Individual Campaign Finance Contributions from FEC Data (updated 10/29/2018)¶
The following Python code does some preliminary exploration of campaign finance data from the Federal Election Committee (FEC) website. It requires that the following three files be downloaded and unzipped into the specified directories (relative to the notebook). As can be seen, the top-level directory is fecYYMMDD where YYMMDD is the value of fdate and should be set to the date that the zip files are obtained from the FEC website.
Candidate master for 2017-2018 - unzip and place cn.txt in directory fecYYMMDD/cm18/
Committee master for 2017-2018 - unzip and place cm.txt in directory fecYYMMDD/cn18/
Contributions by individuals for 2017-2018 - unzip and place itcont.txt in directory fecYYMMDD/indiv18/
Load all Individual Contributions and Merge Information from Committee and Candidate Tables.¶
The individual contribution table is very large. It will be loaded into object ii and only reloaded if ii doesn't exist. The following code will create ii if it doesn't exist and output the first row to show all available fields:
import pandas as pd
pd.set_option('display.width', 220)
pd.set_option('max_rows', 200)
#del ii #DEBUG delete to rerun
year = 2018
fdate = "181029"
try:
ii
except NameError:
yr = year % 100
filepath = "fec"+fdate + "/indiv" + str(yr) + "/itcont.txt"
hdr = pd.read_csv("https://www.fec.gov/files/bulk-downloads/data_dictionaries/indiv_header_file.csv", skiprows=0)
print("READING "+filepath)
ii = pd.read_table(filepath, sep='|', header=None, names=hdr)
print(ii.shape)
hdr = pd.read_csv("https://www.fec.gov/data/advanced/files/bulk-downloads/data_dictionaries/cm_header_file.csv", skiprows=0)
cm = pd.read_table("fec"+fdate + "/cm" + str(yr) + "/cm.txt", sep='|', header=None, names=hdr.columns)
#cm = cm[['CMTE_ID','CMTE_NM','TRES_NM','CMTE_ST1','CMTE_ST2','CMTE_CITY','CMTE_ST','CMTE_ZIP','CMTE_DSGN','CMTE_TP',
# 'CMTE_PTY_AFFILIATION','CMTE_FILING_FREQ','ORG_TP','CONNECTED_ORG_NM','CAND_ID']]
cm = cm[['CMTE_ID','CMTE_NM','TRES_NM','CMTE_DSGN','CMTE_TP',
'CMTE_PTY_AFFILIATION','CMTE_FILING_FREQ','ORG_TP','CONNECTED_ORG_NM','CAND_ID']]
hdr = pd.read_csv("https://www.fec.gov/data/advanced/files/bulk-downloads/data_dictionaries/cn_header_file.csv", skiprows=0)
cn = pd.read_table("fec"+fdate + "/cn" + str(yr) + "/cn.txt", sep='|', header=None, names=hdr.columns)
#cn = cn[['CAND_ID','CAND_NAME','CAND_PTY_AFFILIATION','CAND_ELECTION_YR','CAND_OFFICE_ST','CAND_OFFICE',
# 'CAND_OFFICE_DISTRICT','CAND_ICI','CAND_STATUS','CAND_PCC','CAND_ST1','CAND_ST2','CAND_CITY','CAND_ST','CAND_ZIP']]
cn = cn[['CAND_ID','CAND_NAME','CAND_PTY_AFFILIATION','CAND_ELECTION_YR','CAND_OFFICE_ST','CAND_OFFICE',
'CAND_OFFICE_DISTRICT','CAND_ICI','CAND_STATUS','CAND_PCC']]
ii=ii.merge(cm, left_on='CMTE_ID', right_on='CMTE_ID')
ii=ii.merge(cn, left_on='CAND_ID', right_on='CAND_ID', how='left')
ii['TRANSACTION_DT'] = ii['TRANSACTION_DT'].fillna(0).astype(int)
ii['TRANSACTION_DT'] = pd.to_datetime(ii['TRANSACTION_DT'],format='%m%d%Y',errors='coerce')
ii.rename(columns={'TRANSACTION_DT':'DATE', 'TRANSACTION_AMT':'AMT', 'CAND_PTY_AFFILIATION':'PARTY'}, inplace=True)
#ii['AMOUNT'] = ii['AMT'].apply(lambda x: '{:,}'.format(x))
print(ii.iloc[0,:])
Largest Individual Contributions Grouped by the Name, City, and State of the Contributor¶
