Abstract
Presidential candidates purchase advertising based on each state’s potential to tip the election. The structure of the Electoral College concentrates spending in battleground states, such that a majority of voters are ignored. We estimate an equilibrium model of multimarket advertising competition between candidates that allows for endogenously determined budgets. In a Direct Vote counterfactual, we find advertising would be spread more evenly across states, but total spending levels can either decrease or increase depending on the contestability of the popular vote. Spending would increase by 13 % in the extremely narrow 2000 election, but would decrease by 54 % in 2004. These results suggest that the Electoral College greatly increases advertising spending in typical elections.
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Notes
The two exceptions are Maine and Nebraska, which have used a congressional district method since 1972 and 1996, respectively. Maine’s votes have never been split across candidates, whereas Nebraska’s were split for the first and only time in 2008 when Barack Obama won the state’s 2nd district.
The Electoral College might directly distort a candidate’s policies (or promises, in the case of challengers) to favor voters in battleground states. The winnertakeall rule makes a smaller coalition of voters necessary to win the election, and a politician might be more responsive to these pivotal voters’ preferences through targeted policies instead of broader economic programs. For instance, such incentives help explain the timing of financial support bestowed upon Florida’s Everglades by Presidents Bill Clinton in 1996, George W. Bush in 2004, and Barak Obama in 2012—all announced within months of the general election. Similar reasoning applies to the bipartisan support for corn (ethanol) subsidies in Iowa (Oppel 2011). Empirical studies lend further credence to the view that candidates adopt policies that disproportionately favor battleground states (Garrett and Sobel 2003; Berry et al. 2010; Reeves 2011).
The Electoral College is a nested contest where the final outcome depends on the weighted sum of binary outcomes across statelevel contests. In this literature’s terminology, the contest success function determines a player’s probability of success as a function of all players’ efforts (Skaperdas 1996). We construct an empirical contest success function using our estimates of voters’ preferences and the structure of the candidates’ game. To econometrically analyze such a contest, we present a computational approach to calculate the marginal effect of an agent’s effort (e.g., funds directed towards advertising, R&D, lobbying, etc.). To the best of our knowledge, our work is among the first to estimate an empirical contest model. Appendix A provides more details.
Although rich data exist to describe the informativeness and/or persuasiveness of political ads, the lack of an appropriate instrument makes it hard to identify their relative effects.
Robustness checks with alternative forms do not indicate our results are sensitive to this particular assumption.
Linking individuallevel voter data to TV exposures is difficult but could potentially provide the means to recover heterogeneity in advertising responsiveness. Lovett and Peress (2015) combine multiple data sets to study the targeting of political TV advertising by presidential candidates.
We could generalize the specification of the shocks to allow for correlations across markets at the expense of an increased computational burden in estimation.
We refer to candidates using male pronouns throughout the paper because we analyze past elections and, at the time of this writing, the United States has yet to have a female candidate in the general election for president. Hopefully this changes in the future and potentially leads to a broader movement toward genderneurtral pronouns (Petrow 2014).
To the extent that the opportunity cost of funds is spending on other party activities or elections, it is possible that alterations to the election mechanism might shift the returns to donors’ investments in these related funding opportunities.
We do not consider a donor’s expected utility from an election outcome and assume that R _{ j } is invariant to the actual amount of money donated. This implies two features of donor behavior. First, the marginal utility of donor income must be independent of the amount they donate to the campaign. This is reasonable unless policy changes significantly alter the proportion of a donor’s lifetime income that is offered to the campaign. Second, the expected utility from the candidate winning cannot be contingent on the amount donated. This assumption may be stronger because of common speculation that large donations earn political favors. Nevertheless, such issues are beyond the scope of the paper.
Although building a complete model of endogenous budget formation is challenging, recent work by Urban and Niebler (2013) investigates the relationship between advertising and individual campaign donations.
These benefits could include the perceived monetary value of winning the election, the ability to implement policies consistent with the candidate’s preferences, or simply the candidate’s “hunger” for the office.
