Previous Literature

One of the dominant theories in political economy is the ‘swing voter’ hypothesis. This theory posits that distributive goods will largely be allocated in favour of groups or regions that contain a conspicuous share of voters that are ideologically indifferent between the political parties. While voters with a clear partisan leaning rarely switch their vote to a different party, indifferent voters often do. If voters exchange their ideological stances for public funds and projects, then it is cheaper for politicians to ‘buy’ the votes of these indifferent (swing) voters, and competition for these voters will lead politicians to allocate disproportionate amounts of federal spending to regions or groups with many indifferent voters. Lindbeck and Weibull,Footnote ¹⁶ Dixit and LondreganFootnote ¹⁷ and StrombergFootnote ¹⁸ analyse models that capture this logic.

The logic of distributive politics is also affected by electoral rules. In particular, winner-takes-all systems create incentives to target constituencies that are likely to be pivotal.Footnote ¹⁹ In other words, battleground districts may be favoured both in public policy and campaign resources allocation.Footnote ²⁰ The competitiveness of elections is particularly important in the US context, in which the electoral college system may encourage the channelling of resources toward states that are pivotal in the presidential electoral race.

A competing theory of distributive politics is that parties target spending to loyal voters.Footnote ²¹ This can be a rational strategy in the context of low-turnout elections such as those in the United States. If spending primarily mobilizes voters – either directly as a form of advertising or retrospective voting, or indirectly by buying the support of local elites or groups who engage in get-out-the vote efforts – then the marginal benefit of spending an additional dollar will be highest in areas with the highest density of a party's own voters. Credit-claiming issues may also provide incentives to target core areas. Who will attend the ribbon-cutting ceremonies for new bridges, schools, hospitals and libraries? In a heavily Democratic area the politicians will almost all be Democrats, and they will leave no doubt about which party is responsible for the locale's good fortune. In electorally marginal areas, however, roughly half of the politicians will be Democrats and half will be Republicans, and the impression is not likely to be as partisan or clear. Neither party may benefit much in terms of net votes (although individual politicians, running as incumbents, may benefit).

It is also possible that spending targeted toward loyal voters could simply reflect the fact that politicians are, at least to some extent, policy oriented.Footnote ²² Democratic politicians may prefer to spend on policies that tend to benefit Democratic voters, and likewise for Republicans. These alternate models are not necessarily incompatible with the swing voter hypothesis. For example, loyalists of the out-party may receive disproportionately small shares of the public dollar, while swing areas and loyal areas do equally well.

Finally, other theorists emphasize the importance of factors such as proposal power,Footnote ²³ legislative seniority,Footnote ²⁴ over- and under-representation,Footnote ²⁵ committee structure, presidential leadership and universalism.Footnote ²⁶ If factors such as these are the main drivers of distributive spending, then there may be little relationship between spending and partisanship or ideology.

There are many empirical studies of distributive politics, and the findings are mixed. In terms of the swing-voter hypothesis, studies of the allocation of New Deal spending have found some evidence that states with a more volatile presidential vote received more federal support.Footnote ²⁷ However, Stromberg shows that these findings are not robust to the use of panel data methods with state fixed effects.Footnote ²⁸ Similarly, a more recent study on federal budget allocation by contemporary presidentsFootnote ²⁹ finds that states with more frequent presidential vote swings do not receive more funds. All of these studies use lagged presidential vote returns to measure the fraction of swing voters.

So far, there is little support for the battleground hypothesis, at least with respect to public spending. Wright finds that US states with close presidential races do not receive disproportionately more New Deal spending.Footnote ³⁰ Similarly, Larcinese et al. find no evidence that states with close presidential races receive more federal monies.Footnote ³¹ On the other hand, several studies find that battleground states receive a disproportionate share of the advertising in presidential campaigns.Footnote ³² All of these studies use lagged presidential vote returns to measure the two-party balance in each state.

Several studies find evidence that loyal voters are rewarded. Some studies find a positive relationship between the share of federal spending going to an area and the Democratic vote in the area.Footnote ³³ Since Democrats were the majority party in Congress during the years studied, this supports the idea that federal spoils go to the victors. However, the results might also reflect the behaviour of the Democratic party or characteristics of areas that tend to vote Democratic.Footnote ³⁴ Some studies of US statesFootnote ³⁵ find a positive relationship between federal spending and past vote for the incumbent president's party.Footnote ³⁶

