State Congressional Delegations and the Distribution of Federal Funds

Abstract

Most scholarship on U.S. distributive politics either focuses on the abilities of individual representatives and senators to bring home the bacon or highlights the role the president plays in influencing funding decisions. Little attention is paid to collective efforts in Congress involved in securing grants-in-aid to states. In this paper, we assess how characteristics of House and Senate state delegations affect the collective efforts of a state’s federal officeholders to secure statewide funds. In both the House and Senate, we find that partisan cohesion in a state delegation predicts more federal funds to states. In particular, states receive more funds when larger shares of their delegations are members of a chamber’s majority party. Moreover, we find that the importance of majority party status is increasing over time. These results have important implications for the U.S. federal system and distributive politics.

Keywords

distributive politics congress congressional delegations majority parties federal funds

Introduction

Most scholarship on U.S. distributive politics either focuses on the abilities of individual representatives and senators to bring home the bacon (Alvarez and Saving 1997; Bickers and Stein 1996; Evans 2004) or highlights the role the president plays in influencing funding decisions (Berry, Burden, and Howell 2010; Dynes and Huber 2015; Kriner and Reeves 2012). Little attention is paid to collective efforts in Congress involved in securing grants-in-aid to states (though, see, Lee 2000, 2003; Lee and Oppenheimer 1999). While some federal aid is directed to specific congressional districts (i.e., to individuals or organizations within them), other federal dollars are distributed directly to state governmental entities for use, including discretionary dollars for transportation (including grants for highways, airports, and mass transit), education (including funds to help low-income children and special-needs students), low-income housing, agriculture, health care, homeland security, and more. State delegations of lawmakers in the House and Senate, seeking to secure these forms of aid, need to work collectively to accrue as much as they can for their states.

The collective nature of these efforts, largely unexplored in scholarship on distributive politics, prompt a number of important questions. Here, we focus on the characteristics of both House and Senate state congressional delegations. We ask, Do the partisan dynamics of state congressional delegations affect their abilities to secure these grants-in-aid?

To investigate these questions, we compile Federal Assistance Award Data System (FAADS) data on federal non-formula grants-in-aid dollars distributed directly to state governments or state governmental entities from fiscal years 1984 to 2010. Rather than being distributed to entities or localities in a specific part of a state, these federal dollars are paid out to statewide agencies, and, thus, can have statewide impact. These are dollars that every member of a House or Senate delegation has some interest in securing for credit-claiming and other purposes (Cain, Ferejohn, and Fiorina 1987; Grimmer, Westwood, and Messing 2015). Thus, these data allow us to assess how the dynamics of state congressional delegations affect the distribution of billions of federal dollars across a variety of issue areas including health care, education, transportation, and agriculture.

We find that in both the House and Senate, state delegations with more members in the same party, especially the majority party, receive larger amounts of annual grants-in-aid. Moreover, looking at changes in allocations over time, we find that the share of a delegation in the majority party has become a more powerful predictor since the 1980s, while the share of a delegation in the president’s party has become a less powerful predictor.

In what follows, we lay out competing expectations regarding how characteristics of state congressional delegations may influence the distribution of federal funds, describe our approach to analyzing this phenomenon, present the results of our analyses, and discuss and interpret the findings. All told, our findings indicate that constitutional decision to apportion members of Congress by state alongside the federal system has lasting and meaningful consequences for distributive politics, especially as it interacts with the two-party system.

State Delegations and Distributive Politics

Existing scholarship provides mixed expectations about what kinds of delegation characteristics might translate into more or fewer federal dollars. Different streams of scholarship suggest we might find more dollars are accrued by (1) more bipartisan delegations, (2) more partisan delegations, (3) delegations with more majority party members, (4) delegations with more members in the president’s party, and (5) delegations with less two-party competitive electoral environments.

Bipartisan Delegations

The most common explanation for why bipartisan (or, split) delegations may be particularly effective in Washington is that such delegations bring members of both parties together to support funding for the state. This is an oft-repeated reasoning among those in Washington. For instance, Senator John Ensign (R-NV) described his relationship with Senator Harry Reid (D-NV) in these terms: “He works his side of the aisle and I work mine. Because of that working relationship and trust, we are able to get things done that frankly, members of the same party in some states can’t get done.”¹ Senator Pat Toomey (R-PA) offered a similar analysis: “If Senator Casey [D-PA] and I can agree on policy, and it’s good for Pennsylvania, he can work that issue on his side in a way that I can’t, and I can work it on my side in a way he can’t.”²

Ensign and Toomey are far from the only politicians or political observers to suggest such an advantage for bipartisan delegations. Bipartisan senatorial pairs such as Tom Harkin (D-IA) and Chuck Grassley (R-IA), Pete Domenici (R-NM) and Jeff Bingaman (D-NM), Joe Biden (D-DE) and William Roth (R-DE), and Strom Thurmond (R-SC) and Ernest Hollings (D-SC) have been often viewed as exemplars of the abilities of split delegation to work across the aisle. While all of these are senatorial examples, the logic should extend to the other side of the Capitol. House delegations with members able to work both sides of the aisle may be able to promote their states’ interests in ways one-party delegations cannot.

The separation of powers and bicameral features of the constitutional lawmaking process lend credence to these expectations as well. The various veto points in the legislative process ensure that most successful lawmaking actions require bipartisan agreement (Curry and Lee 2019, 2020; Krehbiel 1998; Mayhew 2005). States represented by bipartisan delegations have representatives and senators who can lobby party leaders and committee chairs on their respective sides of the aisle. Moreover, when both Democrats and Republicans represent a state, leaders in both parties have a stake in seeing to it that the state receives federal funds for their party members to claim credit. After all, scholarship finds a link between federal funds, the credit-claiming actions of lawmakers, and reelection advantages (Bickers and Stein 1996; Grimmer, Westwood, and Messing 2015; Lazarus 2009b; Levitt and Snyder 1997; Stein and Bickers 1994).

Partisan Delegations

At the same time, there are reasons to suspect delegations composed of all or mostly members of the same party may have a leg up instead. One view of parties is that they are established and function, in part, to help solve collective policymaking dilemmas (Aldrich 1995). Parties, by forming long-term and stable coalitions, can help legislators work together to secure better outcomes for the group than might otherwise be the case. There are also strong political incentives for members of the same party to work together, and these same incentives may stoke conflict among members of opposite parties. As Lee (2009) finds, members of congressional parties have collective electoral and power incentives to act as partisan teams to bolster their party’s image and success. Doing so can improve the party’s standing for the next election, help it obtain or keep majority party status, or help it hold together to shape public policy outcomes.

