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The political determinants of Federal Grants allocation on state level: The role of the U.S. President

Name : Wouter B. van Dijk

Address : Vredehofweg 8B

3062EN Rotterdam

The Netherlands

Student number : 306949

E-mail address : woutervandijk88@hotmail.com

University : Erasmus University Rotterdam

Erasmus School of Economics

Master : International Economics and Business Studies

Supervisor : Dr. C. Testa

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AbstractThere is quite some literature and study available analyzing the role of the U.S. President on the allocation of Federal spending for the New Deal period and the period after the implementation of the Congressional Budget and Impoundment  Control Act of 1974 , where this hardly is the case for the period in between. This paper’s prime objective is to analyze the Presidential influence on the allocation of Federal spending in the period ranging from 1960 till 1974. For this study both theoretical as well as empirical studies have been analyzed and tested with three hypothesis. The main findings that came out of this study were that states relatively supporting the incumbent President during the last Presidential election receive less Federal grants, indicating that the President does not target loyal states to improve (re-)election changes. States where the electoral race was very close receive on average per capita more, but swing characteristics do not consistently have significant influence on the allocation of grants. At last hardly any evidence is found of Presidential alignment budgeting. Therefore this paper does not find much evidence of direct Presidential influence on the spending of Federal grants.  

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Table of content Introduction 4

1. Theoretical literature 6

1.1Theory of targeting marginal/swing states & theory of rewarding loyal states 6

1.2 Theory of partisan alignment budgeting 7

2. Empirical literature 8

3. Hypothesis 16

4. Variables 17

4.1 Dependent variable 17

4.2 Independent variables 18

4.2.1 Hypothesis 1 18

4.2.2 Hypothesis 2 19

4.2.3 Hypothesis 3 19

4.3 Control variables 21

Definition of Variables & sources of data 22

5. Results 23

5.1 Hypothesis 1 & 2 23

5.2 Hypothesis 3 25

6. Robustness check 27

6.1 Multi-co-linearity 27

6.2 Auto- or serial correlation 27

6.3 Misspecification 28

6.3.1 Robustness check combined hypothesis 29

6.4 Robustness check individual hypothesis 30

6.4.1 Hypothesis 1 &2 30

6.4.2 Hypothesis 3 31

6.5 Conclusion 31

6.6 Auto-regressive model 32

6.6.1 Auto-regressive model results 32

6.6.2 Multi-co-linearity and Auto- or serial correlation 32

7. Conclusion 33

8. References 34

9. Appendices 38

A Spread and variable overview 38

B Level model analysis 40

C Auto-regressive model analysis 46

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IntroductionThere is quite some literature available on Federal spending and the distribution of it. Theoretical as well as empirical research in the field of Federal spending gives insights on factors that influence the allocation of federal monies to the States. A large part of the literature primarily focuses on the Congressional pork-barrel politics and overrepresentation of small states in Congress. Other studies deal with the influence of important committees, leaving out the influence of the executive. When the influence of the executive is addressed this is most of the time limited to the Presidents right to veto.

Few studies deal with the influence of the executive. According to these studies the President does have power on the appropriation of the budget in the “New Deal’’ era as well as in the period after the implementation of the Congressional Budget and Impoundment Control Act of 1974.

The Congressional Budget and Impoundment Control Act of 1974 was established in order to limit the power of the President to influence Federal spending. After years of democratic control of House and Senate, Congress was able to push this through due to the change in (public) opinion following the Watergate scandal. Thus prior to the Implementation of this Act of 1974 one could expect the President to have substantial influence on the Federal budget and its distribution.

While the theoretical and empirical evidence of Presidential influence is only present in the literature for these periods, there is a lack of research for the period between the Second World War and implementation of the Congressional Budget and Impoundment Control Act of 1974.

As there are significant differences in Federal Grants by state in the period 1960-1974 (see table 1), it is of interest to analyze to what extent pure political factors can explain the differences. For example, an average resident of the state of Wyoming receives more than 3.5 times as much per capita real Federal grants than an average inhabitant of the state Iowa.

This paper’s prime objective is to analyze the Presidential influence on Federal budget allocations to the states between 1960 and the date on which the Congressional Budget and Impoundment Control Act of 1974 was implemented.

There is a lot of literature providing theoretical arguments for why the President may want to divert funds away from a pure social surplus maximization allocation. Amongst others Lindbeck & Weibull (1987) demonstrates that Federal spending is targeted towards the ‘marginal’ or ‘swing’ states. Dixit & Londregan (1996) find that if parties have a comparative advantage (machine politics) to target their loyal voters, they prefer spending Federal resources in favor of their loyal supporters. At last amongst others McCarty (2000) stresses that partisan alignment motivation by the U.S. President may result in a disproportionate allocation of Federal spending towards legislative regions controlled by members of the same party.

The impact of political factors on federal budget allocation has been confirmed by several empirical studies. Wright (1974) and Fleck (2001) find that states where the electoral competition is high, receive on average per capita more Federal spending during the “New Deal” era. Hoover and Pecorino (2005) also find evidence regarding higher relative spending towards states with high electoral competition, but for the period after the passage of the Impoundment Act. Amongst others Levitt & Snyder (1995) find that the average Democratic share of vote in Presidential elections is positively related to Federal assistance expenditures. Larcinese, Rizzo and Testa (2006) and Hoover and Pecorino (2009) find evidence of partisan alignment budgeting.

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Table 1: Average real per capita Federal Grants by state, 1960-1974 1

States Real US$ Per capita States Real US$ Per capitaWyoming 194.17 Mississippi 94.82Montana 156.98 Tennessee 94.47New York 156.83 North Dakota 88.90North Carolina 149.97 Minnesota 88.53Virginia 142.10 Maine 86.90Oklahoma 136.59 Kansas 84.03South Dakota 129.51 New Jersey 83.13Louisiana 126.21 Delaware 82.95Missouri 126.04 Texas 82.02Wisconsin 124.40 New Hampshire 80.69Nevada 123.94 Nebraska 75.60Utah 120.41 South Carolina 75.37Arizona 119.01 Vermont 75.17Illinois 113.37 Idaho 74.47Colorado 111.22 Connecticut 73.41Alabama 110.04 Pennsylvania 72.02Kentucky 108.35 Michigan 70.79Arkansas 106.14 Maryland 68.75Rhode Island 100.11 Indiana 68.44California 98.28 Ohio 66.47Washington 98.12 Florida 66.06Georgia 97.04 West Virginia 63.14Oregon 96.82 New Mexico 59.11Massachusetts 95.24 Iowa 54.96

To briefly summarize the main findings, this study does not find significant evidence that loyal states are rewarded. In fact quite robust evidence is found that states with high share of vote for the incumbent President during the last Presidential election receive less. States where the Presidential electoral race was close are targeted relatively more, but the evidence that states receive more if they on average relatively often change political color is not consistent. Evidence regarding Presidential alignment budgeting is not found, but partisanship does play a role in the distribution of Federal Grants. Overall evidence that the U.S. President is able put large influence on the allocation of Federal Grants in the period between 1960 and 1974 is very modest.

The remainder of this paper is organized as follows. The next section (section 1) will provide an overview of the theoretical literature and theoretical background to the question of why the President may want to divert funds to particular states, groups or people. In section 2 an overview of the existing empirical literature addressing the points stressed in section 1 is presented. Based on the theoretical literature and the empirical literature we formulate in section 3 the main hypothesis of our empirical investigation. In section 4 we describe the data and next we carry out our main empirical analysis in section 5. Section 6 evaluates to what extent the empirical findings are correct and robust against different specifications. In section 7 we present the conclusions.

Theoretical Literature

1 Presented in main text and in Appendix A

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1.1 Theory of targeting marginal and/or swing states & theory of rewarding loyal states

The pre-determined political bias of a person or group is an important determinant for the political choice during elections. When this bias is very large it will be very difficult and/or expensive for a political party to convince this person or group to sway away from this bias. Hence when the bias is small it is less difficult or expensive to alter the pre-determined choice.

With the assumption of decreasing marginal utility of consumption the model of Lindbeck & Weibull (1987) demonstrate that Federal spending is negatively related to the pre-determined absolute level of the political bias. As a result of this, politicians of either political side target indecisive voters also called marginal voters or swing voters.

Dixit & Londregan (1996) questions whether parties would allocate spending to independent voters or to their supporters. According to Dixit & Londregan (1996) when parties are equally able to direct funds to any group, they will target the swing or marginal voters. However if instead of being equally able to target any group, parties have a comparative advantage (machine politics) in targeting their loyal voters, then Federal spending will be allocated to loyal voters or loyal states.

Cox & McCubbins (1986) investigate electoral politics with respect to the stability of coalitions. Their theorem states that a person or group is targeted based on their electoral rate of return. Hence there is a link between the rate of electoral return and the amount of benefits promised to the person or group. Cox & McCubbins (1986) make a distinction between core supporters that voted in favor of the candidate, swing voters and opposition voters. According to them the opposition will, when targeted by a candidate, not change their position. Targeting the opposition voter is not effective to win that vote.

Many scholars have argued that candidates (should) target swing voters in order to increase the election prospects, but according to Cox & McCubbins (1986) this should be seen in light of investment choices. The number of core supporters is well known as it is their reaction to electoral promises. On the other hand swing voters are more risky since both parties could influence them and therefore targeting swing voters is a more risky investment. The trade-off between rate of return and the variance (risk) is important and with it the attitude of the candidate towards the risk and trade-off. In light of these theoretical findings it becomes clear that risk adverse candidates would rather target their core supporters than swing voters. It follows that candidates favor their supporters more than swing voters and favor swing voters more than opposition voters. In other words candidates target loyal voters.

It still holds that groups with high marginal utility of consumption are benefitted. According to Dixit and Londregan (1995) a group has more political power when it has more alignment of beliefs with the ideological field of a political party, but in a model like in Dixit and Londregan (1996) with taxes and redistribution it is not always a blessing to be close to the core supporters of a political party, if being a core supporter means that a person is less sensitive towards higher taxes. If that is the case, it could well be that politicians try to tax their supporters more and redistribute these taxes towards the swing voters.

According to Grier, McDonald and Tollison (1995) the unequal distribution of the electoral votes in the U.S. together with the single winner voting system induces the President to divert funds towards states that have a larger population and are more densely populated. They find that the ‘floor’ votes of state Senator’s influences the amount of vetos initiated by the President. This basically implies that the executive uses his veto more when the important state Senators are against a particular bill.

1.2 Theory of partisan alignment budgeting.

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Snyder (1991) points out that when someone wants to buy votes they target the legislator who is somewhat against the change instead of the strong supporter or the strong opponent. His paper is not directly related to the executive influence, but one can imagine that the idea of buying votes in his paper can be extended towards the political vote bargain. This way the executive administration targets particular legislators in order to advance his agenda. Multiple papers have addressed the same issue; one example is the paper by Groseclose (1996) and Groseclose & Snyder (1996). The main point put forth by these papers is the influence of the executive on particular legislators by logrolling to push his legislative agenda and to win more votes.

