Distribution versus Delegation: Federal Grants-in-Aid, Intergovernmental Policymaking
Authority and Congressional Representation
Pamela Clouser McCann
University of Southern California
Anthony M. Bertelli
New York University
March 22, 2015
DRAFT: PLEASE DO NOT CITE OR QUOTE WITHOUT PERMISSION
Abstract: Congress commonly uses grants-in-aid (GIA) to target funds to states for the
implementation of significant domestic policies. We argue that individual MCs care about both
ex ante and ex post oversight over the spending programs on which they vote, and recognize that
agency costs remain insuring that their constituents maintain their distribution of funds. MCs
prefer to minimize the time and effort needed to reduce such agency loss ex post, and the
decentralization of implementation through GIA has an impact on the costliness of such effort.
We employ a unique dataset that captures all grants-in-aid provisions included in all House and
Senate bills and public laws during the 93rd-111th congresses and find evidence that: MCs are
less likely to support bills as implementation becomes more decentralized when they share
partisanship with the president or both political executives; and when members either share party
affiliation with only their governors or with neither of these political executives, the likelihood of
support rises with decentralization.
Paper prepared for the 3rd Annual Southern California Law & Social Science Forum. We thank
Nathaniel Beck and George Krause for helpful suggestions. Mistakes remain our own.
Congress cannot force the states to implement national policy, but it can provide encouragement.
The majority of significant legislation passed by Congress relies on state implementation of
national policies (McCann 2015). Whether based on language of federal assistance, relief, or
stimulus, money is generally considered an inducement rather than a form of coercion, since
participation in grant programs is typically voluntary. Eligibility for these grants often has a
statutory basis through formulas based on population or need; explicit rules, such as maintenance
of effort requirements, cross-over sanctions, matching funds requirements and other conditions
can impact the distribution of grant dollars actually committed to grantees in the individual
states.
As a result, the design of national-to-state grant programs is manipulable by re-election
seeking legislators who calculate the impact of these programs on their electoral constituency
(Nathan 1987). This claim is rooted in the logic that legislators’ re-election incentive induces
them to prefer to concentrate benefits within their electoral constituencies while spreading costs
across the entire nation (Arnold 1990). Distributions of benefits are thus a means of
representation (Grose 2011). Grants-in-aid (GIA) provide just such a vehicle of benefits as well
as the potential opportunity to stretch federal dollars with matching requirements. Yet, they
represent a decentralized mechanism of policy implementation, leaving policy implementation in
the hands of state rather than federal administrators.
Our focus is on the roll-call voting decision of a member of Congress (MC) and we take a
transaction cost politics approach in theorizing about this decision (e.g., Epstein and O’Halloran
1999; Dixit 1996). We claim that the MC wants to minimize the costs of ex post influence and
monitoring to maintain the share of federal funds to which she feels her constituents are entitled.
The congressional dominance literature asserts that enacting coalitions consider ex ante means
1
for reducing the costs of ex post oversight of the laws they create (e.g., McCubbins and Schwartz
1984; McCubbins, Noll, and Weingast 1989; Macey 1992). Scholars have considered this
problem in a variety of ways, for instance, that Congress is an organization with limited
resources to allocate to different items on its agenda (cf. Baumgartner and Jones 1993; 2015) and
that individual MCs care not only about position taking on proposed legislation, but also about
the extent to which they participate in the legislative process on that proposal (Hall
1996). Consistent with these literatures, we argue that individual MCs care about both ex ante
and ex post oversight over the spending programs on which they vote. Their ex ante concern
surrounds the formula by which funds are allocated, while they recognize that agency costs
remain and consider the cost of engaging in ex post oversight mechanisms—the means of
monitoring and influence over the distribution of funds—available to them. The crux of our
argument is that MCs prefer to minimize the time and effort needed to reduce agency loss ex
post.
Ex post oversight over funds allocation can be done through both formal and informal
mechanisms, and the mechanisms in each category are costly for MC’s to employ. Formal
mechanisms available to MCs include committee hearings involving testimony by administrators
and congressional appropriations to agencies. Informal mechanisms involve contacts and
consultations between congressional and agency staffers and sharing information about agency
choices with the media and relevant organized interests. The core of our theory lies in two
claims. First, an important element of implementation—whether the funds are distributed by
federal administrative agencies or are left to state implementation via GIA—shapes ex post
oversight costs in a meaningful way that influences MCs roll-call voting decisions. Second,
2
party ties between the MC and the political executives at each level, namely, the president and
his state’s governor, also influence the costs of ex post action regarding funds distribution.
Previous studies of GIAs typically utilize annual federal outlays to the states and
localities or compilations of appropriations demarcated as earmarks (e.g., Knight 2008;
Larcinese, Snyder and Testa 2010).
Because we are interested in their impact on roll-call
voting, our focus comes earlier in the legislative process.