The following code aggregates the individual contributions by the name, city, and state of the contributor to get an initial estimate of the total contributions made by each contributor. This is only an initial estimate because, if a single contributor should vary or misspell any of these items, those contributions will be grouped separately. For example, note that items 4, 21, 82, and 113 all appear to be for the same contributor, Richard Uihlein of Lake Forest, IL. The names listed are UIHLEIN, RICHARD; UIHLEIN, RICHARD E.; UIHLEIN, RICHARD; UIHLEIN, RICHARD E. and UIHLEIN, RICHARD E. MR. Of these, only the first and third and the second and fourth appear to be the same. However, the first (item 4) and third (item 68) differ in that the first state is IL and the second state is IN. Then, the second (item 21) and fourth (item 82) differ because the latter item misspells the city as LAKE FORST. Then, the last item (item 113) adds a "MR." to the end of the name for the first time. In any event, the final total following the table shows that over $3.6 billion has already been contributed to federal candidates, PACs, and other committees in the 2017-2018 election cycle.
jj = ii.groupby(['NAME','CITY','STATE'])['AMT'].agg(['sum'])
jj = jj.reset_index(level=['NAME','CITY','STATE'])
jj.columns = ['NAME','CITY','STATE','AMT']
jj['AMOUNT'] = jj['AMT'].apply(lambda x: '{:,}'.format(x))
jj = jj.sort_values(by='AMT',ascending=False)
jj.index = range(1,len(jj)+1)
print(jj[['NAME','CITY','STATE','AMOUNT']].head(200))
print('\nTOTAL = ' + '{:,}'.format(sum(jj['AMT'])))
Largest Individual Contributions Grouped by Committees Connected to Specific Candidates¶
The following code aggregates the individual contributions by the committee name, corresponding candidate name, and party of all committees connected to specific candidates. As can be seen, "Team Ryan" is the top committee despite the fact its candidate, House leader Paul Ryan, is no longer running for reelection. The second committee is "Rick Scott for Florida", connected to Rick Scott, Republican governor of Florida who is running against Democrat Senator Bill Nelson for his Senate seat. As can be seen, the nearly \$32 million that it has raised is nearly three times as much as the nearly $11 raised by Nelson's chief committee, "Bill Nelson for US Senate".
jj = ii[pd.notnull(ii['CAND_ID'])]
jj = jj.groupby(['CMTE_NM','CAND_NAME','PARTY'])['AMT'].agg(['sum'])
jj = jj.reset_index(level=['CMTE_NM','CAND_NAME','PARTY'])
jj.columns = ['CMTE_NM','CAND_NAME','PARTY','AMT']
jj['AMOUNT'] = jj['AMT'].apply(lambda x: '{:,}'.format(x))
jj = jj.sort_values(by='AMT',ascending=False)
jj.index = range(1,len(jj)+1)
print(jj[['CMTE_NM','CAND_NAME','PARTY','AMOUNT']].head(100))
print('\nTOTAL = ' + '{:,}'.format(sum(jj['AMT'])))
Largest Individual Contributions Grouped by Candidates¶
The following code aggregates the individual contributions by the candidate name and party of all candidates. As can be seen, the first three candidates are the same as those listed in the prior section and their amounts are relatively close to those listed above, indicating that most of each candidates' fundraising is being done by one main committee. The amounts are simply the sum of all committees supported with that candidate. For example, the $57,763,577 listed for Paul Ryan equals the $53,458,491 listed for "Team Ryan" and the RYAN FOR CONGRESS, INC. $4,305,086 listed for "Ryan for Congress, Inc." in the prior section.
