Addiction Policy Lobbying
Background
Looked into using API, but the API and codes are for candidates; may be useful to look at which groups donated to candidates that sponsored certain bills…
Importing data: From Data Dictionary:
- In this bulk data, text fields are surrounded by the pipe character (ascii 124). Date and numeric fields are not. Commas separate all fields. *
https://www.opensecrets.org/resources/datadictionary/UserGuide.pdf
Opensecrets methodology: https://www.opensecrets.org/federal-lobbying/methodology
I had to edit the datasets because they are separated by |,| and R doesn’t allow you to import it that way; The first column is only separated by a “,|” and the final column ends with “|”. I did a find and replace with an “;”, starting for “|,|” then “,|” and then “|”. I am not sure I needed the final ; …. I think I could have done a replace with a space, because it imported an empty column. Datasets ending with "_rev" are the cleaned datasets.
Industry dataset and lob_lobbying I cleaned a little different because they have numeric variables. It starts with a “|” and ends with a “|”…but there are dollar amounts so did replace all “|,|” = “;” then “|,” = “;” then “|,” = “;” then “|” with space.
I got variable names from the documentation and did a separate function to add those in after import.
SI_ID = table id
The purpose of this project is to determine there has been a meaningful or notable change in lobbying activity on “alcohol and drug abuse” policy.
H1: There will be spikes in lobbying activity by health industry actors prior to the following laws…
H.R. 6, the Substance Use-Disorder Prevention that Promotes Opioid Recovery and Treatment (SUPPORT) for Patients and Communities Act of 2018, was made law to address the nation’s opioid overdose epidemic.
In December 2016, the 21st Century Cures Act was signed into law. The Cures Act addresses many critical issues including leadership and accountability for behavioral health disorders at the federal level, the importance of evidence-based programs and prevention of mental and substance use disorders, and the imperative to coordinate efforts across government.
The Comprehensive Addiction and Recovery Act (CARA) of 2016 authorizes over $181 million each year (must be appropriated each year) to respond to the epidemic of opioid abuse, and is intended to greatly increase both prevention programs and the availability of treatment programs.
2010 - ACA, had special protections for persons with SUD.
The Mental Health Parity and Addiction Equity Act of 2008 requires insurance groups offering coverage for mental health or substance use disorders to make these benefits comparable to general medical coverage.The Americans with Disabilities Act (ADA) of 1990, as amended in 2008, establishes requirements for equal opportunities in employment, state and local government services, public accommodations, commercial facilities, transportation, and telecommunications for citizens with disabilities—including people with mental illnesses and addictions.
The STOP Act of 2006 authorized:
A grant program providing additional funds to current or former grantees under the Drug Free Communities Act of 1997 to prevent and reduce alcohol use among youth ages 12-20
- The Children’s Health Act of 2000 (PDF | 531 KB) reauthorizes SAMHSA programs that work to improve mental health and substance abuse services for children and adolescents. It also provides SAMHSA the authority to implement proposals that give U.S. states more flexibility in how they use block grant funds, with accountability based on performance. The Act also allows SAMHSA to consolidate discretionary grant authorities, which provides the Secretary of HHS with more flexibility to respond to individuals and communities in need of mental health and substance abuse services. It also provides a waiver from the requirements of the Narcotic Addict Treatment Act, allowing qualified physicians to dispense (and prescribe) Schedule III, IV, or V narcotic drugs, or combinations of such drugs, approved by the Food and Drug Administration (FDA) to treat heroin addiction. Additionally, the Act provides a comprehensive strategy to combat methamphetamine use.
H2: The number of health industry/medical actors will have increased over time, especially medical providers and pharmaceutical companies
H3: The $$ spent on lobbying on the health side will have increased over time
H4: The number of prison/law enforcement group actors will have decreased over time
H5: Decrease in the $$ spent on lobbying by law enforcement actors over time
H6: Increased lobbying of DEA and FDA during the Opioid Crisis
Datasets
Primary dataset is the Lobbying (No Specific Issues) dataset (referred to as “main”). It has all the info that we need except for issue and agency.
library(readr)
#Main
main <- read_delim("C:/Users/talee/Dropbox/1-Research/drug policy and interest groups/Lobbying Data/lob_lobbying_rev.txt",
";", escape_double = FALSE, col_names = FALSE,
trim_ws = TRUE)## Rows: 1255455 Columns: 18## -- Column specification --------------------------------------------------------
## Delimiter: ";"
## chr (18): X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, ...##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.colnames(main)<-c("Uniqid","Registrant_raw","Registrant","Isfirm","Client_raw","Client","Ultorg","Amount","Catcode","Source","Self","IncludeNSFS","Use","Ind","Year","Type","Typelong","Affliate")
#Issue Dataset (No Specific Issue)
issue <- read_delim("C:/Users/talee/Dropbox/1-Research/drug policy and interest groups/Lobbying Data/lob_issue_NoSpecficIssue_rev.txt", ";", escape_double = FALSE, col_names = FALSE, trim_ws = TRUE)## Rows: 2647672 Columns: 6## -- Column specification --------------------------------------------------------
## Delimiter: ";"
## chr (3): X2, X3, X4
## dbl (2): X1, X5
## lgl (1): X6##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.colnames(issue)<-c("SI_ID","Uniqid","IssueID","Issue","Year")
#Agency
agen <-read_delim("C:/Users/talee/Dropbox/1-Research/drug policy and interest groups/Lobbying Data/lob_agency_rev.txt", ";", escape_double = FALSE, col_names = FALSE, trim_ws = TRUE)## Rows: 3630742 Columns: 3## -- Column specification --------------------------------------------------------
## Delimiter: ";"
## chr (3): X1, X2, X3##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.colnames(agen)<-c("Uniqid","AgencyID","Agency")## Don't need this code anymore the dataset above is sufficient.
#Lobbying Issue Specific Dataset -- Would love to use this but the structure of the data makes it very difficult to import. There is an inconsistent # of specific issues for each observation
library(readr)
spec_issue <- read_delim("C:/Users/telsabawi/Dropbox/1-Research/drug policy and interest groups/Lobbying Data/lob_issue_rev.txt",
";", escape_double = FALSE, col_names = FALSE,
trim_ws = TRUE)
colnames(issue)<-c("SI_ID","Uniqid","IssueID","Issue","SpecificIssue","Year")
#Change Year variables to categorical
issue$Year <- as.factor(issue$Year)
main$Year <- as.factor(main$Year)
indus$Year <- as.factor(indus$Year)There are major issues with the underlying data that need to be addressed in order to decrease noise caused by external factors (like the changes in reporting requirements). “Specifically, Each series exhibits a similar trend: steady increases between 1998 and 2009, a high in 2009, and a rapid decline through 2012. They then hold flat from 2012 to 2017 with slight increases in 2018.” ( See LDA at 20) Authors of LDA at 20 suggest creating two separate comparisons before and after 2009 (pg. 266), but this article takes a different approach by using the total number as a control the alchol and drug abuse (ada) specific lobbying.
For counting the number of organizations lobbying on a specific issue, we can deal with the redundancy problem by counting by “client” instead of by report. If you only want to count the issue only once per client, then write the script to count only once per client. OR perhaps, its not bad to county multiple reports because it may mean that the group put in more resources (hired a lobbyiest, and had one of their own people work on it etc). Unclear if a separate report is filed if they lobby agencies ????????
To deal with the redundant data problem for amount spent…you could match lobbyists to client and then subtract the amount the lobbyists say they were paid from the amount the client said that they paid overall…????????
**Need to check to see when acholol and drug abuse were added as an issue because the types of issues that can be selected has changed over time…
Merge Datasets
The variable ‘Uniqid’ is shared by the “Lobbying,” “Lobbyists,” “Lobby issues”, “Agency”, “Lobbying specific issues” datasets. Uniqid is a unique number given to each filed LDA form. - the merge() function in R combines data based on common columns and would be appropriate for this application. In most cases, you join two data frames by one or more common key variables (in this case, ‘Uniqid’). It should be as follows:
merge(data frameA,data frameB,by.x=“Uniqid”)
I realized that I couldnt merge agency with the other datasets because the UniqueId (report) appears multiple times. There is a entry for each agency.So you could use that data to see who is lobbying which agency and how much those groups spend on lobbying, but you can’t combine it with the other datasets because it will add the amount for every agency. (E.g. if one company lobbied FDA and DEA and they spent $20,000 on lobbying total, there will be two entries for $20k, one listing the FDA and the other listing the DEA)
REPORTS WILL HAVE REDUNDANT INFORMATION. A lobbying firm could register on behalf of the client and report the amount that they earned and the client can register and include the amount that they paid the firm in their overall expenses. Also, in some rare circumstances the entire net income of an organization is used as the lobbying amount… (See the LDA at 20 ).
“The Ins and Outs of Calculating Lobbying Totals by Industry We use the individual expenditures in the lobbying table to calculate the total in the lobbying industries table. In most cases it is a straight forward scenario where you just take in account the ind=y. It is more complicated for cases where registrants include their non self filer subsidiaries’ activities (IncludeNSFS=y). For those, we examine the catcode of the parent and the catcode of the subsidiary (self = c and self =b). If they are from different industries then we subtract the total of the subsidiary from the total of the parent and count it toward the other industry. For example look a General Electric in 2007. IncludeNSFS signifies whether a filer includes expenditures from its own self filing. A value of”n’ means that the parent company does include the lobbying expenditures of its subsidiaries in its disclosure form and thus, the expenditures reported by subsidiaries should not be included in the total sum. Conversely, a value of “y” would mean the parent company’s disclosure report does not capture the lobbying expenditures of its subsidiaries and any expenditures by the subsidiaries should be added in addition to the parent companies lobbying expenditures. The field is also used to indicate if the filer is a subcontractor (making business on behalf of) with the “s” value. "
#Merging Data
combined2 <- merge(main, issue, by=c("Uniqid","Year")) # This combines the groups and issue by Uniqid and Year## Warning: One or more parsing issues, see `problems()` for detailsrm(issue)
rm(main)#Made room in the global environment by removing all of these extra datasets and it was still to big to merge...I was able to merge all datasets.
Now I need to calculate the response variables of interest…
- Amount of money spent (minus the redundancies)
Summary Statistics
summary(combined2)## Uniqid Year Registrant_raw Registrant
## Length:2285510 Length:2285510 Length:2285510 Length:2285510
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Isfirm Client_raw Client Ultorg
## Length:2285510 Length:2285510 Length:2285510 Length:2285510
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Amount Catcode Source Self
## Length:2285510 Length:2285510 Length:2285510 Length:2285510
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## IncludeNSFS Use Ind Type
## Length:2285510 Length:2285510 Length:2285510 Length:2285510
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Typelong Affliate SI_ID IssueID
## Length:2285510 Length:2285510 Min. : 419 Length:2285510
## Class :character Class :character 1st Qu.: 782526 Class :character
## Mode :character Mode :character Median :1529336 Mode :character
## Mean :1468007
## 3rd Qu.:2170687
## Max. :2776133
## Issue NA
## Length:2285510 Mode:logical
## Class :character NA's:2285510
## Mode :character
##
##
## Need to remove 2021 because it is not complete
combined2<-subset(combined2, Year!="2021") #removed 2021 because it is not complete## CODE NO LONGER NEEDED AFTER i MERGED DATASET BY BOTH UNIQ AND YEAR
#Checking to make sure the year variables match up so that I can remove the duplicative columns and clean up the data
combined$Year.x <- as.numeric(combined$Year.x)#Changing this variable into a factor since the other year is a factor variable
is.factor(combined$Year.x) #checking to make sure it isn't still a factor
combined$Year.y <- as.numeric(combined$Year.y)
identical(combined$Year.y, combined$Year.x) #making sure these two columns are identical.Descriptive Statistics
combined2$Issue <- as.factor(combined2$Issue)
summary(combined2$Issue) # Revealed that relevant issue is Alcohol & Drug Abuse## Accounting Advertising
## 3856 3675
## Aerospace Agriculture
## 8412 50966
## Alcohol & Drug Abuse Animals
## 4526 6921
## Apparel, Clothing, & Textiles Arts & Entertainment
## 1804 5380
## Automotive Industry Aviation, Airlines & Airports
## 8580 26564
## Banking Bankruptcy
## 33036 4996
## Beverage Industry Chemical Industry
## 2931 8718
## Civil Rights & Civil Liberties Clean Air & Water
## 8118 31060
## Commodities Computers & Information Tech
## 2554 14184
## Constitution Consumer Product Safety
## 3130 23521
## Copyright, Patent & Trademark Defense
## 34607 111014
## Disaster & Emergency Planning District of Columbia
## 12220 1169
## Economics & Econ Development Education
## 20296 66261
## Energy & Nuclear Power Environment & Superfund
## 92094 69247
## Family, Abortion & Adoption Fed Budget & Appropriations
## 4728 256825
## Finance Firearms, Guns & Ammunition
## 51934 2967
## Food Industry Foreign Relations
## 17619 23909
## Fuel, Gas & Oil Gaming, Gambling & Casinos
## 13240 7667
## Government Issues Hazardous & Solid Waste
## 41879 5560
## Health Issues Homeland Security
## 164846 41925
## Housing Immigration
## 22843 31406
## Indian/Native American Affairs Insurance
## 22396 22810
## Intelligence Labor, Antitrust & Workplace
## 4192 45423
## Law Enforcement & Crime Manufacturing
## 25462 11373
## Marine, Boats & Fisheries Media Information & Publishing
## 20873 2346
## Medical Research & Clin Labs Medicare & Medicaid
## 18615 82773
## Mining, Money & Gold Standard Minting, Money & Gold Standard
## 543 339
## Minting/Money/Gold Standard Natural Resources
## 176 41063
## Pharmacy Postal
## 15161 7371
## Radio & TV Broadcasting Railroads
## 20220 9734
## Real Estate & Land Use Religion
## 11072 1294
## Retirement Roads & Highways
## 17560 8470
## Science & Technology Small Business
## 27662 12914
## Sports & Athletics Tariffs
## 2838 5322
## Taxes Telecommunications
## 172647 43603
## Tobacco Torts
## 7180 7837
## Trade Transportation
## 78143 99394
## Travel & Tourism Trucking & Shipping
## 5021 4660
## Unemployment Urban Development
## 1378 11860
## Utilities Veterans Affairs
## 12588 16428
## Welfare
## 4096Most relevant issue is…
Alcohol & Drug Abuse which appears in 4,592 reports Compare this with… Law Enforcement & Crime = 25,884 Health Issues = 167,937
Let’s graph these over time and see how they change…
ada <- subset(combined2, Issue=="Alcohol & Drug Abuse")#subsetting the databases with the relevant issues only
le <- subset(combined2, Issue=="Law Enforcement & Crime")
he <- subset(combined2, Issue=="Health Issues")
rel <-rbind(ada, he, le)#and then appending them so they are in one dataset
summary(rel)## Uniqid Year Registrant_raw Registrant
## Length:194834 Length:194834 Length:194834 Length:194834
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Isfirm Client_raw Client Ultorg
## Length:194834 Length:194834 Length:194834 Length:194834
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Amount Catcode Source Self
## Length:194834 Length:194834 Length:194834 Length:194834
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## IncludeNSFS Use Ind Type
## Length:194834 Length:194834 Length:194834 Length:194834
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Typelong Affliate SI_ID IssueID
## Length:194834 Length:194834 Min. : 419 Length:194834
## Class :character Class :character 1st Qu.: 871808 Class :character
## Mode :character Mode :character Median :1574713 Mode :character
## Mean :1504398
## 3rd Qu.:2188987
## Max. :2776133
##
## Issue NA
## Health Issues :164846 Mode:logical
## Law Enforcement & Crime: 25462 NA's:194834
## Alcohol & Drug Abuse : 4526
## Accounting : 0
## Advertising : 0
## Aerospace : 0
## (Other) : 0Calculating the Number of reports per issue per year
To determine the total number of reports filed per year, can use Uniqid. There will be multiple entries for each issue, but the Uniqid will be the same.
rel$Issue <- factor(rel$Issue) #re-calc levels
rel$Year <- factor(rel$Year) #turn year into a cat varlibrary(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionn_distinct(ada$Uniqid, na.rm = TRUE) #number of unique Uniqueid## [1] 4506n_distinct(le$Uniqid, na.rm = TRUE)## [1] 25291n_distinct(he$Uniqid, na.rm = TRUE)## [1] 162990n_distinct(combined2$Uniqid, na.rm = TRUE)## [1] 1049490This tells us that 4,506 LDA reports have indicated that they lobbied on ada policy. produces counts that measure repeated and non-exclusive occurrences of lobbyists, agencies, and other measures…meaning if an organization has to file reports and their lobbying agency files a report; it is counted double. We could argue that this indicates the issue was more salient (See LDA at 20 pg 264).
