What does a congressional “big spender” look like?
As we begin to approach our next congressional election, it is important to understand the people we vote for, their ideas, as well as what exactly they’ll be doing with their available funding. There have been controversial issues in the past with members of congress being unable to justify their spending, and I think that it would be important to get a general profile on those who are spending more money than the rest. So, in order to do this I asked myself a question; is there a general trend or theme across big spenders in congress, whether it be age, gender, political alignment, or another factor?
In order to answer this question, I performed a variety of data cleaning and organizing techniques. I had access to house disbursement data from quarter 3 of 2021, with specific dollar amounts spent, reasons why, offices, and more. I also had access to a context dataset on these congress members. This database included personal statistics like name, date of birth, gender, political alignment, and more. After cleaning both databases of null values, unnecessary information, duplicates, and more, I was ready to begin my analysis.
Both databases I was using had information that would be significant to compare with the spending data. To perform my analysis, I firstly grouped and sorted my house disbursement data. I grouped by office and returned the summed total amount spent by each. This way, I could easily compare which member’s office was spending more or less money. Next, I merged this grouped data frame with my context database, allowing me to see statistics on each member next to their office’s total spending data. Lastly, after doing some more cleaning on my merged data frame, I utilized Python’s “DateTime” module to subtract all date of birth values from the present day, leaving me with the age in years for each member. This left me with a clean, significant data frame from which further analysis could occur.
From this data frame, I created “profiles” to pull in categories of big spenders. Firstly, I made a big spender data frame from my merged one, only pulling in office members who spent $250,000 or more during a year in office. From this, I created two more groupings; one with age equal to or above 55 years for older members, and the other with age below 55 years. There wasn’t much of a difference in big spenders between age alone, with older big spenders spending an average of $328,619 and younger ones spending an average of $331,574. You can see the distribution of big spenders, comparing the total amount spent to age in years, in Figure 1 above. I needed a more in-depth analysis as spending in comparison with age didn’t have much significant correlation. So, I created more profiles! From my big spender data frame (spending $250,000 or more per year in office), I made two more profile groupings based on the member’s DW-Nominate score. This statistic basically tells you how politically aligned a member is to the Democratic or Republican party, with a positive 1 being the most extreme republican and a -1 being the most extreme democrat. You can see this described distribution of big spenders compared to their political alignment in Figure 2 below. From these two profiles, there again weren’t any significant spending differences, but on average, democratic members were spending a bit more and republican members had more extreme cases (outliers spending over $400,000). So, I synthesized these profiles to create one for young democratic big spenders, young republican big spenders, and the same thing for older members. From these big-spender profiles, I found that on average, older republican big spenders were spending the least amount out of the big spenders, spending $18,700 less on average than old democratic big spenders. I also found that, although not by a significant amount, big-spending old democrats and young republicans were spending the most money in the office on average.
Even though the spending amounts are relatively similar throughout the profiles, I think that these spending differences have further implications for analysis. For example, you could pull individual members of congress for further analysis, or more importantly, analyze these profile trends across multiple years and quarters. It is important to note that these statistics come from a single quarter in 2021 so for more meaningful results comparison is necessary. But overall, although I didn’t exactly answer my question and discover a meaningful trend across big spenders in congress, I found some general pricing themes and it is necessary that these themes be compared to other years and quarters of congressional disbursement data.