To capture the degree of
decentralization in grant administration, we develop a measure of whether provisions in a given
bill are delegated via GIAs to the states, to federal agencies, or through other means. Using a
dataset of 70,827 coded provisions and associated roll-call votes in the House and Senate from
199 major laws enacted during the 93rd to 111th Congresses, we find that MCs are less likely to
support bills as implementation becomes more decentralized when they share partisanship with
the president or their governor, but not both political executives. However, when members
either share party affiliation with both or neither of these political executives, the likelihood of
support rises with decentralization.
We begin with our theoretical argument that MCs consider the costs of oversight in their
roll-call voting decisions in regard to spending programs. Our discussion then turns to the data,
with particular emphasis on the measurement of decentralization in the implementation. We then
describe our statistical modeling technique and present our results before concluding with some
brief remarks.
A Transaction Cost Politics Theory of Roll-Call Voting on Intergovernmental Grants
We argue the costs MCs face for exercising formal ex post oversight are reduced when
administrators at the federal level allocate funds. Intergovernmental delegations of such
3
authority to state officials resulting from the use of GIA provisions lead to the involvement of
constitutionally separated actors in the administrative process. For instance, it is more difficult
for an MC to successfully obtain national hearing testimony from the relevant agency in her
state; and a request for GAO review of funds administration for a particular program will broadly
examine implementation rather than target only the agency in her state. Informal mechanisms
are also more challenging to employ when administration is devolved to the states. At the
federal level, administrators are more identifiable and MCs have more centrality in relevant
policy networks. Each state’s administrators function within issue networks of political
institutions, organized interests and media actors, with individual MCs competing with state
politicians and colleagues in the House and Senate from their own states for influence in those
networks. Overall, implementation via GIA increases ex post oversight costs for MCs.
While decentralization is expected to increase ex post oversight costs for individual MCs,
we argue that party ties with political executives further shape these costs. First, co-partisanship
with the president reduces the costs of employing formal mechanisms when implementation is
centralized. The president can also influence the flow of federal funds—through initial
budgetary requests and agency review—to particular states; partisan differences between the
president and an MC could decrease the flow of national dollars to her electoral constituency. In
American politics, partisanship relates to a politician’s preferences for a variety of spending
priorities from social welfare programs to tax expenditures benefiting corporations, which makes
party
a
particularly
important
proxy
for
policy
conflict
in
regard
to
federal
expenditures. Moreover, the management of such expenditures can impact the re-election
chances of MCs, and presidential co-partisans would be expected to benefit from this fact in the
form of less costly formal influence over funds administration. The absence of presidential co-
4
partisanship may also make informal influence more costly. Access to staff in federal agencies
and their amenability to the MC’s views and concerns about funds administration is less likely if
the MC is from the opposite party from the president.
Second, we expect that co-partisanship with the governor of the MC’s home state reduces
her costs of engaging formal mechanisms of ex post oversight when administration is
decentralized. Formal mechanisms are more costly across federal governing levels for the
reasons given above, but when the MC and governor share party affiliation, policy conflict is
reduced and spending priorities are more similar. Also, federal funds administered in the MC’s
home state as a result of GIA provisions are more likely to benefit her constituents to maintain an
electoral advantage over the opposing party. Having a co-partisan governor can also reduce the
costs of informal mechanisms of ex post enforcement. Although top state agency staff may be
outside of the network of the MC and be more difficult to contact, administrative acceptance of
and availability to a co-partisan MC is likely once the correct administrators are reached. We
test the following hypotheses arising from these claims.
Hypothesis 1 (Presidential Co-Partisanship): An MC who shares the
president’s party affiliation, but not that of his state’s governor, is less likely to
vote in favor of a proposed bill as its implementation becomes more
decentralized.
Hypothesis 2 (Gubernatorial Co-Partisanship): An MC who shares the party
affiliation of his state’s governor, but not that of the president, is more likely to
vote in favor of a proposed bill as its implementation becomes more
decentralized.
Given the lower cost of engaging formal and informal ex post oversight of federally
administered grant programs, MCs sharing party affiliation with both the president and the
governor of their home states should find centralization to have lower ex post costs than
decentralization.
5
Hypothesis 3 (Full Co-Partisanship): An MC who shares the president’s party
affiliation as well as that of his state’s governor is less likely to vote in favor of a
proposed bill as its implementation becomes more decentralized.
When co-partisanship with either executive cannot mitigate ex post oversight costs, ex
post oversight technologies are costlier than in any of the foregoing scenarios. Because of the
high cost of informal federal mechanisms in this scenario, the MC may prefer simply to give the
state more autonomy. Students of pork-barrel politics literature suggests that MCs can still claim
credit for a GIA as a federal benefit to constituents even though we argue here that ex post
oversight is more difficult (Bickers and Stein 1995). With high cost ex post oversight at either
level, an MC will prefer greater decentralization as it provides, at a minimum, a credit claiming
opportunity.
Hypothesis 4 (No Co-Partisanship): An MC who shares neither the president’s
party affiliation nor that of his state’s governor is more likely to vote in favor of a
proposed bill as its implementation becomes more decentralized.