jj = ii.groupby(['CAND_NAME','PARTY'])['AMT'].agg(['sum'])
jj = jj.reset_index(level=['CAND_NAME','PARTY'])
jj.columns = ['CAND_NAME','PARTY','AMT']
jj['AMOUNT'] = jj['AMT'].apply(lambda x: '{:,}'.format(x))
jj = jj.sort_values(by='AMT',ascending=False)
jj.index = range(1,len(jj)+1)
print(jj[['CAND_NAME','PARTY','AMOUNT']].head(100))
print('\nTOTAL = ' + '{:,}'.format(sum(jj['AMT'])))
Largest Individual Contributions Grouped by Committees Not Connected to Specific Candidates¶
The following code aggregates the individual contributions by the committee names of all committees that are not connected to a specific candidate. As can be seen, ActBlue has over four times the contributions of the next largest committee, the "Republican National Committee". ActBlue is a liberal money-gathering platform written about in this recent article.
jj = ii[pd.isnull(ii['CAND_ID'])]
jj = jj.groupby(['CMTE_NM'])['AMT'].agg(['sum'])
jj = jj.reset_index(level=['CMTE_NM'])
jj.columns = ['CMTE_NM','AMT']
jj['AMOUNT'] = jj['AMT'].apply(lambda x: '{:,}'.format(x))
jj = jj.sort_values(by='AMT',ascending=False)
jj.index = range(1,len(jj)+1)
print(jj[['CMTE_NM','AMOUNT']].head(100))
print('\nTOTAL = ' + '{:,}'.format(sum(jj['AMT'])))
Summary of Contributions by Specific Individuals¶
The following code summarizes the contributions given by any specific individual, given their name and, optionally, their city and/or state. The contributions are grouped by the name, city, state, and party of the candidate. If the contribution is to a committee not connected to a candidate, this last item will be "CMTE". The results should be viewed as an estimate, at least until further investigation is done. For example, seaching for "STEYER, THOMAS" did not reveal additional contributions for "STEYER, TOM". Also, both have addresses in Redwood City and San Francisco, CA. It's not clear that these are all for the same person though it is likely that all of the multi-million contribution is from the well-known billionaire. This can be further investigated by looking at the specific contributions (in the next section) and perhaps even referencing the original filings for those contributions on the FEC website.
In any case, the code below looks at the contributions for all of contributors previously shown to have made total contributions of $10 million or more. Note that the search for "ADELSON, SHELDON" included the totals of both "ADELSON, SHELDON", "ADELSON, SHELDON G.", and "ADELSON, SHELDON G" in the third section above. Also, the output reveals a number of misspellings and/or misentries such as for Lake Forest, IL by Richard Uihlein.
def getIndividualSum(name, city, state):
jj = ii[ii['NAME'].str.contains(name, na=False)]
if pd.notnull(city):
jj = jj[jj['CITY'].str.contains(city, na=False)]
if pd.notnull(state):
jj = jj[jj['STATE'].str.contains(state, na=False)]
#if pd.isnull(jj['CAND_ID']):
# jj['PARTY'] = jj['CMTE_PTY_AFFILIATION']
#jj.loc[pd.isnull(jj['CAND_ID']),'PARTY'] = jj.loc[pd.isnull(jj['CAND_ID']),'CMTE_PTY_AFFILIATION']
#jj.loc[pd.isnull(jj['CAND_ID']),'PARTY'] = jj.loc[pd.isnull(jj['CAND_ID']),'CMTE_NM']
jj.loc[pd.isnull(jj['CAND_ID']),'PARTY'] = 'CMTE'
#jj = jj[pd.notnull(jj['CAND_ID'])]
jj['NAME'] = name
kk = jj.groupby(['NAME','CITY','STATE','PARTY'])['AMT'].agg(['sum'])
kk = kk.reset_index(level=['NAME','CITY','STATE','PARTY'])
kk.columns = ['NAME','CITY','STATE','PARTY','AMT']
kk['AMOUNT'] = kk['AMT'].apply(lambda x: '{:,}'.format(x))
#kk.index = range(1,len(jj)+1)
print(kk[['NAME','CITY','STATE','PARTY','AMOUNT']])
print('TOTAL = ' + '{:,}'.format(sum(kk['AMT'])))
getIndividualSum('STEYER, THOMAS', '', '')
getIndividualSum('STEYER, TOM', '', '')
getIndividualSum('ADELSON, SHELDON', '', '')
getIndividualSum('ADELSON, MIRIAM', '', '')
getIndividualSum('UIHLEIN, RICHARD', '', '')
#getIndividualSum('UIHLEIN, ELIZABETH', '', '')
getIndividualSum('SCOTT, RICK', '', '')
getIndividualSum('SOROS, GEORGE', '', '')
getIndividualSum('TRONE, DAVID', '', '')
getIndividualSum('EYCHANER, FRED', '', '')
getIndividualSum('BEZOS, JEFF', '', '')
getIndividualSum('MELLON, TIMOTHY', '', '')
Summary of Contributions by Specific Committees¶
The following code summarizes the contributions given by any specific committee, given their name and, optionally, their city and/or state. The contributions are grouped by the committee name, city, state, and party of the candidate. As before, if the contribution is to a committee not connected to a candidate, this last item will be "CMTE". Also, the code below looks at the contributions for all of committees previously shown to have made total contributions of $10 million or more.
