statistics

In a time of financial struggles facing the US during the time of a pandemic, we were inspired to analyzed consumer spending of their extra money, that is their discretionary money, over the years. We're curious to uncover spending patterns from the past two decades to speculate on the consumer behavioral changes that might be occuring now. Specifically, we sought insights through the following questions:

1. What are the overall average spending of discretionary income?

2. Looking at trends from the 2008 economic recessions, how might consumers expenditure adjust in recovering from the coronavirus shut downs?

   • Compute the annual percent change in expenditure in 2009

3. What affect does demographics have on discretionary spending?

   • Age of reference person

   • Race

   • Gender

   • Highest education level

   • Region of residence

   • Income class

4. Are there any trends that should be further analyzed from the above analysis?