25,291 = reports identifying LE 162,990 = reports identifying health issues
Note: the unique reports were very similiar for most of these to the number of observations in the dataset; which indicates that there are not many Uniqid that are repeated in a particular issue group (the repeats are most likely because tehy are entered multiple times to account for the different issues)
Total UniqId’s in dataset = 1,049,490
Have the number of LDA reports filed identifying ADA changed over time? ADA by YEAR
Since filing requirements changed in 2007/2008 we will expect to see a drastic increase here. The law required that groups go from filing semi-annually to quarterly. And again this may count repeated and non-exclusive occurrences of lobbyists etc.
There will also be a decline in 2012-2018. These declines are attributed to more conservative LDA compliance stemming from increased HLOGA reporting requirements and increased journalistic and academic scrutiny of the reports (LaPira 2016; Thomas and LaPira 2017; LaPira and Thomas 2017). (See LDA at 20 pg 264).
#Unique reports by year for ada, he and le
rpts_ada <- aggregate(data = ada, # Applying aggregate
Uniqid ~ Year,
function(x) length(unique(x))) #uses the number of unique "UniqueId"
rpts_ada # Print counts## Year Uniqid
## 1 1998 112
## 2 1999 129
## 3 2000 137
## 4 2001 102
## 5 2002 151
## 6 2003 131
## 7 2004 140
## 8 2005 145
## 9 2006 148
## 10 2007 122
## 11 2008 237
## 12 2009 169
## 13 2010 164
## 14 2011 148
## 15 2012 205
## 16 2013 196
## 17 2014 233
## 18 2015 232
## 19 2016 301
## 20 2017 301
## 21 2018 374
## 22 2019 341
## 23 2020 288#he
rpts_he <- aggregate(data=he,
Uniqid ~ Year,
function(x) length(unique(x)))
rpts_he## Year Uniqid
## 1 1998 1964
## 2 1999 2101
## 3 2000 2595
## 4 2001 2466
## 5 2002 3151
## 6 2003 2902
## 7 2004 3177
## 8 2005 3555
## 9 2006 3665
## 10 2007 4122
## 11 2008 7908
## 12 2009 10193
## 13 2010 9839
## 14 2011 7581
## 15 2012 9756
## 16 2013 9682
## 17 2014 9825
## 18 2015 10183
## 19 2016 10383
## 20 2017 11522
## 21 2018 11470
## 22 2019 11635
## 23 2020 13315#le
rpts_le <- aggregate(data=le,
Uniqid ~ Year,
function(x) length(unique(x)))
rpts_le## Year Uniqid
## 1 1998 355
## 2 1999 424
## 3 2000 512
## 4 2001 450
## 5 2002 750
## 6 2003 543
## 7 2004 625
## 8 2005 637
## 9 2006 676
## 10 2007 758
## 11 2008 1510
## 12 2009 1438
## 13 2010 1509
## 14 2011 1140
## 15 2012 1425
## 16 2013 1408
## 17 2014 1343
## 18 2015 1469
## 19 2016 1501
## 20 2017 1517
## 21 2018 1722
## 22 2019 1731
## 23 2020 1848#total unique reports by year
rpts_tots <- aggregate(data=combined2,
Uniqid ~ Year,
function(x) length(unique(x)))
rpts_tots## Year Uniqid
## 1 1998 17810
## 2 1999 18511
## 3 2000 19565
## 4 2001 21172
## 5 2002 23253
## 6 2003 26220
## 7 2004 27598
## 8 2005 30549
## 9 2006 31245
## 10 2007 33323
## 11 2008 63727
## 12 2009 64837
## 13 2010 63095
## 14 2011 49260
## 15 2012 62434
## 16 2013 60795
## 17 2014 59672
## 18 2015 59386
## 19 2016 59232
## 20 2017 62488
## 21 2018 63839
## 22 2019 64473
## 23 2020 67006#plotting
#ada by year
plot(rpts_ada$Year, rpts_ada$Uniqid)
plot(rpts_he$Year, rpts_he$Uniqid)
plot(rpts_le$Year, rpts_le$Uniqid)
plot(rpts_tots$Year, rpts_tots$Uniqid)
All issues (except ada) generally follow the same pattern as tots. ada does have the general trend upward but it looks much different. Let’s take a look out whether there is a relationship between the le rpts and the ada rpts. Are these treated as two distinct issues? Also does tots influence ada rpts?
#append counts dataset for rpts
names(rpts_ada)[names(rpts_ada) == 'Uniqid'] <- 'Ada_rpt' #first change variable names so that the datasets merge properly
names(rpts_le)[names(rpts_le)== 'Uniqid'] <- 'Le_rpt'
names(rpts_he)[names(rpts_he)== 'Uniqid'] <- 'He_rpt'
names(rpts_tots)[names(rpts_tots)== 'Uniqid'] <- 'Tots_rpt'
rpts_1 <- merge(rpts_ada, rpts_le, by=c("Year"))
rpt_2 <- merge (rpts_he, rpts_tots, by=c("Year"))
rpts_all <- merge(rpts_1, rpt_2, by=c("Year"))
coplot(rpts_all$Ada_rpt~rpts_all$Year|rpts_all$Le_rpt, pch=16, panel=panel.smooth) 
coplot(rpts_all$Ada_rpt~rpts_all$Year|rpts_all$He_rpt, pch=16, panel=panel.smooth)
coplot(rpts_all$Ada_rpt~rpts_all$Year|rpts_all$Tots_rpt, pch=16, panel=panel.smooth)
library(ggplot2)
rpts_all$Year <- as.numeric(rpts_all$Year)
ggplot(rpts_all, aes(x=Year))+
geom_line(aes(y = Tots_rpt),color = "darkred")+
geom_line(aes(y = Le_rpt),color = "steelblue")+
geom_line(aes(y = He_rpt),color = "black")+
geom_line(aes(y = Ada_rpt),color = "purple")
ggplot(rpts_all, aes(x=Year))+
geom_line(aes(y = Le_rpt),color = "steelblue")+
geom_line(aes(y = He_rpt),color = "black")+
geom_line(aes(y = Ada_rpt),color = "purple")
ggplot(rpts_all, aes(x=Year))+
geom_line(aes(y = Le_rpt),color = "steelblue")+
geom_line(aes(y = Ada_rpt),color = "purple")
Based on the above co-plot, the variance of ada report counts seems to be influenced by le rpt counts, but only after Le reaches a certain count. Same for he and tots… After we plot them all together though….it really looks like ada and le are flatter than increases in total reports filed…
These are interesting in showing trends but there really aren’t many observations (1 per year), so we really can’t do fancier statistics here. Let’s look to see how the industries changed…
Changes in Industry Participation
#Catcode is the variable that represents industry
library(readxl) #importing category names and merging it with combined2
cat_code_industry <- read_excel("C:/Users/talee/Dropbox/1-Research/drug policy and interest groups/Lobbying Data/cat_code_industry.xlsx")
combined3 <- merge(combined2,cat_code_industry,by="Catcode") #merging so that the dataset has the names of the industries...
combined3$Catname <- factor(combined3$Catname)
summary(combined3$Catname)## Civil servant/public employee
## 162955
## Schools & colleges
## 109268
## Employer listed but category unknown
## 107002
## Pharmaceutical manufacturing
## 55133
## Hospitals
## 51584
## Non-Profits
## 29736
## Health & welfare policy
## 28676
## Electric Power utilities
## 27870
## Gas & Electric Utilities
## 27646
## Computer software
## 26800
## Other single-issue or ideological groups
## 23722
## Environmental policy
## 22861
## Biotech products & research
## 22503
## Medical Devices & Supplies
## 22013
## Insurance companies, brokers & agents
## 21693
## Other physician specialists
## 20537
## Indian Gaming
## 20348
## Chemicals
## 20077
## Defense aerospace contractors
## 20021
## Water Utilities
## 19825
## Alternate energy production & services
## 19739
## Industrial/commercial equipment & materials
## 18872
## Defense electronic contractors
## 18122
## Commercial banks & bank holding companies
## 17861
## Data processing & computer services
## 17564
## Real Estate developers & subdividers
## 16989
## Computer components & accessories
## 16889
## Minority/Ethnic Groups
## 16506
## Health care services
## 16418
## Auto manufacturers
## 16312
## Security brokers & investment companies
## 16108
## Electronics manufacturing & services
## 15498
## Accident & health insurance
## 14632
## Sea transport
## 12912
## Life insurance
## 12606
## Telephone utilities
## 12530
## Chambers of commerce
## 12528
## Railroads
## 12295
## Cable distributors & service providers
## 11737
## Major (multinational) oil & gas producers
## 11599
## Credit agencies & finance companies
## 11511
## HMOs
## 10928
## Property & casualty insurance
## 10927
## Education
## 10829
## Tobacco & Tobacco products
## 10829
## Museums, art galleries, libraries, etc.
## 10087
## Attorneys & law firms
## 9913
## Natural Gas transmission & distribution
## 9847
## Airlines
## 9773
## Business services
## 9731
## Aviation services & airports
## 9362
## Welfare & Social Work
## 9231
## Engineering, architecture & construction mgmt svcs
## 9198
## Lobbyists & Public Relations
## 9078
## Food and kindred products manufacturing
## 8967
## Public works, industrial & commercial construction
## 8668
## Forestry & Forest Products
## 8655
## Defense-related services
## 8643
## Computer manufacture & services
## 8619
## Mortgage bankers and brokers
## 8484
## Truck/Automotive parts & accessories
## 8471
## Petroleum refining & marketing
## 8460
## Stone, clay, glass & concrete products
## 8382
## Sea freight & passenger services
## 8249
## Independent oil & gas producers
## 8176
## Health care institutions
## 8171
## Farm bureaus
## 8129
## Milk & dairy producers
## 7813
## Management consultants & services
## 7738
## Medical schools
## 7734
## Pro-business associations
## 7684
## Non-profit foundations
## 7499
## Book, newspaper & periodical publishing
## 7459
## Environmental services, equipment & consulting
## 7368
## Cell/wireless service providers
## 7198
## Commercial TV & radio stations
## 7194
## Health, Education & Human Resources
## 7129
## Vegetables, fruits and tree nut
## 7119
## Restaurants & drinking establishments
## 7114
## Physicians
## 6824
## Wine & distilled spirits manufacturing
## 6793
## Manufacturing
## 6701
## Plastics & Rubber processing & products
## 6680
## Transportation
## 6523
## Steel
## 6491
## Waste management
## 6476
## Accountants
## 6414
## Casinos, racetracks & gambling
## 6327
## Department, variety & convenience stores
## 6327
## Search Engine/Email Services
## 6249
## Aircraft manufacturers
## 6194
## Other non-physician health practitioners
## 6150
## Agricultural chemicals (fertilizers & pesticides)
## 6100
## Oilfield service, equipment & exploration
## 5872
## Coal mining
## 5728
## Fiscal & tax policy
## 5630
## Elderly issues/Social Security
## 5585
## Agricultural services & related industries
## 5573
## Other Communications Services
## 5496
## (Other)
## 626210Lobby by Category
#merge dataset with ada to see who is lobbying on ada policy
ada2 <- merge(ada,cat_code_industry,by="Catcode")
ada2$Catname <- factor(ada2$Catname)