Data and Methods
To evaluate our claims about the influence of decentralization on MCs’ voting decisions,
this study uses the bill, or legislative package, as the unit of analysis with the provision as the
unit coded for congressional choice. As more provisions in a given bill include grants to the
states, more control over the distribution of resources to constituents shifts to state executives, or,
put differently, is decentralized. Alternatively, provisions that delegate such authority to federal
administrators without including specific state GIAs shift the locus of control to the national
level and thus centralize the distribution of funds. Our dataset includes 70,827 coded provisions
from 199 enacted laws from Mayhew’s list of major laws from the 93rd to 111th Congresses
(Mayhew 2005, with web updates). We focus on significant, enacted legislation since these are
6
the legislative packages MCs are most likely to have scrutinized and collect text from the final
law, the House floor-passed bill, and the Senate floor passed bill.
Our binary dependent variable captures the vote choice of each legislator—either a “yea”
(coded one) or a “nay” (coded zero) as the MCs final floor vote. Using THOMAS, we identified
all major actions for the laws in our dataset and extracted specific measures and associated dates
for votes on final passage in both the House and Senate. These votes on final passage were then
matched to the appropriate roll-call results from Voteview.com, yielding a collection of
legislator’s votes on final passage for each law in the dataset.
Dependent Variable
Given our argument that the decentralization of a legislative package is crucial to vote
choice, we measure decentralization through a delegation ratio, namely, the proportion of a bill
or law’s provisions that delegate to the states via GIA. Theoretically, this proportion can vary
along the unit interval from zero to one, where zero represents complete centralization to a
federal agency with no GIA and one for laws that rely on a GIA to implement every provision.
To operationalize intergovernmental delegation via GIA, we begin with a dataset of policy
authority delegation across federal actors (McCann 2015), which codes the delegation structure
for each law’s major provisions as summarized by the Congressional Research Service
(CRS). Specifically, McCann (2015) identifies the entity that received responsibility for
implementing a provision, including no delegation, delegation to national-level actors (such as
federal agencies), state-level actors, jointly to both national and state actors, as well as delegation
to other entities such as the judiciary. Delegation to national-level actors includes establishing
new federal agencies, directing new agencies, and specific delegations to the president. Direct
7
delegation to the states is rare and typically focuses on two policy areas: elections and abortion
(McCann 2015). Joint delegations of authority to both national and state actors are done either
by using GIAs or by simply directing the states and national actors to collaborate on
implementation without providing monetary encouragement. Given our theoretical focus on
GIA, we separate the two types of joint delegations in our coding process.
After carefully reviewing each law’s legislative history, including any and all initial bills
for the House and Senate and which bills received action on the floor, we collected CRS
summaries of all bills that received a floor vote in the House and likewise for the Senate. If no
CRS summary could be located for a Senate or House bill, that bill was coded as missing, which
was the case for 30 House bills and 50 Senate bills in the dataset. Overall, the 513 measures
(199 final laws, 167 House bills, and 147 Senate bills) resulted in a dataset of 43,396 provisions
within congressional bills and 27,432 previously coded provisions from enacted national
laws.
Because the hand-coding process for over 40,000 provisions is extremely laborious, we
relied on automated textual classification using multiple supervised learning algorithms
(Collingwood and Wilkerson 2012). A selection of 7,400 previously hand-coded provisions
provided a training device for our system of classifying the extent of decentralization in
delegations. We used the Rtextools package, which relies on nine machine learning algorithms
(see Jurka et al., 2013 and Collingwood and Wilkerson 2012 for an overview). These algorithms
approached the uncoded set of provisions within these House and Senate bills after being
“taught” the textual patterns within the already handed coded set of provisions.
FIGURE 1 ABOUT HERE
8
Figure 1 provides a summary of decentralization within provisions in final laws averaged
across congresses. As we expect, Congress delegates the implementation of national laws
mainly to national-level actors across time. The second-most common delegation strategy of
Congress is to implement national laws via GIA. Two examples of these patterns of delegation
are the No Child Left Behind Act (NCLB, PL 107-110) and the Affordable Care Act (ACA, PL
111-148), where many provisions decentralize implementation to the states. In the NCLB, 132
provisions were summarized as substantively significant by CRS and included national and state
delegations. Of these provisions, 14 delegated authority to national agencies such as the
Department of Education with no state involvement, 3 delegated directly to the states with no
national involvement, 27 provisions relied on both national and state action for implementation
(joint delegations) without using the GIA mechanism, and 88 provisions relied exclusively on
GIA. The delegation ratio is 0.667 for NCLB (or 88 grant provisions/132 delegating
provisions). In comparison, the delegation ratio for the ACA is 0.256, based on 640 CRS
provisions of which 438 delegated to national actors such as the Department of Health and
Human Services or the Internal Revenue Service, 38 were joint delegations with no GIAs, and
164 provisions included GIAs (or 164/640, given exclusions). Thus, the NCLB is more
decentralized than the ACA.