These partisan political incentives may not only make it easier for co-partisans in a state congressional delegation to work together, but may make it harder for delegation members from opposite parties to collaborate. Incentives for the parties to differentiate themselves may limit or undermine opportunity for cross-party collaboration, even within distributive politics, because cooperation can take an issue off the table for the next election (Gilmour 1995; Lee 2016). Especially in today’s hyper-partisan Congress (Barber and McCarty 2015; Theriault 2008) with power centralized in party leaders (Curry 2015; Hanson 2014; Sinclair 2016), more partisan congressional delegations may be advantaged relative to more split delegations.

Majority Party Delegations

There are different reasons to suspect delegations composed primarily, or entirely, of members of the majority party might be best situated to drive federal funds to their states. After all, there is no shortage of scholarship noting the advantages of majority party status in the House and Senate (Cox and Magar 1999; Cox and McCubbins 2005; Den Hartog and Monroe 2011), including over the distribution of federal funds (Balla et al. 2002; Carsey and Rundquist 1999; Engstrom and Vanberg 2010; Lazarus 2009b; Levitt and Snyder 1995).

In a contemporary Congress driven by partisanship and party conflict, majority-heavy delegations may be advantaged. Indeed, there is clear evidence that party power has increased in recent years in both the House (Aldrich and Rohde 2000) and Senate (Den Hartog and Monroe 2011; Wallner 2013), including within spending policies (Hanson 2014). Efforts to advance party goals via party power may advantage majority-heavy delegations over split-party or minority-heavy delegations. Majority parties may see the distribution of federal funds as a way to provide their members with ways to credit claim and bolster their reelection hopes in the next election.

Presidential Delegations

There are also reasons to suspect affiliation with the president’s party is the key to securing federal funds. Recent scholarship emphasizes the role the president plays in the distribution of federal dollars. Berry, Burden, and Howell (2010) find that the president is able to employ ex post and ex ante influence over federal spending decisions to direct spending to areas represented by co-partisan lawmakers. Kriner and Reeves (2012) and Dynes and Huber (2015) find that presidents direct dollars to politically important and like-minded constituents. Bertelli and Grose (2009) find that executive branch agencies direct money toward states represented by ideologically aligned senators.

None of these studies focuses on the decision-making of legislators, or the efforts of representatives and senators to influence executive branch decisions, but the lessons may apply. Congressional delegations with more members in the president’s party may be best able to secure more federal funds for a number of reasons. These include stronger personal relationships with the president,³ an improved ability to lobby officials in the executive branch and the president’s cabinet,⁴ or the ability of the president’s party in Congress to leverage the president’s veto during the legislative process.

Less Competitive Delegations

Finally, it may be that the partisan electoral dynamics in a state may be of importance. Specifically, state delegations facing more competitive two-party electoral environments may have a harder time working collaboratively to secure federal funds for their states. As Lee (2016) details, more intense two-party competition undermines efforts at bipartisanship among legislators, intensifying partisan positioning and party conflict inside Congress, and this effect is also felt in the states (Hinchliffe and Lee 2016). Delegations that face more muted two-party electoral environments in their state may find party politics less likely to get in the way of collaboration, and may be able to work together effectively to secure funds.

At the same time, stiff electoral competition may also serve as a motivator for members of Congress and senators to try to secure funds. Because federal dollars can translate into credit-claiming opportunities, representatives and senators who feel less secure in office may do more to try to secure more funds to talk about on the campaign trail. It is possible that this electoral motivator helps cross-partisans in a state delegation overcome competitive partisan dynamics and secure more federal dollars rather than less.

Assessing the Effects of Delegation Characteristics

To assess these varying expectations about the characteristics of states’ congressional delegations and federal dollars, we look at data on federal aid to each state during fiscal years (FY) 1984–2010. As with prior research, we rely on the FAADS data as originally collected by Bickers and Stein (1991) and extended by Dynes and Huber (2015). These data report nearly all federal transfers to domestic recipients, with the exception of some defense and federal procurement expenditures and tax expenditures.

For our purposes, we focus on a specific subset of these dollars. Because we are interested in federal money that state congressional delegations have incentives to work collectively to obtain, we focus on non-formula grants-in-aid to which a state government or statewide governmental entity is the recipient. This approach excludes dollars that go to specific individuals or organizations that might reside or work within specific corners of a state and, thus, only benefit individual congressional districts. Such dollars will force individual members to work independently, and perhaps against each other, to secure those funds (Lee 2003). It also excludes formula grant dollars that are distributed via formulas set in authorizing language and that persist over the course of years. For instance, Medicaid and Temporary Assistance for Needy Families (TANF) dollars are distributed based on formulas only periodically updated by Congress and which are primarily impacted by the health and well-being of a state’s residents.

What remains are distributive dollars that members of Congress and senators can impact on a continual basis and that are provided directly to state governmental entities, and thus benefit the state as a whole. These are dollars that congressional delegations have incentives to work together to try to accrue for their states, as they can benefit the entire state and provide credit-claiming opportunities to every representative and senator in the delegation. They represent a substantial amount of money as well: more than $84 billion, annually (in 2010 constant dollars). Moreover, they include prominent federal programs that have large and meaningful consequences for citizens, such as various Medicare programs including hospital insurance, Highway Planning and Construction grants, various Department of Homeland Security programs, various K-12 education grants, and more.

Representatives and senators can affect the distribution of these dollars in a number of ways. Every year, Congress can influence the amount of money available for many of these programs through appropriations bills. Representatives and senators from a state can try to plus-up funding for programs that might be particularly beneficial to their state. For instance, legislators from more rural states may work to increase funding for agricultural grants, while those from states with more transportation infrastructure may work to increase funding to various programs and grants within the Department of Transportation. Legislative earmarks, which were in place during the period we study, likewise provided opportunities for members to get money funneled directly to their states, rather than other states (Engstrom and Vanberg 2010; Grimmer, Westwood, and Messing 2015; Lazarus 2009a; Rocca and Gordon 2012). In addition, representatives and senators can try to shape federal spending decisions through back-channel interactions with executive branch agencies, perhaps pushing these agencies to fund grants in their states rather than in others (Ritchie 2018; Ritchie and You 2019).