summary(ada2$Catname)## Pharmaceutical manufacturing
## 498
## Wine & distilled spirits manufacturing
## 483
## Drug & alcohol treatment hospitals
## 294
## Civil servant/public employee
## 259
## Employer listed but category unknown
## 255
## Beer
## 182
## Schools & colleges
## 170
## Liquor wholesalers
## 149
## Outpatient health services (incl drug & alcohol)
## 137
## Other single-issue or ideological groups
## 126
## Non-Profits
## 113
## Health & welfare policy
## 109
## Professional sports, arenas & related equip & svcs
## 107
## Other physician specialists
## 102
## Psychiatrists & psychologists
## 91
## Hospitals
## 90
## Physicians
## 90
## Mental Health Services
## 67
## Health care services
## 54
## Pharmaceutical wholesale
## 52
## Medical laboratories
## 48
## Other non-physician health practitioners
## 45
## Pharmacists
## 42
## Alcohol
## 40
## Property & casualty insurance
## 38
## Drug stores
## 37
## Tobacco & Tobacco products
## 36
## Dentists
## 31
## Medical schools
## 30
## Minority/Ethnic Groups
## 29
## Attorneys & law firms
## 27
## Household cleansers & chemicals
## 27
## Welfare & Social Work
## 27
## Casinos, racetracks & gambling
## 25
## Non-profit foundations
## 25
## Accident & health insurance
## 24
## Biotech products & research
## 24
## Lobbyists & Public Relations
## 24
## Computer software
## 23
## Health professionals
## 21
## Business services
## 19
## Police & firefighters unions & associations
## 16
## Restaurants & drinking establishments
## 16
## Chemicals
## 15
## Education
## 15
## Health care products
## 15
## Democratic/Liberal
## 14
## Abortion policy/Anti-Abortion
## 13
## Department, variety & convenience stores
## 13
## State & local govt employee unions
## 13
## Liquor stores
## 12
## Churches, clergy & religious organizations
## 11
## Precision instruments
## 11
## Agricultural chemicals (fertilizers & pesticides)
## 10
## Health, Education & Human Resources
## 10
## Transportation unions
## 10
## Electronics manufacturing & services
## 9
## Food wholesalers
## 9
## Industrial/commercial equipment & materials
## 9
## Public school teachers, administrators & officials
## 9
## Chambers of commerce
## 8
## Medical Devices & Supplies
## 8
## Republican/Conservative
## 8
## AIDS treatment & testing
## 7
## Book, newspaper & periodical publishing
## 7
## Christian Conservative
## 7
## Data processing & computer services
## 7
## Health care institutions
## 7
## Indian Gaming
## 7
## Marijuana Production, Sales & Paraphernalia
## 7
## Nurses
## 7
## Optometrists & Ophthalmologists
## 7
## Correctional facilities constr & mgmt/for-profit
## 6
## Food stores
## 6
## Household & office products
## 6
## Nutritional & dietary supplements
## 6
## Services
## 6
## Teamsters union
## 6
## Transportation
## 5
## Amusement/recreation centers
## 4
## Cell/wireless service providers
## 3
## Internet & Online Services
## 3
## Military
## 3
## Petroleum refining & marketing
## 3
## Waste management
## 3
## Aircraft manufacturers
## 2
## Airlines
## 2
## Coal mining
## 2
## Consumer groups
## 2
## Courts & Justice System
## 2
## Defense-related services
## 2
## Defense aerospace contractors
## 2
## Food and kindred products manufacturing
## 2
## Gas & Electric Utilities
## 2
## Life insurance
## 2
## Lodging & tourism
## 2
## Management consultants & services
## 2
## Museums, art galleries, libraries, etc.
## 2
## Oilfield service, equipment & exploration
## 2
## (Other)
## 31Lobbying on Ada by Industry
ada2$Industry <- factor(ada2$Industry)
summary(ada2$Industry)## Abortion Policy/Anti-Abortion Agricultural Services/Products
## 13 11
## Air Transport Beer, Wine & Liquor
## 7 866
## Building Trade Unions Business Associations
## 1 9
## Business Services Casinos/Gambling
## 21 32
## Chemical & Related Manufacturing Civil Servants/Public Officials
## 42 261
## Clergy & Religious Organizations Construction Services
## 11 2
## Defense Aerospace Democratic/Liberal
## 2 14
## Education Electric Utilities
## 224 3
## Electronics Mfg & Equip Employer Listed/Category Unknown
## 39 255
## Environment Food & Beverage
## 1 16
## Food Processing & Sales General Contractors
## 17 1
## Health Professionals Health Services/HMOs
## 436 315
## Hospitals/Nursing Homes Human Rights
## 391 139
## Insurance Internet
## 64 3
## Lawyers/Law Firms Lobbyists
## 27 24
## Lodging/Tourism Marijuana
## 3 7
## Mining Misc Business
## 2 7
## Misc Defense Misc Health
## 2 10
## Misc Issues Misc Manufacturing & Distributing
## 129 27
## Misc Services Misc Transport
## 6 6
## Misc Unions Non-Profit Institutions
## 2 140
## Oil & Gas Other
## 6 30
## Pharmaceuticals/Health Products Printing & Publishing
## 603 7
## Public Sector Unions Railroads
## 31 1
## Real Estate Recreation/Live Entertainment
## 3 111
## Republican/Conservative Retail Sales
## 15 50
## Securities & Investment Telecom Services
## 1 3
## Tobacco Transportation Unions
## 36 16
## TV/Movies/Music Waste Management
## 2 3Lobbying on Ada by Sector
ada2$Sector <- factor(ada2$Sector)
summary(ada2$Sector)## Agribusiness Communic/Electronics Construction
## 64 54 3
## Defense Energy/Nat Resource Finance/Insur/RealEst
## 4 14 68
## Health Ideology/Single-Issue Labor
## 1755 311 50
## Lawyers & Lobbyists Misc Business Other
## 51 1197 666
## Transportation Unknown
## 14 255Lobbying on LE by Category Name
#merge dataset with le to see who is lobbying on le policy
le2 <- merge(le,cat_code_industry,by="Catcode")
le2$Catname <- factor(le2$Catname)
summary(le2$Catname)## Civil servant/public employee
## 5345
## Employer listed but category unknown
## 1519
## Other single-issue or ideological groups
## 1338
## Schools & colleges
## 965
## Police & firefighters unions & associations
## 867
## Courts & Justice System
## 864
## Correctional facilities constr & mgmt/for-profit
## 660
## Non-Profits
## 575
## Security services
## 487
## Computer software
## 386
## Minority/Ethnic Groups
## 371
## Attorneys & law firms
## 350
## Chambers of commerce
## 306
## Human Rights
## 279
## Children's rights
## 263
## Homeland Security contractors
## 238
## Welfare & Social Work
## 236
## Indian Gaming
## 231
## Churches, clergy & religious organizations
## 227
## Defense electronic contractors
## 205
## Psychiatrists & psychologists
## 186
## Electronics manufacturing & services
## 184
## Credit agencies & finance companies
## 177
## Business services
## 170
## Pharmaceutical manufacturing
## 166
## Data processing & computer services
## 165
## Biotech products & research
## 158
## Marijuana Production, Sales & Paraphernalia
## 151
## Computer components & accessories
## 147
## Health & welfare policy
## 145
## Retail trade
## 145
## Republican/Conservative
## 132
## Christian Conservative
## 128
## Employment agencies
## 127
## Women's issues
## 126
## Lobbyists & Public Relations
## 125
## Internet & Online Services
## 124
## Recycling of metal, paper, plastics, etc.
## 124
## Search Engine/Email Services
## 121
## Trial lawyers & law firms
## 120
## Anti-Guns
## 114
## Vendors
## 114
## Nutritional & dietary supplements
## 111
## Pro-Guns
## 110
## Major (multinational) oil & gas producers
## 108
## Non-profit foundations
## 105
## Other Communications Services
## 104
## Commercial banks & bank holding companies
## 103
## Environmental policy
## 101
## Food stores
## 100
## Department, variety & convenience stores
## 99
## Foreign policy
## 98
## Hardware & building materials stores
## 98
## Telephone utilities
## 98
## Hospitals
## 97
## Gay & lesbian rights & issues
## 93
## Drug stores
## 92
## Restaurants & drinking establishments
## 81
## Petroleum refining & marketing
## 78
## Other physician specialists
## 75
## Real Estate developers & subdividers
## 74
## Defense-related services
## 73
## Physicians
## 73
## Social Media
## 73
## Alternate energy production & services
## 70
## Small arms & ammunition
## 70
## Toiletries & cosmetics
## 70
## Cable distributors & service providers
## 69
## State & local govt employee unions
## 67
## Casinos, racetracks & gambling
## 66
## Accountants
## 65
## Technical, business and vocational schools & svcs
## 65
## General business associations
## 64
## Professional sports, arenas & related equip & svcs
## 64
## Chemicals
## 58
## Management consultants & services
## 58
## Telecommunications
## 58
## Defense aerospace contractors
## 56
## Hotels & motels
## 55
## Insurance companies, brokers & agents
## 55
## Transportation unions
## 54
## Democratic/Liberal
## 53
## Mental Health Services
## 52
## Health care products
## 51
## Household & office products
## 50
## Pro-Israel
## 47
## Education
## 46
## Life insurance
## 46
## Nursing homes
## 46
## Abortion policy/Pro-Abortion Rights
## 45
## Property & casualty insurance
## 45
## Truck/Automotive parts & accessories
## 45
## Entertainment Industry/Broadcast & Motion Pictures
## 44
## Labor Unions
## 44
## Fiscal & tax policy
## 43
## Pro-business associations
## 43
## Legal Services
## 42
## Public school teachers, administrators & officials
## 42
## Cell/wireless service providers
## 41
## (Other)
## 2519Lobbying on LE by Industry
le2$Industry <- factor(le2$Industry)
summary(le2$Industry)## Abortion Policy/Anti-Abortion Abortion Policy/Pro-Abortion Rights
## 33 45
## Accountants Agricultural Services/Products
## 65 86
## Air Transport Automotive
## 88 99
## Beer, Wine & Liquor Building Materials & Equipment
## 44 54
## Business Associations Business Services
## 436 867
## Casinos/Gambling Chemical & Related Manufacturing
## 297 96
## Civil Servants/Public Officials Clergy & Religious Organizations
## 6233 227
## Commercial Banks Construction Services
## 105 47
## Crop Production & Basic Processing Dairy
## 7 3
## Defense Aerospace Defense Electronics
## 56 205
## Democratic/Liberal Education
## 53 1189
## Electric Utilities Electronics Mfg & Equip
## 53 953
## Employer Listed/Category Unknown Environment
## 1519 101
## Environmental Svcs/Equipment Finance/Credit Companies
## 12 178
## Fisheries & Wildlife Food & Beverage
## 1 97
## Food Processing & Sales Foreign & Defense Policy
## 108 134
## Forestry & Forest Products General Contractors
## 6 38
## Gun Control Gun Rights
## 114 110
## Health Professionals Health Services/HMOs
## 418 111
## Hospitals/Nursing Homes Human Rights
## 204 1151
## Industrial Unions Insurance
## 7 161
## Internet Lawyers/Law Firms
## 454 519
## Livestock Lobbyists
## 18 125
## Lodging/Tourism Marijuana
## 68 151
## Mining Misc Agriculture
## 16 3
## Misc Business Misc Communications/Electronics
## 663 4
## Misc Defense Misc Energy
## 367 108
## Misc Finance Misc Health
## 116 5
## Misc Issues Misc Manufacturing & Distributing
## 1441 488
## Misc Services Misc Transport
## 11 56
## Misc Unions Non-contribution
## 98 2
## Non-Profit Institutions Oil & Gas
## 705 203
## Other Pharmaceuticals/Health Products
## 249 531
## Printing & Publishing Pro-Israel
## 39 47
## Public Sector Unions Railroads
## 1001 32
## Real Estate Recreation/Live Entertainment
## 157 82
## Republican/Conservative Retail Sales
## 260 470
## Savings & Loans Sea Transport
## 7 16
## Securities & Investment Special Trade Contractors
## 98 12
## Steel Production Telecom Services
## 3 280
## Telephone Utilities Textiles
## 112 1
## Tobacco Transportation Unions
## 32 105
## Trucking TV/Movies/Music
## 18 163
## Waste Management Women's Issues
## 40 126Lobbying on LE by Sector
le2$Sector <- factor(le2$Sector)
summary(le2$Sector)## Agribusiness Communic/Electronics Construction
## 263 2005 151
## Defense Energy/Nat Resource Finance/Insur/RealEst
## 628 433 887
## Health Ideology/Single-Issue Labor
## 1269 3615 1211
## Lawyers & Lobbyists Misc Business Non-contribution
## 644 3774 2
## Other Transportation Unknown
## 8603 309 1519Who is lobbying on both le and addiction?
There are 173 clients that lobbied on both law enforcement and addiction…
library(fuzzyjoin); library(dplyr);
le2$Client <- factor(le2$Client)
ada2$Client <- factor(ada2$Client)
ada_client <-levels(ada2$Client) #created a list of all category levels in Client factor in ada, then put it in a dataframe
ada_client <-as.data.frame(ada_client)
names(ada_client)[names(ada_client) == 'ada_client'] <- "client" #need the variable names to match with the variable name in the other dataframe
le_client<-levels(le2$Client)
le_client <-as.data.frame(le_client)
names(le_client)[names(le_client) == 'le_client'] <- "client"
data_common <- inner_join(ada_client, le_client) #This function checks the data in common on the same variable between two dataframes.## Joining, by = "client"data_common## client
## 1 Agricultural Retailers Assn
## 2 Alachua County, FL
## 3 Alcohol Monitoring Systems
## 4 Alkermes Inc
## 5 Alliance for Children & Families
## 6 Alliance for Safety & Justice
## 7 Allied Domecq Spirits & Wine
## 8 Amalgamated Transit Union
## 9 American Academy of Family Physicians
## 10 American Assn/Marriage & Family Therapy
## 11 American Beverage Licensees
## 12 American Clinical Laboratory Assn
## 13 American College of Emergency Physicians
## 14 American College of Nurse-Midwives
## 15 American Council on Education
## 16 American Council/Regulatory Compliance
## 17 American Counseling Assn
## 18 American Federation of State/Cnty/Munic Employees
## 19 American Foundation for AIDS Research
## 20 American Insurance Assn
## 21 American Optometric Assn
## 22 American Property Casualty Insurance Assn
## 23 American Psychiatric Assn
## 24 American Psychological Assn
## 25 American Psychological Assn Practice Org
## 26 American Psychological Assn Services
## 27 American Public Health Assn
## 28 Americans for Safe Access
## 29 Americans for Tax Reform
## 30 Americans United for Separation of Church & State
## 31 Appriss Inc
## 32 Arinc Engineering Services
## 33 Babyland Family Services
## 34 Bayer Corp
## 35 Berea Children's Home
## 36 Bexar County, TX
## 37 Boehringer Ingelheim Corp
## 38 Boys & Girls Clubs of America
## 39 Broward County, FL
## 40 Cannabis Trade Federation
## 41 Cannabis Trade Federation Action
## 42 Cephalon Inc
## 43 Child Welfare League of America
## 44 Christian Coalition
## 45 Church of Scientology International
## 46 City of Baltimore, LA
## 47 City of Boston, MA
## 48 City of Everett, WA
## 49 City of Farmington, NM
## 50 City of Gainesville, FL
## 51 City of Gary, IN
## 52 City of Huntington Beach, CA
## 53 City of Laguna Beach, CA
## 54 City of Louiseville, KY
## 55 City of Miami Beach, FL
## 56 City of Mission Viejo, CA
## 57 City of Newark, NJ
## 58 City of Philadelphia, PA
## 59 Consortium of Social Science Assns
## 60 Consumer Federation of America
## 61 Consumer Healthcare Products Assn
## 62 Council for Responsible Nutrition
## 63 Cuyahoga County, OH
## 64 DARE America
## 65 DARE New Jersey
## 66 Diageo North America
## 67 Distilled Spirits Council
## 68 Drug Policy Alliance
## 69 Elon University
## 70 Express Scripts
## 71 Facing Addiction
## 72 Family Research Council
## 73 Food Distributors International
## 74 Food Marketing Institute
## 75 Girl Scouts of the USA
## 76 Health Insurance Assn of America
## 77 Heritage Action for America
## 78 Identa Ltd
## 79 Indiana Chamber of Commerce
## 80 Indiana University
## 81 Institute for A Drug-Free Workplace
## 82 International Community Corrections Assn
## 83 Johnson & Johnson
## 84 Joseph E Seagram & Sons
## 85 Justice for Jake
## 86 Kansas Dept of Commerce
## 87 Keeton Corrections
## 88 Kendall-Jackson Wine Estates
## 89 Kids First Coalition
## 90 Magazine Publishers of America
## 91 MAGNA Pharmaceuticals
## 92 Major Cities Chiefs
## 93 Major League Baseball Commissioner's Ofc
## 94 Marijuana Policy Project
## 95 Marshall University Research Corp
## 96 Mcj Inc
## 97 Mental Health America
## 98 Meth Project
## 99 Midwest City Memorial Hospital Authority
## 100 MillerCoors LLC
## 101 Minot State University
## 102 Mothers Against Drunk Driving
## 103 NAACP
## 104 National Assn of Broadcasters
## 105 National Assn of Chain Drug Stores
## 106 National Assn of Convenience Stores
## 107 National Assn of Police Organizations
## 108 National Beer Wholesalers Assn
## 109 National Council/Community Behavioral Healthcare
## 110 National Disability Rights Network
## 111 National District Attorneys Assn
## 112 National Football League
## 113 National Fraternal Order of Police
## 114 National Grocers Assn
## 115 National Mental Health Assn
## 116 National PTA
## 117 National Restaurant Assn
## 118 National Right to Life
## 119 Natl Assn of Drug Court Professionals
## 120 New York University
## 121 Northern Arapaho Indian Tribe
## 122 Ohio State University
## 123 OhioGuidestone
## 124 Oriana House
## 125 Orphan Medical
## 126 Pacific Inst for Research & Evaluation
## 127 Partnership for a Drug Free America
## 128 People for the American Way
## 129 Philip Morris Management
## 130 Physicians Cmte for Responsible Medicine
## 131 Prime Therapeutics
## 132 Property Casualty Insurers Assn of America
## 133 Purdue Pharma
## 134 Rancho Cordova, CA
## 135 Reckitt Benckiser Pharmaceuticals
## 136 Reckitt Benckiser Plc
## 137 RetireSafe
## 138 RTI International
## 139 Salt Lake City, UT
## 140 Salt Lake County, UT
## 141 SAM Action
## 142 Sams, Letha et al
## 143 San Juan County, NM
## 144 SAS Institute
## 145 Save Our Society From Drugs
## 146 Seam Llc
## 147 Shelby County, TN
## 148 Smart Approaches to Marijuana
## 149 Society for Neuroscience
## 150 Southern Ute Indian Tribe
## 151 Starr Commonwealth
## 152 State of Indiana
## 153 State of Oklahoma
## 154 Strategic Applications International
## 155 Susan B Anthony List
## 156 Teamsters Union
## 157 Therapeutic Communities of America
## 158 United Parcel Service
## 159 UnitedHealth Group
## 160 University of Missouri
## 161 University of Virginia
## 162 US Anti-Doping Agency
## 163 US Chamber of Commerce
## 164 Ute Mountain Ute Indian Tribe
## 165 Verde Technologies
## 166 Walgreen Co
## 167 Walgreens
## 168 Warner-Lambert
## 169 Washington County, OR
## 170 Washington State University
## 171 Wine & Spirits Wholesalers of America
## 172 Yellowstone Boys & Girls Ranch
## 173 YMCA of the USANot quite sure what to do with the long list above…
Changes over time in Alchol & Drug Abuse Policy –
Maybe there will be something more meaningful that jumps out when we look at how the activity by industry changes over time…
library(knitr)
table <-table(ada2$Catname, ada2$Year)
kable(table)| 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Abortion policy/Anti-Abortion | 2 | 2 | 2 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 |
| Accident & health insurance | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 8 | 4 | 4 |
| Agricultural chemicals (fertilizers & pesticides) | 0 | 3 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| AIDS treatment & testing | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Aircraft manufacturers | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Airlines | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Alcohol | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 2 | 3 | 4 | 2 | 3 | 4 | 4 | 4 | 1 | 0 | 2 | 2 | 4 | 1 |