The degree to which Congress employs GIA varies considerably over time. The 100th
Congress choose to rely on GIA in about 30% of final enactments, while the 96th Congress used
GIA less than 7% of the time. Our delegation ratio includes both broad grants provided to all
states as well as narrowly targeted grants. An example of a GIA can be found in P.L. 109-059
(SAFETEA-LU, the Safe, Accountable, Flexible, Efficient Transportation Equity Act) where the
Secretary of Transportation is directed to “carry out a grant program to provide financial
9
assistance to states for the cost of local rail line relocation and improvement projects” or in the
Omnibus Budget Reconciliation Act of 1981 (P.L. 97-035) which authorized appropriations to
“provide grants to States to assist eligible household to meet the costs of home energy.”
It is fruitful to consider differences in the average delegation ratio between Senate bills,
House bills, and final enactments as shown in Figure 2. House and Senate bills differ in the
proportion of provisions that are delegated to the states via GIA in every Congress. The most
similar delegation via GIA occurs in the 97th Congress where the Senate chose to delegate 23.5%
of all provisions via grants to the states, the House 23.8% of all provisions, and the final laws
delegated 22.9% of provisions to the states via grants-in-aid. More striking differences in
delegation ratios within House versus Senate bill choices are found in the 99th or 109th
Congresses. In the 99th Congress the Senate included GIA in 14.5% of provisions, the House
just over 8% of provisions and the final enactment included GIA in 9.6% of provisions. The
109th House, alternatively, included GIA in 21.7% of provisions versus the Senate’s 17.8%
reliance on grants. These two chambers resolved their differences in final enactments that
included GIA in only 9% of their provisions. Over time, the Senate relied more heavily on GIA
than did the House in 10 of 19 Congresses. Across congresses, the final enactment may include
more GIA than either chamber’s bill (100th & 105th Congresses), fewer than either bill (102nd,
104th, 107th, 109-111th Congresses), or an average between the two bills (11 remaining
congresses). The highest delegation ratio in enacted laws is 0.786 in P.L. 93-503, the 1974
Amendments to the 1964 Urban Mass Transportation Act.
FIGURE 2 ABOUT HERE
10
Partisanship Regimes
To capture the political party comparisons required to test our hypotheses, we create a
variable partisan regime that takes a value of zero when MCs share party label only with their
governors, a one when members share a party label only with the president, two when an MC
matches the part of both the president and the home state governor, and a three when an MC is of
a different party than both the president and governor. We interact our delegation ratio with this
categorical variable. We expect that as decentralization increases, the probability of voting in
favor of a measure increases for two types of MCs (gubernatorial co-partisans and members
without co-partisan executives) and decreases for the remaining types (presidential co-partisans
and members sharing party with both executives).
Control Variables
We also include a number of control variables expected to influence voting
choices: gubernatorial spending power, state fiscal health, divided legislature, federal deficit,
and whether it is an election year or not. Spending power was coded as a one for those
governors who did have the power to spend unanticipated federal funds without legislative
approval and zero otherwise (Krupnikov and Shipan 2011). State fiscal health is the proportion
of growth in state own-source revenue from one year to the next, lagged by one year. We expect
MCs from states with improving fiscal conditions may not need to provide assistance to their
states, yielding a negative coefficient. Alternatively, MCs from states with improving fiscal
health may be more likely to use GIA in order to claim credit for successful states (Volden
2005), thus a positive coefficient. We include this term and rely on the empirical analyses to
provide support for which mechanism is more common on average. Divided legislature is a
11
dummy variable coded as a one for the years in which the House and Senate are controlled by
different majority parties and we expect this variable to be associated with a reduced probability
of voting for a measure given the increased bargaining costs incurred. The federal deficit or
surplus measure was obtained from the Budget of the United States Government, Fiscal Year
2012 Historical Tables. We utilize the current year’s surplus or deficit as a percentage of the
gross domestic product measured in constant fiscal year 2000 dollars (Deficit). As the federal
deficit increases, resources are more constrained and legislators are expected to be less likely to
vote in favor of a measure. Summary statistics for all variables are provided in the Appendix.
Estimation Strategy
Because individual MCs vote on many bills over time in our sample, we are concerned
about unobserved heterogeneity at the legislator level and use a statistical model that allows us to
address the problem. We employ correlated random effects probit models (e.g., Mundlak 1978;
Chamberlain 1982) to estimate the probability that an MC votes “yea” on a roll-call in our
sample.
The standard probit model with unobserved effects can be written as
𝑃(𝑦𝑖𝑡 = 1|𝑥𝑖𝑡 , 𝑢𝑖 ) = Φ(𝑥𝑖𝑡 + 𝑢𝑖 ). We follow the Chamberlain-Mundlak procedure in assuming
that 𝑢𝑖𝑡 = 𝑥̅𝑖 + 𝑛𝑖 to relax the assumption of the independence of 𝑢𝑖 from 𝑥𝑖𝑡 . Practically
speaking, then, we include time-averaged values of our independent variables, 𝑥̅𝑖 , as additional
regressors in our models. We estimate these models separately on our samples of roll-call votes
in the House and Senate.