The empirical literature finds more support for ‘swing voter’ behaviour outside the United States. In particular, Indian states that are ‘swing’ but are also aligned with the governing parties are found to receive larger shares of public grants.Footnote ³⁷ Dahlberg and JohanssonFootnote ³⁸ find evidence that the more pivotal regions (of twenty) in Sweden were more successful in winning environmental grants from the central government. CramptonFootnote ³⁹ finds a positive correlation between the competitiveness of the race and spending in Canadian provinces that are not ruled by the Liberal party. Milligan and Smart also study CanadaFootnote ⁴⁰ and find that the closeness of an electoral race has a positive effect on spending, at least for seats held by the opposition party. Ward and John find evidence that central government aid to local governments in the UK goes disproportionately to marginal districts.Footnote ⁴¹ CaseFootnote ⁴² finds that during the Berisha administration in Albania, block grants tended to be targeted at swing communes. DenemarkFootnote ⁴³ also finds evidence that marginal seats in Australia receive a disproportionate amount of local community sports grants.Footnote ⁴⁴

Problems with Measures of Attitudes and Partisanship Based on Voting Data: a Simulation

As noted above, almost all of the existing empirical literature uses voting data to measure the percentage of swing voters, partisan balance or the partisan disposition of each state.

One powerful critique of these measures is that voting behaviour is endogenous. Most papers tend to use lagged values of the vote to mitigate the problem, but this is at best a partial solution for several reasons. The first has to do with the relationship between voting and policies. Voters can reward or punish politicians on the basis of their past budget allocations (retrospective voting) or on the basis of their promises about future allocations (prospective voting). Prospective voting is rational in an environment in which politicians keep their pledges. However, in this setting, lagged votes are a function of past promises, which by assumption should be equal to – or at least highly correlated with – current spending. If there is a high correlation, then measures based on past voting are not a satisfactory solution to the endogeneity problem. Even if voters are retrospective – so that past votes should not be automatically correlated with current spending – using lagged votes is potentially problematic. Secondly, budgetary allocations are quite persistent over time, because budgetary processes are sluggish and spending in any given year depends to a large extent on decisions made in previous years. As a result, even in the case of retrospective voting behaviour, lagged votes and current spending are related due to the strong correlation between past and current budgetary allocations. Finally, there is a third reason to suspect that lagged vote measures are not exogenous: omitted variables that are correlated with both voting and budgetary decisions. For example, some groups might be favoured in distributive policies because they are associated with ‘good values’ that citizens wish to preserve (such as farmers), and these groups might vote in particular ways (for example, they might favour conservative parties). The introduction of state fixed effects in panel regressions can deal with this problem when omitted factors are constant over time. Many potential omitted factors, however, are not time invariant. For example, changes in economic conditions, occupational structure, health outcomes, the cost of supplying various public goods or the flow of immigrants can simultaneously affect both political preferences and spending. In some cases we can simply measure these variables, but often measures are unavailable or noisy.Footnote ⁴⁵

Since the measures used in the current literature to test concurrent theories of distributive politics are clearly endogenous under a variety of assumptions, regression estimates that use them are typically biased. The sign and magnitude of the bias, however, are more difficult to determine. In the simplest cases we can compute the expected bias analytically, but most regressions in the literature are fairly complicated, and typically include two or more vote-based measures in the same model. In such cases it is often quite difficult to calculate the signs and relative magnitudes of the biases analytically. We therefore ran a series of simulated regressions. These allow us to gauge the biases in a set of models that is similar to many of the standard models in the literature.Footnote ⁴⁶

The simulations show that the endogeneity of voting data can lead to severely biased estimates. More specifically, using the standard deviation of observed votes, rather than the true number of Independents, can lead to either overestimation or underestimation of the impact of the number of Independents on the allocation of federal spending, depending on the specification and the set of variables included in the regression. The effect of electoral competition is often underestimated, but is sometimes also overestimated. Finally, using the observed votes to measure a region's partisanship leads to systematic overestimation of the impact of the number of partisan voters on spending.

