Inter-Service Rivalry and Distributive Politics

By

Barry Rundquist

University of Illinois at Chicago

and

Lisa Schmit

University of Illinois at Chicago

and Zero Maintenance International, Chicago, IL

Prepared for presentation at the Annual Meeting of the American Political Science Association, Washington, D.C.,
September 2, 2000.

Inter-Service Rivalry and Distributive Politics
A number of studies of distributive politics in Congress have often focused on military procurement spending (e.g., Goss, 1972; Ray, 1980, Mayer, 1991). Recent studies have focused on the paradox that such a highly distributive policy has usually not been found to provide evidence a Congress-centered distributive politics. Recent studies (e.g., Carsey and Rundquist, 1999a and 1999b, Rundquist and Carsey, nd.) have found evidence of a form of distributive politics in military procurement spending. But questions remain regarding the processes responsible for producing distributive patterns in the geographic distribution of military contracts.

The present paper is a departure from previous studies. Because we are able to disaggregate military procurement spending for each of the three major military services, we can explore several service-based processes that may underlie distributive patterns in aggregate military procurement spending.

Theory

Several different political processes may be behind the tendency we have found for places represented by majority party members on House defense committees and liberals in the Senate, as well as places with the appropriate manufacturing capacity and relatively high income levels, to benefit from year-to-year changes in the geographic distribution of prime military contracts (see Rundquist and Carsey, 1999a, 1999b). This is the conclusion of a study covering the 30-plus years from 1963 to 1995. One possible explanation is that the aggregate result reflects similar tendencies in the different military services. In this view, they all face the same political environment on the Hill, they are all subject to the same federal procurement regulations, and during and after the Cold War they have all tended to favor high technology weapons. Therefore, they might all be expected to distribute their procurement awards about the same way.

A second possibility is that the opposite is true. The tendencies that can be observed in the aggregate distribution of military procurement awards may reflect different political processes in each service. In this view, each service has it's own friends on congressional defense committees and its own geographic constituencies (Navy, the coasts; Air Force, the South and Southwest, etc.). Since it is difficult for a service and it's congressional friends to enact weapons programs that benefit it's constituency without the cooperation of the other services and their congressional friends, this view suggests that there will be an inter-service log roll in which all the services and their respective constituencies are benefit. The recurrent tendency for the Army, Navy, and Air Force to have similar shares of the annual military procurement budget is at least superficially consistent with this view.

A third view is that the long-term tendencies observed in aggregate military procurement spending actually reflect shorter term sub-patterns in which different services and areas of the country benefit and are deprived. This is consistent with the aggregate finding that some periods, like the Vietnam War and Reagan buildups, evidence different causal patterns (Carsey and Rundquist, 1999b). It is also consistent with long term trends in the military product line, such as that from wheeled transport favoring the Midwest to air frames favoring St. Louis, Los Angeles, and Seattle, to electronic components favoring Silicon Valley and it's geographic progeny (e.g., Markusen, et. al; 1991; Sandler and Hartley, 1995).

In this study we begin to sort through these theoretical possibilities for explaining aggregate patterns in the distribution of military contracts. Specifically, we ask whether the aggregate patterns: 1) reflect similar patterns of distributive politics in the Army, Navy, and Air Force; 2) are produced by the distributive politics of one or two of the Services procurement processes but not of all three; 3) that such aggregate patterns result from inter-service logrolling whereby different geographic areas get contracts from different military services; and 4) that the evidence of distributive politics in aggregate military procurement data are not reflected in service-level data at all.