getIndividualSum('AMERICAN ACTION NETWORK', None, '')
getIndividualSum('UNITED BROTHERHOOD OF CARPENTERS AND JOINERS', None, '')
getIndividualSum('REPUBLICAN GOVERNORS ASSOCIATION', None, '')
getIndividualSum('NATIONAL ASSOCIATION OF REALTO', None, '')
Top Individual Contributors for OpenSecrets.org on 10/29/18¶
The following code plots a horizontal bar chart of the top individual contributors according to this page on OpenSecrets.org on 10/29/2018. The top 21 contributors are copied into the code but only the top 17 are charted for readability.
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
#import pandas as pd
ind = ['Adelson, Sheldon & Miriam', 'Steyer, Thomas & Kathryn', 'Uihlein, Richard & Elizabeth',
'Sussman, S. Donald', 'Soros, George', 'Simons, James & Marilyn', 'Eychaner, Fred',
'Griffin, Kenneth & Anne', 'Bezos, Jeff & Mackenzie', 'Mellon, Timothy',
'Schwarzman, Steven & Christine','Marcus, George & Judith','Hoffman, Reid','Simon, Deborah',
'Marcus, Bernard & Billi','Jurvetson, Karla','Bloomberg, Michael','Moskovitz, Dustin & Cari',
'Skjodt, Paul & Cynthia','Beal, D. Andrew','Mercer, Robert & Diana']
tot = np.array([87.762600, 42.433582, 34.988529, 21.817700, 15.357486, 13.942810, 12.160800, 10.793100, 10.186170, 10.042100,
8.213800, 8.085825, 8.049133, 7.646186, 7.586125, 6.683146, 6.661024, 5.330730, 5.116000, 5.062774, 4.693524])
per = np.array([0, 1, 0, 1, 1, 1, 1, 0, 0.4, 0, 0, 1, 0.95, 1, 0, 1, 1, 1, 1, 0, 0])
nn = 17 # only go up to Bloomberg for chart
ind = ind[0:nn]
tot = tot[0:nn]
per = per[0:nn]
dem = tot * per
rep = tot - dem
fig, ax = plt.subplots()
fig.set_size_inches(10, 6)
y_pos = np.arange(len(ind))
plt.grid(zorder=0)
ax.barh(y_pos, dem, align='center', color='blue', zorder=3)
ax.barh(y_pos, rep, align='center', color='red', zorder=3, left=dem)
ax.set_yticks(y_pos)
ax.set_yticklabels(ind)
ax.invert_yaxis()
ax.set_xlabel('Millions of Dollars, OpenSecrets 10/29/18 (see http://econdataus.com/camfin181029.html)')
ax.set_title('Top Individual Contributors: All Federal Contributions, 2017-18')
ax.legend(['Democrat','Republican'])
#plt.gcf().subplots_adjust()
plt.tight_layout()
plt.show()
fig.savefig('indcon181029.png')
Top Individual Contributors for OpenSecrets.org on 10/31/18 (updated)¶
The following code plots a horizontal bar chart of the top individual contributors according to this page on OpenSecrets.org on 10/31/2018, just two days after the prior chart. The top 26 contributors are copied into the code but only the top 17 are charted for readability. As can be seen, the contributions have increased markedly for many of the contributors. For example, the contributions from the Adelsons have increased from over \$87 million to over \$113 million and the contributions from Michael Bloomberg have increased from over \$6 million to over \$38 million. This page shows that the key new contributions from the Adelsons were \$25 million in contributions made on 9/12/18 to the Senate Leadership Fund. [This page](https://www.opensecrets.org/donor-lookup/results?name=Bloomberg%2C+Michael&order=desc&sort=D) shows that the key new contribution by Bloomberg was one for \$20 million made on 10/05/18 to the Senate Majority PAC.