We consider the following basic structure. Let j = 1 ,…, J index states, and let t = 1 ,…, T index years. Assume all states have the same population. Let D_j be the fraction of voters in state j that is loyal to party D, let R_j be the fraction that is loyal to party R, and let I_j be the fraction that is independent (swing voters). Also, let $ {{\tilde{D}}_j}\, = \,{{D}_j}/({{D}_j}\, + \,{{R}_j}) $ be the fraction of all loyalists that is loyal to party D, and let $ {{\tilde{R}}_j}\, = \,{{R}_j}/({{D}_j}\, + \,{{R}_j})\, = \,1{\rm{ - }}{{\tilde{D}}_j} $. Let $ {{\tilde{C}}_j}\, = 1{\rm{ - }}|{{\tilde{D}}_j}{\rm{ - }}{{\tilde{R}}_j}| $ be the two-party ‘competitiveness’, or partisan balance, of state j. Let $ X_{{jt}}^{D} $ be the per-capita transfers that party D offers to state j in year t, and let $ X_{{jt}}^{R} $ be the offer made by party R. Let $ S_{{jt}}^{D} $ be the ‘electoral support’ party that D receives in state j in year t, and let $ S_{{jt}}^{R} $ be the support received by party R. Finally, let $ \tilde{V}_{{jt}}^{D} $ be the fraction of votes that party D receives in state j in year t, and let $ \tilde{V}_{{jt}}^{R} \, = \,\,{\rm{1 - }}\tilde{V}_{{jt}}^{D} $. We assume:

$$\,X_{{jt}}^{D} \, = \,{{\alpha }_I}{{I}_j}\, + \,{{\alpha }_C}{{\tilde{C}}_j}\, + \,{{\alpha }_P}{{\tilde{D}}_j}\, + \,\mu _{{jt}}^{D} $$

$$X_{{jt}}^{R} \, = \,{{\alpha }_I}{{I}_j}\, + \,{{\alpha }_C}{{\tilde{C}}_j}\, + \,{{\alpha }_P}{{\tilde{R}}_j}\, + \,\mu _{{jt}}^{R} $$

$$\quad \ \ \ S_{{jt}}^{D} \, = \,{{\beta }_I}X_{{jt}}^{D} {{I}_j}\, + \,(1\, + \,{{\beta }_P}X_{{jt}}^{D} ){{D}_j}\, + \, \varepsilon _{{jt}}^{D} $$

$$\quad \ \ \ S_{{jt}}^{R} \, = \,{{\beta }_I}X_{{jt}}^{R} {{I}_j}\, + \,(1\, + \,{{\beta }_P}X_{{jt}}^{R} ){{R}_j}\, + \, \varepsilon _{{jt}}^{R} $$

$$\tilde{V}_{{jt}}^{D} \, = \,S_{{jt}}^{D} /(S_{{jt}}^{D} \, + \,S_{{jt}}^{R} )\, \quad\qquad\qquad$$

where μ and ɛ are white noise error terms.

If α_I > 0, β_I > 0 and α_C = α_P = β_P = 0, then we have a linearized approximation of the ‘swing voter’ model of Lindbeck and WeibullFootnote ⁴⁷ and Dixit and Londregan.Footnote ⁴⁸ If α_P > 0, β_P > 0, α_I ≥ 0, β_I ≥ 0 and α_C = 0, then we have a version of the ‘machine politics’ modelFootnote ⁴⁹ or what Fishbeck et al. call the mandate model. Finally, if α_C > 0, α_I ≥ 0, β_I ≥ 0, β_P ≥ 0 and α_P = 0, then we have something approximating the model of Milligan and Smart,Footnote ⁵⁰ or the electoral college model of Colantoni et al.,Footnote ⁵¹ StrombergFootnote ⁵² and others.Footnote ⁵³

If researchers had direct measures of I_j, D_j and R_j, then they could construct $ {{\tilde{C}}_j},\,{{\tilde{D}}_j} $ and $ {{\tilde{R}}_j} $, and then directly estimate equations (1) and (2). In almost all cases, however, they do not. Instead, they use measures based on the actual vote shares, $ {{\tilde{V}}^D} $. Beginning with Wright,Footnote ⁵⁴ researchers have often used the standard deviation of $ {{\tilde{V}}^D} $ over a set of elections within each state j as a proxy for I_j. Intuitively, if I_j is large, then $ {{\tilde{V}}^D} $ will vary widely across elections in state j, and the standard deviation of $ {{\tilde{V}}^D} $ in state j will be large.Footnote ⁵⁵ Researchers also tend to use some historical average of $ {{\tilde{V}}^D} $ as a proxy for $ {{\tilde{D}}_j} $, and an analogous average as a proxy for $ {{\tilde{R}}_j} $. Finally, researchers usually use some historical average of $ {\rm{ - }}|{{\tilde{V}}^D} {\rm{ - }}{{\tilde{V}}^R} | $ as a proxy for $ {{\tilde{C}}_j} $.