Our preferred specifications also control for unmodeled differences in legislative design
within policy domains, thus we include fixed effects by policy domain. Our policy domain
indicators rely on the Policy Agendas Projects coding of major topic of each of the laws and bills
12
included in the dataset. To further control for unobservables at the state level, we include state
fixed effects as well.
Results
Our results regarding the influence of co-partisanship and GIA on the probability of
members of Congress voting in favor of a measure on the floor are reported in Table 1.1 We
provide a separate model for each chamber and, given the probit estimation and interacted
variables, include a column for the marginal effect of increasing the grant ratio on the probability
of voting “yea” across categories of co-partisanship to aid in interpretation.
As expected, we find a negative marginal effect of increasing grants to the states for
members who only match the partisanship of their president (Hypothesis 1) and a positive
marginal effect for members who only match their governors (Hypothesis 2), all else in the
model constant. Moreover, we also find support for hypotheses 3 and 4: members who match
both executives’ party labels are less likely and those who match neither are more likely to vote
for passage as the grant ratio increases. Specifically, the probability that a Democratic member
of the House facing a Republican president, but with a Democratic governor will vote for
passage increases by 35% as the grant ratio moves from zero grants in a legislative proposal up
to a law that includes only grants to the states.
A more reasonable interpretation would be to consider a measure that increases the ratio
by 0.1—a law with 100 provisions would switch 10 provisions from centralizing with a federal
agency to delegation to the states with GIA—this change would increase a gubernatorial copartisan’s probability of voting in favor by 3.5%. For presidential co-partisans who only match
1
This table utilizes the grant ratio in the final enactments, although analyses using House and Senate bill ratios are
substantively similar. In effect, we are assuming House and Senate members estimate the extent to which the other
chamber will influence the final ratio in these analyses.
13
the party of their president, though, this same change would result in an 8% lower probability of
voting in favor of the measure.
If we consider, instead, Republican members facing a
Republican president we find support for our final two hypotheses in the House: members who
match both executives (Full Co-Partisans) have a 5.8% lower likelihood and members who
match neither (No Co-Partisans) have a 6% higher likelihood of voting “yea” on final passage.
These differences in voting probabilities are statistically significant and the same patterns hold in
the Senate: Gubernatorial co-partisans are 8.9% more likely, Presidential co-partisans 2.9% less
likely, Full Co-partisans 4% less likely, and finally, No Co-partisans are 8.6% more likely to
vote in favor of a measure that increases the grant ratio by 10%.
In sum, we find support for all four of our hypotheses across both the House and Senate
after controlling for individual-level legislator effects (through the Chamberlain-Mundlak
mechanism), policy-specific differences (policy fixed effects), state-level differences (state fixed
effects), as well as our other controls. The findings with respect to these final control variables
also fit with our expectations and, for the most part, are similar across both chambers. For
instance, we expected that a governor with the power to spend unanticipated federal funds would
be a significant predictor of how national representatives would vote. Previous authors have
found that most national laws include state implementation, thus a governor’s power vis-à-vis his
legislature likely matters.
We find this specific power increases the probability of House
members voting in favor of a measure (10.6%), but has no statistically significant effect on
Senator’s vote choices. As a state’s fiscal health improves, a member of either the House or the
Senate is more likely to vote in favor of passage (6.9% for a 10% improvement in House and
7.2% in Senate), perhaps in order to claim credit for successes during the next election cycle. A
divided legislature decreases the probability that a House member votes in favor of a measure
14
(11.1%), but the possibility of a null effect in the Senate cannot be rejected. An increasing
federal deficit is associated with an increased likelihood of members in both chambers voting in
favor of a measure (although this effect is small: 4.8% and 6% for a 1% increase in the deficit as
a percentage of GDP). Finally, electoral cycles seem to matter in the Senate (positive and
significant: 5.9%), but not in the House.
Conclusion
Our analysis is novel in that it relates the roll-call voting decisions of individual MCs to
the characteristics of delegations.
Specifically, we have examined the way in which the
decentralization of implementation authority shapes those choices. Building our theoretical
claims on the MCs expectation of oversight costs in maintaining their electoral constituency’s
share of grant distributions, we hypothesize and find support for several claims. Ceteris paribus,
members are less likely to vote for bills as their implementation becomes more decentralized
through the inclusion of GIA provisions when they either share the party affiliation of the
president, but not their home state’s governor or share partisan affiliation with both levels. MCs
are more likely to vote in favor of bills as their implementation becomes more decentralized
when they share partisanship with their home state governors, but not the president, all else
equal. Finally, when members share party affiliation with neither of these political executives,
the likelihood that they support a bill increases, ceteris paribus, with decentralization.