| Amusement/recreation centers | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Architectural services | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Attorneys & law firms | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 9 | 4 | 4 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| Aviation services & airports | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Beer | 9 | 7 | 6 | 4 | 6 | 8 | 11 | 12 | 11 | 6 | 17 | 8 | 6 | 5 | 7 | 8 | 11 | 12 | 9 | 5 | 4 | 4 | 6 |
| Biotech products & research | 6 | 3 | 3 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Book, newspaper & periodical publishing | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Building trades unions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Business services | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 5 | 2 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Casinos, racetracks & gambling | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 5 | 4 | 5 | 5 | 4 |
| Cell/wireless service providers | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Chambers of commerce | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| Chemicals | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 3 | 4 | 0 | 0 |
| Children’s rights | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Christian Conservative | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Churches, clergy & religious organizations | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 3 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Civil servant/public employee | 5 | 10 | 10 | 5 | 5 | 4 | 4 | 4 | 6 | 4 | 8 | 9 | 10 | 10 | 5 | 6 | 7 | 7 | 7 | 10 | 30 | 51 | 42 |
| Coal mining | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Commercial TV & radio stations | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Commodity brokers/dealers | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Computer software | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 8 | 4 | 4 |
| Consumer groups | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Correctional facilities constr & mgmt/for-profit | 0 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Courts & Justice System | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Data processing & computer services | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 0 |
| Defense-related services | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| Defense aerospace contractors | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Democratic/Liberal | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 5 | 0 | 3 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Dentists | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 7 | 0 | 3 | 4 | 4 | 4 | 4 |
| Department, variety & convenience stores | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Drug & alcohol treatment hospitals | 5 | 4 | 7 | 6 | 6 | 9 | 11 | 8 | 11 | 5 | 15 | 15 | 21 | 18 | 25 | 24 | 17 | 16 | 15 | 12 | 17 | 12 | 15 |
| Drug stores | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 5 | 8 | 3 | 4 | 4 | 4 |
| Education | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 4 | 3 |
| Electric Power utilities | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Electronics manufacturing & services | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 1 | 0 | 0 | 0 |
| Employer listed but category unknown | 1 | 0 | 4 | 3 | 10 | 17 | 18 | 13 | 9 | 3 | 7 | 2 | 3 | 7 | 14 | 12 | 13 | 15 | 21 | 16 | 23 | 20 | 24 |
| Engineers - type unknown | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Environmental policy | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Express delivery services | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Farm bureaus | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Fiscal & tax policy | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Food and kindred products manufacturing | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Food stores | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Food wholesalers | 2 | 2 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Freight & delivery services | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Gas & Electric Utilities | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| General aviation (private pilots) | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Glass products | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Health & welfare policy | 3 | 4 | 1 | 1 | 3 | 1 | 3 | 3 | 6 | 5 | 11 | 5 | 3 | 4 | 4 | 5 | 5 | 8 | 13 | 12 | 8 | 1 | 0 |
| Health care institutions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 1 | 0 | 0 | 0 | 0 |
| Health care products | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 2 |
| Health care services | 0 | 0 | 1 | 0 | 2 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 2 | 4 | 7 | 4 | 4 | 4 | 4 | 7 | 4 | 4 | 4 |
| Health professionals | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 4 | 4 | 3 | 1 |
| Health, Education & Human Resources | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| HMOs | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Home care services | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Hospitals | 1 | 1 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 4 | 4 | 4 | 4 | 13 | 10 | 12 | 16 | 9 |
| Hotels & motels | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Household & office products | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Household cleansers & chemicals | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 2 | 4 | 4 | 4 | 4 | 0 | 0 | 0 | 0 | 0 |
| Import/Export services | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Indian Gaming | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Industrial/commercial equipment & materials | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 0 |
| International trade associations | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Internet & Online Services | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| Life insurance | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Liquor stores | 1 | 1 | 1 | 0 | 2 | 2 | 2 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Liquor wholesalers | 1 | 4 | 2 | 2 | 2 | 2 | 4 | 4 | 4 | 4 | 6 | 8 | 7 | 17 | 18 | 12 | 12 | 8 | 11 | 4 | 4 | 5 | 8 |
| Lobbyists & Public Relations | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 4 | 4 | 4 | 4 |
| Lodging & tourism | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Management consultants & services | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Marijuana Production, Sales & Paraphernalia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 3 |
| Medical Devices & Supplies | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 0 | 0 | 0 |
| Medical laboratories | 3 | 5 | 2 | 3 | 2 | 1 | 5 | 2 | 3 | 0 | 0 | 0 | 4 | 3 | 4 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 4 |
| Medical schools | 2 | 3 | 4 | 2 | 4 | 3 | 4 | 2 | 1 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Mental Health Services | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 3 | 1 | 1 | 5 | 8 | 8 | 9 | 8 | 9 | 8 |
| Military | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Minority/Ethnic Groups | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 6 | 3 | 4 | 4 | 5 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 |
| Museums, art galleries, libraries, etc. | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Natural Gas transmission & distribution | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Non-profit foundations | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 4 | 4 | 7 | 3 | 0 |
| Non-Profits | 4 | 4 | 4 | 3 | 7 | 3 | 2 | 5 | 3 | 4 | 12 | 10 | 4 | 4 | 4 | 4 | 6 | 4 | 5 | 4 | 5 | 6 | 6 |
| Nurses | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 |
| Nutritional & dietary supplements | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Oilfield service, equipment & exploration | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Optometrists & Ophthalmologists | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| Other non-physician health practitioners | 4 | 5 | 5 | 3 | 3 | 2 | 2 | 1 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 4 | 5 |
| Other physician specialists | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 1 | 0 | 1 | 3 | 2 | 3 | 4 | 9 | 9 | 8 | 7 | 8 | 8 | 11 | 12 | 12 |
| Other single-issue or ideological groups | 8 | 3 | 5 | 8 | 8 | 7 | 4 | 5 | 5 | 13 | 17 | 10 | 3 | 0 | 0 | 3 | 1 | 1 | 7 | 0 | 0 | 9 | 9 |
| Other unions | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Outpatient health services (incl drug & alcohol) | 7 | 7 | 9 | 9 | 10 | 10 | 8 | 3 | 3 | 4 | 4 | 3 | 4 | 4 | 4 | 4 | 4 | 4 | 7 | 9 | 8 | 8 | 4 |
| Petroleum refining & marketing | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Pharmaceutical manufacturing | 1 | 6 | 2 | 2 | 3 | 7 | 12 | 16 | 12 | 8 | 18 | 11 | 13 | 13 | 17 | 28 | 42 | 52 | 57 | 52 | 56 | 42 | 28 |
| Pharmaceutical wholesale | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 6 | 5 | 8 | 9 | 8 | 8 | 4 |
| Pharmacists | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 8 | 9 | 8 | 8 | 4 | 4 |
| Physicians | 0 | 0 | 0 | 3 | 3 | 2 | 2 | 2 | 2 | 1 | 3 | 1 | 3 | 3 | 7 | 5 | 14 | 4 | 12 | 8 | 7 | 7 | 1 |
| Police & firefighters unions & associations | 0 | 1 | 2 | 2 | 2 | 3 | 1 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Precision instruments | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Professional sports, arenas & related equip & svcs | 0 | 0 | 0 | 2 | 4 | 5 | 4 | 9 | 7 | 4 | 13 | 8 | 9 | 3 | 5 | 6 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
| Property & casualty insurance | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 0 | 0 | 4 | 5 | 8 | 13 |
| Psychiatrists & psychologists | 0 | 2 | 1 | 2 | 2 | 1 | 2 | 3 | 4 | 5 | 7 | 6 | 5 | 6 | 8 | 8 | 5 | 4 | 0 | 0 | 11 | 5 | 4 |
| Public school teachers, administrators & officials | 2 | 0 | 0 | 0 | 1 | 2 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Public works, industrial & commercial construction | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Railroads | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Real estate | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Real Estate developers & subdividers | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Recorded Music & music production | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Republican/Conservative | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 2 | 0 | 1 | 3 | 0 |
| Restaurants & drinking establishments | 0 | 2 | 5 | 1 | 2 | 2 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Schools & colleges | 7 | 10 | 9 | 5 | 12 | 9 | 7 | 5 | 4 | 7 | 15 | 4 | 3 | 3 | 4 | 4 | 0 | 0 | 8 | 14 | 21 | 13 | 6 |
| Services | 0 | 0 | 0 | 1 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| State & local govt employee unions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 5 | 4 | 1 |
| Teamsters union | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 0 | 0 |
| Tobacco & Tobacco products | 6 | 8 | 10 | 7 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Transportation | 1 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Transportation unions | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| US Postal Service unions & associations | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 |
| Waste management | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 |
| Welfare & Social Work | 1 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 1 | 5 | 4 | 2 | 2 | 1 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Wine & distilled spirits manufacturing | 7 | 13 | 12 | 9 | 13 | 11 | 13 | 14 | 17 | 16 | 31 | 33 | 28 | 23 | 34 | 28 | 27 | 24 | 24 | 28 | 29 | 22 | 27 |
This has interesting information….but it has a lot of information…thoughts on what to highlight?
library(knitr)
table3 <-table(ada2$Industry, ada2$Year)
kable(table3)| 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Abortion Policy/Anti-Abortion | 2 | 2 | 2 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 |
| Agricultural Services/Products | 0 | 3 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Air Transport | 0 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Beer, Wine & Liquor | 18 | 25 | 21 | 15 | 23 | 23 | 31 | 31 | 37 | 28 | 57 | 53 | 43 | 48 | 63 | 52 | 54 | 45 | 44 | 39 | 39 | 35 | 42 |
| Building Trade Unions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Business Associations | 2 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| Business Services | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 5 | 2 | 3 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Casinos/Gambling | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 5 | 4 | 5 | 5 | 4 |
| Chemical & Related Manufacturing | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 3 | 3 | 3 | 2 | 4 | 4 | 4 | 4 | 4 | 3 | 4 | 0 | 0 |
| Civil Servants/Public Officials | 5 | 10 | 10 | 5 | 5 | 4 | 4 | 5 | 7 | 4 | 8 | 9 | 10 | 10 | 5 | 6 | 7 | 7 | 7 | 10 | 30 | 51 | 42 |
| Clergy & Religious Organizations | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 3 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Construction Services | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Defense Aerospace | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Democratic/Liberal | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 5 | 0 | 3 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Education | 11 | 13 | 13 | 8 | 17 | 14 | 12 | 8 | 7 | 9 | 17 | 5 | 3 | 3 | 4 | 4 | 0 | 0 | 8 | 17 | 25 | 17 | 9 |
| Electric Utilities | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Electronics Mfg & Equip | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 5 | 6 | 12 | 5 | 4 |
| Employer Listed/Category Unknown | 1 | 0 | 4 | 3 | 10 | 17 | 18 | 13 | 9 | 3 | 7 | 2 | 3 | 7 | 14 | 12 | 13 | 15 | 21 | 16 | 23 | 20 | 24 |
| Environment | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Food & Beverage | 0 | 2 | 5 | 1 | 2 | 2 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Food Processing & Sales | 2 | 2 | 3 | 2 | 2 | 0 | 1 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| General Contractors | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Health Professionals | 5 | 7 | 7 | 11 | 10 | 5 | 6 | 7 | 6 | 10 | 13 | 12 | 11 | 13 | 26 | 28 | 39 | 25 | 36 | 38 | 51 | 39 | 31 |
| Health Services/HMOs | 11 | 14 | 15 | 12 | 14 | 11 | 14 | 6 | 7 | 4 | 8 | 6 | 13 | 14 | 16 | 9 | 13 | 16 | 19 | 25 | 23 | 25 | 20 |
| Hospitals/Nursing Homes | 6 | 5 | 9 | 6 | 8 | 9 | 11 | 8 | 11 | 5 | 15 | 15 | 25 | 22 | 29 | 28 | 23 | 24 | 29 | 22 | 29 | 28 | 24 |
| Human Rights | 4 | 4 | 1 | 1 | 3 | 1 | 3 | 4 | 7 | 7 | 17 | 8 | 7 | 8 | 9 | 5 | 5 | 8 | 13 | 15 | 8 | 1 | 0 |
| Insurance | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 0 | 0 | 8 | 13 | 12 | 17 |
| Internet | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| Lawyers/Law Firms | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 9 | 4 | 4 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| Lobbyists | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 4 | 4 | 4 | 4 |
| Lodging/Tourism | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Marijuana | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 3 |
| Mining | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Misc Business | 0 | 2 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Misc Defense | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| Misc Health | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| Misc Issues | 8 | 3 | 5 | 8 | 8 | 8 | 4 | 6 | 5 | 13 | 17 | 10 | 4 | 0 | 0 | 3 | 1 | 1 | 7 | 0 | 0 | 9 | 9 |
| Misc Manufacturing & Distributing | 0 | 1 | 1 | 0 | 0 | 0 | 3 | 3 | 2 | 2 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 0 |
| Misc Services | 0 | 0 | 0 | 1 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Misc Transport | 1 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Misc Unions | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Non-Profit Institutions | 6 | 4 | 4 | 3 | 7 | 3 | 2 | 5 | 3 | 4 | 12 | 10 | 4 | 4 | 4 | 4 | 9 | 8 | 9 | 8 | 12 | 9 | 6 |
| Oil & Gas | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Other | 1 | 0 | 2 | 0 | 3 | 3 | 0 | 0 | 1 | 5 | 4 | 2 | 2 | 1 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Pharmaceuticals/Health Products | 10 | 10 | 5 | 3 | 4 | 9 | 16 | 19 | 15 | 8 | 24 | 13 | 13 | 13 | 19 | 29 | 48 | 57 | 69 | 65 | 66 | 54 | 34 |
| Printing & Publishing | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Public Sector Unions | 0 | 1 | 2 | 2 | 2 | 3 | 1 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 7 | 4 | 1 |
| Railroads | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Real Estate | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Recreation/Live Entertainment | 0 | 0 | 0 | 2 | 4 | 7 | 5 | 9 | 8 | 4 | 13 | 8 | 9 | 3 | 5 | 6 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
| Republican/Conservative | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 2 | 0 | 1 | 3 | 0 |
| Retail Sales | 4 | 3 | 1 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 8 | 5 | 8 | 3 | 4 | 4 | 4 |
| Securities & Investment | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Telecom Services | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Tobacco | 6 | 8 | 10 | 7 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Transportation Unions | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 0 | 0 |
| TV/Movies/Music | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Waste Management | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 |
Perhaps it is worth picking all industries that are over 10 above and graphing them….over time….thoughts?
library(knitr)
table4 <-table(ada2$Sector, ada2$Year)
kable(table4)| 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Agribusiness | 8 | 13 | 17 | 11 | 8 | 0 | 1 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Communic/Electronics | 0 | 0 | 0 | 1 | 4 | 2 | 0 | 4 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 7 | 6 | 12 | 5 | 4 |
| Construction | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Defense | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| Energy/Nat Resource | 3 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 |
| Finance/Insur/RealEst | 3 | 2 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 0 | 0 | 8 | 13 | 12 | 17 |
| Health | 32 | 38 | 37 | 32 | 36 | 34 | 47 | 40 | 39 | 27 | 61 | 50 | 62 | 62 | 90 | 94 | 123 | 122 | 153 | 150 | 169 | 146 | 111 |
| Ideology/Single-Issue | 17 | 11 | 9 | 9 | 11 | 11 | 9 | 10 | 12 | 23 | 39 | 18 | 14 | 9 | 11 | 9 | 7 | 10 | 22 | 16 | 9 | 16 | 9 |
| Labor | 0 | 1 | 2 | 4 | 4 | 7 | 3 | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 9 | 4 | 2 |
| Lawyers & Lobbyists | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 9 | 4 | 4 | 4 | 3 | 4 | 4 | 4 | 4 | 4 | 4 |
| Misc Business | 24 | 35 | 35 | 23 | 32 | 35 | 43 | 52 | 58 | 39 | 87 | 68 | 56 | 54 | 72 | 62 | 70 | 60 | 65 | 53 | 60 | 57 | 57 |
| Other | 23 | 27 | 29 | 16 | 33 | 25 | 19 | 21 | 22 | 22 | 42 | 26 | 19 | 18 | 17 | 16 | 16 | 15 | 24 | 35 | 67 | 77 | 57 |
| Transportation | 1 | 2 | 3 | 1 | 4 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Unknown | 1 | 0 | 4 | 3 | 10 | 17 | 18 | 13 | 9 | 3 | 7 | 2 | 3 | 7 | 14 | 12 | 13 | 15 | 21 | 16 | 23 | 20 | 24 |
Could graph health….