As noted above, there are many reasons why even historical voting measures are not exogenous: (1) rational prospective voting, (2) sluggish budgetary processes and (3) omitted variables that are correlated with both voting patterns and budgetary decisions. Although these three mechanisms are different, they have the same implication: patterns of current votes and current spending are interdependent. In the first case, the relationship between the contemporaneous vote and spending is driven by the link between past promises and current allocations. In the other two, it is due to the correlation of current spending with either past spending (because of inertia) or an omitted variable that is correlated with the vote. Rather than constructing complicated historical averages and autocorrelation structures that attempt to incorporate these features more precisely, we simply analyse the effect of the interdependence between vote and spending using contemporaneous voting data freely in our simulations. Let $ \bar{V}_{j}^{D} \, = \,(1/T)\mathop{\sum}\nolimits_{t\, = \,1}^T \tilde{V}_{{jt}}^{D} $ be the mean of V ^D in state j over a sample of T years, and let $ {{\hat{I}}_j}\, = \,{{[(1/T)\mathop{\sum}\nolimits_{t\, = \,1}^T {{(\tilde{V}_{{jt}}^{D} {\rm{ - }}\bar{V}_{j}^{D} )}^2} ]}^{1/2}} $ be the sample standard deviation. Also, let $ {{\hat{C}}_{jt}}\, = \,1{\rm{ - }}|\tilde{V}_{{jt}}^{D} {\rm{ - }}\tilde{V}_{{jt}}^{R} | $ be the closeness of the election in state j in year t.

We consider the following specifications:

$$\openup 1pt{ & {\rm{Model}}\;1{\rm{a}}\;:\;X_{{jt}}^{D} \, = \,{{a}_I}{{{\hat{I}}}_j}\, + \,{{\mu }_{jt}} \cr & {\rm{Model}}\;1{\rm{b}}\;:\;X_{{jt}}^{D} \, = \,{{a}_C}{{{\hat{C}}}_{jt}}\, + \,{{\mu }_{jt}} \cr & {\rm{Model}}\;1{\rm{c}}\;:\;X_{{jt}}^{D} \, = \,{{a}_P}\tilde{V}_{{jt}}^{D} \, + \,{{\mu }_{jt}} \cr & {\rm{Model}}\;2{\rm{a}}\;:\;X_{{jt}}^{D} \, = \,{{a}_I}{{{\hat{I}}}_j}\, + \,{{a}_C}{{{\hat{C}}}_{jt}}\, + \,{{\mu }_{jt}} \cr & {\rm{Model}}\;2{\rm{b}}\;:\;X_{{jt}}^{D} \, = \,{{a}_I}{{{\hat{I}}}_j}\, + \,{{a}_P}\tilde{V}_{{jt}}^{D} \, + \,{{\mu }_{jt}} \cr & {\rm{Model}}\;2{\rm{c}}\;:\;X_{{jt}}^{D} \, = \,{{a}_C}{{{\hat{C}}}_{jt}}\, + \,{{a}_P}\tilde{V}_{{jt}}^{D} \, + \,{{\mu }_{jt}} \cr & {\rm{Model}}\;3\;:\;X_{{jt}}^{D} \, = \,{{a}_I}{{{\hat{I}}}_j}\, + \,{{a}_C}{{{\hat{C}}}_{jt}}\, + \,{{a}_P}\tilde{V}_{{jt}}^{D} \, + \,{{\mu }_{jt}} \cr & {\rm{Model}}\;4\;:\;X_{{jt}}^{D} \, = \,{{a}_I}{{I}_j}\, + \,{{a}_C}{{{\hat{C}}}_{jt}}\, + \,{{a}_P}\tilde{V}_{{jt}}^{D} \, + \,{{\mu }_{jt}} \cr}$$

We only analyse party D, since analogous specifications for party R would simply duplicate the results. Note that in Model 4 we use the actual value of I_j rather than the vote-based measure. This value approximates the ‘encompassing models’ in Dahlberg and Johansson,Footnote ⁵⁶ which include a survey-based measure of I, but a vote-based measure of V_D.

In each simulation, we set J = 50 and T = 100, that is, fifty states over 100 years. Note that these parameters provide much more data on the time dimension than researchers actually have. We do this to focus attention more on the bias produced by endogeneity than on measurement error bias (which also plagues the literature). In all cases, I, D and R are drawn from independent uniform distributions on [0, 1]. Also, in each simulation, I, D and R are fixed for all 100 years (all t = 1 ,…, 100). Next, we choose values for the parameters α_I, α_C, α_P, β_I and β_c. Finally, we draw μ ^D, μ ^R, ɛ ^D and ɛ ^R from independent uniform distributions. We set the standard deviations of μ ^D and μ ^R to σ_μ, and the standard deviations of ɛ ^D and ɛ ^R to σ_ɛ.