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
#import pandas as pd
ind = ['Adelson, Sheldon & Miriam','Steyer, Thomas & Kathryn','Uihlein, Richard & Elizabeth','Bloomberg, Michael',
'Sussman, S. Donald','Simons, James & Marilyn','Soros, George','Schwarzman, Steven & Christine',
'Eychaner, Fred','Griffin, Kenneth & Anne','Bezos, Jeff & Mackenzie','Mellon, Timothy',
'Simon, Deborah','Hoffman, Reid','Marcus, George & Judith','Marcus, Bernard & Billi',
'Schwab, Charles & Helen','Jurvetson, Karla','Skjodt, Paul & Cynthia','Cameron, Ronald & Nina',
'Moskovitz, Dustin & Cari','Yass, Jeffrey & Janine','Beal, D. Andrew','Mercer, Robert & Diana',
'Laufer, Henry & Marsha','Singer, Paul']
tot = np.array([113.036500, 50.773518, 39.095229, 38.229487, 22.876300, 18.918210, 17.365586, 12.814000, 12.173500, 11.070100,
10.186170, 10.055600, 8.956980, 8.118533, 8.110125, 7.926725, 7.296840, 7.085505, 6.621000, 6.543879,
6.365230, 6.119183, 6.085874, 5.993524, 5.680249, 5.163938])
per = np.array([0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0.4, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0])
nn = 17 # only go up to Bloomberg for chart
ind = ind[0:nn]
tot = tot[0:nn]
per = per[0:nn]
dem = tot * per
rep = tot - dem
fig, ax = plt.subplots()
fig.set_size_inches(10, 6)
y_pos = np.arange(len(ind))
plt.grid(zorder=0)
ax.barh(y_pos, dem, align='center', color='blue', zorder=3)
ax.barh(y_pos, rep, align='center', color='red', zorder=3, left=dem)
ax.set_yticks(y_pos)
ax.set_yticklabels(ind)
ax.invert_yaxis()
ax.set_xlabel('Millions of Dollars, OpenSecrets 10/31/18 (see http://econdataus.com/camfin181029.html)')
ax.set_title('Top Individual Contributors: All Federal Contributions, 2017-18')
ax.legend(['Democrat','Republican'])
#plt.gcf().subplots_adjust()
plt.tight_layout()
plt.show()
fig.savefig('indcon181031.png')
List of All Contributions by Specific Individuals¶
The following code lists each contribution made by any specific individual, given their name and, optionally, their city and/or state. The code first lists those contributions made to committees connected to a candidate and then lists those contributions made to committees not connected to a candidate. Both lists are ordered by date and are generated by calling the function getIndividual2All. The two calls below list all contributions by "STEYER, THOMAS" and "STEYER, TOM". Both names show contributions from San Francisco, CA and Redwood City, CA which are about 22 miles apart. The multi-million dollar contributions by STEYER, THOMAS F. in San Francisco suggest that these contributions are made by the well-known billionaire. However, STEYER, TOM in Redwood City, CA can be seen to have made numerous contributions to DEMOCRACY ENGINE, INC., PAC. Looking at the this organization's most recent filing shows that this contributor has "Fahr LLC" listed as their employer and "Advocacy & Philanthropy" listed as their occupation. This suggests that these contributions were made by the same billionaire.