In our analyses, the dependent variables are the per capita amounts of these federal dollars distributed to each state during each fiscal year from 1984 to 2010 (in 2010 constant dollars). Using time-series cross-sectional (TSCS) analyses, we first look at the distribution of federal dollars described above over the full time period. We then conduct analyses to look at changes in the distribution of funds over time. Combined, these analyses allow us to assess, first, the overall effects of different delegation characteristics and, second, any changes in these effects between 1984 and 2010.

Our primary independent variables measure different characteristics of each state’s House and Senate delegations in each year, and match up with the five characteristics discussed above. These characteristics are measured for each calendar year from 1983 to 2009. In the analyses below, we assess the impact of these delegation characteristics on both federal spending in the upcoming fiscal year (i.e., the impact of 2001 delegation characteristics on federal spending during fiscal year 2002) and, for a subset of years, the impact of these delegation characteristics on spending during the present fiscal year (i.e., the impact of 2001 delegation characteristics on federal spending during fiscal year 2001). We take this approach, because as discussed above, Congress and congressional delegations can shape the distribution of federal funds through the legislative process, and through lobbying and pressure applied to federal agencies (Ritchie 2018; Ritchie and You 2019). The former of these activities affects spending in the subsequent fiscal year, whereas the latter of these activities affects spending during the present fiscal year.

Specifically, for each year, we measure

The degree to which members of each state’s House and Senate delegations are members of the same party in each year, regardless of which party. For House delegations, this is measured as the Percent same party (ranging from 0.5 to 1); and for Senate delegation, this is measured as the dichotomous Same party (either 0 or 1). These variables can assess if having a more bipartisan or partisan delegation results in more or fewer federal dollars.

The degree to which members of each state’s House and Senate delegations are members of the majority party each year in each chamber. For House delegations, this is measured as the Percent in majority (ranging from 0 to 1); and for Senate delegation, this is measured as the Number in majority (either 0, 1, or 2).

The degree to which members of each state’s House and Senate delegations are members of the president’s party each year. For House delegations, this is measured as the Percent in president’s party (ranging from 0 to 1); and for Senate delegation, this is measured as the Number in president’s party (either 0, 1, or 2).

The general election dynamics of each House and Senate delegation across time. Specifically, we measure how competitive two-party politics is in each state. We do this for House delegations with Average electoral margin, measured as the average two-party vote margin of victory for House candidates in each state (ranging from 0 to 50%).⁵ For the Senate, we measure the two-party vote margin of victory won by each senator in each state in their previous election: Senior senator two-party margin and Junior senator two-party margin (both ranging from 0 to 50%).⁶ For all three, lower values indicate more competitive two-party electoral environments.

We also control for various factors that might influence the money distributed to each state in each year. Some of these variables measure other aspects of a state’s House and Senate delegations that are likely to be important. These include,

House—delegation size: the number of representatives in a state’s House delegation. Larger delegations may have a harder time coordinating to secure federal dollars. This variable also serves as a proxy for the state’s population.

House—majority leadership: a dichotomous indicator of whether or not a state’s House delegation has a member in the majority party leadership in each year. These include the Speaker, majority leader, and majority whip.

House—minority leadership: a dichotomous indicator of whethor or not a state’s House delegation has a member in the minority party leadership in each year. These include the minority leader and minority whip.

House—mean LES: the average legislative effectiveness score (LES) (Volden and Wiseman 2014) held by members of each state’s House delegation in each year.

House—mean seniority: the average chamber seniority held by members of each state’s House delegation in each year.

House—# on power committees: the number of members in each state’s House delegation in each year sitting on a “power” committee.⁷

Senate—majority leadership: a dichotomous indicator of whether or not a state has a senator in the majority party leadership during each year. These include the majority leader and majority whip.

Senate—minority leadership: a dichotomous indicator of whether or not a state has a senator in the minority party leadership during each year. These include the minority leader and minority whip.

Senate—mean LES: the average LES held by the members of each state’s Senate delegation in each year.

Senate—mean seniority: the average chamber seniority held by members of each state’s Senate delegation in each year.

Senate—# on power committees: the number of members in each state’s Senate delegation in each year sitting on a “power” committee.⁸

The analyses also include a variable meant to capture the strategic importance of each state to the president in each year. Presidents use their power over federal spending to direct funds to electorally important constituents, including those in battleground states (Kriner and Reeves 2012) and with more like-minded supporters (Dynes and Huber 2015). To these ends, we include two measures: Presidential margin, the two-party popular vote margin of victory for the winning candidate in each state in the previous presidential election; and President favorable, the share of the two-party popular vote for the president in the previous election.⁹

We also include variables that measure aspects of the state and its population that may affect federal grants-in-aid. These variables are measured in the calendar year that overlaps the most with each FY. In other words, for FY 2010, these variables are measured in calendar year 2010.¹⁰

State median income (2015 constant dollars) provides a rough gauge of the relative prosperity of each state. All else equal, poorer states are likely to need more federal funds.

State % non-white is a measure of the percent of each state’s population that is non-white. Lawmakers may not be equally responsive to the demands of different racial constituencies (Broockman 2013; Butler and Broockman 2011), and the racial composition of constituencies may also affect the distribution of federal dollars (Grose 2011).

State population (in thousands) provides a measure of state size. This variable is only included in analyses of Senate delegation characteristics because it is strongly correlated with the House delegation size variable included in the analyses of House delegation characteristics. Lee and Oppenheimer (1999) find that states with smaller populations tend to do better than larger population states in distributive policymaking.

Unless otherwise noted, each analysis also includes fixed effects for each state and each year. These effects ensure that our models are taking into account state- and year-specific factors affecting the distribution of funds not captured by our other covariates. In some models, we also included a lagged dependent variable to account for the fact that the amount of funds a state receives in one year likely influences how much it receives in the next year.

Results

The results of our analyses are presented below. First, we present the results of bivariate analyses. Second, we present the results of several multivariate analyses.