Law Enforcement Lobbying Over Time
library(knitr)
table2 <-table(le2$Catname, le2$Year)
kable(table2)| 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Abortion policy/Anti-Abortion | 0 | 2 | 2 | 2 | 0 | 2 | 2 | 2 | 2 | 3 | 1 | 1 | 0 | 4 | 2 | 0 | 0 | 3 | 0 | 4 | 0 | 0 | 1 |
| Abortion policy/Pro-Abortion Rights | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 9 | 6 | 8 | 9 | 4 | 3 |
| Accident & health insurance | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Accountants | 0 | 0 | 0 | 0 | 4 | 4 | 2 | 2 | 2 | 1 | 8 | 10 | 15 | 6 | 4 | 5 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| Advertising & public relations services | 2 | 2 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Agricultural chemicals (fertilizers & pesticides) | 0 | 0 | 2 | 0 | 1 | 2 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Agriculture | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| Air transport | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 3 | 4 | 4 |
| Air transport unions | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Aircraft manufacturers | 0 | 5 | 3 | 2 | 3 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 | 2 | 0 | 0 | 0 | 0 |
| Aircraft parts & equipment | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Airlines | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 4 |
| Alcohol | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 5 | 4 |
| Alternate energy production & services | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 5 | 7 | 4 | 3 | 4 | 4 | 4 | 4 | 4 | 4 | 7 | 7 | 8 |
| Amusement/recreation centers | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 4 |
| Animal feed & health products | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 5 | 4 | 4 | 4 | 2 | 0 | 0 | 0 |
| Animal Rights | 3 | 2 | 0 | 0 | 0 | 0 | 1 | 4 | 3 | 2 | 3 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Anti-Guns | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 0 | 0 | 4 | 7 | 4 | 4 | 2 | 6 | 2 | 4 | 4 | 4 | 8 | 14 | 19 | 23 |
| Apparel & accessory stores | 0 | 0 | 0 | 0 | 1 | 3 | 4 | 2 | 2 | 1 | 2 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Architectural services | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Attorneys & law firms | 9 | 7 | 4 | 9 | 6 | 9 | 6 | 8 | 16 | 16 | 24 | 27 | 26 | 19 | 19 | 18 | 13 | 16 | 26 | 23 | 21 | 14 | 14 |
| Auto dealers, foreign imports | 0 | 0 | 1 | 1 | 2 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Auto dealers, new & used | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Auto manufacturers | 2 | 1 | 3 | 2 | 2 | 4 | 2 | 0 | 0 | 0 | 4 | 2 | 0 | 0 | 0 | 0 | 1 | 3 | 4 | 1 | 1 | 0 | 4 |
| Automotive, Misc | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Aviation services & airports | 0 | 2 | 5 | 2 | 6 | 3 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Banks & lending institutions | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| Beer | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| Beverages (non-alcoholic) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 2 | 0 | 0 | 0 | 0 | 2 |
| Bicycles & other non-motorized recreational transp | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Biotech products & research | 0 | 0 | 4 | 1 | 4 | 3 | 4 | 7 | 12 | 7 | 13 | 6 | 11 | 9 | 13 | 6 | 7 | 13 | 15 | 10 | 7 | 5 | 1 |
| Book, newspaper & periodical publishing | 1 | 0 | 2 | 0 | 4 | 4 | 3 | 4 | 2 | 1 | 6 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 |
| Builders associations | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 2 | 1 | 0 | 0 |
| Building materials | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Building operators and managers | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 3 | 4 | 0 | 0 | 0 | 0 |
| Bus services | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Business services | 1 | 4 | 3 | 1 | 3 | 7 | 3 | 4 | 12 | 16 | 13 | 4 | 2 | 5 | 14 | 17 | 8 | 8 | 11 | 12 | 11 | 4 | 7 |
| Business tax coalitions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 4 | 4 | 4 | 4 |
| Cable & satellite TV production | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 3 | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| Cable distributors & service providers | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 3 | 5 | 6 | 11 | 10 | 8 | 8 | 5 | 7 | 2 | 0 | 0 | 0 |
| Car rental agencies | 2 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Casinos, racetracks & gambling | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 13 | 9 | 10 | 8 | 7 |
| Cell/wireless service providers | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 4 | 3 | 0 | 2 | 5 | 1 | 0 | 0 | 0 | 0 | 2 | 2 | 5 | 1 | 1 | 3 |
| Chambers of commerce | 0 | 3 | 2 | 3 | 4 | 4 | 5 | 2 | 3 | 8 | 20 | 17 | 10 | 15 | 20 | 20 | 22 | 30 | 30 | 24 | 27 | 19 | 18 |
| Chemicals | 2 | 2 | 3 | 2 | 2 | 3 | 3 | 3 | 2 | 2 | 3 | 0 | 3 | 10 | 8 | 6 | 0 | 0 | 0 | 2 | 0 | 2 | 0 |
| Children’s rights | 0 | 0 | 0 | 1 | 2 | 0 | 4 | 4 | 6 | 13 | 24 | 24 | 22 | 17 | 30 | 13 | 11 | 12 | 13 | 12 | 18 | 17 | 20 |
| Chiropractors | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Christian Conservative | 1 | 2 | 1 | 2 | 5 | 2 | 3 | 4 | 5 | 6 | 8 | 8 | 6 | 12 | 23 | 17 | 8 | 2 | 1 | 0 | 3 | 4 | 5 |
| Churches, clergy & religious organizations | 2 | 2 | 1 | 2 | 1 | 1 | 2 | 4 | 4 | 5 | 14 | 18 | 21 | 10 | 10 | 26 | 21 | 14 | 13 | 11 | 10 | 13 | 22 |
| Civil servant/public employee | 84 | 91 | 111 | 93 | 125 | 100 | 122 | 127 | 122 | 143 | 274 | 277 | 300 | 262 | 336 | 351 | 352 | 340 | 291 | 338 | 380 | 367 | 359 |
| Civil service & government unions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Clothing & accessories | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 1 |
| Commercial banks & bank holding companies | 4 | 1 | 3 | 2 | 3 | 1 | 2 | 1 | 2 | 2 | 18 | 7 | 6 | 6 | 14 | 3 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
| Commercial service unions | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 2 | 3 | 0 | 4 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 6 | 2 | 5 |
| Commercial TV & radio stations | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 4 | 1 | 0 | 1 | 4 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 1 | 0 |
| Commodity brokers/dealers | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Communications & Electronics | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| Communications & hi-tech unions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Computer components & accessories | 2 | 2 | 3 | 3 | 5 | 1 | 2 | 2 | 3 | 5 | 7 | 6 | 9 | 9 | 8 | 12 | 9 | 8 | 4 | 10 | 14 | 10 | 13 |
| Computer manufacture & services | 2 | 1 | 1 | 2 | 1 | 3 | 4 | 2 | 2 | 0 | 2 | 8 | 6 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| Computer software | 4 | 5 | 3 | 2 | 4 | 3 | 3 | 8 | 8 | 11 | 9 | 6 | 11 | 16 | 11 | 17 | 15 | 43 | 41 | 44 | 48 | 40 | 34 |
| Confectionery processors & manufacturers | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Construction equipment | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 4 | 0 | 0 | 0 | 0 | 0 | 2 | 8 |
| Construction, unclassified | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Consumer electronics & computer stores | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Consumer groups | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Corporate lawyers & law firms | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Correctional facilities constr & mgmt/for-profit | 14 | 11 | 12 | 13 | 14 | 21 | 15 | 19 | 16 | 17 | 39 | 37 | 41 | 26 | 30 | 32 | 22 | 20 | 40 | 40 | 55 | 65 | 61 |
| Courts & Justice System | 15 | 11 | 14 | 12 | 16 | 14 | 21 | 29 | 30 | 32 | 48 | 55 | 55 | 37 | 36 | 40 | 44 | 59 | 65 | 63 | 75 | 46 | 47 |
| Credit agencies & finance companies | 3 | 4 | 1 | 4 | 3 | 1 | 2 | 2 | 6 | 10 | 26 | 16 | 9 | 9 | 19 | 12 | 5 | 8 | 5 | 5 | 6 | 10 | 11 |
| Credit reporting services & collection agencies | 0 | 0 | 0 | 1 | 1 | 2 | 5 | 6 | 4 | 4 | 10 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 3 | 0 | 0 | 0 | 0 |
| Crop production & basic processing | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Cruise ships & lines | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Data processing & computer services | 1 | 2 | 1 | 3 | 9 | 3 | 6 | 9 | 9 | 17 | 16 | 9 | 7 | 11 | 12 | 7 | 7 | 6 | 4 | 4 | 6 | 7 | 9 |
| Defense | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 1 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
| Defense-related services | 0 | 0 | 2 | 2 | 2 | 1 | 2 | 5 | 7 | 1 | 5 | 15 | 18 | 0 | 0 | 0 | 0 | 1 | 2 | 4 | 0 | 4 | 2 |
| Defense aerospace contractors | 2 | 3 | 5 | 4 | 7 | 6 | 1 | 0 | 0 | 0 | 4 | 8 | 0 | 0 | 0 | 5 | 2 | 0 | 0 | 5 | 4 | 0 | 0 |
| Defense electronic contractors | 5 | 10 | 13 | 9 | 12 | 7 | 7 | 2 | 7 | 6 | 14 | 15 | 15 | 17 | 15 | 6 | 13 | 8 | 6 | 2 | 8 | 4 | 4 |
| Defense policy, hawks | 0 | 2 | 2 | 2 | 2 | 0 | 2 | 1 | 2 | 2 | 1 | 4 | 3 | 1 | 1 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| Defense Research & Development | 0 | 0 | 1 | 0 | 1 | 2 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Democratic/Liberal | 1 | 2 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 9 | 5 | 3 | 3 | 6 | 4 | 4 | 2 | 2 | 8 | 0 | 0 | 0 |
| Department, variety & convenience stores | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 3 | 8 | 21 | 22 | 16 | 11 | 5 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
| Discount & Online Brokers | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Drug & alcohol treatment hospitals | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 2 | 3 | 1 | 3 | 2 | 4 | 2 | 0 | 0 | 2 | 5 | 4 | 0 | 0 | 0 | 0 |
| Drug stores | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 3 | 2 | 4 | 7 | 7 | 3 | 13 | 11 | 11 | 14 | 15 | 0 | 0 | 0 | 0 |
| Education | 0 | 1 | 2 | 0 | 5 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 8 | 4 | 6 | 2 | 3 | 0 | 4 |
| Elderly issues/Social Security | 1 | 0 | 1 | 1 | 0 | 2 | 0 | 1 | 0 | 1 | 4 | 4 | 3 | 2 | 4 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| Electric Power utilities | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 1 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Electrical contractors | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Electrical lighting products | 0 | 0 | 0 | 1 | 2 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Electronics manufacturing & services | 0 | 2 | 2 | 2 | 6 | 5 | 4 | 2 | 9 | 3 | 2 | 2 | 2 | 0 | 0 | 5 | 15 | 17 | 26 | 30 | 24 | 11 | 15 |
| Employer listed but category unknown | 7 | 6 | 16 | 22 | 71 | 26 | 30 | 23 | 20 | 18 | 56 | 52 | 75 | 35 | 66 | 57 | 55 | 71 | 79 | 119 | 159 | 200 | 256 |
| Employment agencies | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 3 | 11 | 10 | 15 | 8 | 12 | 11 | 12 | 9 | 8 | 6 | 8 | 6 | 4 |
| Energy production & distribution | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Energy, Natural Resources and Environment | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 2 |
| Engineering, architecture & construction mgmt svcs | 3 | 0 | 0 | 0 | 1 | 0 | 1 | 4 | 0 | 0 | 0 | 1 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Engineers - type unknown | 0 | 0 | 0 | 3 | 1 | 2 | 5 | 3 | 3 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Entertainment Industry/Broadcast & Motion Pictures | 0 | 0 | 0 | 3 | 4 | 0 | 1 | 2 | 0 | 4 | 2 | 4 | 4 | 3 | 5 | 5 | 4 | 0 | 0 | 0 | 0 | 0 | 3 |
| Environmental policy | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 4 | 6 | 9 | 12 | 17 | 8 | 9 | 11 | 11 |
| Environmental services, equipment & consulting | 0 | 0 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 1 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Explosives | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 4 | 2 | 0 | 0 | 0 |
| Express delivery services | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 1 | 0 | 0 | 0 | 0 | 2 |
| Fabricated metal products | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Farm bureaus | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Farm machinery & equipment | 0 | 2 | 2 | 2 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 0 | 0 | 0 |
| Farm organizations & cooperatives | 0 | 1 | 1 | 3 | 2 | 3 | 4 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Federal employees unions | 0 | 1 | 1 | 2 | 2 | 2 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 5 | 7 |
| Feedlots & related livestock services | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 4 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Finance, Insurance & Real Estate | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 2 | 3 | 3 | 4 | 9 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Financial services & consulting | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 1 | 0 |
| Fiscal & tax policy | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 3 | 3 | 2 | 2 | 1 | 0 | 0 | 2 | 4 | 4 | 5 | 4 | 4 | 0 | 4 |
| Fishing | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Food & Beverage Products and Services | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Food and kindred products manufacturing | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| Food catering & food services | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Food service & related unions | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 |
| Food stores | 0 | 0 | 0 | 1 | 2 | 0 | 2 | 1 | 6 | 11 | 13 | 20 | 13 | 1 | 5 | 6 | 9 | 1 | 0 | 0 | 1 | 4 | 4 |
| Food wholesalers | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| For-profit Education | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| Foreign Governments | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Foreign policy | 2 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 13 | 6 | 14 | 9 | 17 | 23 |
| Freight & delivery services | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 3 |
| Furniture & wood products | 0 | 0 | 0 | 1 | 2 | 2 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| Gas & Electric Utilities | 0 | 0 | 1 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 12 | 3 | 0 | 0 | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| Gay & lesbian rights & issues | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 4 | 4 | 5 | 13 | 6 | 7 | 2 | 4 | 6 | 8 | 4 | 5 | 5 | 8 | 4 | 4 |
| General business associations | 4 | 1 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 3 | 6 | 8 | 3 | 2 | 2 | 2 | 1 | 4 | 4 | 4 | 4 | 4 | 4 |
| General commerce | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Glass products | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Greeting card publishing | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Ground-based & other weapons systems | 1 | 0 | 2 | 2 | 4 | 2 | 2 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| Hardware & building materials stores | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 15 | 17 | 15 | 6 | 4 | 7 | 4 | 4 | 5 | 4 | 5 | 4 | 5 |
| Health & welfare policy | 2 | 2 | 2 | 0 | 4 | 6 | 6 | 2 | 5 | 5 | 14 | 22 | 16 | 10 | 4 | 4 | 6 | 6 | 10 | 5 | 6 | 4 | 4 |
| Health care institutions | 0 | 0 | 0 | 3 | 2 | 4 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 3 | 6 | 4 | 2 | 3 | 0 | 0 | 0 | 0 |
| Health care products | 2 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 6 | 0 | 5 | 7 | 8 | 6 | 0 | 0 | 0 | 0 | 0 | 4 | 9 |
| Health care services | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Health professionals | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Health worker unions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 |
| Health, Education & Human Resources | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Heavy industrial manufacturing | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Hedge Funds | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 3 | 3 | 0 |