The Study

To explore these possibilities we have combined year-by-year data on the distribution of each service's military procurement awards among U.S. counties for the five years from 1985 to 1990 with data on a variety of other characteristics of the counties. This was a period of Republican Presidents (Reagan and Bush) and for 1985 and '86, a Republican Senate. We have analyzed these data by estimating, for each service and for the three services combined, pooled cross section regression models using robust standard errors. The dependent variable in one model, which is estimated for the three services combines and for each service separately, is the per capita dollar value of military procurement awards. The dependent variable in the other model, again for the services combined and for each service separately, is the number of military procurement awards. Since distributive politics theories emphasize the importance of committee, party, and ideological blocs in Congress, we include lagged (by one-year) values of county representation on Congressional defense committees, by members of the majority party in Congress, and by Congressional liberals or conservatives. In addition, our regression models include controls for county economic capacity (number of people employed in defense-related industries, number of establishments in defense contracting industries, number of military bases), wealth (per capita income), and state and year dummies. The latter measures, which to simplify the presentation of our findings are not included in our summary tables but can be obtained from the authors, provide quasi-experimental controls for unidentified factors characterizing the counties and years that are not explicitly controlled for in the regression models (Beck and Katz, 1995).

The models also include a lagged value of the dependent variable. Such a cross-lagged model should be interpreted as predicting year-to-
year changes in the dependent variables. Thus the test of whether or not counties benefit from committee, party, or ideological representation is whether the coefficients for committee, party, or ideological bloc are positive and statistically significant. Negative coefficients for such variables would indicate that counties represented on a defense committee (say), tend to evidence year-to-year decreases in procurement expenditures. To test for the possibility of inter-service logrolling along geographic lines, we have included in the model for each separate service the other services' levels of per capita expenditures in the preceding year.

For the constant per capita dollar value and the number of contracts for each service, the following general model is estimated:

Defense Awardsit = + B1(Defense Awards1 it-1)+ B2 (Number of Bases) +B3 (Democrat on House Armed Service Commiteeit-1) +B4 (Republican on House Appropriation Commiteeit-1) +B5 (Republican on House Armed Service Commiteeit-1) + B6 (Democrat on Senate Appropriation Commiteeit-1) +B7 (Democrat on Senate Armed Service Commiteeit-1) + B8 (Republican on Senate Appropriation Commiteeit-1) +B9 (Republican on Senate Armed Service Commiteeit-1) + B10 (House Partyt-1) + B11 (Senate Party t-1) + B12 (House ideology) + B13 (Senate ideology) + B14 (Per capita Income ) + B15 (Size of Establishments) +B16 (Number of Defense Establishments) + state and year dummies + e.

Note that the total model omits the measures of each service's awards. Table 1 defines the variables in this equation.

In summary, this research design has several advantages over those used in earlier studies of the distributive politics of military contracting. First, and most important, it distinguishes between the military services. Second, it treats "contracting benefits" as a year-
to-year increase in contracts. Third, it measures military contracting as both the dollar value of contracts per capita and as the number of contracts. Several scholars (e.g., Stein and Bickers, 1995) have argued that the number of contracts is more plausible measure of a political benefit--i.e., because legislators can more easily take credit for bringing in a contract than for increasing the amount of contract expenditures. Fourth, the pooled cross section design controls for unidentified temporal and cross-sectional variation. Fifth, the multi variate model controls for a number of economic factors that may affect the geographic distribution of military contracts. Sixth, using counties as our unit of analysis has the advantage of allowing better control for economic capacity than does using states or congressional districts as units of analysis. Seventh, by restricting the analysis to Army, Navy, and Air Force contracts only, we are able to evaluate the inter-service log rolling hypothesis without having the analysis confounded by other kinds of defense contracting.

Findings

Totals. To see whether the distributive politics of Army, Navy, and Air Force spending is similar to or different from the distributive politics of military procurement spending in the aggregate, we first estimated a model for total DOD awards–Army + Navy + Air Force procurement dollars and the number of Army + Navy + Air Force for each year. This analysis (unlike that in an earlier version of this paper, see Rundquist and Schmit, 2000) excludes military procurement contracting that cannot be associated with one of these three services.