Bivariate Analyses

Figure 1 presents bivariate relationships between our House delegation characteristic variables and the overall per capita non-formula federal grants-in-aid to the states, FY 1984–2010 (in 2010 constant dollars). The dashed line included in each plot is a locally weighted scatterplot smoothing (LOWESS) trend line. Overall, the results show some evidence of a relationship between state delegation characteristics in the House of Representatives and the amount of federal funds transferred to the states. The top-left plot shows there is a slight decrease in the amount transferred to states as the percent of a House delegation within one party increases. This bivariate relationship is statistically significant¹¹ and substantively meaningful as well: on average, a state of median population size (which would be allotted six U.S. House seats) is expected to receive roughly $1.4 billion fewer in federal funds as their House state delegation members changed from split evenly between the parties, to 100 percent in one party.

Figure 1.

House delegation characteristics and per capita non-formula grants-in-aid to the states (2010 constant dollars), FY 1984–2010.

The bottom-right plot shows a similar relationship between the average two-party margin of victory among members of a House delegation and the amount of federal funds transferred to a state. The bivariate relationship is statistically significant¹² and substantively large: an increase from the minimum to maximum value of the average two-party margin variable yields a median population state an additional $1.5 billion in the federal dollars we analyzed.

The relationships are less clear in the other plots, which show some evidence of nonlinearity. For both the percent of a House delegation in the majority party, and the percent of a House delegation in the president’s party, states situated at the highest and lowest ends of each variable do better than those situated in the middle. In other words, states represented by House delegations that are mostly in the majority or minority party, and mostly affiliated or not affiliated with the president’s party, secure more federal dollars than those more evenly split.

Figure 2 presents similar bivariate plots for our Senate delegation characteristics variables. As with the House, the results show some evidence of a relationship between state delegation characteristics in the Senate and the amount of federal funds transferred to the states. The top-left panel shows that states with unified senate delegations (wherein both senators are of the same party) receive slightly more of the federal dollars we analyzed than those with split delegations. This relationship runs counter to the pattern we observed with House delegations. The difference is statistically significant¹³ but the size of relationship is smaller than with House delegations: a state of median population size receives, on average, $192 million more, annually, when it has a unified senate delegation rather than a split delegation.

Figure 2.

Senate delegation characteristics and per capita non-formula grants-in-aid to the states (2010 constant dollars), FY 1984–2010.

Electoral dynamics also seem to matter, as shown in the lower plots. States seem to get more funds as the margin of victory for both their senior and junior senators increases. In other words, less competitive senate elections translate into more of these federal funds. Both of these relationships are statistically significant.¹⁴ Both are substantively meaningful, as well: a state of median population size should expect to receive an additional $1 billion as the senior senator’s electoral margin increases from its minimum to maximum, and an additional $600 million as the junior senator’s margin similarly increases.

As with the House, the remaining plots show less clear relationships, and some evidence of nonlinearity. As with House delegations, the number of a state’s senators in the majority party, or in the president’s party, appears to predict more federal funds at the highest and lowest values, and fewer funds for delegations split along these lines.

Altogether, the bivariate analyses show some significant relationships between the characteristics of state congressional delegations and the distribution of federal funds, but the nature of those relationships is not entirely clear. For instance, the partisan homogeneity of delegations (that is, the percent within just one party) relates to more federal dollars with Senate delegation characteristics, but fewer dollars with House delegation characteristics. Other variables, such as the percent of delegations affiliated with the majority party or the president’s party, show some evidence of nonlinear relationships. The multivariate analyses are aimed at refining our understanding of these relationships.

Multivariate Analyses

Table 1 presents the first set of multivariate analyses. Each analysis is a TSCS linear regression predicting the total amount of per capita non-formula federal grants-in-aid to each state in each fiscal year (in 2010 constant dollars). These analyses assess the effects of delegation characteristics in a year on the federal distributions of funds in the subsequent fiscal year (i.e., the effect of delegation characteristics in 1990 on spending in FY 1991). As noted above, state and year fixed effects are included in each model. Separate analyses were conducted to estimate the effects of House (columns 1 and 2) and Senate (columns 3 and 4) delegation characteristics. Each analysis is presented without (columns 1 and 3) and with (columns 2 and 4) the inclusion of a lagged dependent variable as an additional covariate. In these latter models, because we only have data for FY 1984–2010, the inclusion of the lagged dependent variable results in the loss of one year of cases from the analyses. A number of other model specifications were tried. These are included in the online supplemental appendix to this article, and are referenced at times in the text. None of these additional specifications substantively altered the findings presented here.

Table 1.

Predicting Federal Non-formula Grants-in-Aid Per Capita to States, FY 1984–2010.

	(1)	(2)	(3)	(4)
House—% same party	72.83*	72.67*
House—% same party	(43.45)	(43.17)
House—% majority party	40.79**	31.33*
House—% majority party	(19.09)	(18.68)
House—% president’s party	17.06	0.898
House—% president’s party	(17.22)	(16.82)
House—avg. electoral margin	1.549	1.618
House—avg. electoral margin	(0.997)	(0.994)
Senate—same party			−11.04	−9.417
Senate—same party			(11.09)	(11.07)
Senate—# in majority party			14.69**	12.00*
Senate—# in majority party			(6.703)	(6.620)
Senate—# in president’s party			−13.10**	−14.45**
Senate—# in president’s party			(6.016)	(5.916)
Senate—senior senator margin			−1.723**	−1.228**
Senate—senior senator margin			(0.549)	(0.550)
Senate—junior senator margin			−0.419	−0.0750
Senate—junior senator margin			(0.579)	(0.576)
House—delegation size	−15.34**	−13.58**
House—delegation size	(5.397)	(5.338)
House—majority leadership	−5.392	−10.55
House—majority leadership	(13.88)	(13.79)
House—minority leadership	16.57	11.87
House—minority leadership	(13.03)	(12.79)
House—mean LES	6.287	3.123
House—mean LES	(5.295)	(5.184)
House—mean seniority	−8.831**	−6.030*
House—mean seniority	(3.386)	(3.349)
House—# on power committees	12.43**	13.15**
House—# on power committees	(6.053)	(5.976)
Senate—majority leadership			−112.6**	−85.18**
Senate—majority leadership			(37.78)	(38.44)
Senate—minority leadership			9.993	7.716
Senate—minority leadership			(74.96)	(73.30)
Senate—mean LES			19.26**	10.10
Senate—mean LES			(8.702)	(8.725)
Senate—mean seniority			−0.124	−0.139
Senate—mean seniority			(1.025)	(1.016)
Senate—# on power committees			−7.667	−9.164
Senate—# on power committees			(9.434)	(9.410)
State population (in thousands)			0.00397	0.00155
State population (in thousands)			(0.00661)	(0.00679)
Presidential margin	0.761	−0.119	2.001	0.840
Presidential margin	(1.220)	(1.219)	(1.217)	(1.224)
President favorable	−0.504	−0.543	−1.278	−1.061
President favorable	(1.422)	(1.441)	(1.407)	(1.434)
Median income (in thousands)	−3.418**	−3.317**	−4.194**	−4.100**
Median income (in thousands)	(1.620)	(1.604)	(1.628)	(1.623)
State % non-white	−566.3	−537.5	−979.1	−853.8
State % non-white	(604.9)	(614.0)	(604.3)	(616.3)
(Lagged) Funds per Capita		0.308**		0.300**
(Lagged) Funds per Capita		(0.0275)		(0.0276)
Year fixed effects	✓	✓	✓	✓
State fixed effects	✓	✓	✓	✓
N	1,350	1,300	1,350	1,300
$R^{2}$ within	.839	.855	.841	.856
$R^{2}$ between	.028	.184	.056	.024

