Abstract
Hillary Clinton won the national popular vote for president in 2016, but lost to Donald Trump in the Electoral College. Trump’s margin of victory in several decisive battleground states was smaller than the combined vote for the two leading minor party candidates: Gary Johnson, of the Libertarian Party, and Jill Stein, of the Green Party. The perception that Johnson and Stein “stole” the 2016 presidential election from Clinton is widespread, and potentially consequential for future minor party candidacies, but it has not yet been rigorously tested. In this article, we extend the analysis of minor party voting in the 1992 election from Lacy, D., and B. C. Burden. 1999. “The Vote-Stealing and Turnout Effects of Ross Perot in the 1992 U.S. Presidential Election.” American Journal of Political Science 43 (1): 233–55, by using data from the 2016 Cooperative Congressional Election Study to estimate a multinomial probit model of voting behavior—including outcomes for vote choice and abstention—and calculate the predicted probabilities that Johnson and Stein voters would have voted for another candidate or abstained from voting, had one or both of these candidates been excluded from the ballot. We then reallocate Johnson’s and Stein’s votes accordingly, to estimate Clinton’s and Trump’s counterfactual vote shares nationally and within key battleground states. Our analysis indicates that Johnson and Stein did not deprive Clinton of an Electoral College majority, nor Trump the legitimacy of winning the national popular vote. We estimate that most Johnson and Stein voters would have abstained from voting if denied the choice to vote for their preferred candidate, and that most of Johnson’s remaining voters would have supported Trump.
About the authors
Christopher J. Devine is an Assistant Professor of Political Science at the University of Dayton, in Dayton, Ohio. He is the co-author of Do Running Mates Matter?: The Influence of Vice Presidential Candidates in Presidential Elections (with Kyle C. Kopko), and has published research on topics including campaigns and elections, political parties, ideology, and the U.S. presidency. He can be reached at 937-229-3626.
Kyle C. Kopko is an Adjunct Professor of Political Science at Elizabethtown College in Elizabethtown, Pennsylvania. He has published research on topics including judicial politics, state politics and policy, religion and politics, and the U.S. presidency.
Appendix: Survey Questions and Variable Coding
This appendix describes the survey questions from the 2016 Cooperative Congressional Election Study (CCES) used in the article’s empirical analyses—particularly to estimate the multinomial probit model in Table 3. In each case, we provide the exact question wording from the CCES, as well as the relevant variable name and coded response options. Also, we describe how these variables were recoded in order to conduct our empirical analyses.
Vote
CC16_410: For whom did you vote for President of the United States?
| CCES coding | Recoding |
|---|---|
| 1 – Donald Trump (Republican) | 1 |
| 2 – Hillary Clinton (Democrat) | 2 |
| 3 – Gary Johnson (Libertarian) | 3 |
| 4 – Jill Stein (Green) | 4 |
| 5 – Other | 5 |
| 6 – I didn’t vote in this election | 6 |
| 7 – I’m not sure | 5 |
| 8 – Evan McMullin (independent) | 5 |
If the respondent was not a validated voter in the 2016 general election, our recoded variable classified him/her as abstaining (6). CCES’ voter validation variable is CL_E2016GVM. It reports the method used to cast a ballot; missing data indicate non-voting, as does reporting non-voting in the self-report item above.
Party Identification
pid7: Generally speaking, do you think of yourself as a…?
| CCES coding | Recoding |
|---|---|
| 1 – Strong Democrat | 1 |
| 2 – Not very strong Democrat | 2 |
| 3 – Lean Democrat | 3 |
| 4 – Independent | 4 |
| 5 – Lean Republican | 5 |
| 6 – Not very strong Republican | 6 |
| 7 – Strong Republican | 7 |
| 8 – Not sure | 4 |
Ideology
CC16_340a: How would you rate each of the following individuals and groups? [Yourself]
| CCES coding | Recoding |
|---|---|
| 1 – Very liberal | 1 |
| 2 – Liberal | 2 |
| 3 – Somewhat liberal | 3 |
| 4 – Middle of the road | 4 |
| 5 – Somewhat conservative | 5 |
| 6 – Conservative | 6 |
| 7 – Very conservative | 7 |
| 8 – Not sure | 4 |
National Economy
CC16_302: Over the past year, the nation’s economy has…?