First, Table 1 shows that significant increases in military contract expenditures tended to go to relatively poor counties, counties with relatively more military installations, and counties that had received more military expenditures in the previous year. Counties that experienced significant decreases in military procurement expenditures in this model were relatively richer ones and those represented by a Republican on either of the House defense committees. Thus in this aggregate analysis, there is no evidence of party, committee, or ideological bloc distributive politics, unless one wants to conceive of "negative distributive politics" along the line of Fenno's (1966) and Ferejohn's (1974) discussions of the Cannon/Taber norms of the Rayburn period. We will return to this point.

Second, Table 1 shows that counties that received significant increases in the number of defense contracts tended to have larger military industrial firms and to have received more contracts in the previous year. Counties that received significantly fewer contracts than in the preceding year tended to be those that had more bases, smaller defense contracting firms, and relatively fewer contracts in the preceding year. Thus, this aggregated analysis suggests that, during the late 1980's, no form of congressional distributive politics, either partisan, committee-based, or ideological, was in evidence in the way contract dollars and the number of contract awards were distributed among counties in the U.S. In both models, measures of counties' economic capacity prove to be significantly associated with contracting increases. The only distributive effects were negative ones: counties represented on both House defense committees by a Republican tended to receive year-to-year decreases in military procurement expenditures.

We now examine the extent to which these conclusions about the geographic distribution of combined Army, Navy, and Air Force military procurement expenditures and contracts hold for each service separately.

Army. Increases in the level of per capita Army contract expenditures in counties is positively associated with three things: the level of the previous year's Army expenditures, the level of the previous year's Navy expenditures, and the number of Army bases in the county. There are no distributive politics here, either positive or negative.

Increases in the number of Army awards is positively associated with the number of Army awards in the previous year, the size of military industrial firms, and the number of bases. Decreases in Army contracts are associated with representation by a Republican on Senate Armed Services and the number of military industrial firms. Again, there is no positive evidence of distributive politics.

Navy. Increases in per capita Naval procurement expenditures are positively associated with representation by a Democrat on Senate Armed Services, the level of the previous year's Naval procurement expenditures, the wealth of the county, and the number of Navy installations. Counties represented by Republicans on Senate Armed Services experienced significant decreases. Naval expenditures, therefore, suggest a Democrat-on- Senate-Armed-Services distributive effect, no positive House committee, party, or ideological effects, and a negative effect of representation by a Republican on Senate Armed Services.

Increases in the number of Navy contracts are associated with the previous years number of contracts, the number of Navy bases, and the size of military firms. Decreases in the number of Navy contracts are associated the number of Air Force contracts in the previous year, representation by a House Democrat, and representation by Democrats and conservatives in the Senate. Distributive politics is not evident here but, as we discuss later in the paper, the Air Force negative correlation is consistent with an inter-service log rolling hypothesis.

Air Force. Counties that evidence significant increases in Air Force procurement expenditures tend to have received high levels of such expenditures in the preceding year, to be represented by a House Republicans, to be relatively rich, and to have more Air Force bases. Counties that received significant decreases tend to be represented by a House Democrat and by a Republican on the House Appropriations defense subcommittee, were poorer, and had fewer bases and smaller military industrial firms. There is no evidence of positive distributive politics effects, evidence of a negative partisan effect--
House Democrats suffered--and a Republican-on-House-Appropriations effect, and plenty of evidence that Air Force contracts are going to places with military manufacturing capacity.

Increases in the number of Air Force contracts are associated with the number of contracts in the previous year, representation by a House Republican, being relatively rich, and having more Air Force installations. Decreases are associated with having a Democratic representative, being relatively poor, and having fewer Air Force installations. So places represented by House Republicans benefitted and House Democrats were deprived.. But economic capacity also had a big effect on the number of contracts that counties received.

Inter-Service Logrolling. Our test of inter-service logrolling in these models is to see if there is a negative correlation between the number and amount of each service's contracts and the number or amount of the increase predicted for another service. For example, when predicting the amount of Army contract awards, a negative correlation with the amount of Navy and Air Force contract awards would suggest that Army contracts tend to go to parts of the country where Navy and Air Force contracts do not go. If the same was true in all of the service models, then we could conclude that inter-service logrolling tends to characterize the allocation of defense contracts.