Mebane and Wawro (2002) focus on local Federal expenditures and evaluate whether the distribution of those local Federal expenditures is in line with the ideological beliefs and interests of the U.S. President. It follows that the executive influences the allocation to divert the funds towards constituencies that are important during elections with his power to use the Veto. Mebane & Wawro (2002) divide the distribution of local Federal expenditures between on the one hand voter-oriented spending (to attract support of ordinary voters) and on the other hand spending to reward certain elites (awarding aligned politicians or legislators who have supported the campaign). It follows that both types of local spending (voter-oriented and the elite allocation) is influenced by political motives. After election some of the elites (politicians, legislators etc.) become even part of the Administration. Therefore this can be seen as the President allocating local Federal spending towards aligned legislators and representatives. In line with this point, McCarty (2000) stresses the partisan alignment motivations of the U.S. President. The executive may feel obliged to allocate Federal spending towards aligned constituents.

So the President may want to divert funds away from an socially optimum in order to improve (re-) election changes by for example targeting particular groups such as swing voters or loyal voters, to push his political agenda by for example logrolling and by diverting funds towards partisan politicians.

2 Empirical Literature

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A considerable amount of literature addressing the disproportional spending is based upon data from the “New Deal” era. This period is interesting since at that time Federal spending was quite large and growing. According to Wright (1974) policy is always a result of many interactions between all parts of society, but during the “New Deal” era both chambers and the president were Democratic, simplifying the interaction. This makes it easier to analyze [Wright (1974 : 32)]. It can be stated that this strain of literature started with the paper of Wright (1974).

Arrington (1970) did research on the distribution of agricultural relief programs in the “New Deal” period. According to him one could expect that the “equality of needs” should be close to or equal to the “equality of benefit”. This means that during this period all persons should be threatened equally with respect to the receiving’s of agricultural loans and expenditures by the Federal government based on their needs. From his research it became clear this does not hold. Instead the loans and expenditures went to states where the crisis hit hardest and the relative income drop was highest, meaning states with pre-1929 high incomes received relatively more of the New-Deal aid than states with lower pre-1929 incomes, since these incomes could not drop as much as the high incomes. This result indicates that the program was relief-oriented in order to restore the incomes to the pre-1929. In other words the spending did not have the intention to achieve the promised level of reform.

The Roosevelt administration promised to bring relief recovery and reform. Reform of the economy was promised by Roosevelt directly to the people. Long term fixes of what was structurally wrong were promised. These fixes were expected to bring more equality to some extent and therefore income levels of the poor are expected to rise faster.

In line with the results from Arrington (1970), Reading (1973) found that New-Deal spending did not bring that reform, but only contributed to recovery and relief. For example the independent variable personal income was not significant, while the independent variable that captures the decline in personal income was significant with a positive point estimate. The Administration did not reform in a way that equalizes the incomes between states, but the Administration tried to equalize the incomes with the pre-1929 level of income. At the same time “New Deal” funds and loans were directed towards so-called “National Assets” that restored the levels prior to the crisis. It seemed that Roosevelt did try to get the income levels back on the 1929 level. This insight raises questions on the extent to which political objectives are behind the allocation of funds.

Goss (1972) finds a positive link between a military committee membership and defense related benefits in the House of Representatives. Members use their power to benefit a group of voters in the area they live.

Arrington (1970), Reading (1973) and Goss (1972) all found an unequal or disproportionate distribution. Where Arrington and Reading mostly observed the discrepancies with “New Deal” spending, Goss did find interesting results in that he tested a link between political factors and the distribution of defense related benefits.

Where Arrington noted the possibility and Reading suggested further research in order to identify political drivers and factors of influence on the distribution, Wright (1974) constructed a political model in order to test the political influence and to identify more specific political variables of interest. His approach was to address the findings of Arrington and Reading with an econometric model that analysis the political determinants of New-Deal spending. Among the explanatory variables are the electoral votes per capita, closeness and the variability of vote. This model could explain approximately 58.7% to 79.6% of the variance in per capita spending, but according to Wright the results are questionable due to a possible omitted variable bias and “act as a proxy for some other variable that has nothing to do with political strategy.” [Wright (1974 : 34)]. However from the results of alternative regressions it seems that the political variables do influence and actually lessen the residuals of the regressions compared to earlier models, indicating the importance of the findings of Wright.

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The impact of the President on the distribution of Federal outlays plays an important role in the paper of Wright. From the positive and significant coefficient of the closeness variable and the variable that captures the volatility of voter’s follows that the President’s Administration tend to “reward” marginal states. This supports the theory of disproportionate distribution towards marginal and swing states in order to increase the chance of (re-)election. The other important positive and significant variable is the electoral vote per capita. This result could support the idea of congressional overrepresentation of small states.

At last Wright concluded that the political strategy of buying votes by spending is not very effective. When the economy was growing, income levels dominated the voting choices, but around the 1940’s the political power to influence voting patterns by spending did hardly exist.

Wallis (1984) provides robust evidence supporting the paper of Wight (1974). According to Wallis overall spending did not increase much during the “New Deal” compared to spending between 1900 and 1930, but there was a shift from local spending towards state and Federal spending. In other words the role of the Federal government and to some extent the role of the state governments increased, while the role of the local governments remained stable or declined. One important finding is that states with high incomes tend to spend more of their own revenue. Those states at the same time tend to receive more Federal Grants. This is in line with the earlier findings of a positive link between income and the receiving’s of grants. Wallis findings also coincide with the results of Wright and Arrington who recognized the shift towards income and agricultural relief spending. But where Wright stresses the Presidential reign over the allocation of the budget, Wallis stresses that the Congress did not just rubber stamp the Presidential budget proposals. In fact Congress took discretionary control over the allocation of Federal grants by administrative means or authorized the decision process on state or even local level. This basically supports the idea of congressional pork-barrel politics and given these results one may state that Roosevelt was clearly not “acting on his own.” [Wallis (1984 :159)]

In 1987 Wallis used new data from the New-Deal era and found political factors to influence spending, although to a small extent. He recognized problems in the dataset used by earlier economists and therefore questions their findings with respect to the overwhelming political influence on spending. The results show neither a dominant position of economic factors nor of political factors, although both still contribute to the distribution of spending. Consistent with Wright 1974 the variable `voter variability’ has a positive and significant sign. This supports the idea of targeting states that could change political color between elections. In other words Wallis (1987) found evidence regarding the theory of targeting swing states.

Anderson & Tollison (1991) found evidence that committee membership in the Senate and House of Representatives play an important role in the distribution of Federal “New Deal” spending. Also the electoral vote per capita is found significant and positive. Besides these results they also found evidence of an essential role for the President in the allocation of the “New Deal” budget. They incorporated the variable FDR32 that captures the percentage of state electoral votes for the incumbent President and found this link to be positive and significant. This could indicate that the President targeted states that are loyal with respect to their political beliefs, but this result should be taken with cautious since the variable is not significant in all regression models.

In line with the findings of Wright (1974), Fleck (1999a) found that the variability vote had a positive and significant effect, supporting the swing state theory. In fact from his results it follows that there is a link between the popularity of the President and the relative spending towards swing states instead of the traditional loyal Democratic states. In the same year Fleck (1999c) published a paper proving the importance of state’s turnout in the Presidential elections. From this paper it follows that increasing the turnout would result in more per capita relief. The share of vote for the Democratic President is however not significant, so this does not provide any evidence on the theory of targeting loyal voters.

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With a new model and test Fleck (2001) finds evidence for both the hypothesis of rewarding loyal voters as for the hypothesis of targeting swing states. In the paper of Fleck (2001) loyal voters are characterized by people who vote for the incumbent during general election, but during the primaries their decision depends on how the incumbents’ policy decisions have affected them. Swing voters may or may not vote for the incumbent during general elections. The decision depends on how they have fared under the policy of the incumbent. Swing voters do not vote during the primaries. Fleck (2001) finds evidence that incumbents tend to reward loyal voters to win the state primaries to be able to get re-elected during the general elections. States with a relative large number of loyal voters will be relatively rewarded. However when the competition between parties is high, the general elections will become more important. Ceteris paribus the swing voters will be targeted relatively more, because the importance of the swing voters is higher during the general elections.

Fishback, Kantor & Wallis (2003) analyze the link between “New Deal” programs and political variables. They find that every program is affected by political variables, but there is not one single political variable influencing all the programs. The mean Democratic vote share between 1896 and 1928, the standard deviation of the Democratic vote share between 1986-1932 and the difference of vote share in 1932 and the long term Democratic vote share between 1896 and 1928 were the political variables that have high elasticity’s with the programs initiated during the Roosevelt Administration. The high elasticity’s between the Roosevelt swing votes and some of the programs leave room within the allocation process to accommodate re-election goals by the Administration.

A more recent paper dealing with the determinants of New-Deal spending is the paper written by Fleck (2008). The model predicts quite the same results as the paper of Wright (1974). For example there is evidence of overrepresentation of small states in Congress and states with high vote variability tend to receive more per capital national spending. Fleck extends this by putting more focus on the problems in the 1930’s of where he thinks plays important role in policy making. “New Deal” spending was distributed mostly to area’s that needed them most. Fleck stresses that due to the depression and droughts the electorate valued an increase in spending. Spending was targeted towards constituencies that were hit hardest. So spending went to parts where spending had a high electoral value. In other words spending went to those parts where the electorate valued it most and therefore was more likely to vote for the incumbent. Fleck also stresses that policy will respond, whether politicians search for votes or are more ideologues, to the situation and changes in society in order to win elections or to stay in office.

Papers related to distributive politics addressing more recent years mainly deal with the Presidential veto power, Congressional pork-barrel politics and the more general approach to try to identify political determinants.

The President influences the spending pattern, by initiating the budget. Congress can only approve or disapprove this initiation. If at least 2/3 of congress disapproves the bill is rejected. Notwithstanding the President always has the power to reject any bill or decision as Congress might alter a bill or suggestion. This basically makes him the chief-legislator. Copeland (1983) analyzed which factors induce the President to use his veto. A small number of variables explain a large proportion of the veto’s initiated by the President. Examples of variables influencing the use of the veto are

Depressed economy tends to slightly increase the president using his veto. Congress is ruled by the opposition party it increases the use of the veto. If the president is feeling he has to push his policy. Lack of experience in Congress by the President, causes the President to use the

veto more frequently. So basically when institutions are in conflict the President uses his veto more often. Rohde & Simon (1985) add the link between public support and minority status in Congress. Presidents with a lot of public support are able to reduce the effect of the minority status of

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Congress. This public support also encourages the President to use the veto, indicating a positive link between public support and the number of veto’s. From Kiewiet & McCubbins (1988) it follows that President’s aiming to spend less than Congress have more impact on the final budget than President’s who are aiming to spend more than Congress. They also find that the Congress is more able to influence the final appropriation when it has a dominant majority. However an important finding is that even when Congress has a dominant majority, it seems the President still has influence, even when he wants to spend more than Congress. [Kiewiet & McCubbins (1988 : 712)].