The Wisconsin Advertising Project only tracked political advertising in the top 75 media markets in 2000 and the top 100 markets in the 2004 election. We use the 75 markets that are common to both years.
Presidential campaign strategies are coordinated across the candidate, national party, and related groups. In theory, laws prohibit independent interest groups from explicit coordination with candidate, however, in practice, the effectiveness of these restrictions is unclear (Garrett and Whitaker 2007). Given this, it is appropriate to treat all advertising funds spent in support of a candidate as if it were controlled by one entity.
The price of political advertising in the 60days prior to the general election is subject to laws which require the station to offer the purchaser the lowest unit rate (LUR). The LUR applies to all the terms of the advertising contract, including the priority or preemption level of the ad. This implies it is possible for a political ad to be preempted by a TV station if another advertiser is willing to pay more for a higher priority ad. If an ad is bumped, the TV station is required to deliver the contracted amount of GRPs within a specific time frame (e.g., 24 hours), allowing them to substitute less desirable slots for the original slot. The LUR is only available to candidates, whereas independent groups must pay the market rate. According to the former president of CMAG, wellfinanced candidates in competitive races rarely pay the LUR for a preemptible slot because they want to avoid the possibility that their ads will be bumped by another advertiser (such as another candidate).
In 2012, the Federal Communications Commission (FCC) adopted new rules that required major TV broadcast networks in the top 50 markets to make publicly available electronic copies of all political advertising files. This includes the advertising contracts and their terms of purchase. Until this point, obtaining such contracts was extremely difficulty: Hagen and Kolodny (2008) had to visit each station in one market to collect hardcopies of the relevant files.
A more accurate measure of turnout is the votingeligible population (VEP) because it removes noncitizens and criminals. However, data on the VEP is only available at the state level.
It is impossible to recover candidate’s expectations of these shocks in the candidateside estimation because there are at least as many shocks as advertising observations.
We assume the collection of observed advertising choices constitute a purestrategy equilibrium of the advertising competition game. Although the model may possess multiple equilibria, our estimation strategy does not require us to solve the equilibrium.
The unobserved cost shock therefore absorbs any other differences between the observed choices and the model, such as differences across markets in the degree of uncertainty about outcomes. Note that including the structural error v _{ m j } on the demand side, such as entering in the marginal effect of advertising, is difficult because the nonlinearities in d _{ j }(⋅) prevent inversion of an additively linear error term.
Gerber et al. (2011) find somewhat larger effects using a randomized experiment in the 2006 Texas gubernatorial campaign.
In the counterfactuals, we set the supplyside cost residuals v _{ m j } to zero because we do not expect these shocks to carry over from the Electoral College to the Direct Vote. As explained in Appendix b, we also remove all other crosscandidate withinmarket differences in advertising costs, such that ω _{ m j } = w _{ m }. Note that these residuals contain both the unobserved cost shocks and any errors in a candidates’ estimate of winning the election. Large residuals in some markets tend to be correlated within a state (e.g., the estimated \(\hat {v}_{mj}\) are larger for Bush in all California DMAs in 2000), possibly due to a forecast error in the candidate’s belief about winning the state. However, since state boundaries are meaningless in a Direct Vote, such errors should not persist in our counterfactual.
Appendix C provides a statelevel comparison of voter turnout under each election mechanism.
We define leftleaning markets as those with L _{ m }<0.45 and rightleaning markets as L _{ m }>0.55.
If \(\bar {\delta }_{cj}\) were constant across markets, the country effectively becomes one large undifferentiated market, and advertising would be uniform.
One challenge with polling data is that it is unavailable at the county level. Even statelevel polling data measured at consistent intervals can be difficult to obtain, so one challenge following this approach would be aligning the polling information to the proper period length and geographical unit.
While the advertising primarily spans both September (3rd quarter) and October (4th quarter), it is problematic to use a separate cost for each quarter because a discontinuity in costs would be artificially generated on October 1. Furthermore, 4th quarter ad costs are likely not a good estimate of the true cost of the ad because they include the holiday season.