LES = legislative effectiveness score.

p < .10. **p < .05.

The first two columns of Table 1 focus on the predicted effects of House delegation characteristics. In both models, two delegation characteristics have positive and statistically significant effects on the predicted per capita funds directed to the states: % same party and % majority party. The other two delegation characteristics, % president’s party and avg. electoral margin also have positive coefficients, but the effects are statistically insignificant. The last two columns of Table 1 focus on the predicted effects of Senate delegation characteristics. Consistent with the analyses of House delegation characteristics, the # in majority party has a positive and statistically significant effect on the amount of per capita federal funds distributed to each state. However, other characteristics have different effects. Unlike with House delegation characteristics, the same party variable does not have a statistically significant effect. In contrast, # in the president’s party has a negative and statistically significant effect, predicting fewer federal dollars for states when they have more senators who are co-partisans with the president. Finally, some mixed results are apparent for the level of two-party electoral competition in a state. The average electoral margin of senior senators has a negative and statistically significant effect, suggesting that less competitive state political environments predict fewer federal dollars to states. However, the coefficient for the electoral margins of junior senators is not statistically significant. Across the analyses, then, the consistent takeaway is that states with delegations with more members of the majority party in either chamber appear to receive more federal funds. Other characteristics may matter, but in less consistent ways.

The predicted effects of the delegation characteristics are displayed in Figures 3 and 4. Figure 3 presents the effects for the House delegation characteristics. The top-left panel shows the predicted total amount of per capita dollars distributed to states as their House delegations increase from evenly split between the two parties to 100 percent in just one party. This change predicts an increase from an average of $411 per capita distributed to states with split delegations, to an average of $447 per capita for states with delegations all in one party. For a state of median population size, this predicts a state will receive, on average, $133 million more in federal funds each year when it has a homogeneously partisan delegation compared with when it has a split delegation. The effect is slightly larger for % majority party. States with delegations without any majority party members are predicted to receive an average of $407 per capita, while those with delegations composed entirely of majority party members are predicted to receive $448 per capita. For a state of median population size, this translates into $152 million more in federal funds in a given year if that state had a delegation of just majority party lawmakers compared with if it had a delegation of just minority party lawmakers. The bottom two panels in Figure 3 show flatter and less precise predictions for % president’s party and avg. electoral margin.¹⁵

Figure 3.

House delegation characteristics and predicted per capita non-formula federal dollars to states (from column 1, Table 1).

Figure 4.

Senate delegation characteristics and predicted per capita non-formula federal dollars to states (from column 3, Table 1).

Figure 4 presents predicted effects for Senate delegation characteristics. Consistent with Table 1, sharp effects are seen for # majority party and # in president’s party. Specifically, states with two senators in the majority party are predicted to receive, on average, $30 more per capita than states without any majority party senators. For a median population state, this translates into an additional $111 million in a given year. In comparison, states with two senators in the president’s party are predicted to receive, on average, $26 fewer dollars per capita than states without any senators in the president’s party. For a median population state, this translates into $96 million fewer dollars in a given year. Figure 4 also shows the inconsistent effects of senior senator and junior senator electoral margins. Senior senator electoral margin has a sharply negative effect, with states with the largest margins in the figure (40 percentage points) predicted to receive, on average, $69 fewer dollars per capita, on average, than states with the narrowest margins. This translates into $254 million fewer dollars in a given year for a median population state. In comparison, junior senator margins predict a change of just $17 per capita, on average.¹⁶

Overall, the results of the above analyses show that the only consistent effects are for the share of House and Senate delegations in each chamber’s majority party. In both chambers, these variables have statistically significant and substantively large effects, predicting hundreds of millions more federal dollars for states with more majority party representatives and senators in their delegations. Other characteristics have significant effects in some tests, or in one chamber. However, none of these other characteristics have the consistent effect of majority party status.

Table 2 presents the results of our next set of analyses. These analyses look at the effects of state delegation characteristics on the distribution of federal dollars to states during the same calendar year that we measure the characteristics (rather than in the subsequent fiscal year as in Table 1). The purpose of these analyses is to try to capture the impact of back-channel influence (Ritchie 2018; Ritchie and You 2019). However, we only analyze these effects for odd-numbered years. In even-numbered years, there was typically no change in the delegation characteristics from the previous year (when legislation was set). This is because each Congress convenes in an odd-numbered year and ends the subsequent even-numbered year.¹⁷ Thus, in an even-numbered year, we cannot determine if the effects of the delegation characteristics reflect efforts to shape legislation during the previous year, or back-channel efforts during the present year. In odd-numbered years, in contrast, we have a nice breakpoint of an election, which can and often does change the characteristics of congressional delegations. Consequently, impacts we observe from same-year delegation characteristics in odd-numbered years can more reasonably be chalked up to legislators’ back-channel efforts.

Table 2.

Predicting Federal Non-formula Grants-in-Aid Per Capita to States in Odd-Numbered Years.