Our analysis shows, however, that only one of the eight possible service/service coefficients is significantly negative. This is the coefficient for the number of Naval contracts and the number of Air Force contracts. In other words, it is only Naval contracts that are distributed so that year-to-year benefits favor counties that tend to receive significantly fewer Air Force contracts.

Three of the service-by-service coefficients are significantly positive (or very near significant at the .10 level). Thus counties that have had high levels of Navy and Army contracts in the preceding year tend to have significant year-by-year increases in the per capita amounts of Air Force and Army contracts in the subsequent year. Similarly, counties that have had a larger number of Navy contracts in the preceding year tend to show significant increases in the number of Air Force contracts they receive in the following year.

These findings suggest that the aggregate levels of military contracts we observed above result more from the accumulation of similar patterns in each service than from inter-service log rolling to benefit different areas. This conclusion is supported by the fact that all but one of the insignificant coefficients are positive. But that most of the service-by-service coefficients are insignificant, and that as we have seen, the distribution of all three services' contracts are strongly effected by economic capacity, suggests that the services are independently responding to the same basic constraints in awarding their contracts.

So what can we conclude about logrolling? First, the strong form hypothesized--significant negative correlations for both number and amount of contracts in all equations--is not present. Only Navy contracts exhibit this tendency in that they tend to go to counties that do not receive Air Force contracts. Second, the opposite of logrolling, that each service's contracts go to the same places that do contracting for other services, is upheld. More Air Force contracts tend to go to counties that receive more Army and Navy contracts; more Army contracts tend to go to counties that receive more Navy contracts.

Discussion

Consider the questions with which we began? First, do aggregate characteristics of the geographic distribution of military procurement expenditures reflect similar patterns of distributive politics in the Army, Navy, and Air Force? For the most part, they do. In all of the models, measures of economic capacity like size of military industrial firms and previous year's military contracts, predict year-to-year increases. Similarly, in most of the models, counties with more military installations get more contracts and money.

But there are differences. First, the finding that relatively poor areas get more increases in total procurement expenditures is not supported in any of the service models. In fact, in all three service models, wealthier areas tend to receive significant year-to-year increases in procurement expenditures. Thus the negative coefficient in the totals model should probably be dismissed.

Second, although committee, party, and ideological factors do not appear to affect the total expenditure models, they do appear in a couple of the service models. Specifically, counties represented on the Senate Armed Services Committee by Democrats tend to evidence larger year-to-year increases in Naval procurement expenditures and counties represented by House Republicans tend to receive larger increases in both the number and dollar amount of Air Force contracts. Thus the aggregate models tend to obscure these distributive effects in Naval and Air Force procurement spending.

Nevertheless, the service-by-service analysis does not show a lot of positive distributive effects. In fact we have shown that many of the effects of distributive variables are negative–meaning that committee, party, and ideological factors significantly decrease the number and size of year-to-year procurement awards that counties receive. This finding has led us to discuss negative distributive politics. Such negative distributive effects were found for Republicans on both defense committees in the total expenditure model, Republicans on Senate Armed Services in the Army number of contracts model, Republicans on Armed Services in the Naval expenditures model, House Democrats, Senate Democrats, and Senate conservatives in the Naval number of contracts model, House Democrats and House Republicans on Armed Services and the Appropriations defense subcommittee in the Air Force expenditures model, and House Democrats in the Air Force number of contracts model. In fact, we are able to show that the negative distributive politics tendency for Republicans on House Armed Services and the House DOD Subcommittee in the total expenditures analysis reflects Republicans on Armed Services getting year-to-year decreases from Naval procurement and Republicans on the DOD Appropriations subcommittee getting decreases from Air Force procurement.

In all, there are five times more significant negative distributive effects than there are significant positive ones.