15
16
Figure 1: Average Congressional Choices by Congress in Final Enactments
1
0.9
0.7
0.6
Joint Ratio
0.5
Grant Ratio
0.4
0.3
State Ratio
0.2
National Ratio
0.1
0
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
Delegation Ratio
0.8
Congress
17
Figure 2: Chamber Differences in Average Grant Ratios by Congress
0.35
0.3
AVerage Grant Ratio
0.25
0.2
0.15
0.1
0.05
0
93
94
95
96
97
98
99 100 101 102 103 104 105 106 107 108 109 110 111
Congress
Final Average Grant Ratio
Senate Average of Grant Ratio
House Average of Grant Ratio
18
Table 1: Congressional Vote Choices & Grants-in-Aid
House
Passage
Variables
House
Average Marginal
Effects
Senate
Passage
Match Governor
baseline
Match President
0.162
Avg M.E.
-0.017
0.419
(0.038)**
(0.036)
(0.084)**
Match Both
Match Neither
Grant Ratio
Match Governor x Grant Ratio
baseline
Avg M.E.
(0.133)**
0.234
(0.084)**
0.230
(0.096)*
-0.174
(0.054**)
0.525
(0.078)**
baseline
M.E. ↑GIA
0.894
0.186
0.039
0.431
(0.036)**
(0.034)
(0.099)**
-0.322
-0.283
-0.167
(0.035)**
(0.032)**
(0.057)**
0.350
0.128
(0.001)
M.E. ↑GIA
0.350
(0.063)**
baseline
0.894
(0.063)*
Match President x Grant Ratio
Match Both x Grant Ratio
Match Neither x Grant Ratio
Gubernatorial Spending Power
State Fiscal Health
Divided Legislature
Federal Deficit
(as % GDP)
Election Year
Senate
Average Marginal
Effects
(0.133)*
-1.147
-0.798
-1.188
-0.294
(0.088)**
(0.067)*
(0.191)**
(0.142)^
-0.934
-0.584
-1.291
-0.397
(0.084)**
(0.063)*
(0.198)**
(0.158)^
0.248
0.598
-0.039
0.855
(0.078)**
(0.058)*
(0.153)
(0.108)*
0.106
0.106
0.072
0.072
(0.046)*
(0.046)*
(0.086)
(0.086)
0.690
0.690
0.608
0.608
(0.051)**
(0.051)**
(0.134)**
(0.134)**
-0.111
-0.111
0.011
0.011
(0.016)**
(0.016)**
(0.053)
(0.053)
0.048
0.048
0.060
0.060
(0.007)**
(0.007)**
(0.013)**
(0.013)**
0.006
0.006
0.059
0.0588
(0.011)
(0.011)
(0.029)*
(0.029)*
Grant Ratio
0.114
0.114
0.087
0.087
(averaged, by legislator)
(0.407)
(0.407)
(0.845)
(0.845)
Match with President
-0.405
-0.405
-0.433
-0.433
(averaged, by legislator)
(0.039)**
(0.039)**
(0.104)**
(0.104)**
Match with Governor
-0.103
-0.103
0.091
0.091
(averaged, by legislator)
(0.042)*
(0.042)*
(0.089)
(0.089)
Gubernatorial Power
-0.107
-0.107
-0.145
-0.145
(averaged, by legislator)
(0.067)
(0.067)
(0.142)
(0.142)
State Fiscal Health
0.358
0.358
-0.079
-0.079
(0.588)
(averaged, by legislator)
(0.357)
(0.357)
(0.588)
Divided Legislature
-0.018
-0.018
0.103
0.103
(averaged, by legislator)
(0.078)
(0.078)
(0.171)
(0.171)
Federal Deficit
(averaged, by legislator)
Election Year
0.031
0.031
-0.049
-0.049
(0.014)*
(0.014)*
(0.030)
(0.030)
0.590
0.590
-0.173
-0.173
19
(averaged, by legislator)
Policy Area Fixed Effects
State Fixed Effects
Constant
N
Wald
AIC
BIC
(0.234)*
(0.234)*
(0.399)
(0.399)
Results not shown here, reported in Appendix
Results not shown here, reported in Appendix
0.754
0.798
(0.172)**
(0.288)**
68,324
3,640.04
68559.63
69372.38
14,299
704.18
12992.52
13666.07
Mundlak-Chamberlain corrected random effects probit, including fixed effects for state and policy area with robust s.e. where ^
p<0.1; * p<0.05; ** p<0.01.
20
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25
Appendix
Table 1A: Summary Statistics
Continuous Variables
Grant Ratio
State Fiscal Health (lag)
Federal Deficit as a % of GDP
Categorical Variables
Votes
Copartisan Regime
Gubernatorial Power
Divided Legislature
Election Year
Legislator Specific Averages:
Gubernatorial Power
State Fiscal Health (1 year lag)
Divided Legislature
Federal Deficit as a % of GDP
Election Year
Legislator-to-President
Distance
Legislator-to-Governor
Distance
Grant Ratio
Continuous Variables
Grant Ratio
State Fiscal Health (1 year lag)
Federal Deficit as a % of GDP
Categorical Variables
Votes
Copartisan Regime
Gubernatorial Power
Divided Legislature
Election Year
Legislator Specific Averages:
Gubernatorial Power
State Fiscal Health (1 year lag)
Divided Legislature
Federal Deficit as a % of GDP
Election Year
Legislator-to-President
Distance
Legislator-to-Governor
Distance
Grant Ratio
House Final Enactments
N
Mean
79002
0.160
79824
0.087
2.131
0.5
69214
79828
79744
79828
79828
N
79824
79824
79828
79828
79828
-2.666
0's
16263
30418
36160
65899
34056
Mean
0.495
0.087
0.174
-2.666
0.573
s.d.
