Forecasting Popular Vote and Electoral College Vote Results: Partisan-Bounded Economic Model

Abstract

The Partisan-Bounded Economic Model forecasts popular vote and Electoral College vote results in the 2024 presidential election based on economic growth, presidential popularity, and shifts in party identification within the electorate. Due to the partisan vote and voter inertia, presidential elections are unlikely to result in lopsided outcomes even in the face of economic turmoil or prosperity. In the Partisan-Bounded Economic Model, outliers in the annualized GDP growth are adjusted to fall within a fixed range of 5 percent to -5 percent. Any GDP growth values exceeding 5 percent or falling below -5 percent are re-coded as 5 percent and -5 percent, respectively. The model predicts that Vice President Kamala Harris will secure 52.3 percent of the party vote, 49.4 percent of the total popular vote, and 59.1 percent of the Electoral College votes.

Researchers have noted ample influence of economic performance on the voters’ decisions in presidential elections (Campbell 2008; Erikson 1989; Fiorina 1981; Lewis-Beck 1985, 1988; Lewis-Beck and Stegmaier 2000; Tufte 1978), and many forecast models evince a solid influence of economic conditions on popular vote results (Campbell 2020; Erikson and Wlezien 2021; Lewis-Beck and Tien 2021; Lockerbie 2021). Nonetheless, partly due to the partisan vote and voter inertia, presidential elections are unlikely to result in lopsided outcomes even in the face of economic turmoil or prosperity. Roughly 60% of the electorate are partisan voters today, with approximately 90% of them consistently voting for candidates of their parties. ¹ Also, incumbent presidents in reelection campaigns are likely to receive a certain number of votes because of their visibility, name recognition, and voters’ risk aversion. In his explanation of “voter inertia,” Campbell (2008) explains that undecided voters may give the benefit of the doubt to the candidate who currently holds the office.

Therefore, the influence of economic performance on electoral results could have limitations. For instance, whether the economic growth is 10% or -10%, its effect on electoral results might not significantly differ from that of 5% or -5% growth. As described in further detail below, in the partisan-bounded economic model, outliers in the annualized gross domestic product (GDP) growth are truncated to fall within a fixed range of 5% to -5%. Any GDP growth values exceeding 5% or falling below -5% in the original measurement are re-coded as 5% and -5%, respectively.

Second, most of the forecast models have focused on the percentage of the party vote won by in-party candidates as the predicted variable. In contrast, the partisan-bounded economic model forecasts the percentage of the total popular vote and the percentage of the Electoral College vote won by in-party candidates, as well as party-vote percentages. Forecasting could differ somewhat from scientific analysis.  In general, the merit of forecasting models is evaluated based on predictive success, narrow probability bounds, and length of lead times (Dowding 2021). However, in social science, the focus is on explaining and testing the influences of various factors on voters’ decisions rather than merely predicting the winner. During presidential elections, most voters contemplate whether to keep the incumbent president (or their party) in office or replace them with another candidate (Campbell et al. 2010; Fiorina 1981; Lewis-Beck 1988). Thus, for the dependent variable, the percentage of total popular vote won by in-party candidates rather than just party-vote percentages may offer a more theoretically coherent measure of the aggregate outcome of voter decisions.

Furthermore, regarding predictive success in forecasting, it is important to note that the popular vote result may not always align with the Electoral College outcome, which can ultimately determine the outcome of the election. For five times in American history, a candidate who had not won a plurality of popular votes in the nation won a majority of Electoral College votes, thereby leading to their electoral victories. This so-called split result could occur largely due to the unequal representation of popular votes and candidates’ wasted votes. Popular votes across the states are not equally represented in the Electoral College. The Connecticut Compromise guarantees two senatorial seats and a minimum of one House seat to all states regardless of population size. Consequently, popular votes in less-populous states are overrepresented and votes in populous states are underrepresented in the allocation of Electoral College votes. When a loser of national popular vote wins in a lot of overrepresented states, she is likely to win a larger proportion of electoral votes relative to the size of the popular votes she won, thereby increasing the likelihood of a split result (Saeki 2025).