Whereas the literature about “New Deal” spending mainly evolved from the paper of Wright (1974), the more recent papers that address the Congressional pork-barrel politics are triggered by the paper written by Atlas, Gilligan, Hendershott and Zupan (1995). This paper analyses the distribution of per capita Federal spending across states between 1972 and 1990. They incorporate the independent variables Senators per capita and Representatives per capita. From the preliminary test as well as the regression results it follows that small states are overrepresented in the Senate. In other words small states are able to attract more per capita Federal spending, because they have per capita more representatives in Congress. This is not the case for the small state tax burden. This means that ceteris paribus states with small population tend to receive more of the NET pie than states with a large population. Lee (1998) also tests whether less populated states are overrepresented, but controls his regressions for differences in the needs for Federal spending of states. He finds that cooperation between state Senators could lead to a disproportional distribution. Spill-over effects of Federal spending between congressional districts makes Congressman want to cooperate. Spending is managed to do good to states, not to a single Congressional district that receives it. Also state level benefit more from the programs based on formula than on the Congressional district level. To summarize these results, geographic distribution could be influenced by the coalition of Senators. In line with the results from Atlas et al. (1995) is the significant influence of the inverse of the reciprocal of the representation index, indicating an overrepresentation of small populated states in the Senate.

Carsey & Rundquist (1999) put more focus on the committee’s membership of states in Congress and the link between defense related benefits. In line with partisan alignment theories comes their result that states represented by Democratic majority on the Committee receive more defense related benefits. Bickers & Stein (2000) find also evidence of partisan influence on the budget allocation while comparing the 103rd and 104th Congresses. The 104th

Congress is dominated by the Republicans and the 103rd by the Democratic Party. The shift towards the “blue” did not alter absolute spending much, but it did influence the domestic public policy. The Republicans partially changed the targets that are rewarded by Federal spending based on their interests. In other words the distribution of domestic Federal expenditures was different under the reign of the Republicans than under the reign of the Democrats. The results give evidence of the presence of Republican pork and Democratic pork politics. From this paper it does not become clear whether these changes in the distribution of expenditures can also be seen as direct increases of re-election changes of representatives or the President.

Levitt & Poterba (1999) study the link between Congressional distributive politics and the economic growth and performance of states. Besides the results of the influence of political factors on economic growth they also find evidence of political influence on the budget allocation. An important point in their paper is the choice of dependent variables. Instead of focusing on a single dependent spending variable they capture multiple dimensions of spending in order to search for patterns of significance, instead of focusing on a single significance level of a single dependent variable. From their results it follows that for example the variable capturing the seniority of Democratic Congressman has a positive sign, however this does not seem to influence the distribution of spending significantly. They do not find direct evidence of partisan alignment influence of committee membership on the distribution and there is no evidence of the positive link between political competition within a state and

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the receiving’s of Federal spending. The Congressional vote share on the other hand does seem to influence the distribution significantly for multiple spending categories. In other words they find evidence regarding the hypothesis of rewarding loyal voters, but do not find evidence with respect to the hypothesis’s of partisan budgeting or targeting swing voters.

Knight (2005) focuses on the analysis of more specific bills that are more easily influenced by Congress. Moreover the paper identifies the chamber in Congress that appropriates the specific bills to be able to make more accurate estimates. The results find strong evidence of overrepresentation by per capita delegates. This overrepresentation results in an inefficient allocation of public goods based upon Samuelson conditions [Knight (2005 : 21)].

Several papers have a more general approach on the political determinants of Federal spending. These papers incorporate various explanatory variables to search for political links with the allocation of the budget. Levitt & Snyder (1995) find evidence that the average Democratic share of vote in Presidential elections is positively related to the receiving’s of Federal assistance expenditures. These expenditures can be divided between:

All programs, Programs with little variation Programs without much variation.

This finding strongly supports the theory of rewarding loyal voters. The paper also stresses the importance of voter turnout on the allocation of Federal expenditures. States with higher turnout during Presidential elections obtain higher levels of outlays. They do not find evidence of state overrepresentation in Senate and the evidence on partisan budget allocation is very modest, at least for the latest programs in their paper. Democratic Representatives cannot easily target their voters with the programs. Above that the majority party might only be able to direct Federal money towards very broad defined programs allocated to broad defined areas. These programs cannot be changed swiftly due to political or partisan motives.

Hoover & Pecorino (2005) use more specific and less aggregated data on the period ranging from 1983 till 1999. They incorporate congressional variables, variables capturing the link with the President and economic/ demographic control variables. The dependent variables are categorized into 6 different categories: overall spending, retirement, other, wages, grants and procurement. According to Hoover & Pecorino politicians could possibly influence some categories more than other. Much of the earlier studies mainly focus on overall spending, but in theory some parts of the budget are more prone to political influences than other programs making the estimates of political influence less significant for overall spending than for those particular programs. Hoover and Pecorino also notice the abnormal results from the variable electoral votes per capita. Possibly due to a lack of change in the data, they try to reduce the problem by running regressions without these electoral votes per capita and instead incorporate the variable population.

The Congressional variables are the number of House of Representatives by state and the two Senators per state, both divided by the state population. These variables in combination with the electoral votes per capita can measure the extent of overrepresentation of small states in congress. From the regression output it follows that the electoral votes per capita are negatively related to all spending categories, except for Federal Grants. The state Senator representation variable has a positive sign and is significant in all regressions, except for the category: other. This means the Senators from small states tend to be able to attract more per capita resources from the Federal government than lager senators from larger states. In contrast with the results of Atlas et. al. (2005) the state House representation does not significantly influence any spending category. This changes after removing the electoral votes per capita, then the coefficient becomes negative (opposite sign compared to the sign found by Atlas et. al. (2005) and significant at 10%). The regression results of the Congressional variables provide evidence that small states are overrepresented in the

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Senate, but not in the House subject to electoral votes per capita being included in the analysis. Hoover & Pecorino include two dummy variables that capture the leadership status of the state Senate. The results are again mixed and insignificant except for the case of wages. Apparently the state Senate minority leader tends to receive less spending on wages from the Federal government.

Besides the Congressional variables Hoover & Pecorino (2005) also incorporate the influence of the President on the budget. This makes the paper closer related to this study. The alignment of the President with the state Governor has a significant and positive influence on the amount of overall spending of Federal government to states and on procurements, but a negative impact on wages. This variables does not seem to influence retirement, other and grants significantly. The percentage of state delegation to the House aligned with the party of the incumbent President has a positive sign, but is solely significant with the Federal grants as dependent variable. Apparently the alignment between the President with the state delegates to the House causes the Grants to increase significantly. The results for the number of Senators aligned with the incumbent President are quite similar for grants. Although the coefficient is smaller, the point estimate is positive and highly significant. The alignment of state Senators with the sitting President is not significant in the other five spending categories.

Hoover & Pecorino (2005) also incorporate variables that capture the alignment between the representation of the states and the majority party of the House of Representatives and the Senate. This way they test the political influence of both chambers in Congress on the diversion of spending to states. The results of the regression output show mixed relations. For example in the case of the House of Representatives there is a positive and significant link between the percentage of state House delegation and the majority party in the House of Representatives for the spending categories grants and other, while there is a negative and significant relation with wages. In the case of the Senate there is also a negative and significant link between the amount of state Senators with the same political color as the majority party in the Senate for wages, but not for the other spending categories. Important note is that the signs change between the different categories.

Hoover & Pecorino include the political variable that captures the absolute difference between the percentage of vote for the winning President and the runner-up (margin). They also constructed the dummy variable that takes on the value one when the incumbent President won during last elections in that state and zero otherwise. At last they constructed an interaction variable between the dummy variable and the variable capturing the difference in vote.

From the significant coefficient of the dummy variable it follows that states which voted for the incumbent President tend to receive less funds. The sign of the dummy variable is negative for all spending categories and significant in almost all cases. The significant negative sign does not support the idea of rewarding loyal voters.Except for the insignificant positive sign of the grants category the variable margin also has a negative and significant coefficient. This indicates that the administration spends more money on state where electoral competition was high. To some extent this supports the theory of targeting swing states. At last the interaction term between the margin and dummy variable that takes on the value of one when the state voted for the incumbent President during the last election, zero otherwise, has a positive and significant coefficient in most spending categories. Combining the results of the three variables lead to the conclusion that states where the difference in votes was large tend to receive less Federal spending irrespective of whether they voted for the winning President or the runner-up than states where the elections were a close-call. This basically tells us that there is not much evidence in favor of or against the hypothesis of rewarding loyal voters. It does provide evidence on the hypothesis of targeting swing states.

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Larcinese, Rizzo and Testa (2006) give new evidence on the determinants of Federal spending to states by extending the Congressional literature adding and putting more focus on variables that capture the influence of the President on the allocation of Federal outlays. Their main goal is to analyze the impact of the U.S. president on the distribution of Federal outlays to states. The period of interest ranges from 1982 till 2000. The paper bears some resemblance with the paper of Hoover and Pecorino (2005), but the hypothesis under consideration, goals and the choice of variables makes this paper quite different as can also be seen from the comparison of the results later on.

The first hypothesis tests the swing bias theory. It predicts that states where the electoral competition between political parties is high are targeted by politicians in order to get those states to vote in favor of the politician to (re-)win the elections. One way to test this theory is to use a variable that captures the election results, or more specific a variable that contains information about past changes of political color of states. Larcinese, Rizzo and Testa (2006) constructed the dummy variable that takes on the value of one when a state swung during last Presidential elections. More interesting are the amount of swings in more than just the last election to identify which states are really swing states over time and not just switched during one single election due to particular one-time motivations. Therefore they constructed two other swing variables. The first is the average amount of swings during the last four elections. The second variable divides the amount of swings from 1964 onwards by the amount of elections under consideration.

From their research it follows that none of the three variables influences the Federal outlays by state significantly. There is no evidence in favor of the swing hypothesis. The same results come from the variable that measures the difference in percentage vote between the winner of the last Presidential elections and the runner-up. Where Hoover and Pecorino did find a significant relationship between the margins of the election results on overall spending, Larcinese, Rizzo and Testa (2006) found no significant evidence that states with a very small margin in the last election receive more Federal outlays.

Larcinese, Rizzo and Testa (2006) found evidence on the ideological bias hypothesis. From the significant coefficient of the variable that captures the share of vote for the incumbent President during the last election it follows that states that ideologically lean towards the executive tend to receive more Federal outlays.