The Constitution specifies the number of a state’s electoral votes as equal to its number of Senators (two) plus its number of Representatives (proportional to its Census population). This allocation implies that each elector in a small state represents fewer voters compared to larger states: as of 2008, each of Wyoming’s three electoral votes represented about 177,000 voters, compared to 715,000 for each of the 32 electors in Texas.
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Acknowledgments
We thank JeanPierre Dubé, Matt Gentzkow, Ron Goettler, Mitch Lovett, Sridhar Moorthy, Michael Peress, Stephan Seiler, Ron Shachar, V. Seenu Srinivasan, Ali Yurukogu, Ali Yurukoglu, and seminar participants at Chicago Booth, Columbia, Erasmus, Helsinki (HECER), Iowa, Kellogg, Leuven, MIT Sloan, NYU Stern, Princeton, Stanford GSB, Toronto, University of Pennsylvania, USC, WUStL, Yale, Zürich, NBER IO, QME, SICS, and SITE for providing valuable feedback. All remaining errors are our own.
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Appendices
Appendix A: Estimation in a contest
This appendix describes a general approach to estimating contest models. The method is applicable to any problem where an agent (or agents) exerts observed effort to pass a threshold while facing some unobservables. The general objective function under a threshold goal for agent j ∈ J≥1 choosing effort allocations \(A_{j}\in \mathbb {R}_{+}^{M}\) is
where \(R_{j}\in \mathbb {R}_{\ge 0}\) is the agent’s prize for success, \(A\in \mathbb {R}_{\geq 0}^{JM}\) is the collection of effort choices across agents, \(\omega \in \mathbb {R}_{\geq 0}^{M}\) is the cost of effort, and θ ∈ Θ^{k} is a set of structural parameters. d _{ j }∈{0,1} is the success function that determines whether agent j wins the prize, such that \(\mathbb {E}\left [d_{j}\right ]\) is the probability of success that integrates over the unobservable \(\eta \in \mathbb {R}^{JM}\) with known CDF F _{ η }. An important component of d _{ j } is the scoring function \(s_{jm}\left (A,\eta ;\theta \right )\in \mathbb {R}_{\geq 0}\), which determines the agent’s score given all effort choices and the unobservables. For all m, we assume s _{ j m }(A,η;θ) is strictly increasing in A _{ j m } and η _{ j m } and strictly decreasing in A _{ k m } and η _{ k m }, for k≠j. These conditions ensure that s(A,η) is invertible in η, which is necessary for the computational approach. We suppress θ for the rest of the exposition.
Our paper estimates θ using GMM based on the FOCs of the agent’s problem with respect to A _{ j }. To compute the FOC, one requires a method to estimate the marginal effect of (say) advertising \(\frac {\partial \mathbb {E}\left [d_{j} \left (A,\eta ;\theta ^{v}\right )\right ]}{\partial A_{mj}}\) on the probability of winning the contest. Below we discuss ways to calculate this quantity under different conditions.
Single agent with scalar uncertainty
The simplest application involves a single agent (J = 1) who chooses a single effort level A≥0 (M = 1) with a scalar unobservable η in an attempt to exceed threshold \(\overline {V}\). The success function is
The scoring function produces a value v = s(A,η), which is a random variable due to the presence of η. Assume the scoring function can produce a value high enough to exceed the threshold, such that \(s(A,\eta )\geq \overline {V}\) for some combination of A and η. Otherwise, the agent is guaranteed to fail and she will not exert any effort. The probability of success can be expressed as
where F _{ v }(⋅) is the unknown CDF of the score v. Let s ^{−1}(A,v) be the inverse of s(A,η) through its second argument. Given the known distribution function for η, we use a changeofvariables to reexpress the cumulative distribution F _{ v } in terms of η,
where \(\eta ^{*}\equiv \eta ^{*}\left (A,\overline {V}\right )=s^{1}\left (A,\overline {V}\right )\) is the critical value of the shock that equates the score and the threshold, such that \(s\left (A,\eta ^{*}\right )=\overline {V}\). Thus, given a particular value of A , the marginal effect of A on the probability of success is:
If s ^{−1}(A,v) and ∂ s ^{−1}(A,v)/∂ A have closed forms, then knowledge of f _{ η } yields an analytic expression for the marginal effect of effort.