Also, except for Maine and Nebraska, a candidate who fails to win a plurality of popular votes within states does not garner any electoral votes. ² In competitive states where the margin of loss is minimal, this discrepancy significantly decreases the ratio of electoral votes to popular votes won by the candidate. Thus, the likelihood of a split result increases substantially when the national popular vote winner accrues wasted votes across numerous states (Saeki 2025). The partisan-bounded economic model tests the unique influence of predictor variables on electoral college results and analyzes their effects and predictive power.

As presented in Table 1, the partisan-bounded economic model forecasts an electoral victory for Vice President Kamala Harris. It predicts that Vice President Harris will secure 52.3% of the party vote, 49.4% of the total popular vote, and 59.1% of the Electoral College votes. For presidential popularity, the model uses the presidential approval rating in June or a month close to June during an election year, as reported by the Gallup Poll. For the shift in party identification within the electorate, it records the percentages of Democratic and Republican voters during an election year (second or third quarter) and calculates the change in party affiliation of the incumbent president relative to four years prior. For the current year, the annualized GDP growth in the second quarter stood at 5.2% (5% for the truncated GDP measurement), whereas the presidential approval rating was at 38%. Additionally, there was a quadrennial decrease of 2.0% in Democratic voters.

Table 1 2024 Presidential Election Results Forecast

Forecast Models and Outlier in the 2020 Election

To date, researchers have analyzed the influence of economic performance, presidential popularity, and other factors on electoral outcomes, based on the results of presidential elections spanning the years between 1948 and 2020. Several models correctly predict a popular vote winner in 16 out of 19 elections but result in incorrect predictions for the 1960, 1968, and 1976 elections (viz. Lewis-Beck and Tien 2021). If Richard Nixon is assumed to have won the popular vote in the 1960 election (Alexander 2019; Campbell 2008; Gaines 2001; Longley and Pierce 1996), the models make a correct prediction in 17 out of 19 elections.

Donald Trump’s victory in the 2016 election stunned analysts and pundits. Before the vote count, researchers and the media mostly expected a victory for Hillary Clinton. The New York Times announced that Clinton held an 85% chance of winning, and ABC News reported that Clinton had a 95% chance of winning. In parallel, a majority of econometric models employed by scholars forecasted Clinton’s victory. Subsequently, the unexpected triumph of Trump raised questions about the robustness of forecasting models. Nonetheless, most econometric models forecast popular vote results rather than Electoral College vote results, and a great majority of models correctly predicted Clinton as a winner of the popular vote.

Rather than the 2016 election, the 2020 election presented a methodological challenge for forecast models. The year 2020 witnessed abnormal economic performance due to the coronavirus pandemic. The unemployment rate peaked at 14.7% during the year, and the annualized GDP growth rate in the second quarter of 2020 plummeted to -33.3%, marking a record low since the Great Depression. While the nation’s annual GDP growth rate averages approximately 3% from 1947 until 2020, the extraordinary economic conditions during the 2020 election year resulted in outlier values for the economic indicators. These outlier values would weaken the correlation between the economic performance and electoral outcomes, thereby underestimating the influence of economic conditions on electoral results. Also, these outliers inflate the predicted values in electoral results, thereby reducing the predictive power of forecast models.

Presidential Elections and Economic Conditions

For many decades, scholars have noted a significant influence of the economic condition of the nation on the voters’ decisions and the results of presidential elections (Campbell 2008; Erikson 1989; Fiorina 1981; Lewis-Beck 1985, 1988; Lewis-Beck and Stegmaier 2000; Tufte 1978). Most of the forecast models for presidential elections incorporate various indicators of economic performance (Abramowitz 2021; Campbell 2020; Erikson and Wlezien 2021; Lewis-Beck and Tien 2021; Lockerbie 2021), and they all have evinced a significant influence of economic conditions on electoral results.

The postulation of economic influence on vote decision is largely based on the retrospective voting thesis. The so-called reward-punishment model exemplifies genuine retrospective voting during elections (Key 1966; Tufte 1978). Key contends that the vote decisions by the populace are founded on the voters’ evaluation ex post of the president’s performance ex ante (1966). Subsequently, Tufte (1978) observes that the electorate makes vote decisions largely based on the economic performance under the present administration. Therefore, according to the retrospective voting thesis, elections are quasi-referendums on the heretofore presidential performance, often pertaining to the state of the national economy.