Their final section analyzes the possible influence of partisanship on the distribution of Federal outlays to states. A set of dummies is created and implemented in multiple regressions to capture the political alignments between: Governor-President, Governor-double state Senator, Governor-majority of state Representatives, double state Senators-President, majority of state Representatives-President, double state Senators-majority Senate and the majority of state Representatives-majority House.

The following results of Hoover and Pecorino’s (2005) analysis on the influence of alignments on the distribution of Federal spending bear resemblance to the paper of Larcinese, Rizzo and Testa (2006): states where the Governor is of the same party as the President tends to receive more overall Federal spending. Without electoral votes in the regression this variable becomes insignificant just like the link between the alignments of the Governor with the President on the receiving’s of Federal grants. The percentage of House delegation that is of the same party as the incumbent President is positive and significantly related to overall spending subject to population being included in regression. This variable has a positive sign and is highly significant with respect to the distribution of Federal grants in all models. The link between the amount of state Senators aligned with the President and the distribution of Federal grants is quite similar. Again the coefficient is positive and highly significant (at 1%). There is no evidence that the number of Senators aligned with the President influences overall spending significantly. There is also no evidence that the state Representatives and state Senators aligned with respectively the majority party in the House of Representatives and majority party in the Senate receive more overall Federal spending.

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The alignment between the state Representatives with the majority party of the House however does influences the allocation of Federal grants significantly.

Since Larcinese, Rizzo and Testa (2006) only use the variable Federal outlays as dependent variable it is quite reasonable to compare their results with the output of Hoover and Pecorino (2005) for the regression that has overall spending as dependent variable. Larcinese, Rizzo and Testa (2006) provide evidence on the influence of the alignment between the state Governor and the President on the allocation of Federal outlays. This link is found positive and significant (at 5%). The dummy variable that captures whether there is an alignment between the majority of state delegates with the President has a positive sign and is highly significant (at 1%). There is no significant evidence that the alignment between the President and two state Senators influence the distribution of Federal outlays. Also the coefficients capturing the links between the state Governor with the majority party in the House, majority party in the Senate, majority party state Representatives and the double state Senators are found insignificant. At last there is no significant evidence that the alignment between the representation of state in House and Senate and the majority party in the House and Senate influence spending.

They do find evidence that the amount of Senators per capita influences the budget significantly. This is in line with earlier findings that states with small population tend to receive relatively more. In other words this provides evidence on the theory that small-states are overrepresented in the Senate. Levitt and Snyder (1995) stressed the importance of turnout, but Larcinese, Rizzo and Testa (2006) did not find a significant influence of turnout on the distribution of Federal outlays. They do find evidence that when a Democratic President is in charge spending is approximately $137-139 more than when the executive is Republican.

Committee membership does not influence the distribution of Federal spending on a significant basis, except for the number of members in the Ways & Means committee. Except for the result of the Ways & Means committee the findings correspond to the findings of Levitt & Poterba (1999).

In 2009, Hoover & Pecorino extend their analysis of their 2005 paper by testing whether there is a difference in spending after the Republican take-over in 1994. Their dataset includes the years 1983-2004 to be able to test for a break in 1994 by using time interacted dummies. This relatively long panel dataset consists of variables that have a trend over time. They recognize the possibility of statistical problems with these time trending variables. It could influence the results and make the coefficient of the trended variables highly significant when in fact after de-trending they are not. To account for this problem Hoover and Pecorino normalize the variables that could contain a trend over time. They take the level of a state and divide it by the un-weighted average of all states in that year and they do this with all the possible trending variables. To portray the period from 1994 till 2004 they interpret the interaction between the time dummy with the period from 1983-1996. They use the sum of both coefficients to be able to analyze the period after 1994.

The interaction terms provide evidence that after the Republican take-over in 1994 spending became larger in states that have more aligned Senators and Representatives with the majority party (Republican). Especially the alignment of the state Representatives and the majority party in the House of Representatives has a significant effect on Federal spending. The change of Governor-President, Senators-President and House-President alignments measured by the time interaction dummies and the prior 1994 coefficients are not significant, except for the minor case of the influence of state Senator-President alignment on procurements. The overall lack of change of the influence on spending by the Presidential alignment variables could indicate that the President’s Administration did not alter spending after the Republican take-over, because of its power over the budget. This link is however not clear and there is no direct evidence on this matter.

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The amount of Senators per capita changes significantly. The time interaction dummy is positive, but the size of the coefficient is not very large. At last the time interaction dummy for income is significant and has a negative sign. This provides significant evidence that after 1994 the distribution of retirement spending, grants, wages, procurements and total spending became more distributive.

My paper is closely related to the papers of Hoover and Pecorino (2005) and the paper of Larcinese, Rizzo and Testa (2006). The latter is the basis for my research into the impact of the President on the distribution on Federal grants in the period ranging from 1960 (1954) till 1974. This paper will make an attempt to fill the mentioned gap in the current literature by evaluating the impact of the U.S. President on the allocation of Federal Grants to states for the specified period.

3 HypothesisGiven the theoretical explanations this study constructs in line with the paper of Larcinese, Rizzo and Testa (2006) three hypotheses to be tested:

The first hypothesis: Federal grants are disproportionately targeted towards ‘marginal’ and/or ‘swing’ states.

Second Hypothesis: Federal grants are disproportionately target towards states that are relatively supporting the incumbent President.

The third hypothesis: Alignment between Governors and/or Senators and/or members of the House of Representatives with the President and with each other is awarded with higher receiving’s of Federal grants by those states.

4 Variables

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4.1 Dependent variable

This study uses Federal Grants as dependent variable for 48 US states2 for the period ranging from 1960 till 1974. Levitt & Poterba (1999) stress the importance of the difference in dependent variables in literature related to distributive spending. It follows that the choice of the dependent variable while doing research in the field of the determinants of Federal spending has a crucial impact on the results. For example the determinants of Federal grants can be quite different from the determinants of Medicare or defense related spending. Intuitively the impact of politicians and the President on some parts of Federal spending can be larger than other parts. For example the spending on Medicare is probably relatively stable and is related to the amount of inhabitants of a particular region and the health situation or status. On the other hand Grants are more redistributive and the receiving’s of Grants by states fluctuate more. Levitt and Snyder (1997) study the impact of Federal spending on election outcomes. They find that some parts of Federal spending do influence the election outcome more than others. In particular, discretionary spending categories such as Federal grants with its high variation do influence the election outcome significantly. Based on these findings it is important to make a distinction between parts of Federal spending. Federal grants is an informative dependent variable while doing research in the field of Presidential influence on the distribution of Federal funds. The Federal grants category is subject to political influence and therefore highly relevant for this analysis.

In the preceding years of our period of interest the amount of Federal grants rose sharply. After the Roosevelt administration Truman (1945-1953) and later Eisenhower (1953-1961) together almost tripled the total amount of Federal grants. Lyndon B. Johnson initiated the ‘Great Society’ initiative and expanded the role of the Federal government. More grants were initiated, a large part of which went to urban regions in order to improve life in these regions. During the Johnson’s Administration, the Federal government tried to achieve multiple national goals. For example the administration believed that urban regions and cities were not able to improve their situation significantly and made this one of their goals. These extreme increases during the reign of Johnson were put to a stop by the Nixon Administration. He believed that grants and the allocation of grants were inefficient, overlapping, too administrative and not effective enough. His ‘New-Federalism’ features National revenue sharing with local governments. These local governments were better able to manage resources more effectively and efficiently. In practice it was questionable if this reduces the power of the Federal government, since revenue sharing was basically subject to compulsory conditions made by the Federal government.3 The increase in the total amount of Federal grants to states and the importance of the Federal government into the distribution by not only appropriating the grants but also setting up rules and conditions as to who receives the Grants makes it a proper dependent variable to test the political influence. The data on Federal grants by state is constructed by manually extracting the budget figures from the historical abstracts published by the U.S. Census Bureau of Statistics. Federal grants used here is the sum of the grants of the following categories: public assistance, employment security, health services, other welfare services, education and all other. It includes some small undistributed grants, but due to the relative size of the undistributed grants this does not influence the overall results substantially.4 The panel data on Federal grants ranges from 1960 (1954) till 1974. The upper limit of the range is chosen based on the fact that the

2 Alaska, District of Colombia and Hawaii have been excluded to allow for comparison with previous research. By way of voting, Hawaii became a state of the U.S.A in 1959. In that same year Alaska joined the states of the U.S too. These relative ‘new’ states with geographical important positions (especially during the cold war) could therefore perhaps receive disproportionate amount of (Grants-) spending. Testing for political influence, without these underlying factors could lead to incorrect conclusions. With respect to the District of Colombia large amounts of bureaucratic money will go towards this relative small state for administrative reasons. Above that, this state is not represented in Congress to limit the state’s influence on Federal decisions. 3 Canada, Ben (2003)

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primary objective is to describe the influence of the U.S. President on the allocation of Federal Grants for the period prior to the passage of the Budget, Control and Impoundment Act in 1974. The lower bound of the range is set based on data availability issues for particular variables as will be clarified in the next part where the choice of independent variables is evaluated.

The extracted data on Federal Grants are converted into real values using the Consumer Price Index (CPI) (base year 1974) and divided by the state population to get the real per capita Federal Grants. Table 1 reports the state’s average received real per capita Federal Grants between 1960 till 1974. The differences between the allocations of Federal grants towards states are enormous. As said this gap can be entirely due to the needs of states, but it is quite justifiable to ask to what extent pure political factors are behind this difference. In order to test the hypothesis the following equation will be estimated:

The estimation of the linear regression model relies on panel data on the 48 states over the time period under consideration. The term P stands for the set of political variables used to test the particular hypothesis and the X represents the standard set of control variables. State fixed effects and time fixed effects are included in all regressions to account for state and year heterogeneity. When testing the influence of political variables on the distribution of spending to states it is quite logical to assume that some state individual time-invariant characteristics and some year specific characteristics may influence the estimates of the political variables. By adding both fixed effects in the model these individual characteristics are removed. As an economic and political model, the within states auto-correlation of the variables on both sides of the equation are very likely to affect the results. To account for this and limit the influence of auto-correlation on the results, the White cross section coefficient covariance method is used in all regression analysis.

4.2 Independent variables

The choice of independent variables5 while analyzing the influence of political players, with special attention to the influence of the U.S. President on the allocation of Federal grants, is evaluated in this part of the paper. It will be divided according to the three hypotheses.

4.2.1 Hypothesis 1

The first hypothesis concerns the influence of ‘swing’ and/or ‘marginal’ states on the distribution of Federal Grants. Comparable with the variable margin of Hoover and Pecorino (2005) the variable closeness captures the absolute difference between the percentage of votes of the winner of the Presidential elections and the percentage of vote for the runner-up. The sign of the variable is expected to be negative, indicating that states where the Presidential election is a close-call, receive on average more Federal grants.