	(1)	(2)
House—% same party	93.15*
House—% same party	(56.22)
House—% majority party	43.55*
House—% majority party	(25.51)
House—% president’s party	8.085
House—% president’s party	(22.95)
House—avg. electoral margin	1.241
House—avg. electoral margin	(1.274)
Senate—same party		−18.55
Senate—same party		(14.29)
Senate—# in majority party		20.77**
Senate—# in majority party		(8.630)
Senate—# in president’s party		−10.15
Senate—# in president’s party		(7.778)
Senate—senior senator margin		−1.071
Senate—senior senator margin		(0.714)
Senate—junior senator margin		0.119
Senate—junior senator margin		(0.746)
House—delegation size	−14.20**
House—delegation size	(6.949)
House—majority leadership	−1.784
House—majority leadership	(18.45)
House—minority leadership	23.64
House—minority leadership	(16.71)
House—mean LES	4.765
House—mean LES	(6.862)
House—mean seniority	−10.15**
House—mean seniority	(4.482)
House—# on power committees	7.997
House—# on power committees	(7.822)
Senate—majority leadership		−101.5**
Senate—majority leadership		(48.56)
Senate—minority leadership		13.56
Senate—minority leadership		(94.35)
Senate—mean LES		13.62
Senate—mean LES		(11.00)
Senate—mean seniority		−1.304
Senate—mean seniority		(1.332)
Senate—# on power committees		−17.20
Senate—# on power committees		(11.99)
Presidential margin	−1.123	−0.421
Presidential margin	(1.596)	(1.581)
President favorable	0.529	−0.216
President favorable	(1.928)	(1.898)
Median income (in thousands)	−3.125	−3.843*
Median income (in thousands)	(2.096)	(2.108)
State % non-white	−9.559	−491.0
State % non-white	(817.9)	(808.1)
State population (in thousands)		0.00613
State population (in thousands)		(0.00871)
Year fixed effects	✓	✓
State fixed effects	✓	✓
N	650	650
$R^{2}$ within	.881	.884
$R^{2}$ between	.054	.140

LES = legislative effectiveness score.

p < .10. **p < .05.

The important takeaway from Table 2 is that the same delegation characteristics with consistent effects in Table 1 have consistent effects in Table 2. For both House and Senate delegations, the share of the delegation in the majority party has a positive and statistically significant effect. The sizes of the effects are similar, as well. A change from a House delegation with no majority party members to one with all majority party members predicts, on average, a $43 increase in per capita dollars. In the Senate, a change from a delegation with no majority party senators, to one with two majority party senators, predicts, on average, a $42 increase in per capita dollars. There are some other similarities to the results in Table 1. With House delegations, % same party has a positive and statistically significant effect. In the Senate, # president’s party has a negative coefficient, although the effect is not statistically significant. In any case, the primary takeaway from these analyses aligns with the primary takeaway from the analyses in Table 1. States with House and Senate delegations with more majority party lawmakers receive more statewide federal grants-in-aid.

It is important to note that some of the control variables have consistent effects across our analyses. In almost every analysis of House delegation characteristics, the size of a state’s House delegation (measured as the number of House representatives from a state) has a negative and statistically significant effect. This could mean that states with more House members have a more difficult time coordinating to secure federal dollars. In the Senate analyses, the size of a state’s population does not have a significant effect. Among the other delegation controls, the average seniority of a House delegation has a negative and statistically significant effect in every analysis. The mean LES of a House delegation, however, is insignificant in every analysis. Neither seniority nor LES has consistent effects in the analyses of Senate delegation characteristics.¹⁸ The presence of party leaders in House and Senate delegations does not consistently affect the distribution of these federal dollars. However, the number of delegation members a state has on House power committees has strong positive effects in Table 1.

The presidency-focused variables have inconsistent and insignificant effects across all of the analyses. In some analyses, the coefficients have negative effects, and, in some, the directions of the coefficients are positive. There does not appear to be any pattern. These null findings do not indicate that presidents are unimportant in influencing the distribution of the statewide federal dollars we analyze. Rather, our analysis cannot pick up on any presidential influence. In contrast, the measures of state need have substantial effects. A state’s median income is negative and statistically significant in almost every analysis. States with lower median incomes, in other words, typically receive more federal dollars. The percent of a state’s population that is non-white also has a consistently negative effect, but the coefficients are never statistically significant.

The next set of analyses assesses whether the most influential delegation characteristics have shifted over time. In these analyses, we interact a continuous variable for Year with each of our delegation characteristics.¹⁹ The regression results are found in the online supplemental appendix (Table A1 and A2) and include the same covariates (with the exception of year fixed effects). Because the tables are very large and unwieldy, and in the interest of saving space, we present only the predicted effects in Figures 5 and 6.

Figure 5.

The over-time influence of house delegation characteristics on the predicted per capita non-formula federal dollars to states.

Figure 6.

The over-time influence of senate delegation characteristics on the predicted per capita non-formula federal dollars to states.

Figure 5 shows the over-time effects of House delegation characteristics. Two of the characteristics—% majority party and % president’s party—have effects that clearly change over time. The top-right panel of Figure 5 demonstrates this for % majority party. In this panel, the effect of the percent of a House delegation in the majority party on per capita funds distributed to each state is shown at three points in our time series: the first year (FY 1984), the middle year (FY 1997), and the final year (FY 2010). The panel shows that in the first year (1984), the effect of % majority party is negative. That is, at the start of the time series, states with House delegations with more majority party members were predicted to receive fewer statewide federal dollars. However, at the end of the time series, the effect is positive. In 2010, states with House delegations with more majority party members are predicted to receive more federal dollars.

The bottom-left panel shows an opposite over-time effect for % president’s party. In the first year of the time series (1984), % president’s party has a positive effect. In early years, state House delegations with more members in the president’s party receive more federal dollars. However, by the final year of the time series (2010), the effect is clearly negative. States with more House members in the president’s party receive fewer federal dollars.

Figure 6 shows similar over-time effects for Senate delegations. The top-right panel shows the effect of # in the majority party over time. In the first year of the time series, the effect is generally negative (if not completely linear). Generally, states with no majority party members were predicted to receive more federal dollars than those with one or two majority party members in FY 1984. At the end of the time series, the effect is reversed. In FY 2010, states with one or two majority party members are predicted to receive more federal dollars than those with no majority party senators. For # in the president’s party, the opposite over-time effect is even more pronounced. At the start of the time series, Senate delegations with more senators in the president’s party receive more federal dollars, but at the end of the time series, Senate delegations with more senators in the president’s party receive fewer federal dollars.