0.411
0.038
0.137
0.990
0.057
min
0.000
-0.617
10.100
1's
52951
11870
35390
13929
45772
min
0.000
-0.332
0.000
-9.775
0.000
79828
0.293
0.357
0.000
1.000
79828
0.526
79828
0.160
Senate Final Enactments
N
Mean
17381
0.161
17578
0.085
0.315
0.026
0.000
0.026
1.000
0.354
s.d.
0.186
0.133
max
0.786
1.236
17578
0's
2801
6758
5716
14785
7064
N
17532
17578
17578
17578
17578
-2.731
0.5
2430
5486
Mean
0.610
0.085
0.160
-2.731
0.598
2.105
1's
11827
2904
9664
2820
10514
s.d.
0.396
0.041
0.132
0.904
0.055
min
0.000
-0.617
10.100
2's
min
0.000
-0.102
0.000
-9.667
0.000
max
1.000
0.283
1.000
0.525
1.000
17578
0.303
0.346
0.000
1.000
17578
17578
0.523
0.161
0.316
0.022
0.000
0.013
1.000
0.321
79828
2043
s.d.
0.188
0.119
8194
max
0.786
1.236
2.400
2's
11538
max
1.000
0.347
1.000
2.400
1.000
2.400
3's
26
Table 2a: Congressional Vote Choices & Grants-in-Aid (State & Policy Fixed Effects Reported)
Variables
House
Passage
House
Passage Full
Senate
Passage
Senate
Passage Full
Match Governor
Match President
baseline
0.176
baseline
0.162
baseline
0.428
baseline
0.419
(0.028)**
(0.038)**
(0.061)**
(0.084)**
0.209
0.186
0.467
0.431
(0.027)**
(0.036)**
(0.063)**
(0.099)**
-0.299
-0.322
-0.143
-0.167
(0.020)**
(0.035)**
(0.044)**
(0.057)**
0.127
0.350
0.972
0.894
(0.053)*
(0.063)**
(0.128)**
(0.133)**
baseline
-1.086
baseline
-1.147
baseline
-1.138
baseline
-1.188
(0.088)**
(0.088)**
(0.203)**
(0.191)**
-0.893
-0.934
-1.288
-1.291
(0.088)**
(0.084)**
(0.214)**
(0.198)**
0.224
0.248
-0.104
-0.039
(0.076)**
(0.078)**
(0.185)
(0.153)
Match Both
Match Neither
Grant Ratio
Match Governor x Grant Ratio
Match President x Grant Ratio
Match Both x Grant Ratio
Match Neither x Grant Ratio
Gubernatorial Spending Power
0.106
0.072
(0.046)*
(0.086)
0.690
0.608
(0.051)**
(0.134)**
-0.111
0.011
(0.016)**
(0.053)
Federal Deficit
0.048
0.060
(as % GDP)
(0.007)**
(0.013)**
Election Year
0.006
0.059
(0.011)
(0.029)*
State Fiscal Health
Divided Legislature
Grant Ratio
0.902
0.114
0.937
0.087
(averaged, by legislator)
(0.358)*
(0.407)
(0.896)
(0.845)
Match with President
-0.399
-0.405
-0.496
-0.433
(averaged, by legislator)
(0.035)**
(0.039)**
(0.078)**
(0.104)**
Match with Governor
-0.120
-0.103
0.005
0.091
(averaged, by legislator)
(0.035)**
(0.042)*
(0.078)
(0.089)
Gubernatorial Power
-0.107
-0.145
(averaged, by legislator)
(0.067)
(0.142)
State Fiscal Health
0.358
-0.079
(averaged, by legislator)
(0.357)
(0.588)
Divided Legislature
-0.018
0.103
(averaged, by legislator)
(0.078)
(0.171)
Federal Deficit
0.031
-0.049
(averaged, by legislator)
(0.014)*
(0.030)
Election Year
0.590
-0.173
(averaged, by legislator)
(0.234)*
(0.399)
Macroeconomics
baseline
baseline
27
Civil Rights
Health
Agriculture
Labor/Employment
Education
Environment
Energy
Transportation
Law/Crime
Social Welfare
Community Development/Housing
Banking/Finance/Domestic Commerce
Defense
Space/Science/Technology/Communications
Foreign Trade
International Affairs/Foreign Aid
Government Operations