The closeness variable may not always be a very good proxy for analyzing the impact of swing states. When a state changed ‘color’ during the last election it can be marked as a swing state during that particular election. Therefore the closeness variable may not be a good variable to measure the impact of swing states on the allocation. A better way to test the hypothesis is to take an average of the so-called swings over more than one election. Therefore, in line with the paper of Larcinese, Rizzo and Testa (2006) a variable is constructed capturing the average amount of swings during the past four elections at time t. (from now on called swing_1) A significant and positive estimate of this variable would indicate that ‘purple’ states receive more Federal grants than states that vote consistently for the same party

4 For more information visit the website of the U.S. Census Bureau of Statistics.5 Table 2 in appendix A reports the summary statistics of all variables used.

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4.2.2 Hypothesis 2

The second hypothesis can then be tested by the variable sharevote, which measures the share of vote for the incumbent President. According to the hypothesis the point estimate is expected to be positive. This indicates that the more states support the incumbent President, the more Federal grants they receive. In other words states where a large part of the electorate vote for the incumbent receive more Federal grants per capita.

Another relevant variable is the state’s electoral weight in the Presidential elections. Therefore this study adds the electoral vote’s per capita variable (from now on called electvotepercap) to account for this. To summarize there will be two specifications of the model to test both the first and the second hypothesis. The first specification includes the political set containing the following political variables: sharevote, closeness and electvotepercap and the second specification incorporates the set of political variables: sharevote, swing_1 and electvotepercap.

4.2.3 Hypothesis 3

The third hypothesis will be tested with multiple variables capturing the links between the Representatives in Congress, state Governors and the President. In line with the paper of Larcinese, Rizzo and Testa (2006) the testing of the third hypothesis will be divided into two parts. The first set of dummy variables capture the links between the state Governor and the U.S. President (from now on called Gov-Pres) and the state Governor with the bicameral legislature (Gov-Congress).

One point that needs attention here is the dominant position of the Democratic Party in both chambers. From 1955 till 1981 the Democratic Party had the majority in both the Senate and the House of Representatives. From this fact it becomes clear that checking the influence of divided government is only possible by evaluating the change in spending between having a Republican President or a Democratic President. By definition having a Republican seated in the White House means there is a divided government, because the Democratic Party dominates Congress in our entire period of interest. Therefore creating and implementing two variables where one captures the link between the state Governor and the majority party in the Senate and the other captures the relationship between the state Governor and majority party in the House of Representatives would lead to a ‘near singular matrix’ since the variables are equal to each other. The solution here is to introduce a variable that captures the political status of the Governor. So the variable basically contains data that captures whether the political status of the Governor is Democratic (=aligned with Congress) or Republican (=no alignment with Congress). (From now on called Gov-Congress)

In line with the paper of Hoover and Pecorino (2005) a dummy is created that takes on the value of 1 when the party of the President is the same as the party of the state Governor and zero, otherwise. In line with the paper of Larcinese, Rizzo and Testa (2006) this paper considers also the link between the Governor and the majority party of both chambers in Congress. Again the Democratic Party is the majority party in both chambers so this paper only considers the alignment between the state Governor and the majority party of Congress. This way it is obvious that this dummy takes on the value of 1 when the state Governor is Democratic and zero if the state executive is Republican. It may also be possible that the alignment between the party of the state Governor and the state delegates in both chambers may lead to a disproportionate allocation to the constituencies under consideration. So the political set of variables in the first specification consists of: Gov-Pres, Gov-Congress.

This study also considers the possible influence of many more alignments between political actors on the allocation of Federal Grants. Testing the alignment variables separate from each other is not optimal, because there is a possibility of correlation between the dummies. This could lead to biased estimates and might even lead to wrong conclusions concerning the impact of particular alignments on the allocation. So besides the described alignment from the first specification the second specification adds the alignment between the state

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Governor with the majority party of the state delegates to the House and the alignment between the party of the state Governor with the party of both state Senators. In line with Larcinese et al. (2006) two dummies are constructed to test the possible influence of both alignments. The first dummy variable takes on the value of 1 when the party of both state Senators is the same as the party of the state Governor and zero otherwise (from now on called Govstatehouse) The other dummy equals one when the party of the majority of state delegates to the House of Representatives is the same as the party of the state executive and zero otherwise (from now on called Govstatesenate). With the same reasoning one can imagine that an alignment between the state delegates and the President would result in a disproportionate distribution of Federal grants. Therefore along the same line; two dummies are created to capture the alignments between the party of both Senators and the party of the President (from now on called Housep) and the party of the majority of state delegates and the President (from now on called Senatep).

At last two more dummies are introduced to test if the internal alignments in the House and in the Senate facilitate some parts of Federal grants spending. The first dummy takes the value of 1 when both state Senators are from the same party as the majority party in the Senate, zero otherwise (from now on called Senatestatemajor). The other dummy takes the value of 1 when the majority of State delegates to the House of Representatives has the same political color as the majority of the House of Representatives (from now on called Housestatemajor). Again note the Democratic majority in both chambers in our period of interest. This way the first of the latter two dummies can be interpreted as a dummy that measures the influence of having two Democratic state Senators in the Senate. Reasoning along the same line, the latter measures the effect of having a Democratic majority of state Representatives in the House.

To limit any possible omitted variable bias some other variables should be included in the regression analysis. For the “New Deal” period Wright (1974) and Fleck (2008) and for later periods Atlas et.al (1995) and Knight (2004) found evidence that small states are overrepresented in Congress. Levitt & Snyder (1995) found no evidence in favor of overrepresentation of small states in Congress, but they stress the importance of turnout on the distribution of spending like Fleck (1999c). Although Larcinese, Rizzo and Testa (2006) found no evidence of influence on turnout this paper incorporates two variables that contain data on state Senators per capita (from now on called Senatorpercap) and percentage of state’s turnout (from now on called turnout) during electoral Presidential elections in regression. There ought to be some caution with respect to the interpretation of the coefficient of the variable containing data on the amount of Senators per capita. By law, there are always two Senators per state. If this variable is included in regression without per capita measuring, this would basically be a constant term. Any variable that influences the dependent variable, but which is not taken into the regression equation influences the estimate of the constant term.

Turnout and Senatorpercap representation are included in specifications introduced in the Robustness chapter.

In line with the paper of Larcinese, Rizzo and Testa (2006) this paper assumes that there is a delay between the political appropriation of Federal Grants and the actual period in which of Federal Grants are received by states. The first year after the appropriation, 60% is actually spent by the states and the other 40% is spent the year after. To account for the delay, the political independent variables (or dummy variables) will be reconstructed to get weighted independent political variables. The following formula is used for construction:

P is the set of political variables by state s. t denotes the respective years. Note that after reconstruction the dummy variables (Gov-Pres, Gov-Congress, Govstatehouse, Govstatesenate, Housep, Senatep, Housestatemajor, Senatestatemajor) become weighted

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variables. These variables do not only take on the values zero or 1 but also values between zero and 1. (Also the variables sharevote, closeness, electvotepercap, Senatorpercap, swing_1, swing_2 are reconstructed)

4.3 Control variables

Control variables are used throughout all the analysis. The choice of control variables is in line with the paper of Larcinese, Rizzo and Testa (2006). They are incorporated to account for particular state characteristics and so to reduce the effect of omitted variable bias in order to be able to isolate and estimate the political influence on the distribution of Federal grants. When evaluating the distribution of Federal spending towards states one could imagine that a state with a larger population will receive more than a state with a small population in absolute numbers. To take this effect into account we use state population (from now on called pop) as independent variable in regression. There are however more demographic characteristics that could lead to an altered distribution of Federal spending to states. Therefore in line with the paper of Larcinese et. al. (2006) we incorporate the percentage of elderly in a state and the percentage of young ones who are not allowed to vote. The percent of elderly (from now called pop65) is the percentage of the total state population with the age above 65. The percentage of young people is the percentage of people with the age between 5 and 17 years in a state (from now on called pop517). One can also imagine that especially in the case of Federal grants it is straightforward to incorporate variables that capture the economic conditions in a state. When Federal grants are primarily redistributive in a sense that they should contribute to equalization of income and overall economic condition of a state, then income per capita and the unemployment rate are large and significant drivers in the allocation of Federal grants. Therefore we incorporate the variables state income per capita (from now on called realincomepercap) and the unemployment rate by state (from now on called unemployment) to account for this.

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5 Results5.1 Hypothesis 1 & 2

The output in the first regression table (Table 4) 6 estimates whether the loyal voters are rewarded and or the electoral competition influences the distribution of Federal grants to states.7 The estimate of the variable sharevote is negative and highly significant in both the first and second column.8 This does not provide evidence regarding the hypothesis of rewarding loyal voters. In fact the sign of the coefficient is the opposite one would expect given the hypothesis of rewarding loyal states. In other words the higher the state share of vote for the incumbent President during the last Presidential election the lower the amount of Federal Grants received. The difference in spending between a state with the maximum share of Presidential vote (sharevote) and the minimum sharevote depends on the specification considered. In the first column the difference leads to a gap of $41.49 and the second specification considered results in a gap of $50.46. Which implies that one standard deviation is worth around $6.13-7.45.9

The closeness or margin of the past election does influence the allocation of Federal Grants too. The negative sign of the variable in the first column is significant, indicating the smaller the absolute difference between the percentages of votes for the President and the runner-up in a state during the past election, the larger the amount of receiving’s of Federal Grants by that state. The estimate of the coefficient implicate that the difference between the state with the smallest absolute difference in percentages of vote and the state with the largest absolute difference in percentages of vote result in a difference of spending of $20.05 with a standard deviation of $3.36. This result provides evidence in favor of the hypothesis that states with high electoral competition (swing states) are targeted relatively more. On the other hand the result of the second specification reported in the second column does not provide significant evidence regarding the hypothesis of targeting swing states. The long term swing variable swing_1 has a negative point estimate and the coefficient is not found significant. This indicates that the average amount of swings during the last four elections is negatively related to the receiving’s of Federal grants, but the estimate is not significant. So the sign is incorrect with respect to the hypothesis. The state’s weight in the Presidential elections has no significant influence on the allocation of per capita Federal grants.

Taking the negative estimate of the sharevote variable with the negative estimate on the closeness variable could lead to a combination of results to identify which state is targeted relatively more: electoral competitive states that had a small share of vote for the incumbent President during the last Presidential elections are targeted most. State where the electoral

6 All tables are presented in the Appendix7 In the short dataset (1960-1974) unemployment is found significant. Leaving out unemployment and using the long dataset (1954-1974) would most likely result in a misspecification of the model. Therefore the remainder of this paper will analyze the short dataset where unemployment is included.8 Concerning the estimates of the control variables in the baseline specifications of the model only the state population and the state’s percentage of total population with the age between 5 and 17 have significant impact on the allocation of Federal Grants. State’s with higher population and states with higher percentage of people with age between 5 and 17 receive less real per capita Federal Grants. The percentage of aged population, the real per capita income and the unemployment rate do not seem to influence the allocation of real per capita Federal Grants. Whereas the sign of the coefficient of the state real per capita income remains stable, the signs of the coefficients of the percentage of aged population and the unemployment rate highly dependents on the choice of independent variables. 9 The difference in spending are calculated by taking the difference of the multiplications between the estimated coefficient and the maximum and minimum levels of the variables considered. The value of one standard deviation is calculated by multiplying the standard deviation of the variable by the estimated coefficient.