However, due to the partisan vote and voter inertia, a period of economic turmoil or prosperity would not drastically decrease or increase the popular vote won by in-party candidates in presidential elections. Scholars note that a great majority of voters make voting decisions based on their partisanship and that voters’ party identification is very stable. The authors of The American Voter note that “Few factors are of greater importance for our national election than the lasting attachment of tens of millions of Americans to one of the Parties” (Campbell et al. 1960). The analysts suggest that the results of presidential elections are generally predictable due to the stable and prevalent partisan behavior of the electorate. In comport with the Michigan model, Philip Converse’s (1966) concept of the normal vote exemplifies the assumption of stability in presidential elections. The normal vote is a minimum aggregate proportion of votes that presidential candidates of the two major parties are expected to receive. Converse explains that the results of presidential elections are regularly within the parameter of normal voting due to the solid influence of partisanship on vote decisions and the stability of party identification within the populace. Furthermore, in addition to the direct influence of partisanship on voters’ decisions, partisanship may influence voters’ evaluation of the economy. Several scholars observe that voters have a more positive evaluation of economic conditions when the party of the incumbent president is the party they support (e.g., Bartels 2002; Brady, Ferejohn, and Parker 2022; Gerber and Huber 2010).

Also, some voters are risk averse or lack significant information and therefore tend to vote for the incumbent president (or in-party candidates). During an election, many voters know or hear about the incumbent president. In contrast, the information about the challenger is relatively less available, less clear, and less proven. Most voters do not know about the challenger as well as they do about the sitting president. As a result, some voters—specifically undecided voters with less political knowledge—may vote for the incumbent president (Campbell 2008).

Consequently, electoral outcomes are generally stable over time. Even at a time of extreme economic downturns or upswings, electoral results are unlikely to significantly deviate from the outcomes in other elections. For instance, when GDP growth is higher than 10% or falls below -10%, its influence on electoral results would not differ drastically from that of 5% economic growth or -5% growth. In the partisan-bounded economic model, values higher than 5% or lower than -5% in the original measurement of economic growth are re-coded as 5% and -5%, respectively, for the secondary measurement of economic growth.

Method and Variables

In the partisan-bounded economic model, ordinary least squares estimates are analyzed for electoral results spanning from 1948 to 2020. The predicted variables include the percentage of the two-party vote won by in-party candidates, the percentage of total popular votes won by in-party candidates, and the percentage of Electoral College votes won by in-party candidates. The regressors include economic growth, presidential popularity, and shifts in party identification within the electorate.

For economic growth, GDP growth in the second quarter (annualized) of the election year is used. For the secondary measurement of economic growth, values higher than 5% or lower than -5% in the original GDP growth data are re-coded to 5% and -5%, respectively. Thus, the secondary measurement truncates GDP growth values to a fixed range of 5% to -5%. The thresholds of 5% and -5% were determined based on the standard deviation of the sample and the z-scores for outliers. The standard deviation of the original GDP growth measurement is 8.62, indicating significant variation across the 19 elections studied. The z-score for GDP growth during the 2020 election is -3.75, where a z-score higher than 3.0 or lower than -3.0 generally suggests an outlier. Preliminary analysis tested various truncation values, and the chosen threshold values of 5% and -5% were found to minimize the standard deviation and z-scores. After truncation, the standard deviation of GDP growth in the sample is reduced to 2.66, whereas the z-score for the truncated GDP growth (-5%) during the 2020 election is -2.15.

For presidential popularity, the model uses the presidential approval rating in June or a month close to June during an election year, reported by the Gallup Poll. A few analysts posit that in-party candidates under a second-term president would rather face an electoral disadvantage, whereas the voters would prefer out-party candidates (Abramowitz 2021; Norpoth 2021). In-party candidates during the 1952 election (Adlai Stevenson) and 2008 election (John McCain) failed to win popular votes. However, George H. W. Bush, during the 1988 election after the Reagan presidency, Gore, during the 2000 election, and Hillary Clinton, during the 2016 election, won more popular votes than out-party candidates did. In addition, Nixon possibly won more popular votes than Kennedy did in the 1960 election after the Eisenhower presidency (Alexander 2019; Campbell 2008; Gaines 2001; Longley and Pierce 1996). Therefore, the hypothesis in question is not strongly supported. In a preliminary analysis, a dummy variable for a second-term presidency was included in the empirical test and the variable was insignificant.