We started this paper by referring to three possible explanations of the aggregate patterns in the geographic distribution of military procurement spending. The analysis just reported seems to reject one type of explanation, that different political patterns characterize different services' procurement, such that different places benefit from the procurement spending. We find that all three services tend to allocate year-to-year increases to places with manufacturing capacity, near-by military installations, and a recent history of receiving contracts from the service.

These findings are consistent with the first line of explanation we considered--that the services are subject to similar constraints when they award defense contracts. But we also have found two positive and ten negative distributive effects in our models. Since these effects tend to be different in different services, we cannot conclude that the services' procurement processes are totally similar. Rather, it appears that they are similar in responding to the presence of installations, previous contracting, and local industrial capacity, and different in the nature of the political influences they are subjected to.

For example, our analysis suggests that the Navy tends to respond positively to counties represented by Democrats on Armed Services, while the Air Force responds positively to places represented by House Republicans. The Army responds negatively by awarding fewer contracts to counties represented by Senate Armed Services Republicans. The Navy reduces the expenditures going to counties represented by Senate Armed Services Republicans and gives fewer contracts to counties represented by House and Senate Democrats and Senate conservatives. The Air Force punishes counties represented by House Democrats and House Republicans on either military committee.

Explaining this pattern of differential political responsiveness is probably beyond the capability of the authors. But the right answer is no doubt the general one that distributive politics in a democracy is inherently unstable and the bigger the prize to be distributed, the more conflict there is over it and the less stable it will be. The snap shot we have taken of the late 1980s, a period of intensifying partisanship in American politics, split partisan control of Congress and the Presidency, and the end of the Cold War, is of temporary geographic winners and losers in conflict over a declining military procurement pie. A possible explanation of all the negative distributive effects may be precisely that the Reagan build up was building down and the Cold War was ending (Defense spending generally started to decline in 1985). Perhaps the places that had gained most during Reagan build up were those represented on Congressional defense committees and in the majority party and the significant reductions in contracts and expenditures simply reflects that they had more to lose.

Perhaps also, the late 1980s context led to a different-political-
strategies-for-different-services pattern that may be what we are observing in our analysis. The important point for distributive theorists, however, is less that different distributive political strategies may have involved different congressional institutions and different military services, than that distributive politics was going on.

Distributive politics is not something that is fixed in one place so that the beneficiaries will always be (say) on particular committees or in one party or in one branch of government. This analysis, which we have designed to control for many of the temporal and cross sectional factors that may affect defense contracting, and that therefore allows us to observe the operation of distributive processes in Congress in a way that has heretofore been impossible, reveals 1) that there was a lot of distributive politics going on, and 2) that in the late 1980's, it was pretty messy.

Conclusion

This paper is a first cut at disaggregating the distributive politics of military procurement by military service. Data on the service-by-service distribution of military contracts among U.S. counties were analyzed for a relatively short time period in the mid- to late-1980s.

What we have found is that several common factors, the presence of military bases, high levels of previous benefits and contracts, and industrial capacity, affect the distribution of each services contracts. We have also found that each service's contract distributions were affected differently by different distributive characteristics of Congress and that these distributive effects were both positive and negative. We have speculated that the negative distributive effects may reflect earlier positive benefits in the sense that places represented on defense committees and in the majority party in the House and Senate may have benefitted significantly during the Reagan build up in the early 1980s and had more to lose in the late 1980s. We also speculate that the different distributive foci of the different services may also reflect the turbulent partisan and defense environment of the late '80s. We also observe that because congressional distributive politics operates in a majority voting context, it should not be expected to be particularly stable. Rather places that benefit should be expected to use any institutional leverage they can obtain in the government to further their efforts to obtain federal resources. The struggle to do so should result in some institutions advantaging different areas at different times.

Table1 : Operationalization of Variables

Defense Awards

Measured as per capita constant county defense expenditures Awards is measured as military prime contract expenditures and as the number of contracts. Defense awards is measured separately as Army awards, Air Force Awards, Navy Awards and to sum of all three.