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competition is high are targeted relatively more while at the same time states with a relative high share of vote for the incumbent President during the last Presidential election receive less.

The signs of the estimates of the variable Closeness support the hypothesis of targeting electoral volatile (marginal or swing) states. This is in line with multiple previous papers in the empirical literature. For example Wright (1974), Wallis (1987), and Fleck (1999a) all find significant evidence supporting this hypothesis. They estimate the impact of electoral volatility of states on Federal spending. Hoover and Pecorino (2005) uses the margin of the percentage of vote between the Winner of the Presidential elections and the runner-up. This quite similar variable to the variable closeness also has a negative and significant impact on the allocation of Federal Grants in the period ranging from 1983 to 1999. On the other hand as in the paper of Larcinese, Rizzo and Testa (2006) the coefficient of the long term swing (swing_1) variable is not found to be significant. Also Levitt & Poterba (1999) do not find significant support for the hypothesis.

The finding that the state share of vote for the incumbent President is negatively related to the amount of Federal grants received indicate that the larger the state’s support for the incumbent President the lower the amount of Federal grants received is. According to Hoover and Pecorino a state that voted for the incumbent President in the last Presidential elections receive less Federal spending (idem. for Federal grants). Seemingly in line with the interpretation of the estimate on sharevote in the second column, loyal voters are not rewarded during the period of aligned Presidential reign. Larcinese, Rizzo and Testa (2006) also found a negative sign of this dummy variable, but in their case the estimate was not significant.

According to Lindbeck & Weibull (1987) spending is negatively related to the level of the pre-determined absolute political bias. In combination with the decreasing marginal utility of consumption it becomes straightforward that states that are not ideologically pre-determined receive relatively more Federal spending. From the paper of Cox & McCubbins (1986) it followed that risk adverse politicians would rather spend money towards loyal constituencies than towards constituencies that voted for the opposition, because of the trade-off between return and risk. The findings in the first column of table 4 may be explained by the combination of both theoretical papers.

According to the model of Cox & McCubbins (1986) the allocation of Federal spending can be compared with a simple investment choice model where the rate of return on one side and the level of risk on the other determine the allocation decision. Loyal voters are more easily targeted and the reaction on it by the loyal voters is known (low-risk). Risk adverse legislators or candidates would rather target the loyal voters because of the knowledge of the reaction of loyal voter. With the same reasoning risk-loving legislators or candidates that want to be (re-)elected target Federal spending towards (or the promise of targeting the Federal spending) constituencies not ideologically supporting the legislator or candidate. In other words the risk loving legislator or candidate targets states that do not support his or her party in the past election in the hope to win over this state from the opposition precious state. Another theoretical interpretation of the negative point estimate of the sharevote variable is provided by the model of Dixit and Londregan (2006). If a loyal voter has strong ideological preferences that to some extent overrule the marginal utility of consumption it could mean that the loyal voter is less sensitive towards for example higher taxes than the opposition voter is. With the same reasoning Federal grants may be diverted towards constituencies that are not ideologically supportive. This could be towards swing states, but depending on the ideological preferences of the loyal voters and on the ideological preferences of the opposition voters could also be towards the opposition voter. Cox & McCubbins (1986) noted the importance of knowledge of the preferences of the loyal electorate and the efficiency in targeting the loyal voter in explaining why a person or administration would want to divert Federal resources towards the loyal voters. This argument assumes a relative lack of

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information about the ideological preferences of the opposition voter. The combination of the arguments could perhaps give a theoretical interpretation of the results in the second column: Given the information on the very high ideological preferences of the loyal voters, that to some extent overrule the marginal utility of consumption, in combination with the lack of information on the opposition voter, a candidate or legislator may want to divert Federal grants towards the opposition voter. This could either be because the ideological preferences of the loyal voter are higher than the ideological preferences of the voter who voted for the rival candidate or because the known loyal preferences are overestimated compared to the preferences of the opposition voter. The state’s share of vote for the incumbent (sharevote) President is then negatively related to the amount of Federal grants received.

5.2 Hypothesis 3

This part of the paper evaluates to what extent the political alignments between the state delegates in Congress, majority party in the House of Representatives, majority party in the Senate; state Governors and the Executive influence the allocation of Federal Grants to states. The first column in table 5 does not provide significant evidence on that the stronger the alignment between the state Governor and the President (Gov-Pres) the higher the allocation of Federal grants is towards a state. The larger the number of consecutive years with alignment between the state Governor and the majority party in Congress (Gov-Congress) does not significantly influence the distribution of Federal grants. Although the sign of the coefficient of the Gov-Pres dummy correspond to the idea of a positive link between the alignment and the amount of Federal spending of Federal Grants, the significance is not high enough. Note: both chambers in Congress are constantly dominated by the Democratic party in the years under consideration. In other words the interpretation of the insignificant coefficient of the variable capturing the link between the party of the state Governor and the majority party in the House, is that having a Democratic state executive at t-1 and t-2 does not increase the amount of Federal Grants at t=0 towards that particular state.

The significance and the impact of both coefficients (Gov-Pres & Gov-Congress)10 in the second column hardly change compared to the estimates in the first column. Both do not significantly influence the distribution of Federal grants. The estimation results in the second column of table 5 do provide significant evidence that partisanship influences the allocation of Federal grants. The consecutive years of alignment between the state Governor with the party of the majority of state delegates to the House (Govstatehouse) is negative and highly significantly related to the amount of Federal Grants received. At the same time the stronger the alignment between the states Governor with both state Senators (Govstatesenate) the more Federal Grants received by these particular states. The stronger the alignment between the party of the President and the party of the majority of state delegates to the House (Housep) the lower the Federal Grants received. The internal alignment in the House of Representatives has a positive and significant impact on the receiving’s of Federal Grants. There is no evidence that the internal alignment in the Senate (Senatestatemajor) and the alignment between the both state Senators with the President (Senatep) influence the allocation of Federal grants significantly.

The absolute difference between the maximum and the minimum consecutive years of alignment between the Governor with the party of the majority of the state delegates to the

10 The democrat John F. Kennedy took seat in the White House in 1960. After his death in 1963 Vice-President Johnson took office and after being elected in 1964 he remained in office till the next election in 1968 where he did not participate in order to get re-elected. The republican Nixon was elected during that election and took office till 1974 (end of our period of interest). In other words from 1960 till 1968 both Chambers in Congress and the President himself were democratic. The variables discussed are therefore identical until the republican President Nixon took office. Individual estimates of both variables do not change after leaving one of the two out. Therefore both variables are included in further specifications.

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House (Govstatehouse) is $6.87 with a standard deviation of $3.11. The absolute difference between the maximum and minimum in case of the Govstatesenate alignment is $6.13 with a standard deviation of $2.81. Using the same method on the Housep variable the absolute difference for this alignment in Federal grants received is $10.77 with std.dev of $4.89. At last using the same method on the Housestatemajor variable the absolute difference of spending for this alignment is $10.79 with a std. dev. of $5.07.

The empirical findings do not give much support to the idea that the U.S. President is able to direct Federal resources towards states with aligned state executives nor towards aligned state delegates, except for the majority party of the state delegates to the House. The result on the internal benefit allocation in the House raise the idea that actually the House of Representatives is the powerful and influential political players who are able to direct resources to the aligned states. This supports the idea that the House of Representatives is an important chamber in U.S. politics with respect to the distribution of Federal Grants towards states.

The insignificance of the alignment variable between the President and the state Governor does not support the empirical finding of for example Larcinese, Rizzo and Testa (2006) and Hoover and Pecorino (2005). In line with our finding, the political alignment between the President and majority party of state delegates to the House of Representatives is positive related to the amount of Federal grants states receive, in the paper of Hoover and Pecorino (2005). The estimate is highly significant if Federal grants is the dependent variable. Hoover and Pecorino (2005) also find significant results regarding the alignment between the majority party of the state delegates to the House of Representatives and the majority party in the House. The point estimate of their variable is positive and significant. Once again the estimate is only significant in case of Federal grants being the dependent variable. This is in line with the estimation results of this study.

A vast amount of empirical literature addresses the influence of the Senate on the distribution of Federal spending like for example the paper written by Lee (1998). A smaller amount of the empirical literature however addresses the internal power of the Senate to benefit states which have both state Senators in the Senate from the same party as the majority party in the Senate. Empirical research of for example Larcinese, Rizzo and Testa (2006), Hoover and Pecorino (2005) do not find significant evidence that the alignment between state Senators with the majority in the Senate influences Federal spending. This is also in line with the finding of this study. The internal alignment in the Senate does not significantly influences the allocation of Federal Grants.

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6 Robustness CheckIn this section we will estimate the robustness of our results taking into account some factors which may affect our estimations.

6.1 Multi-co-linearity

The first is the possible presence of multi-co-linearity between the independent variables. Presence of multi-co-linearity does not violate the OLS assumptions. It does however increase the standard error of the variables. With a large confidence interval due to the high standard error the coefficient of the variable needs to be higher to be significant. In other words it is more difficult to find significant estimates with the presence of multi-co-linearity. The method to detect the presence of multi-co-linearity is to calculate the Variance Inflation Factors of the independent variables. When large, the VIF’s for a particular variable indicate the presence of multi-co-linearity with the other independent variables. It also measures to what extent the standard errors are inflated. Therefore the first step in the process of analyzing the robustness of the findings in the previous chapter is to calculate the Variance Inflation Factors for the explanatory variables in the basic specifications of the model. Common rule of thumbs in literature: VIF values larger than 4 or 10 indicate the presence of multi-co-linearity between the particular independent variable and the rest of the independent variables. If the calculated Variance Inflation Factor exceeds at least one of the two values, multi-co-linearity between the specific variable and the other independent variables is present.

Table 6 gives the Variance Inflation Factors for all the individual independent variables from table 4 and 5. Following both Rule of Thumbs it is easy to see the lack of multi-co-linearity between the independent variables from table 4 and table 5. Apparently this model does not suffer much from multi-co-linearity.

6.2 Auto- or serial correlation

Auto-correlation or serial correlation is the second statistical phenomenon that is possibly influencing the estimation results. Auto-correlation does not bias the coefficient estimates, but it does violate the assumption of uncorrelated error terms. A possible lack of variability and a small trend in the level variables could give incorrect estimates. To detect the presence of auto-correlation, the residuals of the basic regression specifications will be regressed on the independent variables of the respective regression specification and the lagged residuals.