Regarding partisanship in the electorate, again, the authors of The American Voter stress the influence of voters’ partisanship on electoral results (Campbell et al. 1960). However, the relationship between voters’ partisanship and their vote decision could potentially be bidirectional. Morris Fiorina stresses that some voters adjust their partisanship ex post in accordance with their vote decision ex ante for the forthcoming election (Fiorina 1981, 2017). Thus, the shift in party identification within the electorate could serve as both a cause and a precursor to the electoral outcomes.

For the partisan shift variable, the percentages of Democratic and Republican voters during an election year (second or third quarter) as reported by Pew Research Center and Gallup Poll are recorded. The change in the party of the incumbent president from four years prior is then calculated. Independent voters leaning to the Democratic or Republican parties are excluded from the calculation. For instance, during the 2020 election, the percentage of partisan voters supporting the Republican Party was 26%, which represented a 1% decrease from 27% in 2016. For the 2024 election, the percentage of Democratic voters was 30%, reflecting a 2% decline from 32% in 2020.

Popular Vote Estimate

Figure 1 reports the scatter plot for the observed values of the percentage of the two-party vote won by in-party candidates (Vote Party) and the GDP growth (GDP). As shown on the graph, the GDP growth in 2020 (Trump, -33.3%) and the one in 1980 (Carter, -11.6%), to a lesser degree, are outliers. Figure 2 presents the scatter plot for the same dependent variable and the re-coded measure of economic growth (GDP truncated), whereas the outlier values of the GDP growth are rescaled within the fixed range between 5% and -5%. The values of GDP (truncated) for the 2020 (-33.3% for GDP) and 1980 (-11.5%) elections are -5%, whereas the values for the 1972 (6.9% for GDP) and 1984 (5.1%) elections are 5%. Figure 2 exhibits a linear pattern among the measured values.

Figure 1 Party Vote and GDP: 1948–2020

Figure 2 Party Vote and GDP (truncated): 1948–2020

Next, multivariate equations are estimated to test the influences of independent variables. Table 2 reports the results of estimates. For the percentage of the party vote won by an in-party candidate, the result of equation 1 shows that presidential popularity (approval) is significant, with a positive coefficient. In-party candidates are likely to win a higher percentage of the party vote when an incumbent president is popular. Partisan shift and GDP and in equation 1 are insignificant. For equation 2, GDP (truncated) replaces GDP. Approval remains significant, and partisan shift remains insignificant. GDP (truncated) is significant, with a positive coefficient. In-party candidates are likely to win a higher percentage of the party vote when the national economy is growing substantially. The value of adjusted R² for equation 2 suggests that the estimate explains 80% of the variation in the percentage of the party vote won by in-party candidates.

Table 2 Popular Vote Won by In-Party Candidates 1948–2020

Note: Entries are ordinary least squares regression coefficients, and standard errors are in parentheses.

⁺ p<0.10;

* p<0.05;

^** p<0.01;

^*** p<0.001.

Figure 3 presents the observed (actual) values, estimated (fitted) values, and errors (residuals) of equation 2. Figure 3 illustrates that equation 2 predicts the party vote results very well. However, figure 3 reveals relatively large residuals for the 1960, 1972, 1976, 2000, and 2020 elections. Nixon in the 1972 election, as well as Trump in the 2020 election, won a large number of votes relative to the values of their popularity and economic condition. Inversely, in the 1960 (Nixon), 1976 (Ford), and 2000 (Gore) elections, in-party candidates won a disproportionately small number of votes relative to the economic performance and the popularity of the incumbent president. Overall, equation 2 accurately predicts the correct popular vote winner for at least 17 out of 19 elections. Assuming Richard Nixon won the popular vote in the 1960 election (Alexander 2019; Campbell 2008; Gaines 2001; Longley and Pierce 1996), the model’s accuracy increases to 18 out of 19 elections. The model incorrectly predicts a victory by Ford in the 1976 election (predicted 51.5%, actual 48.9%).

Figure 3 Percentage Party Vote Won by In-Party Candidate

Apropos of the percentage of the total vote won by in-party candidates, approval remains significant, with a positive coefficient, in both equation 3 and equation 4. Also, GDP (truncated) remains significant in equation 4. Furthermore, the shift in voters’ party identification (partisan shift) is significant in both equation 3 and equation 4. The positive values of coefficients indicate that in-party candidates are likely to win a larger proportion of total popular votes when the voters of their parties increase. The value of adjusted R² for equation 4 suggests that approval, GDP (truncated), and partisan shift jointly explain 74% of the variation in the percentage of the total vote won by in-party candidates.

