Quantifying Trickle Down in Real Average Earnings

I studied a handful of economic measures to see their affect on Real Average Earnings. I used the following series from the St. Louis Fed FRED system:

• Average Hourly Earnings of All Employees, Total Private (CES0500000003)
• Consumer Price Index for All Urban Consumers: All Items in U.S. City Average (CPIAUCSL) base 100=1982
• Job Openings: Total Nonfarm (JTSJOL)
• Quits: Total Nonfarm (JTSQUL)
• Unemployment Rate (UNRATE)
• Employment-Population Ratio (EMRATIO)
• Labor Force Participation Rate (CIVPART)
• Corporate profits after tax (CP)

Although many series went back to 1948, some only had more recent data starting in 2003. I limited the study to where the data overlapped. The first thing I did was deflate average hourly earnings by the CPI to get real average earnings. The inflation spike from the COVID era made a real dent (no pun intended). Next, I ran a time series model, but didn't find a strong time signal in the data.

I followed up with a multiple regression model. Only two features were statistically significant:

• JOLTS quits
• Corporate profits

The employment population ratio and JOLTS opens were marginally significant, but fell outside the normal range for significance. Both had slightly negative coefficients.

The adjusted R-squared was 0.838: the model accounted for 83.8% of the real average earnings. The F-statistic of 190.8 with probability of 5.24e-83 showed the model was statistically significant.

The factors with statistical significance were JOLTS quits and corporate profits. The coefficients suggest the following:

an increase of one million quits is associated with an increase of approximately $1.13 in real average earnings (job market tightness).

an increase of one trillion in corporate profits is associated with an increase of approximately $0.31 in real average earnings (nice trickle down!).

I didn't start looking at this data with any goal in mind, especially of quantifying trickle down economics. I just wanted to discover correlations and relationships. I am often surprised by what data reveals, but there are many other factors at play. Big changes happen during periods of turmoil, like the Great Recession of 2008, and COVID. Real earnings appeared to spike during these deep recessionary events, at least temporarily. I may add additional factors to the model in the future: Labor Productivity Index, Union Membership Rate, GDP Growth Rate.