Table 7 & 8 show the results of regressing the residuals on the independent variables and the lagged residuals. Unfortunately the coefficients of residuals of multiple orders are found significant, indicating that this model suffers from severe auto-correlation of the error terms.11 The presence of auto-correlation of the error terms harms the empirical findings of this model.

6.3 Misspecification

The third possible issue with using this model is a misspecification, which may be caused by an omitted variable. An omitted variable that is correlated with both the dependent variable and one or more of the independent variables, could give wrong estimates on the individual correlated independent variable. The individual estimate contains now information regarding that variable, but also information regarding the omitted variable. Interpretation of the estimate is difficult and often leads to wrong conclusions. In other words the estimate of the independent variable will contain information about the variables itself and the omitted variable. In this case, the estimates will be biased and inconsistent. Heteroscedasticity of the residuals could indicate the presence of an omitted variable bias. Although heteroscedasticity

11 More information concerning the method and steps can be found at Wooldridge, J. M. 2009. “Introductory Econometrics: A modern approach.” Cengage learning: 417-419

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could also be present due to other reasons like an incorrect functional form. The method that will be used in this paper to detect issues with the specifications and the model is to check the robustness of the empirical findings, by setting-up different specifications of the model. Robust estimates will not radically change under alternative specifications of the same model. With this method it will become clear whether certain changes between estimates on single variables are caused by a lack of robustness.

So in order to check the robustness of the results, alternative specifications of the model will be tested. The robustness check performed in this study follows the work of Larcinese, Rizzo and Testa (2006) by specifying alternative regression equations. The misspecification check is divided into two parts. The first part basically tests the robustness of all the individual significant variables found in the previous chapter of all three hypothesis together and adds particular variables. The second part checks the robustness by setting up alternative specifications to test the single hypothesis. However testing the first and second hypothesis separated from each other is not preferable, so the first and second hypothesis will be tested together, but with different sets of variables.

6.3.1 Robustness check combined hypothesis

The first robustness check equation includes and tests all the significant variables of all three hypothesis of the previous chapter. In order to test the combined significant variables while testing different hypothesis this study sets up a combined robustness check. Therefore the following model is constructed and used:

Likewise the paper of Larcinese, Rizzo and Testa (2006) the second specification of the combined robustness check adds a dummy variable that takes on the value of one, if the gubernatorial elections took place in that year (Govelectionyr). This dummy variable will also be interacted with the Gov-Pres dummy. Both variables will be included. The empirical findings in the previous chapter did not indicate a large influence of the alignments with the state Governor on the allocation of Federal Grants. As mentioned this contradicts some of the empirical findings in the preceding empirical literature. Nevertheless the President may want to divert funds towards states with aligned Governors who are running for (re-)elections. If this is found significant, but the alignment dummy between the President and the state Governor not, it could indicate that aligned states are targeted during Gubernatorial election years, but receive less Federal grants in years of no Gubernatorial elections to make up for the difference.

The third robustness check equation adds two variables that have been found significant in the existing empirical literature. The variable containing data on the percentage of people in a state casting their vote during the Presidential elections (turnout) and variable containing data on the amount of state Senators per capita are added (Senatorpercap).

Note that also these political variables are reconstructed to get weighted dependent variable to account for the delay between the political appropriation and the receiving’s of Federal Grants.

The first robustness check equations tests all the significant variables of all three hypothesis of the previous chapter. To recall from the previous chapter the following variables were found to have significant influence on the allocation of Federal Grants: sharevote, closeness, Govstatehouse, Govstatesenate, Housep and Housestatemajor. These set of political variables will be included in the first combined Robustness check equation together with the control variables (as always).

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The first column in table 9 reports the estimates of the first robustness check specification. The sign of the coefficients of the explanatory variables did not change.12 In fact except for some minor changes to the size of the coefficients, the estimates are quite the same to the empirical findings in the previous chapter. Except for the coefficient of the closeness variable that becomes significant at 10% instead of 5%, the significance levels of the explanatory variables remain the same. Adding Gov-Pres, Govelectionyr and the interaction of the two does not change the estimates of the individual significant variables much. The interaction term and the Govelectionyr variable do not significantly influence the allocation. It is worth noting however that is the coefficient of the Gov-Pres variable is now significant at 10%. Indicating that the alignment between the states Governor with the U.S. President is positively related to the amount of Grants received. This variable however becomes insignificant again in the third specification. Again the estimates of the variables that were also in the first specification remain quite stable and are therefore quite robust. The turnout at the Presidential elections and the Senatorpercap representation does not influence the allocation much nor the estimates of the variables that were also included in the first specification. The insignificant coefficient of the turnout variables does not provide evidence that the state turnout influences the distribution of Federal Grants. This contradicts the finding of Fleck (1999c) and the point stressed by Levitt & Snyder (1995) on the importance of the turnout on the distribution of Federal Spending. However the finding is in line with the paper of Larcinese, Rizzo and Testa (2006). The insignificance of the Senatorpercap variable however contradicts the finding of Larcinese, Rizzo and Testa (2006) but also multiple papers by other scholars like Wright (1974), Fleck (2008), Atlas et. al (2005) and Knight (2004).

6.3.2. Robustness check individual hypothesis

This check departs from the original specification of the model to test the first and second hypothesis. But instead of using the variable closeness the variable lastelecswing13 is introduced into the equation. The second specification replaces the lastelecswing variable for a longer term swing variable (swing_2). Instead of taking the average amount of swings over the past four Presidential elections like the swing_1 variable, the swing_2 variable takes the average amount of swings from the Presidential elections in 1936 onwards. To give an idea about this variable in 1972 the last presidential elections in the period under consideration took place. This was the 9th election since the election in 1936. In other words the amount of swings during the nine elections is divided by the total amount of nine elections under consideration. On the other hand the first Presidential election in our period of interest took place in 1960. The amount of swings from 1936 onwards is divided by six, the respective number of elections. Due to the high number of elections accounted for, this variable is better able to express the state’s long term swing characteristics. Given the positive and significant coefficients of the lastelecswing and the swing_1 variables, the variable swing_2 is expected to have positive and significant influence on the amount of Federal grants received by states. A final specification with respect to the first and second hypothesis incorporates the swing_1 variable again, but also adds the statepreswon dummy and the interaction of the two variables.

12 Concerning the estimates of the control variables in the robustness check chapter, the coefficients are not very robust. For example the sign of the coefficient of the unemployment variable changes between different specifications. The estimates and significance levels of the controls are different between on the one hand table 10 and on the other the original specifications of the original model. The percentages of young ones is not significant anymore, while instead the variables capturing the percentage of aged population becomes significant. Above that: instead of the sign of the coefficient being negative, the sign becomes positive. These radical changes between estimates due to alternative specifications and the choice of independent variables indicate that the model is not very robust with respect to the estimates of the controls. 13 Takes basically only last election swing from the swing_1 variable.

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This study continues along the same line by constructing alternative specifications of the model to test the third hypothesis and to check the robustness. In specifying the model this study follows the steps made by the paper of Larcinese, Rizzo and Testa (2006). First the states with an alignment between the state Governor with the majority of state delegates to the House (Govstatehouse) and/or with an alignment between the Governor and the double state Senators (Govstatesenate) may benefit them with respect to the receiving’s of Federal grants. The second alternative specification checks if the alignment between the President with the majority party of the state delegates to the House (Housep) and the alignment with both state Senators (Senatep). Last specification includes the internal alignment between the state delegates to the House (Housestatemajor) and both state Senators with the majority party in the House and the Senate (Senatestatemajor)

Hypothesis 1 & 2

Table 10 reports the estimation results. Again the coefficient of the sharevote variable is negative and highly significant. Whether a state swung during the last Presidential election (Lastelecswing) does not seem to influence the allocation of grants significantly. This does not provide significant evidence regarding the hypothesis of targeting swing states. Again the state representation (electvotepercap) does not influence the distribution. The longer term swing variable included in the second specification does influence the allocation of grants significantly. The sign of the coefficient is positive, indicating that long term (since election 1936) swing states receive more Federal grants then states that voted more consistently for the same political party. The final specification includes the swing_1 variable again, but together with the statepreswon variable and the interaction of those two. Again the swing_1 variable is not found significant, nor the statepreswon seem to significantly influence the allocation of Federal grants. On the other hand the interaction variable between statepreswon and swing_1 is negative related to the grants received and significant at 5%. So the average amount of swings during the past four elections only influences the allocation significantly if the state voted for the incumbent President during the past election. This influence is however negative. There is no targeting of swing states where the incumbent President won in the last Presidential election, in fact those states receive less. The state per capita representation in Congress does again not influence the allocation of Federal grants significantly.

Hypothesis 3

From the estimates presented in the first column of table 11, only the Govstatehouse dummy is significant. With respect to this variable the finding is in line with the previous finding, but note that the size of the coefficient is substantially larger. Contradicting previous findings is the insignificant coefficient of the Govstatesenate variable. Instead of being significant at 1% in the previous chapter, the variable now does not influence the allocation of Federal grants. The second specification includes the Housep and Senatep variables. Again only the coefficient of the Housep variable is significant, but note again the coefficient is substantially larger compared to the finding of the original specification. The third and final specification of the robustness check also provides substantially different estimates compared to the estimates from the original specification. The Housestatemajor variable is now significant at 10% instead of 1%, but above that the coefficient of the Senatestatemajor variable is now significant at 5%. So subject to a particular choice of independent variables the internal alignment in the Senate influences the allocation of Federal grants. The robustness check on the third individual hypothesis shows that the findings with respect to the third hypothesis are not robust against different specification.

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6.4 Conclusion

The main finding from these additional specifications is that subject to particular choice of explanatory variables the significance levels may change. This is especially the case for the individual estimates on the alignment variables. Based on this finding it could very well be that an additional, not included, but probably correlated variable with at least one or more of the explanatory variables, influences the estimates. Larcinese, Snyder and Testa (2009) note the absence of good measures of the true underlying partisan relationship and ideological mind-set in the geographical area’s when focusing on data on votes and the outcome of elections. Data on votes and election outcomes are just a proxy to the real underlying partisan relationship and ideological mind-set. The problem with this kind of data according to them is that decisions on voting behavior are endogenous to Federal spending. They propose to use survey data on party identification and the ideological beliefs to address this issue. Survey data is more exogenous to Federal spending and therefore a better proxy. From the results they do not find evidence in favor of the hypothesis of targeting swing states or states with high electoral competition. Neither do they find evidence regarding the hypothesis of rewarding the loyal voters. They do find mixed evidence on the topic of partisan spending patterns. The final major important finding of the paper is that spending has little to no effect on the decision of voters. Based on this paper and on the inconsistent estimates between the columns of the regression output table it is rather questionable to what extent the approach of this study will find any correct and solid evidence regarding the hypotheses. Highlighting and correcting this problem pointed out by Larcinese, Snyder and Testa (2009) is out of the scope of this work and will be left for further research.