Public Lands/Water Management
Connecticut
Maine
Massachusetts
New Hampshire
Rhode Island
Vermont
Delaware
New Jersey
New York
Pennsylvania
Illinois
Indiana
Michigan
Ohio
0.404
(0.035)**
-0.020
(0.026)
-0.126
(0.035)**
0.236
(0.025)**
-0.105
(0.023)**
0.896
(0.032)**
0.200
(0.026)**
0.265
(0.032)**
0.574
(0.041)**
-0.216
(0.034)**
0.045
(0.028)
0.669
(0.024)**
0.580
(0.023)**
0.016
(0.041)
0.106
(0.043)*
-0.405
(0.064)**
0.265
(0.021)**
0.273
(0.038)**
baseline
-0.120
(0.156)
-0.254
(0.106)*
-0.172
(0.185)
-0.233
(0.137)^
-0.064
(0.352)
0.049
(0.182)
-0.049
(0.107)
-0.080
(0.095)
-0.096
(0.103)
-0.198
(0.102)^
-0.056
(0.114)
-0.155
(0.107)
-0.225
0.437
(0.070)**
0.438
(0.078)**
0.035
(0.096)
0.519
(0.058)**
0.040
(0.057)
0.544
(0.062)**
0.271
(0.055)**
0.283
(0.072)**
0.610
(0.081)**
0.493
(0.086)**
0.417
(0.081)**
0.461
(0.059)**
0.590
(0.070)**
0.260
(0.081)**
1.087
(0.139)**
0.445
(0.124)**
0.269
(0.045)**
0.835
(0.104)**
baseline
0.082
(0.133)
-0.118
(0.155)
-0.368
(0.222)^
-0.050
(0.214)
0.110
(0.263)
-0.159
(0.107)
-0.186
(0.166)
-0.223
(0.197)
0.694
(0.151)**
-0.162
(0.169)
-0.042
(0.143)
-0.076
(0.185)
-0.052
28
Wisconsin
Iowa
Kansas
Minnesota
Montana
Nebraska
North Dakota
South Dakota
Virginia
Alabama
Arkansas
Florida
Georgia
Louisiana
Mississippi
North Carolina
South Carolina
Texas
Kentucky
Maryland
Oklahoma
Tennessee
West Virginia
Arizona
Colorado
Idaho
Montana
Nevada
New Mexico
Utah
Wyoming
(0.102)*
-0.237
(0.114)*
0.032
(0.136)
-0.137
(0.125)
-0.221
(0.123)^
-0.215
(0.112)^
-0.130
(0.146)
-0.207
(0.094)*
-0.140
(0.175)
-0.322
(0.096)**
-0.224
(0.110)*
-0.210
(0.118)^
-0.206
(0.098)*
-0.298
(0.108)**
-0.380
(0.132)**
-0.275
(0.121)*
-0.073
(0.105)
-0.192
(0.103)^
-0.453
(0.095)**
-0.146
(0.109)
-0.224
(0.111)*
-0.373
(0.115)**
-0.087
(0.118)
-0.149
(0.154)
-0.502
(0.141)**
-0.404
(0.127)**
-0.531
(0.189)**
-0.414
(0.188)*
-0.023
(0.178)
-0.228
(0.160)
-0.186
(0.148)
-0.435
(0.128)
-0.424
(0.187)*
-0.007
(0.112)
-0.057
(0.130)
0.019
(0.140)
0.251
(0.131)^
-0.415
(0.149)**
-0.010
(0.128)
-0.204
(0.191)
-0.139
(0.246)
-0.349
(0.122)**
-0.300
(0.148)*
-0.311
(0.140)*
-0.220
(0.121)^
-0.217
(0.160)
-0.440
(0.158)**
-0.599
(0.211)**
-0.574
(0.186)**
-0.341
(0.224)
-0.099
(0.094)
-0.061
(0.174)
-0.587
(0.156)**
0.032
(0.222)
-0.161
(0.143)
-0.672
(0.182)**
-0.224
(0.164)
-0.588
(0.188)**
-0.189
(0.104)^
-0.305
(0.178)^
-0.089
(0.154)
-0.485
(0.200)*
-0.329
29
(0.203)*
-0.285
(0.098)**
-0.231
(0.146)
-0.079
(0.107)
-0.028
(0.090)
-0.036
(0.220)
California
Oregon
Washington
Alaska
Hawaii
Constant
N
Wald
AIC
BIC
(0.212)
-0.128
(0.155)
0.035
(0.236)
-0.077
(0.177)
0.105
(0.175)
-0.158
(0.132)
0.888
0.754
0.768
0.798
(0.063)**
(0.172)**
(0.159)**
(0.288)**
68,388
759.31
71629.39
71738.99
68,324
3,640.04
68559.63
69372.38
14,431
210.12
13458.55
13549.47
14,299
704.18
12992.52
13666.07
Mundlak-Chamberlain corrected random effects probit, including fixed effects for state and policy area with robust s.e. where ^
p<0.1;
*
p<0.05;
**
p<0.01.
30