Altogether, these over-time results reinforce the findings in the previous models. Not only do the overall results indicate that House and Senate delegations with more majority party members receive more of the statewide federal dollars we analyze, but the over-time analyses show that the effect of having more majority party members in a state delegation has been increasing. Interestingly, as this has happened, the effect of a state having more representatives or senators in the president’s party has become less helpful. This suggests that as much partisan change has happened in Congress, with increased party polarization and party organizational strength increasing since the 1980s, majority party status in state congressional delegations has become more important for the distribution of federal funds to states.

We conducted a number of additional analyses included in the online supplemental appendix to this paper. In addition to those already mentioned, we also ran analyses looking for nonlinear effects in our delegation characteristics, analyses looking at the percent and number of Democrats and Republicans in each delegation (reflecting on the results of Albouy 2013), and results excluding the presidential variables. Across all of these specifications, our general results hold. State delegations with more representatives and senators in the majority are able to secure more statewide federal funds.

Conclusion

In this paper, we assess the influence of state congressional delegation characteristics on the federal dollars distributed to state governments and state governmental entities. Our findings indicate that the characteristics of congressional delegations matter, but in specific ways. More homogeneously partisan delegations translate into more of these federal dollars for states, with the share of House and Senate delegations in each chamber’s majority party playing the biggest role. States with more House members and more senators in the majority party receive more statewide federal grants-in-aid. Moreover, this effect increased over time from the early 1980s to 2010.

Because we narrowed our analyses of federal aid to non-formula dollars that are distributed directly to state governments and state governmental agencies, our results suggest that the dynamics of state congressional delegations matter because they affect the abilities of members of Congress from the same state to work together toward common goals that would benefit the entire state. That states with delegations that are more majoritarian are better able to secure these dollars reflects the growing importance of parties in the contemporary Congress. Although large, bipartisan coalitions are often needed to enact legislation on Capitol Hill (Curry and Lee 2019, 2020; Krehbiel 1998), it appears majority party status continues to mean something. At the very least, our results suggest that majority party status in state congressional delegations helps states accrue federal funds.

At the same time, our results only provide an initial look at the collective dynamics of congressional delegations. Our results cannot and do not investigate how exactly delegations work together (or fail to work together) to achieve collective goals like directing federal funds to their states. While we discussed prominent scholarship on the nature of distributive politics, the nature of our analyses cannot uncover specific mechanisms. However, our results are suggestive of factors likely to be important in these collective efforts. In short, delegations empowered by majority status, and less encumbered by partisan divisions, are better able to direct funds to their states. Future research should investigate the mechanisms of this empirical result.

Finally, our findings indicate that the distribution of federal dollars is meaningfully impacted by the constitutional decision to apportion members of Congress by state alongside the federal system. Delegations of legislators in the U.S. Congress share a commonality of state residence that binds them together in important ways. At least in this study, we find it likely affects the distribution of federal dollars within the United States.

Supplemental Material

Online_Appendix – Supplemental material for State Congressional Delegations and the Distribution of Federal Funds

Supplemental material, Online_Appendix for State Congressional Delegations and the Distribution of Federal Funds by James M. Curry and Christopher P. Donnelly in Political Research Quarterly

Footnotes

Acknowledgements

We thank Adam Dynes, Kris Kanthak, and Wendy Schiller for their helpful comments and suggestions on earlier versions of this article.

Authors’ Note

Christopher P. Donnelly is currently an independent researcher.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

James M. Curry

Notes

Supplemental Material

Replication code and data available at: . Supplemental materials for this article are available with the manuscript on the Political Research Quarterly (PRQ) website.

References

Albouy

David

. 2013. “Partisan Representation in Congress and the Geographic Distribution of Federal Funds.” The Review of Economics and Statistics 95:127–41.

Aldrich

John H.

1995. Why Parties? The Origin and Transformation of Political Parties in America. Chicago: The University of Chicago Press.

Aldrich

John H.

Rohde

David W.

2000. “The Republican Revolution and the House Appropriations Committee.” The Journal of Politics 62 (1): 1–33.

Alvarez

R. Michael

Saving

Jason

. 1997. “Congressional Committees and the Political Economy of Federal Outlays.” Public Choice 92 (1): 55–73.

Balla

Steven J.

Lawrence

Eric D.

Maltzman

Forrest

Sigelman

Lee

. 2002. “Partisanship, Blame Avoidance, and the Distribution of Legislative Pork.” American Journal of Political Science 46 (3): 515–25.

Barber

Michael J.

McCarty

Nolan

. 2015. “Causes and Consequences of Polarization.” In Solutions to Political Polarization in America, edited by Persily

Nathaniel

, 15–58. New York: Cambridge University Press.

Barone

Michael

Cohen

Richard E.

2007. The Almanac of American Politics, 2008. Washington, DC: National Journal Group.

Berry

Christopher R.

Burden

Barry C.

Howell

William G.

2010. “The President and the Distribution of Federal Spending.” American Political Science Review 104 (4): 783–99.

Bertelli

Anthony M.

Grose

Christian R.

2009. “Secretaries of Pork? A New Theory of Distributive Public Policy.” The Journal of Politics 71 (3): 926–45.

10.

Bickers

Kenneth N.

Stein

Robert

. 1991. Federal Domestic Outlays, 1983-1990: A Data Book. Armonk: M.E. Sharpe.

11.

Bickers

Kenneth N.

Stein

Robert

. 1996. “The Electoral Dynamics of the Federal Pork Barrel.” American Journal of Political Science 40 (1): 1300–26.

12.

Broockman

David E.

2013. “Black Politicians Are More Intrinsically Motivated to Advance Blacks’ Interests: A Field Experiment Manipulating Political Incentives.” American Journal of Political Science 57:521–36.

13.

Butler

Daniel E.

Broockman

David E.

2011. “Do Politicians Racially Discriminate Against Constituents? A Field Experiment on State Legislators.” American Journal of Political Science 55:463–77.

14.

Cain

Bruce

Ferejohn

John

Fiorina

Morris

. 1987. The Personal Vote: Constituency Service and Electoral Independence. Cambridge, MA: Harvard University Press.

15.

Caro

Robert A.

1990. The Years of Lyndon Johnson: Means of Ascent. New York: Vintage Books.

16.

Carsey

Thomas M.