Multiple markets and a competing agent
Our presidential election application involves an extension to a competing agent and multiple markets, where each market requires an action and has an agentmarket specific unobservable. The success function in such a contest is:
The scoring function is
where A _{ m } and η _{ m } are those values relevant to market m across all agents. For a given market, we can rewrite the success function in the following way:
Intuitively, an agent’s score relative to his opponent’s in market m must be greater than his deficit across all other markets. We refer to these terms as the internal margin I M _{ m j } and external margin E M _{ m j }. The inversion to obtain a critical η ^{∗} is now in terms of the internal margin instead of a single agent’s score. Note that an inverse of I M _{ m j } through η _{ m j } exists due to our shape assumptions on s(A,η). The probability of success can now be expressed as
Given values for A _{−m } and η _{−m }, E M _{ m j } is just a scalar representing agent j’s deficit or surplus outside market m. Thus the critical value of agent j’s shock in market m is the \(\eta _{mj}^{*}\equiv \eta _{mj}^{*}\left (A_{m},EM_{mj},\eta _{mk}\right )\) that sets
We combine this result with Monte Carlo simulation over the opponent’s shock inside market m and the collection of shocks outside market m to produce an estimate of the marginal effect of A _{ j m }. The key steps are:

1.
Simulate r = 1,…,N S draws over \(\eta _{mk}^{r}\) and \(\eta _{m}^{r}=\left \{ \eta _{nj}^{r}, \eta _{nk}^{r} \right \}_{n\neq m}\) using the known distributions \(f_{\eta _{mj}}\). In our application, these are N(0,σ ^{2}), which can be generalized at higher computational cost.

2.
Use \(\eta _{m}^{r}\) to calculate the JM values of \(EM_{mj}^{r}\equiv EM_{mj}^{r}\left (A_{m}, \eta _{m}^{r} \right )\), given some particular value A _{−m }.

3.
Solve for the critical values \(\eta _{mj}^{r*}\equiv \eta _{mj}^{r*}\left (A_{m},EM_{mj}^{r},\eta _{mk}^{r}\right )\) that set \(IM_{mj}^{r}\left (A_{m},\eta _{mj}^{r*},\eta _{mk}^{r}\right )=EM_{mj}^{r}\) for all m and j.

4.
The estimate of the marginal effect of A _{ m j } is:
$$ \widehat{\frac{\partial\mathbb{E}\left[d\left( A,\eta\right)\right]}{\partial A_{mj}}}=\frac{1}{NS} \underset{r}{\sum}f_{\eta_{mj}} \left( \eta_{mj}^{r*}\right)\frac{\partial\eta\left( A_{m},EM_{mj}^{r}, \eta_{mk}^{r}\right)}{\partial A_{mj}}\,. $$(16)where \(\partial \eta (\cdot )/\partial A_{mj}\equiv \partial IM_{mj}^{1}(\cdot )/\partial A_{mj}\) and evaluated at \(\eta _{mj}=\eta _{mj}^{r*}\).
The next two sections discuss the details of implementing this general form of the derivative in the Direct Vote and Electoral College, respectively.
Application to a Direct Vote
The realities of an election place certain restrictions on the empirical analogues of the score functions. Under a Direct Vote, Eq. 16 describes the derivative if it is always feasible for the internal margin of votes to exceed the external margin of votes in all markets. In practice, bounds on the internal margin are implied by the number of voters in the market, giving us the following expression for the derivative in a Direct Vote:
where \(N_{m}={\sum }_{c\in C_{m}}N_{c}\) is the total number of voters in the market. If \(N_{m}<EM_{mj}^{r}\), then there does not exist a \(\eta _{mj}^{r*}\) to equate the internal and external margins and the derivative at the r ^{th} draw is zero.