We focus on four different cases. In Cases 1 and 2 there is no partisan targeting, that is, α_P = 0. In addition, we assume there is no partisan voter response to transfers, that is, β_P = 0. The difference between the two cases is the value of σ_μ, the degree to which the distribution of transfers across states is determined by random, idiosyncratic factors. In Case 1, σ_μ = 0.2, which indicates that the idiosyncratic factors are relatively important. In Case 2, σ_μ = 0.03, so the idiosyncratic factors are less important. In Cases 3 and 4 there is partisan targeting, with α_P = 0.5. We also assume there is a partisan voter response, with β_P = 0.5. The difference between the two cases is again the value of σ_μ, with σ_μ = 0.2 in Case 3 and σ_μ = 0.03 in Case 4. Within each case, we vary the parameters α_I and α_C. We fix β_I = 1 and σ_ɛ = 0.09 throughout the simulations.

For each vector of parameters we run 10,000 simulated regressions. Table 1 presents the results in four panels, each of which corresponds to one of the fours cases. Within each case, the rows correspond to different values for α_I and α_C. The true values are reported in the first two columns. The remaining columns report the averages of the estimates of the parameters of interest for various models $ ({{\hat{\alpha }}_I},\,{{\hat{\alpha }}_C},\,{{\hat{\alpha }}_C}) $.Footnote ⁵⁷ To give an example, if we take Model 2a, the first row gives the average estimates of, respectively, α_I (0.01) and α_C (0.09) when the true values of these parameters are both equal to zero.

Table 1

Simulation Results

We observe a number of patterns. First, in most cases the average estimates of a_P are biased upward. That is, there is a strong tendency to find ‘partisan targeting’ predicted by the mandate model or machine politics model, even when it does not exist. The effect is large when idiosyncratic factors have a large impact on transfers, which is a direct result of the assumption that independent voters respond to transfers in their voting behaviour. When one party happens to spend more than the other party in a state – whether due to the exogenous factors captured in μ ^D and μ ^R, or to actual partisan targeting – then many independent voters will vote for that party, producing a spurious additional correlation between transfers and votes.

Secondly, the average estimates of a_I tend to be biased downward, but are sometimes biased upward. They can even have the wrong sign: this appears to be especially true when σ_μ is low and a_I is high. The average estimates of a_I are not even monotonic in the true value of α_I, as we can see in Models 2b and 3 of Case 3.

Also, the average estimates of a_I are often biased even when the true I_J is used (Model 4): this is because the other vote-based measures are endogenous and may be correlated with I_j. In fact, the bias on a_I can be even larger using the true I_j: this is especially the case when the true I_j is low.

Thirdly, the average estimates of a_C are sometimes biased downward and sometimes biased upward. When σ_μ is low the coefficient is generally underestimated, while if σ_μ is high then the coefficient can be biased both upwards and downwards, depending on the specification.

The difficulty in recovering the true parameters is well illustrated with Model 3, which is similar to many specifications used in the empirical literature. Here when σ_μ is high (Cases 1 and 3), the estimate of α_P is systematically and substantially upward biased. If instead σ_μ is low (Cases 2 and 4), then we obtain a much more precise estimate of α_P. This precision comes at the cost, however, of a deterioration in the estimates of α_I. In fact, there appears to be a trade-off between the consistency of $ {{\hat{\alpha }}_P} $ and the consistency of $ {{\hat{\alpha }}_I} $. The intuition is straightforward. As noted above, a large degree of random variation in the allocation of spending induces more support to be directed at partisans simply by voters’ reaction to the spending. Many Independents therefore act as if they are partisans, generating a spurious positive correlation between observed votes and observed spending. At the same time, however, a more random allocation of funds facilitates the identification of the electoral response to spending. Since independent voters respond to spending, random variations in the allocation of funds will produce large fluctuations in their voting behaviour. Therefore the standard deviation of the vote is a relatively good measure of the proportion of independent voters. In fact, this means that we encounter a type of contradiction: the swing voter hypothesis is testable using voting data to measure the number of swing voters only insofar as it is false; that is, only insofar as funds are randomly allocated rather than targeted to independent voters.

The Data

We analyse US federal budget allocation to states from 1978 to 2002. We consider three dependent variables: (1) total federal spending per capita, (2) total spending other than direct transfers to individuals per capita and (3) federal grants per capita. The second variable should allow us to isolate the most manipulable items in the budget, since it removes the largest of the ‘non-discretionary’ or ‘entitlement’ programs, such as Social Security, Medicare, pensions for public officials, AFDC (TANF), etc.Footnote ⁵⁸ The third variable is arguably the most targetable; while it is much smaller than the first or second, it still constitutes an important part of state finances. In all cases, our dependent variables are outlays.