Rundquist

Barry

. 1999. “Party and Committee in Distributive Politics: Evidence from Defense Spending.” The Journal of Politics 61 (4): 1156–69.

17.

Cox

Gary W.

Magar

Eric

. 1999. “How Much Is Majority Status in the U.S. Congress Worth?” American Political Science Review 93 (2): 299–309.

18.

Cox

Gary W.

McCubbins

Mathew D.

2005. Setting the Agenda: Responsible Party Government in the U.S. House of Representatives. New York: Cambridge University Press.

19.

Curry

James M.

2015. Legislating in the Dark: Information and Power in the House of Representatives. Chicago: The University of Chicago Press.

20.

Curry

James M.

Lee

Frances E.

2019. “Non-Party Government: Bipartisan Lawmaking and Party Power in Congress.” Perspectives on Politics 17 (1): 47–65.

21.

Curry

James M.

Lee

Frances E.

2020. The Limits of Party: Congress and Lawmaking in a Polarized Era. Chicago: The University of Chicago Press.

22.

Den Hartog

Chris

Monroe

Nathan W.

2011. Agenda Setting in the U.S. Senate: Costly Consideration and Majority Party Advantage. New York: Cambridge University Press.

23.

Dynes

Adam M.

Huber

Gregory A.

2015. “Partisanship and the Allocation of Federal Spending: Do Same-Party Legislators or Voters Benefit from Shared Party Affiliation with the President and House Majority?” American Political Science Review 109 (1): 172–86.

24.

Engstrom

Erik J.

Vanberg

Georg

. 2010. “Assessing the Allocation of Pork: Evidence from Congressional Earmark.” American Politics Research 38 (6): 959–85.

25.

Evans

Diana

. 2004. Greasing the Wheels: Using Pork Barrel Projects to Build Majority Coalitions in Congress. New York: Cambridge University Press.

26.

Gilmour

John B.

1995. Strategic Disagreement: Stalemate in American Politics. Pittsburgh: University of Pittsburgh Press.

27.

Grimmer

Justin

Westwood

Sean J.

Messing

Solomon

. 2015. The Impression of Influence: Legislator Communication, Representation, and Democratic Accountability. Princeton: Princeton University Press.

28.

Grose

Christian R.

2011. Congress in Black and White: Race and Representation in Washington and at Home. New York: Cambridge University Press.

29.

Hanson

Peter

. 2014. Too Weak to Govern: Majority Party Power and Appropriations in the U.S. Senate. New York: Cambridge University Press.

30.

Hinchliffe

Kelsey L.

Lee

Frances E.

2016. “Party Competition and Conflict in State Legislatures.” State Politics & Policy Quarterly 16 (2): 172–97.

31.

Krehbiel

Keith

. 1998. Pivotal Politics: A Theory of U.S. Lawmaking. Chicago: The University of Chicago Press.

32.

Kriner

Douglas L.

Reeves

Andrew

. 2012. “The Influence of Federal Spending on Presidential Elections.” American Political Science Review 106 (2): 348–66.

33.

Lazarus

Jeffrey

. 2009a. “Giving the People What They Want? The Distribution of Earmarks in the U.S. House of Representatives.” American Journal of Political Science 54 (2): 338–53.

34.

Lazarus

Jeffrey

. 2009b. “Party, Electoral Vulnerability, and Earmarks in the U.S. House of Representatives.” The Journal of Politics 71 (3): 1050–61.

35.

Lee

Frances E.

2000. “Senate Representation and Coalition Building in Distributive Politics.” American Political Science Review 94 (1): 59–72.

36.

Lee

Frances E.

2003. “Geographic Politics in the U.S. House of Representatives: Coalition Building and Distribution of Benefits.” American Journal of Political Science 47 (4): 714–28.

37.

Lee

Frances E.

2009. Beyond Ideology: Politics, Principles, and Partisanship in the U.S. Senate. Chicago: The University of Chicago Press.

38.

Lee

Frances E.

2016. Insecure Majorities: Congress and the Perpetual Campaign. Chicago: The University of Chicago Press.

39.

Lee

Frances E.

Oppenheimer

Bruce I.

1999. Sizing Up the Senate: The Unequal Consequences of Equal Representation. Chicago: The University of Chicago Press.

40.

Levitt

Steven D.

Snyder

James M.

1995. “Political Parties and the Distribution of Federal Outlays.” American Journal of Political Science 39 (4): 958–80.

41.

Levitt

Steven D.

Snyder

James M.

1997. “The Impact of Federal Spending on House Election Outcomes.” Journal of Political Economy 105 (1): 30–53.

42.

Mayhew

David R.

2005. Divided We Govern: Party Control, Lawmaking, and Investigations, 1946–1990. New Haven: Yale University Press.

43.

Ritchie

Melinda N.

2018. “Back-Channel Representation: A Study of the Strategic Communication of Senators with the U.S. Department of Labor.” The Journal of Politics 80 (1): 240–53.

44.

Ritchie

Melinda N.

You

Hye Young

. 2019. “Legislators as Lobbyists.” Legislative Studies Quarterly 44 (1): 65–95.

45.

Rocca

Michael S.

Gordon

Stacy B.

2012. “Earmarks as a Means and an End: The Link between Earmarks and Campaign Contributions in the U.S. House of Representatives.” The Journal of Politics 75 (1): 241–53.

46.

Sinclair

Barbara

. 2016. Unorthodox Lawmaking: New Legislative Processes in the U.S. Congress. 5th ed. Washington, DC: CQ Press.

47.

Stein

Robert

Bickers

Kenneth N.

1994. “Universalism and the Electoral Connection: A Test and Some Doubts.” Political Research Quarterly 47 (2): 295–317.

48.

Theriault

Sean M.

2008. Party Polarization in Congress. New York: Cambridge University Press.

49.

Volden

Craig

Wiseman

Alan E.

2014. Legislative Effectiveness in the United States Congress: The Lawmakers. New York: Cambridge University Press.

50.

Wallner

James I.

2013. The Death of Deliberation: Partisanship and Polarization in the United States Senate. New York: Lexington Books.

51.

Zito

Salena

. 2015. “Pa. Senators Casey, Toomey Test Bipartisan Alliance.” Pittsburgh Tribune-Review, January 10. https://archive.triblive.com/news/politics-election/pa-senators-casey-toomey-test-bipartisan-alliance/.

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