Using the notation from the body of the paper, the marginal effect of advertising on the probability of winning is:
where \(EM_{mj}^{r}\equiv EM_{mj}^{r}\left (A_{m},\eta _{m}^{r}\right )\) is the external margin in market m and \(\eta _{mj}^{r*}=\eta _{mj}\left (A_{m},EM_{mj}^{r},\eta _{mk}^{r}\right )\) is the critical value of the shock that equates the external and internal margins. We have suppressed an indicator function that sets the derivative for the r ^{th} draw equal to zero when \(N_{m}<E{M_{m}^{r}}\). Note that an interior solution requires us to solve a system of J M(1 + N S) equations in as many unknowns. Setting N S = 10, 000 helps ensure an accurate Monte Carlo approximation to the integral, but makes computing the equilibrium nontrivial.
Application to the Electoral College
The Electoral College introduces an added layer of complexity because of the statelevel contests. We need to introduce additional notation corresponding to multiple markets intersecting a single state and markets intersecting multiple states. Let M _{ s(m)} be the set of all markets intersecting state s(m), where m is the focal market for the derivative. Let C _{ m s } denote the set of counties intersecting both state s and market m. Finally, let \(\tilde {S}\left (m\right )\) denote the set of states that market m intersects.
The Electoral College involves two relevant thresholds for winning an election: (i) the electoral vote margin and (ii) the statelevel popular vote margin. For a given r, the derivative is nonzero only if the electoral votes attainable through advertising in market m are greater than the electoral vote deficit implied by the shocks in all other markets:
If a market is “in play” based on the electoral votes, then the statelevel margin can be separated into an internal and external margin as above. If a market intersects multiple states there is a potentially relevant internal and external margin for each:
We therefore calculate an \(\eta _{mj}^{*rs}\) , as described above, for each state \(s\in \tilde {S}\left (m\right )\). Next, we define the relevant critical value, \(\eta _{mj}^{*r}\), to be the smallest of these shocks that yields enough electoral votes to offset the electoral vote deficit implied by the r ^{th} set of draws.
The derivative for the r ^{th} draw is therefore:
and the overall derivative is
Using the notation from the body of the paper, the marginal effect of advertising is equal to:
where ∂ η(⋅,⋅,⋅)/∂ A _{ m j } is evaluated at \(\eta _{mj}^{r*}\). The derivative of η(⋅) with respect to A _{ m j } in our application is \(\frac {\alpha }{1+A_{mj}}\) because A _{ m j } and η _{ m j } are perfectly substitutable within the utility function as follows: \(\delta _{cj}=\bar {\delta }_{cj}+ {\alpha }\log \left (1+A_{mj}\right )+\eta _{mj}\). Intuitively, the derivative we seek equals the probability of drawing a critical value \(\eta _{mj}^{*}\) times the derivative of this critical value with respect to advertising. The condition on the right requires that the state be pivotal in the election’s outcome: the number of electoral votes at stake, E _{ s }, must be larger than the candidate’s electoral deficit outside that state, otherwise the derivative at the r ^{th} draw is zero. A benefit of this approach is that the Monte Carlo integration is effectively over η _{ m k } and E M _{ m j }, combined with an analytic expression for \(\eta _{mj}^{*}\), instead of the original M×J dimensional integral.
Note that the above characterizes the marginal effect of effort in a contest with a general contest success function. One primary focus of the theoretical contest literature has been on the derivation of analytically tractable success functions (Skaperdas 1996). In practice, contests such as elections have their own specific success functions implying CDFs for the probability of success that may inherently not be analytically tractable. We show that the above approach is beneficial by compressing a large multidimensional integration problem into unidimensional external and internal margins.