| HMOs | 0 | 0 | 1 | 0 | 2 | 0 | 2 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Home care services | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Homeland Security contractors | 4 | 6 | 5 | 3 | 6 | 4 | 8 | 7 | 16 | 11 | 16 | 20 | 14 | 6 | 4 | 6 | 4 | 4 | 14 | 14 | 19 | 23 | 24 |
| Hospitals | 2 | 0 | 1 | 1 | 3 | 2 | 3 | 2 | 2 | 2 | 3 | 7 | 9 | 4 | 4 | 9 | 4 | 8 | 9 | 4 | 4 | 6 | 8 |
| Hosting/Cloud Services | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| Hotels & motels | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 4 | 4 | 10 | 11 | 15 | 6 |
| Household & office products | 2 | 1 | 1 | 3 | 4 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 4 | 5 | 4 | 7 | 8 |
| Household appliances | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Household cleansers & chemicals | 0 | 2 | 3 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Human Rights | 6 | 5 | 9 | 10 | 9 | 13 | 17 | 5 | 8 | 5 | 13 | 9 | 12 | 16 | 21 | 18 | 16 | 12 | 8 | 20 | 16 | 16 | 15 |
| Independent oil & gas producers | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Independent power generation & cogeneration | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Indian Gaming | 0 | 0 | 0 | 3 | 2 | 5 | 3 | 8 | 3 | 2 | 9 | 13 | 15 | 13 | 23 | 21 | 12 | 15 | 16 | 19 | 20 | 16 | 13 |
| Industrial/commercial equipment & materials | 2 | 1 | 1 | 0 | 2 | 1 | 0 | 0 | 3 | 1 | 0 | 1 | 5 | 0 | 0 | 0 | 4 | 8 | 8 | 0 | 0 | 0 | 0 |
| Insurance | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Insurance companies, brokers & agents | 5 | 5 | 7 | 4 | 10 | 2 | 5 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 0 | 3 | 2 |
| Internet & Online Services | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 1 | 2 | 0 | 8 | 4 | 1 | 0 | 4 | 6 | 9 | 8 | 13 | 15 | 23 | 24 |
| Investment banking | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Investors | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 6 | 5 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Labor Unions | 1 | 3 | 2 | 1 | 2 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 5 | 9 | 7 | 1 | 1 | 0 | 1 |
| Law schools | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 4 | 4 | 4 | 4 |
| Legal Services | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 4 | 1 | 0 | 0 | 0 | 3 | 10 | 7 | 7 |
| Life insurance | 0 | 3 | 4 | 3 | 7 | 4 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 | 4 | 0 | 0 | 0 | 3 |
| Liquor wholesalers | 2 | 3 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 0 |
| Live theater & other entertainment productions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 4 |
| Lobbyists & Public Relations | 0 | 0 | 1 | 1 | 3 | 2 | 2 | 4 | 5 | 5 | 3 | 8 | 19 | 14 | 16 | 13 | 7 | 8 | 8 | 6 | 0 | 0 | 0 |
| Lodging & tourism | 1 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 3 | 0 | 0 | 0 | 0 | 0 |
| Long-distance telephone & telegraph service | 0 | 0 | 1 | 0 | 1 | 0 | 6 | 1 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Major (multinational) oil & gas producers | 3 | 5 | 3 | 4 | 5 | 4 | 2 | 4 | 2 | 3 | 10 | 4 | 4 | 3 | 7 | 9 | 6 | 4 | 4 | 5 | 7 | 6 | 4 |
| Management consultants & services | 0 | 2 | 3 | 1 | 4 | 1 | 1 | 2 | 3 | 5 | 8 | 5 | 6 | 0 | 0 | 0 | 5 | 1 | 4 | 1 | 2 | 0 | 4 |
| Manufacturers of railroad equipment | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Manufacturing | 0 | 0 | 0 | 1 | 2 | 1 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 3 | 3 | 1 | 4 | 3 | 4 | 0 | 0 | 0 |
| Manufacturing unions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Marijuana Production, Sales & Paraphernalia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 4 | 5 | 8 | 14 | 24 | 48 | 41 |
| Marketing research services | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| Medical Devices & Supplies | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 9 | 12 | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 5 |
| Medical laboratories | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 2 | 1 | 3 | 0 | 1 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 4 |
| Medical schools | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 2 | 3 | 3 | 6 | 4 | 4 | 4 | 4 | 4 | 0 | 0 | 0 | 0 |
| Mental Health Services | 0 | 2 | 1 | 0 | 2 | 0 | 2 | 3 | 4 | 5 | 7 | 8 | 2 | 0 | 0 | 0 | 2 | 4 | 2 | 0 | 1 | 3 | 4 |
| Metal cans & containers | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Metal mining & processing | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| Military | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 4 | 0 | 0 | 0 | 0 |
| Milk & dairy producers | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Mining | 0 | 1 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Mining services & equipment | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 3 | 0 | 0 | 0 | 0 |
| Minority/Ethnic Groups | 2 | 0 | 0 | 5 | 6 | 7 | 8 | 12 | 6 | 11 | 29 | 26 | 26 | 19 | 33 | 28 | 31 | 22 | 19 | 22 | 24 | 18 | 17 |
| Miscellaneous retail stores | 0 | 0 | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Mortgage bankers and brokers | 1 | 2 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Motion Picture production & distribution | 0 | 1 | 1 | 2 | 4 | 1 | 2 | 1 | 2 | 2 | 9 | 3 | 4 | 2 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Movie Theaters | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Museums, art galleries, libraries, etc. | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 3 | 8 | 4 | 4 | 0 |
| Natural Gas transmission & distribution | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Non-Contribution, Miscellaneous | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Non-profit foundations | 1 | 1 | 0 | 1 | 2 | 1 | 1 | 2 | 0 | 0 | 2 | 4 | 4 | 4 | 8 | 1 | 4 | 7 | 20 | 10 | 10 | 10 | 12 |
| Non-Profits | 9 | 7 | 14 | 13 | 25 | 18 | 18 | 19 | 22 | 28 | 61 | 45 | 44 | 30 | 27 | 31 | 24 | 19 | 23 | 23 | 29 | 21 | 25 |
| Nuclear plant construction, equipment & svcs | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Nurses | 0 | 2 | 3 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 3 | 2 | 0 | 0 | 0 | 1 | 4 | 4 |
| Nursing homes | 0 | 1 | 1 | 0 | 0 | 1 | 2 | 1 | 2 | 2 | 6 | 5 | 2 | 0 | 2 | 4 | 4 | 4 | 4 | 4 | 1 | 0 | 0 |
| Nutritional & dietary supplements | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 21 | 10 | 13 | 10 | 11 | 12 | 12 | 11 | 5 | 0 | 0 |
| Office machines | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 5 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 |
| Oil & Gas | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 |
| Oilfield service, equipment & exploration | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| Online Entertainment | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| Optometrists & Ophthalmologists | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
| Other | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Other commercial unions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 2 | 5 |
| Other commodities (incl rice, peanuts, honey) | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| Other Communication Electronics | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 6 | 3 | 5 | 5 | 1 |
| Other Communications Services | 0 | 2 | 3 | 2 | 2 | 2 | 3 | 4 | 3 | 7 | 12 | 6 | 3 | 2 | 3 | 8 | 6 | 15 | 4 | 4 | 5 | 5 | 3 |
| Other construction-related products | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Other financial services | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 3 | 4 | 4 | 4 | 3 | 0 | 0 | 0 | 0 | 0 |
| Other non-physician health practitioners | 3 | 3 | 1 | 0 | 3 | 0 | 1 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| Other physician specialists | 2 | 1 | 5 | 1 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 8 | 4 | 12 | 11 | 13 | 8 | 0 | 0 |
| Other single-issue or ideological groups | 12 | 11 | 15 | 14 | 24 | 11 | 16 | 26 | 28 | 28 | 56 | 53 | 62 | 62 | 80 | 88 | 78 | 89 | 115 | 94 | 117 | 134 | 125 |
| Other transportation unions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 5 | 10 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Other unions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 |
| Outpatient health services (incl drug & alcohol) | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 2 | 1 | 0 | 0 | 2 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Paper & pulp mills and paper manufacturing | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Paper, glass & packaging materials | 0 | 0 | 0 | 0 | 1 | 2 | 2 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Payday lenders | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Personal health care products | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Petroleum refining & marketing | 0 | 2 | 0 | 0 | 0 | 4 | 6 | 1 | 0 | 2 | 8 | 12 | 6 | 3 | 0 | 3 | 0 | 5 | 5 | 4 | 5 | 6 | 6 |
| Pharmaceutical cannabis | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 3 | 0 |
| Pharmaceutical manufacturing | 5 | 12 | 13 | 7 | 6 | 1 | 1 | 3 | 9 | 8 | 14 | 5 | 7 | 10 | 6 | 1 | 10 | 10 | 8 | 3 | 7 | 12 | 8 |
| Pharmaceutical wholesale | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Pharmacists | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 3 | 5 | 5 | 4 | 4 | 0 | 0 | 0 | 0 | 0 |
| Photographic equipment & supplies | 0 | 0 | 0 | 0 | 4 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Physicians | 1 | 4 | 3 | 3 | 2 | 2 | 1 | 2 | 2 | 1 | 7 | 3 | 4 | 4 | 11 | 4 | 3 | 3 | 4 | 0 | 1 | 4 | 4 |
| Plastics & Rubber processing & products | 0 | 0 | 2 | 2 | 1 | 3 | 2 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Pleasure boats | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Plumbing & pipe products | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 2 | 3 | 4 | 3 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Plumbing, heating & air conditioning | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 1 |
| Police & firefighters unions & associations | 14 | 16 | 21 | 22 | 19 | 19 | 18 | 14 | 18 | 17 | 49 | 42 | 48 | 51 | 70 | 60 | 65 | 61 | 55 | 41 | 48 | 51 | 48 |
| Power plant construction & equipment | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| Precision instruments | 2 | 1 | 0 | 3 | 2 | 0 | 3 | 3 | 4 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 0 | 0 | 4 | 4 | 0 | 2 |
| Printing and publishing (printed & online) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Private Equity & Investment Firms | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 4 | 5 | 4 | 7 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
| Pro-Arab | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Pro-business associations | 2 | 7 | 9 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 17 |
| Pro-Guns | 7 | 8 | 5 | 6 | 4 | 1 | 1 | 1 | 2 | 1 | 4 | 3 | 4 | 3 | 4 | 5 | 7 | 7 | 6 | 9 | 8 | 8 | 6 |
| Pro-Israel | 0 | 0 | 3 | 1 | 2 | 3 | 2 | 3 | 4 | 3 | 4 | 2 | 5 | 1 | 3 | 0 | 0 | 0 | 2 | 4 | 2 | 3 | 0 |
| Professional sports, arenas & related equip & svcs | 7 | 9 | 3 | 2 | 2 | 2 | 1 | 3 | 2 | 1 | 3 | 3 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 8 | 5 | 4 |
| Property & casualty insurance | 0 | 0 | 2 | 4 | 3 | 10 | 9 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 6 |
| Psychiatrists & psychologists | 6 | 5 | 3 | 6 | 3 | 2 | 6 | 6 | 7 | 9 | 14 | 11 | 10 | 1 | 0 | 6 | 12 | 15 | 20 | 8 | 16 | 10 | 10 |
| Public official (elected or appointed) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 4 | 5 | 4 | 4 |
| Public school teachers, administrators & officials | 3 | 4 | 3 | 3 | 3 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 | 4 | 1 | 0 | 0 | 3 | 4 | 5 |
| Public works, industrial & commercial construction | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 4 | 4 | 9 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Railroads | 0 | 0 | 0 | 0 | 6 | 2 | 6 | 6 | 5 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| Real estate | 0 | 0 | 2 | 0 | 1 | 1 | 0 | 2 | 2 | 1 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Real estate agents | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 5 | 3 | 2 | 4 | 4 | 4 | 5 | 4 | 4 | 3 | 0 | 0 |
| Real Estate developers & subdividers | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 5 | 2 | 6 | 7 | 5 | 8 | 2 | 0 | 5 | 6 | 4 | 4 | 4 | 4 | 6 | 4 |
| Recorded Music & music production | 0 | 3 | 1 | 3 | 3 | 0 | 5 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| Recreation/Entertainment | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 4 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Recycling of metal, paper, plastics, etc. | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 7 | 10 | 9 | 12 | 13 | 16 | 15 | 10 | 8 | 8 | 8 | 5 |
| Republican/Conservative | 4 | 4 | 2 | 2 | 4 | 2 | 4 | 3 | 2 | 2 | 5 | 6 | 10 | 7 | 8 | 5 | 5 | 3 | 2 | 3 | 17 | 16 | 16 |
| Resorts | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Restaurants & drinking establishments | 0 | 0 | 1 | 3 | 2 | 1 | 1 | 2 | 2 | 3 | 7 | 3 | 6 | 5 | 4 | 4 | 4 | 8 | 8 | 4 | 5 | 4 | 4 |
| Retail trade | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 8 | 18 | 21 | 23 | 15 | 10 | 9 | 7 | 7 | 8 | 5 | 2 | 0 | 7 |
| Rural electric cooperatives | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| Satellite communications | 0 | 0 | 3 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Savings banks & Savings and Loans | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Schools & colleges | 7 | 18 | 15 | 15 | 38 | 28 | 20 | 21 | 31 | 33 | 83 | 72 | 76 | 66 | 54 | 56 | 57 | 59 | 48 | 59 | 40 | 35 | 34 |
| Sea freight & passenger services | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Sea transport | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Search Engine/Email Services | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 10 | 17 | 11 | 23 | 24 | 12 | 15 |
| Securities, commodities & investment | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 1 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Security brokers & investment companies | 0 | 1 | 2 | 1 | 3 | 0 | 0 | 0 | 0 | 3 | 8 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 0 | 0 |
| Security services | 6 | 10 | 10 | 12 | 18 | 17 | 27 | 23 | 18 | 23 | 38 | 29 | 30 | 21 | 23 | 29 | 25 | 24 | 26 | 26 | 30 | 13 | 9 |
| Services | 0 | 0 | 0 | 1 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Ship building & repair | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Small arms & ammunition | 3 | 4 | 2 | 0 | 0 | 0 | 1 | 1 | 2 | 0 | 4 | 4 | 3 | 0 | 6 | 4 | 3 | 4 | 0 | 0 | 7 | 11 | 11 |
| Small business associations | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| Smelting & non-petroleum refining | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Social Media | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 7 | 12 | 12 | 9 | 9 | 20 |
| Space vehicles & components | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Sporting goods sales & manufacturing | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| State & local govt employee unions | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 1 | 4 | 4 | 4 | 5 | 1 | 4 | 4 | 3 | 5 | 3 | 4 | 1 | 4 | 5 |
| Steel | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Stone, clay, glass & concrete products | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| Sugar cane & sugar beets | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Taxicabs | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 |
| Teachers unions | 2 | 1 | 0 | 3 | 1 | 1 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 2 | 1 | 3 | 2 | 4 | 5 | 5 |