| CCES coding | Recoding |
|---|---|
| 1 – Gotten much better | 5 |
| 2 – Gotten better | 4 |
| 3 – Stayed about the same | 3 |
| 4 – Gotten worse | 2 |
| 5 – Gotten much worse | 1 |
| 6 – Not sure | 3 |
Age
birthyr: In what year were you born?
| CCES coding | Recoding |
|---|---|
| [Year] | 2016 – Year |
Gender
gender: Are you male or female?
| CCES coding | Recoding |
|---|---|
| 1 – Male | 0 |
| 2 – Female | 1 |
Education
educ: What is the highest level of education you have completed?
| CCES coding | Recoding |
|---|---|
| 1 – No high school degree | 1 |
| 2 – High school graduate | 2 |
| 3 – Some college | 3 |
| 4 – 2-year college degree | 3 |
| 5 – 4-year college degree | 4 |
| 6 – Post-graduate degree | 5 |
Race/Ethnicity
race: What racial or ethnic group best describes you?
| CCES coding | Recoding (African American) |
|---|---|
| 1 – White | 0 |
| 2 – Black | 1 |
| 3 – Hispanic | 0 |
| 4 – Asian | 0 |
| 5 – Native American | 0 |
| 6 – Mixed | 0 |
| 7 – Other | 0 |
| 8 – Middle Eastern | 0 |
| CCES coding | Recoding (Latino) |
|---|---|
| 1 – White | 0 |
| 2 – Black | 0 |
| 3 – Hispanic | 1 |
| 4 – Asian | 0 |
| 5 – Native American | 0 |
| 6 – Mixed | 0 |
| 7 – Other | 0 |
| 8 – Middle Eastern | 0 |
Also, Latino = 1 if respondent answered Yes (1) on “hispanic” (Are you of Spanish, Latino, or Hispanic origin or descent?)
Born Again Christian
pew_bornagain: Would you describe yourself as a born-again or evangelical Christian, or not?
| CCES coding | Recoding |
|---|---|
| 1 – Yes | 1 |
| 2 – No | 0 |
Battleground
inputstate:
| Colorado | 1 |
| Florida | 1 |
| Iowa | 1 |
| Michigan | 1 |
| Nevada | 1 |
| New Hampshire | 1 |
| North Carolina | 1 |
| Virginia | 1 |
| Ohio | 1 |
| Pennsylvania | 1 |
| Wisconsin | 1 |
We code residents of battleground states as 1, other respondents as 0.
Polarization
CC16_340g/CC16_340h: How would you rate each of the following individuals and groups? [The Democratic Party/The Republican Party]
| CCES coding | Recoding |
|---|---|
| 1 – Very liberal | 1 |
| 2 – Liberal | 2 |
| 3 – Somewhat liberal | 3 |
| 4 – Middle of the road | 4 |
| 5 – Somewhat conservative | 5 |
| 6 – Conservative | 6 |
| 7 – Very conservative | 7 |
| 8 – Not sure | 4 |
To measure perceptions of partisan polarization, for each respondent we subtract perceived Democratic Party ideology from perceived Republican Party ideology. Then, we take the difference score’s absolute value, to create a 0 (no difference) to 6 (maximum difference) scale.