Data Source: PRIME CONTRACTS ON CD-ROM.
Per Capita Income	Measured as per capita constant income in counties*. Data Source is the Regional Economic Information System.
Number Military Base	The number of military bases in a county.
Establishments

Dummy	Measured as the number of establishments in a county that fall into the 19 chosen SIC Codes. Data Source is County Business Patterns for 1985-
1990.
Size of Establishments	Measured as the size of establishments that fall into the 19 chosen SIC codes*. Data Source is County Business Patterns for 1985-1990.
Number of Counties	Measured as the number of counties in a district. Data Source is the Consolidated Federal Funds Report.
Dem on Appropriations Com in House	Dummy variable indicating whether or not the county was represented on the House Appropriations Committee by a Democrat for a given year. Coded 1=yes and 0=no. Data Source is the Congressional Quarterly.
Rep on Appropriations Com in House	Dummy variable indicating whether or not the county was represented on the House Appropriations Committee by a Republican for a given year. Coded 1=yes and 0=no. Data Source is the Congressional Quarterly.
Dem on Armed Service Com in House	Dummy variable indicating whether or not the county was represented on the House Armed Service Committee by a Democrat for a given year. Coded 1=yes and 0=no. Data Source is the Congressional Quarterly.
Rep on Armed Service Com in House	Dummy variable indicating whether or not the county was represented on the House Armed Service Committee by a Republican for a given year. Coded 1=yes and 0=no. Data Source is the Congressional Quarterly.
Dem on Appropriations Com in Senate	Dummy variable indicating whether or not the county was represented on the Senate Appropriations Committee by a Democrat for a given year. Coded 1=yes and 0=no. Data Source is the Congressional Quarterly.
Rep on Appropriations Com in Senate	Dummy variable indicating whether or not the county was represented on the Senate Appropriations Committee by a Republican for a given year. Coded 1=yes and 0=no. Data Source is the Congressional Quarterly.
Dem on Armed Service Com in Senate	Dummy variable indicating whether or not the county was represented on the Senate Armed Service Committee by a Democrat for a given year. Coded 1=yes and 0=no. Data Source is the Congressional Quarterly.
Rep on Armed Service Com in Senate	Dummy variable indicating whether or not the county was represented on the Senate Armed Service Committee by a Republican for a given year. Coded 1=yes and 0=no. Data Source is the Congressional Quarterly.
House Ideology	The average ideology of a representative based on his or her Conservative Coalition Score. The higher the score, the more conservative. Data Source is the Congressional Quarterly.
Senate ideology	The average ideology of a state's Senate delegation based on Conservative Coalition Scores. Data Source is CQ
Party of Rep	The average party of the Representative. 1=Democrat and 0=Republican
Part of Senators	Measured as 2=2 Democratic Senators, 1=split and0=2 Republicans
Constant dollars are measured in 1982 dollars.