| Teamsters union | 0 | 0 | 0 | 0 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 4 | 4 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 4 | 0 | 0 |
| Technical, business and vocational schools & svcs | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 3 | 3 | 4 | 4 | 5 | 4 | 6 | 8 | 8 | 8 | 8 |
| Telecommunications | 2 | 2 | 6 | 3 | 0 | 0 | 0 | 0 | 7 | 5 | 2 | 6 | 11 | 5 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Telecommunications Devices | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Telephone utilities | 5 | 8 | 3 | 3 | 4 | 3 | 2 | 0 | 2 | 3 | 2 | 2 | 8 | 14 | 9 | 3 | 4 | 5 | 8 | 0 | 2 | 4 | 4 |
| Textiles & fabrics | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Title insurance & title abstract offices | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 4 | 2 |
| Tobacco & Tobacco products | 0 | 0 | 1 | 1 | 4 | 2 | 2 | 2 | 2 | 3 | 1 | 4 | 5 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Toiletries & cosmetics | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 5 | 2 | 1 | 7 | 5 | 4 | 3 | 6 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 7 |
| Transportation | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 8 | 7 | 0 | 0 | 0 | 0 | 0 | 0 |
| Transportation unions | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 3 | 2 | 4 | 4 | 5 | 4 | 5 | 6 | 4 | 4 | 5 |
| Travel agents | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Trial lawyers & law firms | 2 | 1 | 2 | 2 | 2 | 3 | 2 | 3 | 2 | 3 | 8 | 10 | 9 | 4 | 4 | 0 | 0 | 0 | 6 | 11 | 14 | 17 | 15 |
| Truck & trailer manufacturers | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Truck/Automotive parts & accessories | 0 | 0 | 0 | 0 | 2 | 4 | 2 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 5 | 8 | 9 | 4 |
| Trucking | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Trucking companies & services | 0 | 4 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| Vending Machine Sales & Services | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Vendors | 0 | 0 | 2 | 0 | 2 | 4 | 2 | 2 | 2 | 3 | 13 | 21 | 19 | 7 | 9 | 8 | 4 | 0 | 0 | 1 | 5 | 6 | 4 |
| Venture capital | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Veterinarians | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 4 | 5 | 4 | 4 | 4 | 0 | 0 | 0 | 0 |
| Video rental | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Waste management | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 4 | 3 | 0 | 1 | 4 | 2 | 0 | 4 | 4 | 4 | 4 | 4 | 4 |
| Water Utilities | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 6 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Welfare & Social Work | 6 | 9 | 8 | 9 | 5 | 9 | 10 | 9 | 3 | 9 | 19 | 13 | 10 | 15 | 20 | 12 | 4 | 6 | 16 | 10 | 10 | 15 | 9 |
| Wheat, corn, soybeans and cash grain | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Wine & distilled spirits manufacturing | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 |
| Women’s issues | 2 | 2 | 0 | 0 | 0 | 4 | 4 | 3 | 2 | 5 | 1 | 4 | 5 | 17 | 19 | 10 | 8 | 8 | 5 | 7 | 4 | 6 | 10 |
library(knitr)
table4 <-table(le2$Industry, le2$Year)
kable(table4)| 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Abortion Policy/Anti-Abortion | 0 | 2 | 2 | 2 | 0 | 2 | 2 | 2 | 2 | 3 | 1 | 1 | 0 | 4 | 2 | 0 | 0 | 3 | 0 | 4 | 0 | 0 | 1 |
| Abortion Policy/Pro-Abortion Rights | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 9 | 6 | 8 | 9 | 4 | 3 |
| Accountants | 0 | 0 | 0 | 0 | 4 | 4 | 2 | 2 | 2 | 1 | 8 | 10 | 15 | 6 | 4 | 5 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| Agricultural Services/Products | 0 | 3 | 5 | 5 | 5 | 5 | 6 | 3 | 1 | 1 | 0 | 1 | 0 | 0 | 8 | 10 | 8 | 8 | 10 | 7 | 0 | 0 | 0 |
| Air Transport | 0 | 7 | 12 | 4 | 13 | 4 | 5 | 1 | 1 | 0 | 1 | 0 | 3 | 0 | 0 | 4 | 4 | 9 | 2 | 1 | 3 | 4 | 10 |
| Automotive | 4 | 2 | 4 | 4 | 8 | 10 | 7 | 4 | 2 | 2 | 6 | 2 | 0 | 0 | 0 | 0 | 1 | 3 | 8 | 6 | 9 | 9 | 8 |
| Beer, Wine & Liquor | 4 | 4 | 3 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 4 | 0 | 0 | 0 | 8 | 9 | 4 |
| Building Materials & Equipment | 3 | 1 | 2 | 0 | 1 | 2 | 1 | 3 | 2 | 0 | 2 | 3 | 4 | 5 | 4 | 8 | 0 | 0 | 0 | 0 | 0 | 2 | 11 |
| Business Associations | 6 | 11 | 11 | 5 | 5 | 6 | 7 | 4 | 5 | 11 | 26 | 25 | 13 | 17 | 22 | 22 | 27 | 36 | 40 | 32 | 35 | 27 | 43 |
| Business Services | 9 | 18 | 16 | 14 | 25 | 26 | 32 | 31 | 35 | 47 | 70 | 48 | 56 | 35 | 56 | 62 | 50 | 42 | 49 | 45 | 51 | 23 | 27 |
| Casinos/Gambling | 0 | 0 | 1 | 3 | 2 | 5 | 5 | 9 | 3 | 3 | 10 | 13 | 15 | 13 | 23 | 21 | 12 | 28 | 29 | 28 | 30 | 24 | 20 |
| Chemical & Related Manufacturing | 2 | 4 | 8 | 4 | 3 | 7 | 8 | 7 | 3 | 3 | 6 | 1 | 3 | 10 | 8 | 9 | 0 | 0 | 4 | 4 | 0 | 2 | 0 |
| Civil Servants/Public Officials | 99 | 102 | 125 | 105 | 141 | 114 | 143 | 156 | 152 | 175 | 322 | 332 | 355 | 299 | 372 | 391 | 396 | 402 | 360 | 405 | 460 | 417 | 410 |
| Clergy & Religious Organizations | 2 | 2 | 1 | 2 | 1 | 1 | 2 | 4 | 4 | 5 | 14 | 18 | 21 | 10 | 10 | 26 | 21 | 14 | 13 | 11 | 10 | 13 | 22 |
| Commercial Banks | 4 | 1 | 3 | 2 | 3 | 2 | 2 | 1 | 2 | 2 | 18 | 7 | 6 | 6 | 14 | 3 | 4 | 4 | 4 | 4 | 4 | 5 | 4 |
| Construction Services | 3 | 0 | 0 | 3 | 2 | 2 | 8 | 9 | 5 | 5 | 4 | 2 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Crop Production & Basic Processing | 0 | 0 | 1 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| Dairy | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Defense Aerospace | 2 | 3 | 5 | 4 | 7 | 6 | 1 | 0 | 0 | 0 | 4 | 8 | 0 | 0 | 0 | 5 | 2 | 0 | 0 | 5 | 4 | 0 | 0 |
| Defense Electronics | 5 | 10 | 13 | 9 | 12 | 7 | 7 | 2 | 7 | 6 | 14 | 15 | 15 | 17 | 15 | 6 | 13 | 8 | 6 | 2 | 8 | 4 | 4 |
| Democratic/Liberal | 1 | 2 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 9 | 5 | 3 | 3 | 6 | 4 | 4 | 2 | 2 | 8 | 0 | 0 | 0 |
| Education | 10 | 23 | 20 | 18 | 48 | 30 | 25 | 24 | 36 | 34 | 90 | 80 | 82 | 75 | 63 | 72 | 80 | 74 | 68 | 73 | 58 | 51 | 55 |
| Electric Utilities | 0 | 0 | 2 | 0 | 3 | 2 | 2 | 1 | 0 | 0 | 3 | 21 | 3 | 0 | 0 | 9 | 7 | 0 | 0 | 0 | 0 | 0 | 0 |
| Electronics Mfg & Equip | 12 | 12 | 10 | 12 | 26 | 15 | 19 | 23 | 31 | 36 | 36 | 31 | 35 | 39 | 32 | 42 | 48 | 78 | 81 | 91 | 97 | 73 | 74 |
| Employer Listed/Category Unknown | 7 | 6 | 16 | 22 | 71 | 26 | 30 | 23 | 20 | 18 | 56 | 52 | 75 | 35 | 66 | 57 | 55 | 71 | 79 | 119 | 159 | 200 | 256 |
| Environment | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 4 | 6 | 9 | 12 | 17 | 8 | 9 | 11 | 11 |
| Environmental Svcs/Equipment | 0 | 0 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 1 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Finance/Credit Companies | 4 | 4 | 1 | 4 | 3 | 1 | 2 | 2 | 6 | 10 | 26 | 16 | 9 | 9 | 19 | 12 | 5 | 8 | 5 | 5 | 6 | 10 | 11 |
| Fisheries & Wildlife | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Food & Beverage | 0 | 0 | 2 | 3 | 3 | 1 | 1 | 2 | 2 | 5 | 9 | 3 | 6 | 5 | 4 | 6 | 8 | 10 | 8 | 4 | 5 | 4 | 6 |
| Food Processing & Sales | 1 | 0 | 1 | 1 | 3 | 0 | 2 | 2 | 6 | 11 | 13 | 20 | 14 | 1 | 5 | 6 | 9 | 1 | 0 | 1 | 1 | 4 | 6 |
| Foreign & Defense Policy | 2 | 4 | 3 | 3 | 2 | 0 | 2 | 2 | 4 | 8 | 3 | 6 | 3 | 1 | 1 | 4 | 4 | 13 | 6 | 14 | 9 | 17 | 23 |
| Forestry & Forest Products | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| General Contractors | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 4 | 8 | 11 | 3 | 0 | 2 | 0 | 0 | 1 | 2 | 1 | 0 | 0 |
| Gun Control | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 0 | 0 | 4 | 7 | 4 | 4 | 2 | 6 | 2 | 4 | 4 | 4 | 8 | 14 | 19 | 23 |
| Gun Rights | 7 | 8 | 5 | 6 | 4 | 1 | 1 | 1 | 2 | 1 | 4 | 3 | 4 | 3 | 4 | 5 | 7 | 7 | 6 | 9 | 8 | 8 | 6 |
| Health Professionals | 12 | 15 | 16 | 11 | 10 | 6 | 8 | 9 | 9 | 12 | 26 | 19 | 19 | 11 | 22 | 26 | 25 | 34 | 35 | 21 | 26 | 18 | 28 |
| Health Services/HMOs | 0 | 2 | 4 | 0 | 6 | 1 | 7 | 9 | 8 | 11 | 8 | 11 | 10 | 1 | 3 | 3 | 2 | 4 | 2 | 0 | 4 | 7 | 8 |
| Hospitals/Nursing Homes | 2 | 1 | 2 | 4 | 5 | 8 | 7 | 5 | 7 | 5 | 15 | 14 | 15 | 6 | 9 | 19 | 14 | 19 | 20 | 8 | 5 | 6 | 8 |
| Human Rights | 10 | 7 | 11 | 16 | 22 | 27 | 37 | 27 | 29 | 39 | 93 | 87 | 83 | 64 | 92 | 69 | 72 | 56 | 55 | 64 | 72 | 59 | 60 |
| Industrial Unions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Insurance | 5 | 8 | 16 | 11 | 21 | 16 | 20 | 4 | 2 | 1 | 3 | 0 | 4 | 2 | 4 | 0 | 5 | 8 | 9 | 5 | 2 | 4 | 11 |
| Internet | 0 | 2 | 4 | 2 | 5 | 5 | 2 | 2 | 3 | 7 | 14 | 29 | 23 | 12 | 17 | 17 | 24 | 33 | 31 | 49 | 53 | 51 | 69 |
| Lawyers/Law Firms | 13 | 10 | 6 | 11 | 11 | 12 | 9 | 11 | 18 | 20 | 34 | 37 | 38 | 26 | 27 | 19 | 13 | 16 | 32 | 37 | 45 | 38 | 36 |
| Livestock | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 4 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Lobbyists | 0 | 0 | 1 | 1 | 3 | 2 | 2 | 4 | 5 | 5 | 3 | 8 | 19 | 14 | 16 | 13 | 7 | 8 | 8 | 6 | 0 | 0 | 0 |
| Lodging/Tourism | 1 | 2 | 0 | 1 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 7 | 7 | 4 | 10 | 11 | 15 | 6 |
| Marijuana | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 4 | 5 | 8 | 14 | 24 | 48 | 41 |
| Mining | 0 | 1 | 2 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 3 | 0 | 0 | 0 | 0 |
| Misc Agriculture | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| Misc Business | 14 | 11 | 12 | 13 | 14 | 21 | 15 | 19 | 16 | 17 | 39 | 37 | 44 | 26 | 30 | 32 | 22 | 20 | 40 | 40 | 55 | 65 | 61 |
| Misc Communications/Electronics | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| Misc Defense | 6 | 8 | 12 | 9 | 15 | 11 | 13 | 18 | 26 | 12 | 27 | 38 | 32 | 6 | 4 | 11 | 4 | 5 | 18 | 18 | 19 | 29 | 26 |
| Misc Energy | 0 | 0 | 5 | 1 | 3 | 0 | 0 | 1 | 2 | 1 | 5 | 9 | 10 | 8 | 9 | 6 | 6 | 4 | 4 | 4 | 9 | 11 | 10 |
| Misc Finance | 0 | 0 | 0 | 1 | 2 | 3 | 6 | 8 | 6 | 8 | 18 | 8 | 6 | 10 | 13 | 7 | 6 | 5 | 3 | 1 | 4 | 1 | 0 |
| Misc Health | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Misc Issues | 19 | 13 | 16 | 16 | 25 | 14 | 18 | 32 | 34 | 34 | 65 | 63 | 70 | 64 | 84 | 94 | 85 | 93 | 120 | 98 | 121 | 134 | 129 |
| Misc Manufacturing & Distributing | 11 | 7 | 5 | 9 | 26 | 16 | 18 | 15 | 17 | 11 | 21 | 19 | 24 | 13 | 29 | 24 | 32 | 45 | 29 | 25 | 28 | 30 | 34 |
| Misc Services | 0 | 0 | 0 | 1 | 4 | 4 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Misc Transport | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 12 | 7 | 0 | 0 | 0 | 4 | 18 | 3 |
| Misc Unions | 1 | 5 | 3 | 1 | 2 | 5 | 2 | 3 | 2 | 3 | 0 | 4 | 2 | 0 | 0 | 4 | 5 | 11 | 7 | 7 | 9 | 7 | 15 |
| Non-contribution | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Non-Profit Institutions | 10 | 8 | 14 | 14 | 29 | 19 | 19 | 21 | 22 | 28 | 63 | 49 | 48 | 34 | 37 | 33 | 29 | 26 | 46 | 41 | 43 | 35 | 37 |
| Oil & Gas | 3 | 7 | 5 | 4 | 6 | 8 | 8 | 6 | 4 | 7 | 21 | 16 | 10 | 6 | 7 | 12 | 6 | 9 | 9 | 9 | 15 | 15 | 10 |
| Other | 6 | 12 | 9 | 10 | 5 | 9 | 10 | 9 | 3 | 9 | 19 | 13 | 10 | 15 | 20 | 12 | 5 | 9 | 20 | 10 | 10 | 15 | 9 |
| Pharmaceuticals/Health Products | 7 | 12 | 23 | 8 | 12 | 4 | 5 | 12 | 22 | 17 | 33 | 13 | 44 | 45 | 52 | 28 | 31 | 36 | 37 | 24 | 19 | 24 | 23 |
| Printing & Publishing | 1 | 1 | 2 | 0 | 4 | 4 | 3 | 4 | 2 | 2 | 8 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 |
| Pro-Israel | 0 | 0 | 3 | 1 | 2 | 3 | 2 | 3 | 4 | 3 | 4 | 2 | 5 | 1 | 3 | 0 | 0 | 0 | 2 | 4 | 2 | 3 | 0 |
| Public Sector Unions | 18 | 20 | 24 | 28 | 24 | 24 | 22 | 18 | 23 | 21 | 53 | 46 | 53 | 53 | 76 | 66 | 70 | 67 | 61 | 47 | 57 | 65 | 65 |
| Railroads | 0 | 0 | 0 | 0 | 7 | 2 | 6 | 6 | 5 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| Real Estate | 2 | 2 | 2 | 0 | 6 | 1 | 0 | 7 | 4 | 10 | 13 | 11 | 11 | 4 | 4 | 11 | 10 | 12 | 12 | 9 | 9 | 10 | 7 |
| Recreation/Live Entertainment | 7 | 9 | 3 | 2 | 2 | 2 | 1 | 3 | 2 | 3 | 4 | 3 | 6 | 1 | 4 | 0 | 0 | 1 | 0 | 2 | 10 | 9 | 8 |
| Republican/Conservative | 5 | 6 | 3 | 4 | 9 | 4 | 7 | 7 | 7 | 8 | 13 | 14 | 16 | 19 | 31 | 22 | 13 | 5 | 3 | 3 | 20 | 20 | 21 |
| Retail Sales | 0 | 0 | 1 | 4 | 6 | 3 | 6 | 7 | 12 | 20 | 63 | 74 | 62 | 36 | 32 | 29 | 22 | 26 | 29 | 9 | 7 | 4 | 18 |
| Savings & Loans | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Sea Transport | 1 | 0 | 4 | 0 | 1 | 0 | 1 | 4 | 1 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Securities & Investment | 0 | 1 | 2 | 4 | 8 | 2 | 2 | 3 | 4 | 9 | 16 | 9 | 9 | 2 | 1 | 4 | 0 | 0 | 0 | 9 | 8 | 5 | 0 |
| Special Trade Contractors | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 3 | 1 |
| Steel Production | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Telecom Services | 2 | 4 | 13 | 8 | 7 | 5 | 7 | 9 | 16 | 12 | 19 | 23 | 21 | 18 | 20 | 18 | 14 | 22 | 13 | 11 | 6 | 6 | 6 |
| Telephone Utilities | 5 | 8 | 4 | 3 | 5 | 3 | 8 | 1 | 2 | 3 | 7 | 2 | 8 | 14 | 9 | 3 | 4 | 5 | 8 | 0 | 2 | 4 | 4 |
| Textiles | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Tobacco | 0 | 0 | 1 | 1 | 4 | 2 | 2 | 2 | 2 | 3 | 1 | 4 | 5 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Transportation Unions | 2 | 1 | 1 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 6 | 11 | 17 | 6 | 4 | 4 | 5 | 6 | 6 | 6 | 8 | 4 | 5 |
| Trucking | 0 | 4 | 3 | 0 | 0 | 2 | 2 | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| TV/Movies/Music | 0 | 4 | 2 | 8 | 18 | 1 | 10 | 8 | 10 | 12 | 16 | 10 | 12 | 5 | 9 | 11 | 7 | 0 | 0 | 1 | 1 | 5 | 13 |
| Waste Management | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 4 | 3 | 0 | 1 | 4 | 2 | 0 | 4 | 4 | 4 | 4 | 4 | 4 |
| Women’s Issues | 2 | 2 | 0 | 0 | 0 | 4 | 4 | 3 | 2 | 5 | 1 | 4 | 5 | 17 | 19 | 10 | 8 | 8 | 5 | 7 | 4 | 6 | 10 |
library(knitr)
table5 <-table(le2$Sector, le2$Year)
kable(table5)| 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Agribusiness | 1 | 3 | 9 | 7 | 17 | 7 | 11 | 8 | 9 | 15 | 17 | 29 | 26 | 9 | 17 | 18 | 20 | 9 | 12 | 8 | 1 | 4 | 6 |
| Communic/Electronics | 20 | 31 | 35 | 33 | 65 | 33 | 49 | 47 | 64 | 72 | 100 | 97 | 101 | 89 | 87 | 91 | 97 | 138 | 133 | 154 | 159 | 139 | 171 |
| Construction | 6 | 1 | 3 | 3 | 3 | 4 | 12 | 14 | 8 | 5 | 11 | 17 | 15 | 11 | 5 | 12 | 0 | 0 | 1 | 2 | 1 | 5 | 12 |
| Defense | 13 | 21 | 30 | 22 | 34 | 24 | 21 | 20 | 33 | 18 | 45 | 61 | 47 | 23 | 19 | 22 | 19 | 13 | 24 | 25 | 31 | 33 | 30 |
| Energy/Nat Resource | 4 | 8 | 15 | 7 | 18 | 10 | 10 | 8 | 7 | 11 | 34 | 50 | 25 | 16 | 20 | 29 | 20 | 22 | 20 | 17 | 28 | 30 | 24 |
| Finance/Insur/RealEst | 15 | 16 | 24 | 22 | 47 | 32 | 36 | 29 | 26 | 41 | 102 | 61 | 60 | 39 | 59 | 42 | 32 | 37 | 33 | 33 | 33 | 35 | 33 |
| Health | 21 | 30 | 45 | 23 | 33 | 19 | 27 | 37 | 49 | 45 | 82 | 57 | 88 | 63 | 86 | 76 | 72 | 93 | 94 | 53 | 54 | 55 | 67 |
| Ideology/Single-Issue | 48 | 46 | 45 | 52 | 68 | 60 | 78 | 81 | 85 | 105 | 200 | 189 | 193 | 179 | 252 | 216 | 209 | 212 | 226 | 235 | 268 | 281 | 287 |
| Labor | 21 | 26 | 28 | 31 | 28 | 31 | 25 | 24 | 27 | 26 | 62 | 63 | 72 | 59 | 80 | 74 | 80 | 84 | 74 | 60 | 74 | 76 | 86 |
| Lawyers & Lobbyists | 13 | 10 | 7 | 12 | 14 | 14 | 11 | 15 | 23 | 25 | 37 | 45 | 57 | 40 | 43 | 32 | 20 | 24 | 40 | 43 | 45 | 38 | 36 |
| Misc Business | 54 | 66 | 62 | 59 | 97 | 91 | 95 | 98 | 97 | 120 | 248 | 223 | 229 | 156 | 211 | 215 | 188 | 220 | 240 | 213 | 264 | 260 | 268 |
| Non-contribution | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Other | 127 | 147 | 169 | 149 | 224 | 173 | 199 | 214 | 217 | 251 | 508 | 492 | 516 | 433 | 502 | 534 | 531 | 525 | 507 | 540 | 581 | 531 | 533 |
| Transportation | 5 | 13 | 24 | 8 | 31 | 19 | 21 | 18 | 9 | 4 | 10 | 8 | 3 | 0 | 8 | 16 | 12 | 12 | 10 | 7 | 16 | 32 | 23 |
| Unknown | 7 | 6 | 16 | 22 | 71 | 26 | 30 | 23 | 20 | 18 | 56 | 52 | 75 | 35 | 66 | 57 | 55 | 71 | 79 | 119 | 159 | 200 | 256 |
We could graph some of these industries in law enforcement with ada and see the differences…
#########################################################OLD CODE / JUNK CODE###############################################################
#CODE NO LONGER NEEDED.