It is important to note that there is a lag between the appropriation and spending of federal funds. This is relevant when estimating the effect of particular institutional and political variables, since current federal outlays have normally been appropriated in previous calendar years. For this reason, we will always consider lagged values of the political explanatory variables.

As noted above, one of the main independent variables of interest is the percentage of swing voters in a state. We use poll data to measure the share of ‘Independents’ (and also the share of Democrats and Republicans). This data comes from exit polls conducted by various news organizations – CBS News, CBS News/New York Times, ABC News, ABC News/Washington Post and Voter News Service.Footnote ⁵⁹ Voters are surveyed briefly after leaving the polling booth, and asked how they voted. They are also asked to provide their party identification (Democrat, Republican, other or Independent) and their ideological leaning (liberal, conservative, moderate or don't know).Footnote ⁶⁰ Importantly, these questions are designed to tap into voters’ general self-identification, rather than how they have just voted. Two typical forms of the party identification question are: ‘Regardless of how you voted today, do you normally think of yourself as a [Democrat], [Republican], [Independent], [Something Else]?’; and ‘Do you normally think of yourself as a [Democrat], [Republican], [Independent]?’ Two common forms of the ideology question are: ‘On most political matters, do you consider yourself [liberal], [moderate], [conservative]?’; and ‘Regardless of the party you may favor, do you lean more toward the liberal side or the conservative side politically [liberal], [conservative], [somewhere in between]?’Footnote ⁶¹

Using this information, we can construct state-level variables that report the percentage of voters that declare themselves Democratic, Republican or Independent. Due to the relatively small number of respondents in some states in some years, we aggregate the results over four-year periods (two elections). We also drop any cases with fewer than 100 respondents. This approach yields a sample size of 1,174 state-years for our analysis of total spending and grant spending, which is 2.1 per cent smaller than the maximum possible amount. In the resulting sample, the average number of respondents per state-period is about 3,700 and the median is about 3,300. Almost 87 per cent of the cases have more than 1,000 respondents, and only 1 per cent of the cases have fewer than 250. We assess the reliability of these variables with respect to exogeneity and measurement error problems in Section 4.1 below.

Endogeneity and Measurement Error in Survey Data

One concern is how well survey data can capture the distribution of partisanship within states. This issue is discussed extensively in Erikson et al.Footnote ⁶² They conclude that the partisanship measures derived from surveys correlate in the expected way with other observable measures, including other polls, election returns and party registration. We present some of our own checks below, and the results make us confident that these data capture the underlying distribution of partisanship by state relatively well and that they are preferable to using simple voting results.

Figure 1 plots the share of Democratic vote by state (averaged across all years) by the share of Democratic partisans in the survey data. Figure 2 does the same for Republicans. There is a clear positive correlation between votes and partisanship, especially for the Republican party. Although our purpose is to go beyond what can be captured by voting data, the correlation between the exit poll measures and observed votes is reassuring and suggests that our measure is a reliable indicator of partisanship. Of course, actual votes also include non-partisans, and final election results are crucially affected by the leaning, in a particular election, of independent voters. Hence, Figure 3 reports the aggregate Democratic share of votes at presidential elections and the share of Democratic supporters from exit polls: it clearly shows that partisanship is much more stable than what electoral results would suggest, and that using voting to measure partisanship can therefore be problematic.Footnote ⁶³ In Figure 4 we report the standard deviation (over the period we consider) of presidential Democratic votes by state and compare with the standard deviation of party identification: again, this figure suggests that partisanship is much less volatile than voting. Hence, the exit poll data confirm the stable pattern of party identity variables found by other studies and support the notion that party identity is a long-term stable personal characteristic as opposed to the variable pattern of voting data.Footnote ⁶⁴

Fig. 1

Democratic Vote Share and Partisanship by State

Fig. 2

Republican Vote Share and Partisanship by State

Fig. 3

Aggregate Democratic Vote and Partisanship over Time

Fig. 4

Standard Deviation of Democratic Vote and Partisanship by State Note: In this graph the states are listed in alphabetical order. The state code placement corresponds to the standard deviation of the Democratic vote in the state. The corresponding triangles represent the standard deviation of Democratic partisanship.