Table 2: Total DOD Awards

Per Capita $	Number of Awards

total defense awards_1	0.817031***	0.968432***
hparty_1	-0.013

-2.00065**
hdasdm_1	0.009776

0.070663
hrapdm_1	-.02852*

-1.49642
hrasdm_1	0.016737

1.713571
hid_1	-0.00021

-0.0165
sdap_1	-0.01857

-2.01609
sdas_1	0.012296

2.057435
srap_1	-0.02099

-5.70683**
sras_1	-0.01816

-0.12477
sparty_1	-0.01573

-1.82081
sid_1	-8.66E-06

-0.09611
pccinc_1	3.91E-06**

0.001194**
crsze_1	1.23E-06

0.000109
establ_1	-4.9E-05

-0.13665**
basenb

R2=

0.0386**

.70

34.9302**

.99

*** significant at .000 level

** significant at .05 level

* significant at .09 level

Table 3: Total Service Awards

Per Capita $	Number of Awards
total service awards_1	0.820082***

526.0231***
hparty_1	- 0.01271	264.8189
hdasdm_1	0.006148

-364.088
hrapdm_1	-0.02804*

22523.03
hrasdm_1	0.01709

15464.79
hid_1	-0.00022

-183
sdap_1	-0.01865

-1265.96
sdas_1	0.009017

2398.679
srap_1	-0.01592

24.85191
sras_1	-0.01762

-2364.62
sparty_1	-0.01405

-3241.14
sid_1	-6.4E-05

-34.7699
pccinc_1	3.60E-06**

-1.13513
crsze_1	1.21E-06

3.561763**
establ_1	-4.2E-05

-2.50815
basenb

R2

0.034662**

.70	-78116.6**

.61

*** significant at .000 level

** significant at .05 level

* significant at .09 level

Table 4: Total Army Awards

Per Capita $	Number of Awards
army awards	0.768467***	1.00259***
rafn4_1	0.017629

-0.00716
navy awards_1	0.005305**	0.000859
hparty_1	-0.00345

-0.2853
hdasdm_1	0.003117

0.046252
hrapdm_1	-0.01764

-0.28942
hrasdm_1	0.018704

0.652267
hid_1	1.94E-05

0.000487
sdap_1	0.005042

-0.36408
sdas_1	-0.01095

-0.10316
srap_1	-0.00811

-1.47343
sras_1	-0.00134

-0.6635*
sparty_1	0.002756

-0.41267
sid_1	-6.9E-05

-0.02133
pccinc_1	7.48E-07

8.97E-05
size_1	1.30E-06

8.98E-05
establ_1	-4.1E-05

-0.01612*
basearnb	0.05407**

4.516429
R2	.67	.97

*** significant at .000 level

** significant at .05 level

* significant at .09 level

Table 5: Total Air Force Awards

Per Capita $	Number of Awards
air force awards_1	0.832268***

0.97436***
army awards_1	0.005656

0.009177
navy awards_1	0.002676

0.008274
hparty_1	-0.00324

-0.87782**
hdasdm_1	0.005227

0.965857
hrapdm_1	-0.00616

-1.32019
hrasdm_1	-0.00583**

0.041945
hid_1	-8.9E-05

-0.00819
sdap_1	0.001581

-0.81336
sdas_1	-0.00498

1.777235
srap_1	-0.00199

-0.6793
sras_1	-0.00111

-0.3258
sparty_1	-0.00491

0.066658
sid_1	-6.9E-05

0.000224
pccinc_1	9.18E-07**

0.000306*
size_1	4.45E-07

-5.3E-05
establ_1	-4.79E-06

-0.02994
baseafnb	0.032955**

18.17555***
R3	.79	.98

*** significant at .000 level

** significant at .05 level

* significant at .09 level

Table 6: Total Navy Awards

Per Capita $	Number of Awards
navy awards_1	0.80949***	0.998026***
army awards_1	0.018918	-0.01102
air force awards_1	0.029306	-0.0449**
hparty_1	-0.00566

-0.91715
hdasdm_1	-0.00321

0.208642
hrapdm_1	-0.00535

0.717897
hrasdm_1	0.006424

1.391737
hid_1	-0.00012

-0.01567
sdap_1	-0.02548

-0.92422
sdas_1	0.024906*

0.256257
srap_1	-0.00445

-3.47143
sras_1	-0.01532*

0.420435
sparty_1	-0.01208

-1.50993
sid_1	6.78E-05

-0.07931**
pccinc_1	1.38E-06*

0.000599**
size_1	-6.13E-07

0.000078
establ_1	-6.51E-06

-0.06543**
basenvnb	0.029368*

31.34574***
R2	.65	.97

*** significant at .000 level

** significant at .05 level

* significant at .09 level

Notes

* This paper would not have been possible if Kenneth Mayer had not generously shared a data set with us a couple years ago. We also gratefully acknowledge the support of the National Science Foundation for the larger study of which this is a small part, and the continuing contributions of Tom Carsey.

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