Social Media
CC16_300_5: In the past 24 hours have you… (check all that apply)
| CCES coding | Recoding |
|---|---|
| 1 – Selected | 0 |
| 2 – Not selected | 1 |
CC16_300d_1: Did you do any of the following on social media (such as Facebook, YouTube or Twitter)? [Posted a story, photo, video or link about politics]
| CCES coding | Recoding |
|---|---|
| 1 – Selected | 1 |
| 2 – Not selected | 0 |
| 9 – Not asked (CC16_300_5=2) | 0 |
CC16_300d_3: Did you do any of the following on social media (such as Facebook, YouTube or Twitter)? [Read a story or watched a video about politics]
| CCES coding | Recoding |
|---|---|
| 1 – Selected | 1 |
| 2 – Not selected | 0 |
| 9 – Not asked (CC16_300_5=2) | 0 |
CC16_300d_4: Did you do any of the following on social media (such as Facebook, YouTube or Twitter)? [Followed a political event]
| CCES coding | Recoding |
|---|---|
| 1 – Selected | 1 |
| 2 – Not selected | 0 |
| 9 – Not asked (CC16_300_5=2) | 0 |
CC16_300d_5: Did you do any of the following on social media (such as Facebook, YouTube or Twitter)? [Forwarded a story, photo, video or link about politics to friends]
| CCES coding | Recoding |
|---|---|
| 1 – Selected | 1 |
| 2 – Not selected | 0 |
| 9 – Not asked (CC16_300_5=2) | 0 |
To measure social media use, we sum responses to all five questions (0–5).
Disapprove Institutions
CC16_320a/CC16_320b/CC16_320c/CC16_320d/CC16_320e: Do you approve of the way each is doing their job…? [Obama/Congress/Supreme Court/Governor/Legislature]
| CCES coding | Recoding |
|---|---|
| 1 – Strongly approve | 1 |
| 2 – Somewhat approve | 2 |
| 3 – Somewhat disapprove | 4 |
| 4 – Strongly disapprove | 5 |
| 5 – Not sure | 3 |
To measure disapproval of governing institutions, we calculate the mean disapproval rating on all five questions (1–5).
Government Spending
CC16_426_1/CC16_426_2/CC16_426_3/CC16_426_4/CC16_426_5: State legislatures must make choices when making spending decisions on important state programs. Would you like your legislature to increase or decrease spending on the five areas below? [Welfare/Health Care/Education/Law Enforcement/Transportation and Infrastructure]
| CCES coding | Recoding |
|---|---|
| 1 – Greatly increase | 0 |
| 2 – Slightly increase | 1 |
| 3 – Maintain | 2 |
| 4 – Slightly decrease | 3 |
| 5 – Greatly decrease | 4 |
Our government spending index represents the respondent’s average score on the items above, rescaled to 0–1.
Environment
CC16_333a/CC16_333b/CC16_333c/CC16_333d: Do you support or oppose each of the following proposals? [Give Environmental Protection Agency power to regulate Carbon Dioxide emissions/Raise required fuel efficiency for the average automobile from 25 mpg to 35 mpg/Require a minimum amount of renewable fuels (wind, solar, and hydroelectric) in the generation of electricity even if electricity prices increase somewhat/Strengthen enforcement of the Clean Air Act and Clean Water Act even if it costs US jobs]
| CCES coding | Recoding |
|---|---|
| 1 – Support | 0 |
| 2 – Oppose | 1 |
Our environmental regulation index represents the respondent’s average score on the items above.
Abortion
CC16_332a: Do you support or oppose each of the following proposals? [Always allow a woman to obtain an abortion as a matter of choice]
| CCES coding | Recoding |
|---|---|
| 1 – Support | 0 |
| 2 – Oppose | 1 |
CC16_332c/CC16_332d/CC16_332e/CC16_332f: Do you support or oppose each of the following proposals? [Prohibit all abortions after the 20 th week of pregnancy/Allow employers to decline coverage of abortions in insurance plans/Prohibit the expenditure of funds authorized or appropriated by federal law for any abortion/Make abortions illegal in all circumstances]
| CCES coding | Recoding |
|---|---|
| 1 – Support | 1 |
| 2 – Oppose | 0 |
Our abortion index represents the respondent’s average score on the items above.