Although party identification is more stable over time than vote choice, it is not perfectly stable. It is likely that some of the observed instability represents real changes in respondents’ partisan loyalties. Some of the instability, however, might indicate unreliability in the measure, which is known to be a significant problem in surveys.Footnote ⁶⁵ For example, a desire to avoid cognitive dissonance could induce voters to align their party identification response to the party for which they voted most recently.Footnote ⁶⁶ For such voters, party identification is equivalent to voting data, and therefore equally endogenous. Table 2 provides some information about the possible magnitude of the problem in the exit poll data. The figures in the table show that, although there is a substantial overlap between party identification and reported voting choices, almost one in four voters declares himself/herself an Independent in spite of having voted for one of the two major parties. More than 28 per cent of respondents who reported having voted Democratic do not report a Democratic party identification. The percentage is 36 per cent for those voting Republican. Importantly, an overwhelming majority of self-declared Independents votes for one of the two major parties rather than for a third party or an Independent candidate. When we aggregate party identification at the state level, we again find an overall positive correlation between voting results and party identification in the states. This correlation is 0.31 for the Democratic party and 0.53 for the Republican party. Although the correlations between voting choices and party identification are positive, they are hardly overwhelming. The ‘slack’ indicates that party identification is not simply another measure of vote choice. Of course, we cannot entirely rule out cases of positive dissonance and other survey-related problems. It is indeed reasonable to presume that survey responses are not, for various reasons, entirely accurate. Our claim is more modest: using party identification constitutes a movement in the right direction, and therefore improves on existing studies.

Table 2

Cross tabulation of party ID and presidential voting decisions (percentage)

Examining split-ticket voting for different groups of identifiers helps us assess the degree to which party identification captures the relative degree of ‘independence’ in the vote choices of self-identified Independents and partisans. Data from the American National Election Study are revealing. A ticket splitter is defined as a respondent who voted for at least one Democrat and at least one Republican, in the elections for president, US House and US Senate held during the year of the survey.Footnote ⁶⁷ For those respondents who voted in at least two races during the year of the survey, 42 per cent of pure Independents, 32 per cent of leaning Independents and 22 per cent of self-identified Democrats and Republicans were ticket splitters. These figures are similar to those in exit polls. In the exit poll data we have respondents’ vote choices for president, US House, US Senate and governor. For those respondents who voted in at least two races, 33 per cent of Independents (including leaners) and 22 per cent of partisans split their tickets in some fashion. Panel data allows us to use the initial party identification for each respondent and thereby mitigate the possibility of reverse causation (ticket splitters who self-identify as Independents, and those casting straight tickets who self-identify as partisans). We examine the 1992–1994–1996 elections, using party identification in 1992.Footnote ⁶⁸ For those respondents who cast votes in at least four of the six or seven possible races, 75 per cent of pure Independents, 52 per cent of leaning Independents and 44 per cent of partisans exhibited at least one instance of a split. Thus, while party identification is not a perfect measure, it is quite stable over time and captures ‘independence’ in voting to a considerable extent.

Effects of Government Expenditures on Voting

Our previous results cast some doubt on the idea that voters are responsive to the receipt of federal funds. In fact, one of the premises of the swing voter model is that politicians can buy votes by allocating spending to certain groups: swing voters are then simply cheaper to buy, given their lack of unconditional attachment to a given party. Hence, in this section we turn to the other side of the coin and ask whether voters do in fact respond to favourable spending by rewarding incumbent politicians.

The relationship between spending and vote depends on how rational voters use their ballot to provide incentives to politicians. If voters are retrospective, they reward politicians for their past performance – that is, they are more likely to vote for an incumbent if they received more federal transfers when he or she was in power. On the other hand, if voters are prospective, then campaign promises should be the main driver of voting patterns.Footnote ⁷⁹ The use of individual-level data can advance our understanding of voting behaviour in response to governmental transfers. Some recent studiesFootnote ⁸⁰ examine individual-level survey data on ‘expressed political support’ and ‘vote intentions’, and find that beneficiaries of targeted transfers declare an increased political support or propensity to vote for the government implementing them, thus providing indirect evidence of retrospective voting behaviour. On the other hand, Elinder et al.Footnote ⁸¹ use survey data on individual voting from the Swedish Election Studies, and find that voters respond to promises rather than to implemented policies, which suggests that prospective voting is important.

In our work, we estimate the impact of federal spending on individual voting decisions using voting records from exit polls, which have the desirable feature of collecting information from actual voters when they exit the polling station. Our data also allow us to control for partisanship and ideology, which to a large extent mitigates possible endogeneity problems for the spending variable. On the other hand, since we have information on federal budget allocations to the states (outlays) but not on spending proposals, we can check whether voters respond to received transfers (that is, if they behave retrospectively), but not whether they react to promises (that is, if they are prospective).