def getIndividual2Candidate(name, city, state):
jj = ii[ii['NAME'].str.contains(name, na=False)]
if pd.notnull(city): jj = jj[jj['CITY'].str.contains(city, na=False)]
if pd.notnull(state): jj = jj[jj['STATE'].str.contains(state, na=False)]
jj = jj[pd.notnull(jj['CAND_ID'])]
jj['AMOUNT'] = jj['AMT'].apply(lambda x: '{:,}'.format(x))
jj = jj.sort_values(by='DATE',ascending=True)
jj.index = range(1,len(jj)+1)
kk = jj[['NAME','DATE','AMOUNT','CITY','STATE','CAND_NAME','PARTY']]
print(kk.head(100))
print('\nSUBTOTAL = ' + '{:,}'.format(sum(jj['AMT'])))
def getIndividual2Committee(name, city, state):
jj = ii[ii['NAME'].str.contains(name, na=False)]
if pd.notnull(city): jj = jj[jj['CITY'].str.contains(city, na=False)]
if pd.notnull(state): jj = jj[jj['STATE'].str.contains(state, na=False)]
jj = jj[pd.isnull(jj['CAND_ID'])]
jj['AMOUNT'] = jj['AMT'].apply(lambda x: '{:,}'.format(x))
jj = jj.sort_values(by='DATE',ascending=True)
jj.index = range(1,len(jj)+1)
kk = jj[['NAME','DATE','AMOUNT','CITY','STATE','CMTE_NM']]
print(kk.head(100))
print('\nSUBTOTAL = ' + '{:,}'.format(sum(jj['AMT'])))
def getIndividual2All(name, city, state):
getIndividual2Candidate(name, city, state)
getIndividual2Committee(name, city, state)
getIndividual2All('STEYER, THOMAS', None, '')
getIndividual2All('STEYER, TOM', None, '')
List of All Contributions for Sheldon and Miriam Adelson¶
The two calls below list all contributions by "ADELSON, SHELDON" and "ADELSON, MIRIAM". Sheldon Adelson is the billionaire founder, chairman and chief executive officer of Las Vegas Sands Corporation and Miriam Adelson is his wife. The lists show that the two contributed \$160,400, \$32,587,800 \$179,300, and \$33,037,600 for a total of \$65,965,100. However, [a page on OpenSecrets.org](https://www.opensecrets.org/overview/topindivs.php) lists the total contributions to be \$87,762,600. Another page shows the contribution details. Comparing this to the details below shows that it contains \$22 million of contributions for September not contained in the FEC data. That accounts for all but \$202,500 of the difference between \$87,762,600 and \$65,965,100. The cause of that additional \$202,500 could be determined by comparing all of the contribution details. In any case, following are the \$32 million of contributions shown by OpenSecrets for September, including the $22 million not in the FEC data:
| Contributor | Amount | Date | Recipient |
|---|---|---|---|
| ADELSON, SHELDON G, LAS VEGAS, NV | \$1,000,000 | 09/14/18 | Ending Spending Action Fund |
| ADELSON, MIRIAM DR, LAS VEGAS, NV | \$1,000,000 | 09/14/18 | Ending Spending Action Fund |
| ADELSON, MIRIAM DR, LAS VEGAS, NV | \$5,000,000 | 09/13/18 | America First Action (in FEC on 10/28) |
| ADELSON, SHELDON G, LAS VEGAS, NV | \$5,000,000 | 09/13/18 | America First Action (in FEC on 10/28) |
| ADELSON, SHELDON, LAS VEGAS, NV | \$10,000,000 | 09/12/18 | Congressional Leadership Fund |
| ADELSON, MIRIAM, LAS VEGAS, NV | \$10,000,000 | 09/12/18 | Congressional Leadership Fund |
Problem in Using the Bulk Files¶
The above shows a problem with using the three bulk files listed at the beginning of this notebook. Even though these bulk files were downloaded on October 29th, they still did not yet contain some contributions from September. In fact, the \$10 million to "America First Action" just appeared in the update to the FEC bulk files on October 28th. It is possible to see the other contributions by going to the Filings page on the FEC website, typing type in and selecting the name of the committee or candidate to whom the contribution was made, and finding the filing that contains the contribution.
For example, select the first "Congressional Leadership Fund" entry for the committee (the second is for prior years), order the documents by date, search back in time until you find the document titled "OCTOBER QUARTERLY 2018 | Original", and open the pdf version of the filing. Searching for Adelson then shows the $10,000,000 contributions on page 17 of 396. The same process can be followed to find the \$5,000,000 contributions on page 6 of the October Quarterly filing for "America First Action". However, the same process does not seem able to find the \$1,000,000 contributions.