Gun Control
CC16_330a/CC16_330d: On the issue of gun regulation, do you support or oppose each of the following proposals? [Background checks for all sales, including at gun shows and over the Internet/Ban assault rifles]
| CCES coding | Recoding |
|---|---|
| 1 – Support | 0 |
| 2 – Oppose | 1 |
CC16_330b/CC16_330e: Prohibit states and local governments from publishing the names and addresses of all gun owners/Make it easier for people to obtain concealed-carry permit]
| CCES coding | Recoding |
|---|---|
| 1 – Support | 1 |
| 2 – Oppose | 0 |
Our gun control index represents the respondent’s average score on the items above.
Immigration
CC16_331_1/CC16_331_3: What do you think the U.S. government should do about immigration? Select all that apply. [Grant legal status to all illegal immigrants who have held jobs and paid taxes for at least 3 years, and not been convicted of any felony crimes/Grant legal status to people who were brought to the US illegally as children, but who have graduated from a U.S. high school
| CCES coding | Recoding |
|---|---|
| 1 – Selected | 0 |
| 2 – Not selected | 1 |
CC16_331_2/CC16_331_7: What do you think the U.S. government should do about immigration? Select all that apply. [Increase the number of border patrols on the U.S.-Mexican border/Identify and deport illegal immigrants]
| CCES coding | Recoding |
|---|---|
| 1 – Selected | 1 |
| 2 – Not selected | 0 |
Our immigration index represents the respondent’s average score on the items above.
Criminal Justice
CC16_334a/CC16_334b: Do you support or oppose each of the following proposals? [Eliminate mandatory minimum sentences for non-violent drug offenders/Require police officers to wear body cameras that record all of their activities while on duty]
| CCES coding | Recoding |
|---|---|
| 1 – Support | 0 |
| 2 – Oppose | 1 |
CC16_334c/CC16_334d: Do you support or oppose each of the following proposals? [Increase the number of police on the street by 10%, even if it means fewer funds for other public services/Increase prison sentences for felons who have already committed two or more serious or violent crimes]
| CCES coding | Recoding |
|---|---|
| 1 – Support | 1 |
| 2 – Oppose | 0 |
Our criminal justice index represents the respondent’s average score on the items above.
Military Intervention
CC16_414_1/CC16_414_2/CC16_414_3/CC16_414_4/CC16_414_5/CC16_414_6: Would you approve of the use of U.S. military troops in order to…? Please check all that apply. [Ensure the supply of oil/Destroy a terrorist camp/Intervene in a region where there is genocide or a civil war/Assist the spread of democracy/Protect American allies under attack by foreign nations/Help the United Nations uphold international law]
| CCES coding | Recoding |
|---|---|
| 1 – Selected | 1 |
| 0 – Not selected | 0 |
Our military intervention index represents the respondent’s average score on the items above.
Racial Prejudice
CC16_422c/CC16_422d: I am angry that racism exists/White people in the U.S. have certain advantages because of the color of their skin]
| CCES coding | Recoding |
|---|---|
| 1 – Strongly agree | 1 |
| 2 – Somewhat agree | 2 |
| 3 – Neither agree nor disagree | 3 |
| 4 – Somewhat disagree | 4 |
| 5 – Strongly disagree | 5 |
CC16_422e/CC16_422f: I often find myself fearful of people of other races/Racial problems in the U.S. are rare, isolated situations]
| CCES coding | Recoding |
|---|---|
| 1 – Strongly agree | 5 |
| 2 – Somewhat agree | 4 |
| 3 – Neither agree nor disagree | 3 |
| 4 – Somewhat disagree | 2 |
| 5 – Strongly disagree | 1 |
Our racial prejudice index represents the respondent’s average score on the items above, rescaled to 0–1.
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