Category: Data Visualization

The 2016 election is rapidly approaching and one of the major issues of this years race is campaign fiance reform. I am not big into politics, but I am well aware of the Citizens United vs FEC ruling. One thing I do not know however is on what scale politicians actually receive donations. I set out to see how much an average senator or congressman actually receives in a given year.

My intuition led me to believe that these men and women were pulling millions of dollars each year in donations, but that may be based on watching a little to much House of Cards. First thing I needed was the data. Luckily for me all politicians at the state level are required to file info on their finances. Even luckier for me there is an amazing website that databases it all and has an easy to use API

Followthemoney.org

First I wanted to start at the state level, looking at state senators and assemblymen. My guess was that these people were not pulling in the big bucks when it came to campaign donations. I downloaded a data set from follow the money which contained records of donations to lawmakers in the state of New Jersey. From there I cleaned it up and visualized it.  Heads up lots of bar charts coming!

New Jersey leaning democratic I expect the democrats to pull in a little more money than the republicans.

WOW that’s a big difference. However, there seems to be an issue. Our data doesn’t look complete. Look at 2012 and 2014, there is missing data for both parties. The total amount is lower than it was back in 1997. Know that this data might be incomplete all analysis must be taken with a grain of salt. Let move on to Senate vs. House.

Senators in New Jersey serve one two-year term and two four-year terms every ten years is considered a 2-4-4 term system. This means that this year all the State senator seats are up. This makes me question the data even more as 2015 is relatively low compared to say 2011, another year were all seats were up. State House members serve 2 years. I have two conflicting trains of thought. One is that Senators will receive more donations as the contributor gets more bang for their buck to put it bluntly. Two is that assemblymen get more donations as they are up for election more frequently and constantly need to replenish the war chest. Lets see.

Looks like Senators out do Assemblymen. Look at 2011, this year all State Senate seats were up for election. A grand total of around 31 million was raised that year. That’s pretty impressive , but where is all this money coming from? Lets take a look. I’m going to stick to 2011 as it seems to be the most complete our of all the years.

And our winner is Uncoded with a distant second, unitemized contributions. What does this mean? According to followthemoney.org, unitemized contributions are donations that are under the report-able limit. They are aggregated and listed under this heading. For New Jersey, the limit is $300 dollars from an individual. As for uncoded, this money can come from various industries or most prominently previous years. Uncoded gives an idea of how much these politicians have stocked up in the war chest.

As for the other General Trade Unions comes in third and Lawyers & Lobbyists in forth at around half of General Trade Unions. This is interesting as my previous beliefs on donations are based on big conglomerates or super pacs donating massive amounts of money, not general trade unions. Nevertheless, this is the state level maybe when we look at the federal level there will be much more, for lack of a better word, interesting donators.

Pretty interesting. If you wanna take a look at the data set yourself. I’ve included it here. NJlegDon.  My code is copied below if you wanna check it out (very unoptimized and also in Python!).

-Marcello

 

"""

@author: Marcello

Campaign Donation NJ totals
data sourced from: followthemoney.com

goal of program is to breakdown campaign donations to NJ Senators and 
Congressmen who are currently in office.
"""
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

def summarytablegen(variable_list,varname):
 
 index = np.arange(len(variable_list))
 donation_summary2=pd.DataFrame(columns=columns_4_summary, index=index) 
 donation_total={} 
 total_year = 0
 i=0
 for variable in variable_list:
 for year in election_year:
 df1=df.loc[(df['Election_Year'] == year )& (df[varname] == variable)]
 total_donation = df1['Total_$'].sum()
 donation_total[str(year)] = total_donation
 total_year = total_year+total_donation
 donation_total['Variable']=variable
 donation_total['Grand Total']=total_year
 donation_summary2.loc[i] = pd.Series(donation_total)
 i=i+1
 donation_total={} 
 total_year = 0
 return donation_summary2

# data preprocessing, removing unnecessary columns

df = pd.DataFrame.from_csv('NJlegDon.csv')

df=df.reset_index()

column_names = df.columns.values.tolist()

columns_to_drop = ['request','Election_Year:token','Election_Year:id','Lawmaker:token',
'Office:token','Office:id','General_Office:token','General_Office:id',
'General_Party:token','General_Party:id','Contributor:token','Type_of_Contributor:token',
'Type_of_Contributor:id','General_Industry:token','Broad_Sector:token','In-Jurisdiction:token',
'In-Jurisdiction:id','#_of_Records']

df = df.drop(columns_to_drop, 1)

# drop all negative donations

df = df[df['Total_$'] >= 0]

# %%find total donations for canidate by year
Lawmaker_Id = list(set(df['Lawmaker'].tolist()))
election_year = list(set(df['Election_Year'].tolist()))
Industry = list(set(df['General_Industry'].tolist()))
party = list(set(df['General_Party'].tolist()))
office= list(set(df['General_Office'].tolist()))

str1 =','.join(str(e) for e in election_year)
str1=str1.split(',')

columns_4_summary = ['Variable','Grand Total']
columns_4_summary.extend(str1)


dflawmaker=summarytablegen(Lawmaker_Id,'Lawmaker')
dfIndustry=summarytablegen(Industry,'General_Industry')
dfparty = summarytablegen(party,'General_Party')
dfoffice = summarytablegen(office,'General_Office')


 
#%% 
# breakdown by party 

party = pd.melt(dfparty, id_vars=['Variable'], value_vars=str1,var_name='year', value_name='Donations')

colors = ["windows blue", "red"]
ax = sns.barplot(x="year", y="Donations",hue="Variable", data=party,palette=sns.xkcd_palette(colors))
ax.set( ylabel='Donation Total')