Electoral College Vote Estimate

The forecast model for Electoral College vote results is estimated using the same independent variables, which include economic conditions, presidential popularity, and shifts in voters’ party identification. The dependent variable is the percentage of Electoral College votes won by in-party candidates. ³

Table 3 presents the results of the estimate, and Approval and GDP (truncated) in equation 5 are significant, with positive coefficients. In addition, the shift in party identification in the electorate (partisan shift) is significant, with a positive coefficient. Thus, when partisan voters of in-party candidates increase, the candidates are likely to win more electoral votes. The value of adjusted R² suggests that approval, GDP (truncated), and partisan shift jointly explain 79% of the variation in the percentage of the Electoral College vote won by in-party candidates. Thus, the explanatory power of the estimate is substantial.

Table 3 Percentage of the Electoral College Vote

Note: Entries show coefficients, and standard errors are in parentheses.

* p<0.05;

^** p<0.01;

^*** p<0.001.

Figure 4 presents the observed (actual) values, estimated (fitted) values, and errors (residuals) of equation 5. Figure 4 demonstrates that, overall, equation 5 predicts the percentage of the Electoral College vote won by in-party candidates very well. Still, figure 4 reveals relatively larger residuals for the 1988, 2000, and 2020 elections. Bush in the 1988 election and Trump in the 2020 election won many electoral votes relative to the values of independent variables. During the 2020 election, Trump won 43% of electoral votes, but the equation estimates 24% of electoral votes for him. For the 2000 election, Gore won a disproportionately small number of votes relative to the values of independent variables. Gore won 49.5% of total electoral votes. While when the electoral votes of Florida are discounted, Gore won 51% of electoral votes. However, the equation estimates 71% for his electoral votes.

Figure 4 Percentage of Electoral College Vote: Actual and Predicted Values

Overall, equation 5 predicts accurate Electoral College vote winners for 16 out of 19 elections. The model incorrectly predicts electoral victories by Nixon (1960: actual 44%, predicted 55%), Ford (1976: actual 45%, predicted 52%), and Gore (2000: actual 49.5%, predicted 71%). For the 2000 election, researchers explain that Gore would have won in a Florida statewide recount (e.g., deHaven-Smith 2005; Edwards 2019; Foley 2019). ⁴ Accordingly, if the 2000 election is discounted, the model predicts accurate Electoral College vote winners for 16 out of 18 elections. For the 2016 election with the split results, the model proffers a largely accurate estimate for Hillary Clinton. In 2016, Clinton won 43% of electoral votes and the predicted value is 45%.

CONCLUSION

The nation is currently experiencing ample economic growth. At President Biden’s inauguration in 2021, the unemployment rate was 6.2%. In contrast, the unemployment rate decreased to 3.8% in the second quarter of 2024, nearing a 50-year low. Also, the GDP expanded by 5.2% in the second quarter.

However, despite favorable economic indicators, President Biden’s popularity among the electorate is currently low. According to the Gallup Poll survey conducted in April 2024, the presidential approval rate was 38%. In comparison, President Trump’s approval rate in April 2020 was 46%. On July 21, 2024, President Biden announced that he was dropping out of his reelection bid and endorsed Vice President Kamala Harris as his replacement. Although this event is highly unusual, it is unlikely to exert a unique influence on the electoral results. A historical comparison can be made with March 31, 1968, when President Lyndon Johnson withdrew from his reelection bid. Following his withdrawal, Vice President Hubert Humphrey was selected as the Democratic nominee at the tumultuous Democratic Party National Convention. Despite President Johnson’s withdrawal from reelection campaign, the partisan-bounded economic model provides a reliable prediction for the 1968 election. Humphrey secured 49.6% of the party vote, and the partisan-bounded economic model predicts 49.0% for him.

The partisan-bounded economic model predicts that Vice President Kamala Harris will secure 52.3% of the party vote, 49.4% of the total popular vote, and 59.1% of the Electoral College votes. Thus, the electoral result of the 2024 election is likely to be closely contested, resulting in a victory with a narrow margin for either candidate. Regardless of the winner, a significant proportion of the nation is expected to remain deeply dissatisfied with the result.