Module Assignment 2

For this module assignment, I wanted to see the spending of the US house using the US House disbursement data of the third quarter of 2021. I specifically want to look at the services that cost the most and what offices are spending the most. My question “How much is being spent on services in the US House and why some offices are spending more than other offices?” will guide me to finding the answers I want from the dataset.

My audience for this paper would be the office of management and budget in the US House. This audience is the perfect fit for my data because they would be interested in the spending of the house, where the money goes, and how it was spent. They would also be interested to see the top spending offices and the top spending categories to find themes that they can work on in the future.

I printed the dataset for quarter 3 of 2021 from the US House website and then filtered it to only have “DETAIL” values in the “SORT SEQUENCE” column.

I then grouped my new dataset by the “CATEGORY” column to see how much each category is spending in total in that quarter. It looks like there are 11 different categories that the US House pays its employees for.

To find the highest spending category, I sorted the dataset above to show the Categories in a descending order based on the “AMOUNT” column. The Personnel Compensation category comes first, Personnel benefits comes next with a huge difference between the two. The third most spending category is Other services also with a huge difference. The least spending Categories were Transportation of things, benefits to former personnel, and franked mail.

I then wanted to look at the offices that are spending the US House money. I grouped my first dataset by the“OFFICE” column to see all the offices and the sum of their spending.

I was curious to see what offices are spending the most because most of the time the name of the office would explain why that office is spending more or less money than the other offices. To get the highest spending office, I sorted my values to show the most spending office.

I then used the index numbers from the code above to find the top 3 most spending offices in the US House using idxmax. It looks like government contributions, Chief Administrative Officer of the House, and clerk of the House are the most spending offices in the house in the third quarter of 2021.

The results above make sense to me because the offices that spent the most were offices that require a lot of money. For example, government contributions cover a lot of spending in different areas. That means it would require to spend way more money than a specific office that only spends for a certain area/field instead for all areas. Similarly, The Chief Admin Officer of the House is a large office and serves all the employees in the house. It offers its services to all employees which means it is reasonable that it is one of the most spending offices. Finally, the Clerk of the House is a big figure in the house and it is expected that they would require so much spending.

For my visualization part of the assignment, I chose to look at the different categories and how much is being spent on each to find themes to my data. I first used the initial dataset to create a histogram and then I grouped it by the “CATEGORY” column. I chose histograms because I thought they would present the data better than any kind of graph. here are my results.

It was hard to find a specific number of bins for all categories because the amount of spending for each category was different. You can see that some categories barely have any bins with the number of bins that I chose. For example, personnel benefits barely has any bins while personnel compensation has a lot of bins. An explanation for this is that each category is different in its spending, some spend a lot on a few while others spend a little on many. One of the categories with barely any bins was benefits to former personnel, it looks like people spend a variant amounts on this category. To investigate it, I changed my number of bins to show up to 100,000 to see if the chart will change. When I did so, the results showed that benefits to former personnel can go up to around $75,000 while other categories like personnel compensation stop at $50,000. This makes sense because the House pays that much to only a few employees, that is why benefits to former personnel did not show up as one of the most spending categories. Other categories look like they never spend this much at a time because the bins were small and did not pass half the graph although they could be spending more but with smaller amounts.

The data above shows me that the House spends the most on their personnel, especially compensations and benefits. This could have been expected as these categories require more money than other categories like Mail or equipment. Other services category was also a high spender, this is not a surprise as other services can include anything that is not a category mentioned by the House. Additionally, the data revealed that the most spending offices were government contributions, the Chief administrative officer of the House, and the Clerk of the House. These results fit because all these offices either manage a huge number of people/fields instead of specific offices that only manage a few. Additionally, all these offices are big enough to be the most spending offices in the house.