Problem in Using the Original Filings¶
A big problem with the original filings is that it's only possible to search them by the committee or candidate to whom the contribution is made. There is apparently no way to automatic way to search by contributor. As a result, some of the reports that use the filings seem to have omissions. For example, the headline of a recent article is Sheldon Adelson Breaks Spending Record on Midterm Elections, Surpassing $100M. The article states:
A new \$25 million donation puts the billionaire benefactors’ total spending toward helping Republicans hold on to both chambers of Congress at \$113 million, Bloomberg reported. The donation was to the Senate Leadership Fund, a super PAC with ties to Senate Majority Leader Mitch McConnell.
The \$113 figure is about \$25 million more than the \$87,762,600 listed by [OpenSecrets.org](https://www.opensecrets.org/overview/topindivs.php). In fact, the table above does not show a recent contribution to the Senate Leadership Fund. Searching their filings, page 9 of the one titled ["OCTOBER MONTHLY 2018"](http://docquery.fec.gov/pdf/691/201810199125775691/201810199125775691.pdf) shows \$12.5 million contributions by each of Sheldon and Miriam Adelson on 9/12/2018. This again suggests that there is no simple, automatic method for searching the filings by contributor. The FEC site does contain a CSV version of the filing that contains the contribution but it may not be a simple matter to check and read this file. Hence, it appears to be easy to miss contributions that are in the original filings but have not yet been added to the FEC bulk files. As can be seen above, it appears that it can sometimes take over a month for all filings to be added. This would seem to be a problem if rapid disclosure of contributions is desired.
getIndividual2All('ADELSON, MIRIAM', None, '')
getIndividual2All('ADELSON, SHELDON', None, '')
#getIndividual2All('ADELSON, SHELLEY', None, '')
List of All Contributions for Rick Scott¶
The call below lists all contributions by "SCOTT, RICK". Rick Scott is the current governor of Florida who is running for the Senate against current Florida Senator Bill Nelson. What is of special interest is that, not only is Rick Scott the federal candidate receiving the most funds after House Speaker Paul Ryan, he appears to be making most of the contributions. Out of the total of \$35,511,899 in contributions, \$27,050,190 appears to be from Scott. According to his Wikipedia page, Scott's net worth stood at \$255 million as of August 2018. That means that Scott has contributed over 10 percent of his net worth to his own campaign.
getIndividual2All('SCOTT, RICK', None, '')
For a Specified Committee, List All Individual Contributions¶
The following fuction getIndividual2Committee1 lists all contributions made to specific committees and sorted by date. The two following calls list the contributions for the committees "Congressional Leadership Fund" and "Senate Leadership Fund".
The most recent contribution shown from Sheldon and Miriam Adelson to the "Senate Leadership Fund" is for \$12.5 million each on 7/26/2018. As mentioned above, page 9 of that committee's filing titled "OCTOBER MONTHLY 2018" shows identical contributions being made on 9/12/2018. The fact that the contributions are otherwise identical may be a reason why the more recent ones were missed by OpenSecrets.
def getIndividual2Committee1(cmte_nm):
jj = ii[ii['CMTE_NM'].str.contains(cmte_nm, na=False)]
jj['AMOUNT'] = jj['AMT'].apply(lambda x: '{:,}'.format(x))
jj = jj.sort_values(by='DATE',ascending=True)
jj.index = range(1,len(jj)+1)
kk = jj[['NAME','DATE','AMOUNT','CITY','STATE','CMTE_NM']]
kk['NAME'] = kk['NAME'].str[0:40]
print(kk.tail(100))
print('\nSUBTOTAL = ' + '{:,}'.format(sum(jj['AMT'])))
getIndividual2Committee1('CONGRESSIONAL LEADERSHIP')
getIndividual2Committee1('SENATE LEADERSHIP')
For a Specified Candidate, List All Individual Contributions¶
The following fuction getIndividual2Candidate1 lists all contributions made to a specific candidate and sorted by date. The following call lists the contributions to Rick Scott. As mentioned above, \$27,050,190 of the \$35,511,899 in contributions came from Scott himself but the output shows many of the other contributors.