We analyse voting decisions in presidential, gubernatorial, Senate and House elections. In the first three cases, the swing voter model would posit that incumbents are rewarded for voters’ receipt of federal funds, and therefore the dependent variable is a dummy equal to 1 if the voter chooses the incumbent (or a candidate from the incumbent's party). In the case of the House, we cannot predict how the funds flowing to a state should affect voting for particular incumbents, since many states have House incumbents from different parties running simultaneously. Moreover, we only know the state of each voter, not his/her district. Thus, in this case the dependent variable is a dummy equal to 1 if a vote is cast for a Democratic candidate, and the explanatory variable of interest is an interaction term between the amount received and the share of Democratic representatives from the state.Footnote ⁸²

Table 4 reports our estimations when a state's total federal expenditure is used as the explanatory variable. It is clear that the fact that a state receives more federal funds does not induce its citizens to cast more votes in favour of incumbents. The coefficient of total federal expenditure can even be negative, and never reaches a 5 per cent significance level, in spite of the very large number of observations. On the contrary, partisanship and ideology have large effects. These results are consistent with BartelsFootnote ⁸³ and others, who find that partisanship has a large impact on voting at both the presidential and congressional level.

Table 4

Effects of Spending on Voting Decisions

Note: The table reports probit coefficients. All regressions include a constant, year dummies, state fixed effects and the following control variables: income per capita, percentage of the population that is school age, percentage of the population over 65, total population, unemployment rate and dummy equal to 1 for unit-periods in which a natural disaster occurred. The House regressions also include dummies for Democratic partisanship, Republican partisanship, liberal ideology and conservative ideology. Partisan match is a dummy equal to 1 if the voter has the same partisanship as the incumbent politician. Ideology match is a dummy equal to 1 if the voter is liberal and the incumbent politician is a Democrat, or if the voter is conservative and the incumbent is Republican. Robust z-statistics in parentheses (clustered by state). * significant at 10%; ** significant at 5%; *** significant at 1%.

When we use targetable spending, our results do not show substantial variations, with the exception of a positive coefficient on the probability of voting for an incumbent governor. Even in this case, however, the significance level (10 per cent) appears rather weak for a sample of this size. For presidential elections we again encounter a negative coefficient, although it is only significant at the 10 per cent level. Grants are totally insignificant in the president, governor and senator equations. Instead, they appear to have a positive impact on the probability of voting for a Democrat in Congress when the majority of state representatives in Congress are Democrats. This is the only coefficient that is significant at the 5 per cent level. Although this might be explained by the inability to identify voters’ districts,Footnote ⁸⁴ it is also consistent with related findings by other studies.Footnote ⁸⁵

Overall, the evidence that receiving more federal funds induces voters to reward incumbent politicians is rather weak.Footnote ⁸⁶ One possible objection to this conclusion is that, according to swing voter models, both candidates converge on the same platform in equilibrium; hence in equilibrium, we should expect no effect, but this does not imply that voters would not react to spending proposals. The idea that electoral competition induces platform convergence appears, in reality, to contradict the historical evidence. The two major American parties have often proposed very different platforms on spending, as well as on other matters.Footnote ⁸⁷ Although identifying causal relationships is not straightforward, there appears to be a clear correlation between platform proposals and implemented policies, which is consistent with the ‘mandate’ model.Footnote ⁸⁸ In addition, numerous studies of taxation, spending and macroeconomic policies find clear correlations between the partisan composition of Congress and policy outcomes, which are consistent with a model of policy divergence.Footnote ⁸⁹ At the district level, the situation does not appear much different: individual candidates for the House have also been shown to systematically assume divergent positions.Footnote ⁹⁰ In addition, Poole and Rosenthal and Lee et al. document stark differences in the roll call voting positions of Democrat and Republican representatives elected from districts with very similar partisan balances.Footnote ⁹¹

Another possible explanation for our findings is that although parties (or candidates) do not converge, our estimates nonetheless capture equilibrium behaviour that masks structural coefficients. For example, if candidates typically manage to meet voters’ expectations (or fulfill their campaign promises) regarding spending, then we may find little correlation in the data because we do not observe ‘out-of-equilibrium’ behaviour. Since rational prospective voters reward politicians based on the expectation that they will be faithful to their election pledges, we would only observe a reaction of voters to past policy if promises are not kept. Such a reaction is unlikely whenever a large share of campaign pledges is enacted, as is the case for the United States.Footnote ⁹² Thus, while our findings are consistent with the hypothesis that voters rarely respond with their votes to public spending in a clear and systematic way, further research is clearly needed to rule out other possibilities.