#Plot counts
ggplot(data_count_2, aes(Year1, Uniqid)) +
geom_point()
geom_smooth()
names(data_count_1)[names(data_count_1) == 'Uniqid'] <- 'Ada' #changed names so wont have two varaibles with the same name when we merge
uniq_no_rpt <- merge(data_count_2,data_count_1,by="Year1")
uniq_no_rpt$Year1 <- as.numeric(as.character(uniq_no_rpt$Year1))
lm4 <- lm(uniq_no_rpt$Ada ~ uniq_no_rpt$Year1 + uniq_no_rpt$Uniqid)
summary(lm4)#combined3 <- merge(combined2,indus,by=“Client”) #groups by issue, agency, among spent, and industr– too big to merge. How to fix: https://www.programmingr.com/r-error-messages/cannot-allocate-vector-of-size/ #combined2 <- merge(combined,main,by=“Uniqid”)#This adds the total amount that they spent lobbying (not just on drug policy)
#Change Issue into factor combined2\(Issue <- as.factor(combined2\)Issue) summary(combined2$Issue) # Revealed that relevant issue is Alcohol & Drug Abuse
Alcho_DA <- subset(combined2, combined2$Issue==“Alcohol & Drug Abuse”) #n=23,981
combined3 <- merge(Alcho_DA,indus,by=“Client”) # Yay. Complete dataset #n=462,412
rem(combined2)#remove extra datasets <-
#There is something wrogn with the Amount variable in combined2.
combined2[, 15] <- as.numeric(as.character( combined2[, 15] )) # had to make sure that Amount was numeric because error occurred when trying to aggregate; as.character put in to preserve info. See here (https://stackoverflow.com/questions/18045096/r-error-sum-not-meaningful-for-factors)
combined2$Amount <- as.numeric(combined2$Amount) #can also use this notation instead.
typeof(combined2$Year.x) # Year is an integer, so need to change it to factor.
combined2$Year.x <- as.factor(combined2$Year.x) #changed year to factor
x <-as.data.frame(!is.na(as.numeric(combined2$Amount))) #checking to see if there is a mistake in one of the data entries in Amount, True = numeric, False = not numeric. There are a lot of entries that are "FALSE" meaning they are not numeric. Thankfully this problem didn't carry over to combined3 dataset.FIGURED OUT THE ISSUE. THERE ARE "NA". SO, NEED TO TELL function NA IS TRUE.
sum(combined2$Amount, na.rm=TRUE)
#switched to combined 3 dataset because was having too much problems with combined2
typeof(combined3$Amount)#tells us type of integer, which was factor
combined3$Amount <- as.numeric(combined3$Amount) # chagned it to numeric
sum(combined3$Amount)
names(combined3)[names(combined3) == 'Year.x'] <- 'Year1' #renamed the variable in case it was causing problems
typeof(combined3$Year1)#it is an integer and needs to change it to a factor
combined3$Year1 <-as.factor(combined3$Year1) #changed year to factor
is.factor(combined3$Year1)#checked and it is a factor
colnames(combined3)[9]<-"unk" #for some reason there is a blank, unnamed column in the dataset and dplyr didn't like that so I named the column
#Aggregated spending by year for alch and drug abuse
tots <- aggregate(combined3$Amount, by=list(Category=combined3$Year1), FUN=sum)
#Aggregated spending by year for all issues
tots_all <-aggregate(combined2$Amount, by=list(Category=combined2$Year.x), FUN=sum)$238,355,107,883 – amount reported spent on lobbying since 1999 by groups reporting lobbying on alcohol and drug abuse policy.
Total spending on all issues $5,789,444,000,000
Have the reported amounts spent by groups that say they lobbied on alcohol and drug abuse policy changed over time?
H1 = Reported amounts spent by groups lobbying on ALDA has increased over time.
Has this increase been constant or have there been ebbs and flows?
names(tots)[names(tots) == 'Category'] <- 'Year'
names(tots)[names(tots) == 'x'] <- 'Amount'#renamed columns
#Plot year by amount for drug policy and alcho.
library(ggplot2)
library(scales)
pl1 <- ggplot(data=tots, aes(x=Year, y=Amount, group=1)) +
geom_line(color="blue")+
geom_point()
pl1+scale_y_continuous(labels = comma)There is something very wrong with the data (I’m guessing)…was the data aggregated differently after 2001?
I checked the data dictionary and didn’t see anything noted about changes in measurement… (https://www.opensecrets.org/resources/datadictionary/UserGuide.pdf)
Ok I figured out the problem. When you combine with Agency, it includes a different entry for each agency….so, we need to go back and use a different dataset to caculate total’s etc. Bah.
library(readr) # The following dataset will give us the dollar amounts. It will not contain a link by industry however...
main <- read_delim("C:/Users/talee/Dropbox/1-Research/drug policy and interest groups/Lobbying Data/lob_lobbying_rev.txt",
";", escape_double = FALSE, col_names = FALSE,
trim_ws = TRUE)
colnames(main)<-c("Uniqid","Registrant_raw","Registrant","Isfirm","Client_raw","Client","Ultorg","Amount","Catcode","Source","Self","IncludeNSFS","Use","Ind","Year","Type","Typelong","Affliate")
issue <- read_delim("C:/Users/talee/Dropbox/1-Research/drug policy and interest groups/Lobbying Data/lob_issue_NoSpecficIssue_rev.txt", ";", escape_double = FALSE, col_names = FALSE, trim_ws = TRUE)
colnames(issue)<-c("SI_ID","Uniqid","IssueID","Issue","Year")
combined4 <- merge(main,issue,by="Uniqid")
ADA2 <- subset(combined4, combined4$Issue=="Alcohol & Drug Abuse")
#THIS NEW DATASET ADA2 SHOULD WORK...AND COMBINED4 WILL BE ALL OF THE LOBBYING ACTIVITY, redoing the above analysis
typeof(combined4$Amount)#tells us type of integer, which was factor
combined4$Amount <- as.numeric(combined4$Amount) # chagned it to numeric
sum(combined4$Amount, na.rm = TRUE)
names(combined4)[names(combined4) == 'Year.x'] <- 'Year1' #renamed the variable in case it was causing problems
typeof(combined4$Year1)#it is an integer and needs to change it to a factor
combined4$Year1 <-as.factor(combined4$Year1) #changed year to factor
is.factor(combined4$Year1)#checked and it is a factor
colnames(combined4)[23]<-"unk" #for some reason there is a blank, unnamed column in the dataset and dplyr didn't like that so I named the column
####redoing the same thing for the ADA2 dataset
typeof(ADA2$Amount)#tells us type of integer, which was factor
ADA2$Amount <- as.numeric(ADA2$Amount) # chagned it to numeric
sum(ADA2$Amount, na.rm = TRUE)
names(ADA2)[names(ADA2) == 'Year.x'] <- 'Year1' #renamed the variable in case it was causing problems
typeof(ADA2$Year1)#it is an integer and needs to change it to a factor
ADA2$Year1 <-as.factor(ADA2$Year1) #changed year to factor
is.factor(ADA2$Year1)#checked and it is a factor
colnames(ADA2)[23]<-"unk" #for some reason there is a blank, unnamed column in the dataset and dplyr didn't like that so I named the column
#Aggregated spending by year for alch and drug abuse
tots <- aggregate(ADA2$Amount, by=list(Category=ADA2$Year1), FUN=sum)
#Aggregated spending by year for all issues
tots_all <-aggregate(combined4$Amount, by=list(Category=combined4$Year1), FUN=sum)New total spending across all issues $449,804,493,484 Spending by groups lobbying on ADA = $847,775,127
Have the reported amounts spent by groups that say they lobbied on alcohol and drug abuse policy changed over time?
H1 = Reported amounts spent by groups lobbying on ALDA has increased over time.
Has this increase been constant or have there been ebbs and flows?
names(tots)[names(tots) == 'Category'] <- 'Year'
names(tots)[names(tots) == 'x'] <- 'Amount'#renamed columns
getOption("scipen")
options(scipen = 10L) #changing R settings to decrease the use of scientific notation
#Plot year by amount for drug policy and alcho.
library(ggplot2)
library(scales)
ggplot(tots, aes(Year, Amount)) +
geom_point()
geom_smooth()
#Turns out there was something wrong with the ggplot code, not the merging of the datasets....uggghhhh so annoyed....Does this look different than how overall spending changed?
names(tots_all)[names(tots_all) == 'Category'] <- 'Year'
names(tots_all)[names(tots_all) == 'x'] <- 'Amount'#renamed columns
getOption("scipen")
options(scipen = 10L) #changing R settings to decrease the use of scientific notation
#Plot year by amount for drug policy and alcho.
library(ggplot2)
ggplot(tots_all, aes(Year, Amount)) +
geom_point()
geom_smooth()Overall spending definitely looks different… also it does not increase every year…
The changes in spending of groups lobbying on drug policy exist even after controlling for general changes in spending…
Let’s look at this systematically…
We need to remove some outliers: 2021, n and y
tots2<-subset(tots, Year!="2021") #removed 2021 because it is not complete
tots_all2 <-subset(tots_all, Year!="2021" & Year!="n" & Year!="y" ) #n and y must be errors in the dataset
#Removing outliers from other datasets while I am at it!
ADA2 <- subset(ADA2, Year1!="2021" & Year1!="n" & Year1!="y" )
combined4 <-subset(combined4, Year1!="2021" & Year1!="n" & Year1!="y" )Currently year is a factor variable. As a factor variable, we can learn the changes in spending that occur by year after controlling for overall changes in spending. This changes in spending doesn’t mean that this is changes in spending specifically on lobbying on alcohol and drug abuse policy, but rather amount of money spent can be seen as an indicator of power of the groups lobbying.
We cant run a regression with year as a factor variable because there are as many levels as there are observations.
#need to combine tots into one dataset,
names(tots_all2)[names(tots_all2) == 'Amount'] <- 'TotAmt' #must rename Amount for total so that there are two different varaibles...
#merge
tots_comb <- merge(tots_all2,tots2,by="Year")tots_comb$Year <- as.numeric(as.character(tots_comb$Year)) #changing year to numeric
lm2 <- lm(tots_comb$Amount ~ tots_comb$Year + tots_comb$TotAmt)
summary(lm2)The residuals of this model look a bit off…
“we did assume that these residuals were normally distributed, with mean 0. In particular it’s worth quickly checking to see if the median is close to zero, and to see if the first quartile is about the same size as the third quartile. If they look badly off, there’s a good chance that the assumptions of regression are violated.” https://bookdown.org/ekothe/navarro26/regression.html#regressiontests
After controlling for changes in total amount of spending, in the aggregate in ADA policy, we see an increase of $2,262,476.48 every year by groups lobbying ADA policy (p=0.0085)
So in this case, the model performs better than you’d expect by chance (F(2,20)=3.975, p=0.04), but the p value isn’t that low…R2 says that the model accounts for only 28.4% of the variability….this means that the overal changes in spending dont account for all of the overall changes in spending in drug abuse…
Did the number of groups lobbying predict the differences in amount spent?
names(data_count_1)[names(data_count_1) == 'Year1'] <- 'Year' #changed name to allow for
tots3 <- merge(tots_comb,data_count_1,by.x ="Year")
tots3$Year <- as.numeric(as.character(tots3$Year)) #Changed year to numeric
lm3 <- lm(tots3$Amount ~ tots3$Year + tots3$TotAmt + tots3$Uniqid)
summary(lm3) The number of groups seems to be explaining the variance in amount per year, more than overall changes in amount spent per year…so, we should drop the total amount variable…but how do the total number of groups lobbying in drug policy compare to the changes in groups lobbying overall? Could the overall increases in lobbying groups explain the difference?
How do the numbers of groups lobbying in drug policy compare to the overall number of groups lobbying?
```r
data_count_2 <- aggregate(data = main, # Applying aggregate
Uniqid ~ Year,
function(x) length(unique(x)))
data_count_2 # Print counts
#Plot counts
ggplot(data_count_2, aes(Year, Uniqid)) +
geom_point()
geom_smooth()