def getIndividual2Candidate1(cand_name):
jj = ii[ii['CAND_NAME'].str.contains(cand_name, na=False)]
jj['AMOUNT'] = jj['AMT'].apply(lambda x: '{:,}'.format(x))
jj = jj.sort_values(by='DATE',ascending=True)
jj.index = range(1,len(jj)+1)
kk = jj[['NAME','DATE','AMOUNT','CITY','STATE','CAND_NAME','PARTY']]
print(kk.head(100))
print(kk.tail(100))
print('\nSUBTOTAL = ' + '{:,}'.format(sum(jj['AMT'])))
getIndividual2Candidate1('SCOTT, RICK')
List All Individual Contributions above a Specific Amount¶
The following fuction getAllFromDate lists all contributions more than a specific amount and made after a specific date. The following call lists all contributions of \$500,000 or more.
from datetime import datetime
#import pandas as pd
#pd.options.display.max_colwidth = 99
def getCandidateFromDate(date, minamt):
#jj = ii[ii['NAME'].str.contains(name, na=False)]
#if pd.notnull(city): jj = jj[jj['CITY'].str.contains(city, na=False)]
#if pd.notnull(state): jj = jj[jj['STATE'].str.contains(state, na=False)]
jj = ii[ii['AMT'] >= minamt]
jj = jj[jj['DATE'] < datetime.now()]
jj = jj[pd.notnull(jj['CAND_ID'])]
jj['AMOUNT'] = jj['AMT'].apply(lambda x: '{:,}'.format(x))
jj = jj.sort_values(by='DATE',ascending=True)
jj.index = range(1,len(jj)+1)
kk = jj[['NAME','DATE','AMOUNT','CITY','STATE','CAND_NAME','PARTY']]
print(kk.tail(100))
print('\nSUBTOTAL = ' + '{:,}'.format(sum(jj['AMT'])))
def getCommitteeFromDate(date, minamt):
#jj = ii[ii['NAME'].str.contains(name, na=False)]
#if pd.notnull(city): jj = jj[jj['CITY'].str.contains(city, na=False)]
#if pd.notnull(state): jj = jj[jj['STATE'].str.contains(state, na=False)]
jj = ii[ii['AMT'] >= minamt]
jj = jj[jj['DATE'] < datetime.now()]
jj = jj[pd.isnull(jj['CAND_ID'])]
jj['AMOUNT'] = jj['AMT'].apply(lambda x: '{:,}'.format(x))
jj = jj.sort_values(by='DATE',ascending=True)
jj.index = range(1,len(jj)+1)
kk = jj[['NAME','DATE','AMOUNT','CITY','STATE','CMTE_NM']]
kk['NAME'] = kk['NAME'].str[0:35]
kk['CMTE_NM'] = kk['CMTE_NM'].str[0:35]
print(kk.tail(100))
print('\nSUBTOTAL = ' + '{:,}'.format(sum(jj['AMT'])))
def getAllFromDate(date, minamt):
getCandidateFromDate(date, minamt)
getCommitteeFromDate(date, minamt)
getAllFromDate(None, 500000)
List All Contributions from the Koch Brothers¶
The following calls simply attempt to list all of the contributions connected with the Koch brothers, Charles and David. As can be seen, \$3,308,900 is connected to Charles Koch, \$109,772 is connected to David Koch, and \$2,335,000 is connected to Koch Industries. This adds up to \$5,753,672 a relatively small amount compared to the other top contributors. However, this does not include any "dark money" which is not disclosed to the FEC for any of the contributors.
getIndividual2All('KOCH, CHARLES', None, 'KS')
getIndividual2All('KOCH, DAVID ', None, 'NY')
getIndividual2All('KOCH INDUSTRIES', None, '')
getIndividual2All('CHARLES G. KOCH', None, '')
Note: The Jupyter Notebook from which this post is generated can be found at http://econdataus.com/camfin181029.ipynb. It is based on bulk data downloaded from the FEC website on 10/29/2018. Links to additional Jupyter Notebooks can be found at http://econdataus.com/jupyter.html.