Appendix B: Advertising and advertising prices
We construct a marketcandidate observed aggregate advertising level and advertising price (A _{ m j } and w _{ m j }) based on two observed variables. Expenditure_{ m j a d } is CMAG’s estimate of the dollars spent by candidate j in market m on an advertisement a in daypart d. CPP _{ m d } is SQAD’s reported advertising price for the 18 and over demographic in market m during daypart d. We use the CPP from the 3rd quarter of the election year.^{Footnote 29}
Let the daypart level of advertising by candidate j in market m be:
where \(\mathbb {A}_{tmjd}\) is the set of advertisements for a candidate in a market and daypart. Then total advertising by candidate j in market m is:
The marketspecific advertising price for candidate j is defined as follows:
where
In other words, we use a weighted average across the dayparts in which candidate j advertised in market m if the candidate did in fact advertise there, or a weighted average based on both candidates advertising in all markets within each daypart if the candidate did not advertise in the market.
The advertising price in our candidateside estimation is ω _{ m j } = w _{ m j } + v _{ m j } where v _{ m j } is the candidate’s marketspecific unobservable component of advertising. (Recall that the SQAD prices are forecasts) When we analyze the cost per marginal vote, we use C P P _{ m } in all markets to highlight the role of diminishing marginal effectiveness and political leaning in the costs of acquiring an additional vote. Finally, when we solve the Direct Vote counterfactual, we use w _{ m j } as the price of advertising. This avoids odd implications from large local residuals that likely do not relate to costs, but retains a source of local variation in advertising. We remove both the candidate and local market ad price variation in the final simulation by setting an equal price per thousand people (CPM) such that \(\tilde {w}_{mj}=((\frac {1}{2M}{\sum }_{j=1}^{2}{\sum }_{m=1}^{M}CPM{}_{mj})\times Pop)/100\).
Appendix C: A comparison of voter turnout in the Electoral College and the Direct Vote in 2000
Turnout in the 2000 Direct Vote increases by 0.9 %, or about 1.8 million voters. The popular vote in four states—Iowa, New Mexico, Oregon, and Wisconsin, all with thin margins—flips from Gore to Bush. Gore, however, gains enough votes in the Democratic stronghold of California to win the election even though his national vote margin shrinks from about 543,000 to 390,000.
An important distinction between the Electoral College and a Direct Vote is a state’s relative influence in the election outcome. Under the Electoral College, a state’s influence is fixed and proportional to its fraction of the total electoral votes.^{Footnote 30} The Electoral College essentially protects states from political losses if a state implements policies that make it more difficult or disqualifies certain voters from casting their votes. Furthermore, the winnertakeall rule gives partisan members of a state’s government strong motivation to influence voter turnout to favor their own political party (as witnessed recently in the form of voter identification and antivoter fraud laws proposed in many states).
In contrast, in a Direct Vote, a state’s relative influence in the election outcome is endogenous—it is proportional to the percent of its population that turns out to vote relative to national voter turnout. Figure 7 depicts the difference in representation of a state between each electoral mechanism and the representation that their population constitutes as percentage of the US population over age 18. States are ordered on the left axis by increasing size of their voting age population. On the top, the series of positive bars reflect the electoral college’s protection of small states. On the bottom, large states such as California, Texas and Florida are underrepresented in both the electoral college and a Direct Vote. Underrepresentation in the Direct Vote arises from a smaller fraction of the state’s voting age population actually voting. Other states such as Georgia, Arizona and Nevada also are underrepresented in a Direct Vote. Minnesota, Wisconsin, Michigan and Ohio are however overrepresented in a Direct Vote. A Direct Vote therefore eliminates both the electoral college’s protection of small states and the tie in to state population size, as a state is now represented only by its voters turning out for the election.
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Gordon, B.R., Hartmann, W.R. Advertising competition in presidential elections. Quant Mark Econ 14, 1–40 (2016). https://doi.org/10.1007/s1112901691656
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DOI: https://doi.org/10.1007/s1112901691656
Keywords
 Advertising
 Politics
 Empirical game
 Presidential election
 Electoral college
 Direct vote
 Resource allocation
 Contest
JEL Classification
 D72
 L10
 M37