6.5 Autoregressive model

This study will continue by constructing an alternative model to empirically test the panel data on the 48 states.14

The auto-regressive model will incorporate a lagged dependent variable on the right hand side of the equation. By adding this lagged dependent variable as explanatory variable in the regression equation it reduces the auto-correlation of the dependent variable. This kind of model can bring the dynamic political influences to light.

Suppose the allocation of Federal grants is partly based on current political and economic decision and on the past allocation of Federal Grants. As discussed the Federal Grants are influenced by the Federal government by their power to appropriate and set conditions. It is quite common to imagine that the Federal grants are not simple allocated totally irrespective of prior allocations. Not only are they signed and distributed for a longer period than one year in many cases, some institutions or parts of the society need and receive that part of spending year after year and actually are dependent on it. Excluding the lagged dependent variable could therefore lead to severe problems when using and testing these kinds of economic and political spending models with OLS.15 The auto-regressive model to be tested becomes:

14 Two more models were tested. Instead of testing level variables, the shares were tested in the first model. The second model distinct itself by taking the first difference of the level variables. The results did not differ substantially from the original model and therefore both models and results will not be discussed in this paper. 15 See Keele & Kelly (2006)

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6.5.1 Results Autoregressive model

This study proceeds by performing an analysis on the auto-regressive model, by taking the same steps as the original analysis. So the first step is to run four regressions to test the combined first and second hypothesis and the third hypothesis. The next step is to evaluate the possible presence of auto-correlation and multi-co-linearity.

-Hypothesis 1 & 2

Table 12 reports the test results with respect to the first and second hypothesis. As usual the first column incorporates the closeness variable and the second column incorporates the swing_1 variable. Like the finding from the original model the sharevote is negatively and significantly (10% instead of 1%) related to the amount of Federal grants received by states.16 The closeness variable however has a positive sign, but is not found significant. Nor is the swing_1 variable significantly related to the amount of Federal Grants received. The state per capita representation in Congress or in other words the state per capita electoral votes does not influence the distribution of Grants. This is in line with the findings of the original model. With respect to the hypothesis of rewarding loyal states and the hypothesis of targeting swing states the results of the auto-regressive model does not find any positive evidence. In fact the negative point estimate of the sharevote variable contradicts the hypothesis of rewarding loyal states.

-Hypothesis 3

With respect to the hypothesis of awarding aligned political players there is no significant evidence that the alignments between the Governor and the President (Gov-Pres) and between the Governor and Congress (Gov-Congress) influence the distribution of Federal Grants. The second column of table 13 reports the test results on all alignment variables. Only the coefficient of the Govstatesenate variable is found significant (at 10%). Alignment between the states Governor with double state Senators is awarded with higher amount of Federal grants to the particular state. There is no significant evidence that any of the other alignments are able to attract Federal grants or are awarded with Federal grants.

6.5.2 Multi-co-linearity and Auto- or serial correlation

The levels of the Variance Inflation Factors for the explanatory variables reported in table 14 are not above the discussed rules of thumb. Based on these low VIF’s values, the standard errors and the significance levels are not heavily influenced by this statistical phenomenon. Multi-co-linearity of the independent explanatory variables is not a big issue with the use of this model.

Along the same line the lagged residuals together with the independent variables are regressed on the residuals to check for the presence of auto-correlation. Adding a lagged dependent variable on the right hand side is a common way to limit the influence of autocorrelation of political and economic models Therefore this model is expected to be less influenced by this statistical phenomenon. Tables 15 and 16 report the estimates of the independent variables and the lagged residuals. From the significant coefficient of the first and second order lagged residuals it follows that this model still suffers from auto-correlation. However it must be said that the auto-correlation is substantially less compared to the original model.

16 Concerning the estimates of the control variables in the baseline regressions specifications of the auto-regressive model only the state unemployment rate and the real percapita Federal Grants have in only one single case significant influence. State’s with higher unemployment rates receive less (First column table 11) while states with higher real percapita income receive more real per capita Grants (second column table 11). The sign of the coefficient of the unemployment rate variable is on the other hand positive in the other baseline specifications of the auto-regressive model.

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7 ConclusionThe goal of this paper was to determine the possible political influence, with special attention to the role of the U.S. President, on the allocation of Federal Grants prior to the impoundment power act before 1974. The amounts of real per capita Federal Grants received by states differ substantially for the period between 1960 and 1974. This can be due to difference in the state’s needs, but it is relevant to ask to what extent political factors may affect these large differences in allocation.

From a theoretical point of view the President may want to divert funds away from a social optimum in order to improve (re-)election changes by for example targeting particular groups such as ‘swing’ voters or ‘loyal’ voters, or to push his political agenda by for example logrolling and by diverting funds towards partisan politicians.

This paper does not find evidence that loyal states are rewarded by the receiving more per capita real Federal Grants. Instead, states which supported the incumbent President more receive on average less than states which supported the President less. States where the electoral race in the last Presidential election was close are targeted and so receive more Federal Grants. This supports the hypothesis of targeting marginal states. The margin may not always be a good proxy for testing the influence of swing states. Instead variables containing information about the average amount of swings over different periods were implemented. From the results it seems that only the amount of swings in the long term (average amount of swings since the 1936 elections) contributed to the disproportionate allocation of Federal Grants. However interacting the swing variable capturing the average amount of swing over the last four Presidential elections the variable capturing information whether the state voted for the incumbent President did influence the allocation. The impact is however negative, indicating that swing states that did not vote for the incumbent President in the last Presidential election receive less per capita real Federal Grants.

Evidence regarding Presidential partisan alignment budgeting is hardly found. It does not seem that the President is targeting states with an aligned Governor, nor states with aligned state Senators. Alignment between state delegates to the House and the President even result in significantly less Federal grants received by that particular state.

Evidence is found that partisan budgeting is influencing the allocation of Federal Grants significantly. The internal alignment between state delegates to the House and the majority party in the House result in higher receiving’s of Federal grants to that particular state. Also the political alignment between the state Governor with the political party of both state Senators increases the amount of Grants received. If the state Governor is however aligned with the majority party of the state delegates to the House it results in significantly less Grants received.

Larcinese, Snyder and Testa (2009) put forth the idea that focusing on the voting data and outcome of elections is just a proxy to identify the true underlying partisan relationship and ideological mind-set of people in specific geographical areas. The problem may be the endogeneity of the relation between voting and Federal spending. Based on this paper and on the inconsistent and no robust findings, it is highly questionable to what extent this study found any correct and solid evidence regarding the hypotheses.

Possibly a better way to determine the political, to be specific, the influence of the Executive on the distribution of Federal Grants, subject to availability, is to use survey data. This is however out of reach for this study and will be left for further research.

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Rich, Michael J. 1989. “Distributive Politics and the Allocation of Federal Grants.” The American Political Science Review 83 (February): 193–213.

Rohde, David, and Dennis M. Simon. 1985. “Presidential Veto and Congressional Response: A Study of Institutional Conflict.” American Journal of Political Science 29 (August): 397–427.

Snyder, Jamers M. 1991. “On Buying Legislatures.” Economics and Politics 3 (July): 93-109

Wallis, John J. 1984. “The Birth of Old Federalism: Financing the New Deal, 1932-1940.” Journal of Economic History 44 (March): 139-59

Wallis, John J. 1987. “Employment, Politics and Economic Recovery in the Great Depression.” Review of Economics and Statistics 69 (August): 516–20.

38

Wright, Gavin. 1974. “The Political Economy of New Deal Spending: An Econometric Analysis.” Review of Economics and Statistics 56 (February): 30–8.

9 Appendices

► A: Spread and variable overview Table 1: Average real percapita Federal Grants by state, 1960-1974 17

States Real US$ Per capita States Real US$ Per capitaWyoming 194.1679399 Mississippi 94.81646769Montana 156.9829901 Tennesee 94.46717339New York 156.8266555 North Dakota 88.89683463North Carolina 149.9669593 Minnesota 88.5308621Virginia 142.100618 Maine 86.90203058Oklahoma 136.593601 Kansas 84.0273453South Dakota 129.5133094 New Jersey 83.13438753Louisiana 126.2106237 Delaware 82.95180103Missouri 126.0427082 Texas 82.01738839Wisconsin 124.4010101 New Hampshire 80.69359374Nevada 123.9382632 Nebraska 75.60125689Utah 120.4098441 South Carolina 75.36603426arizona 119.0065075 Vermont 75.17077297Illinois 113.3734789 Idaho 74.46503484Colorado 111.2199831 Connecticut 73.41452649alabama 110.0418814 Pennsylvania 72.0235032Kentucky 108.3482617 Michigan 70.78582887

17 also presented in the main text

39

Arkansas 106.1397985 Maryland 68.75028958Rhode Island 100.1094149 Indiana 68.43602639California 98.28270223 Ohio 66.46961179Washington 98.11954455 Florida 66.06310933Georgia 97.04045277 West Virginia 63.13783048Oregon 96.8173541 New Mexico 59.11234366Massachusetts 95.23596797 Iowa 54.95582752

Table 2: Summary statisticsVariable Mean Maximum Minimum Std. Dev. ObservationsFedgrants 136,509 356,8868 7,820479 65,81462 720Closeness 16,10597 74,2 0,2 12,30957 720Electvotepercap 0,003662 0,010863 0,001569 0,001598 720Gov-Congress 0,373611 1 0 0,419448 720Govelectionyr 0,347222 0,6 0 0,248485 720Gov-Pres 0,548747 1 0 0,45475 720Govstatehouse 0,520278 1 0 0,452261 720Govstatesenate 0,453408 1 0 0,457237 720Housep 0,512778 1 0 0,454008 720Housestatemajor 0,576944 1 0 0,470163 720lastelecswing 0,463217 1 0 0,460457 720Pop 4073993 21174000 291000 4178036 720Pop517 0,259519 0,313883 0,171732 0,017714 720Pop65 0,1034 0,19 0,021739 0,029742 720Realincomepercap 4560,937 64402,12 637,1191 3967,474 720Senatep 0,430598 1 0 0,455433 720Senatestatemajor 0,453408 1,00E+00 0 0,488003 720Senatorpercap 0,00132 0,007242 9,64E-05 0,001405 720Sharevote 52,25025 80,9 12,9 10,04547 720

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Statepreswon 0,711944 1 0 0,418716 720Swing_1 0,399653 1 0 0,170442 720Swing_2 0,333164 0,75 0 0,152536 720Turnout 61,71625 79,7 25,3 11,6787 720Unemployment 4,846528 13,5 1,8 1,4959999 720

► B: Level model analysis

Table 4: Swing & Ideological bias

Dependent variable: Real percapita Federal Grants, 1960-1974

Dependent variable (1) Fedgrants (2) Fedgrants

Sharevote -0.6102 (-6.5998) *** -0.742 (-5.4572) ***

Closeness -0.2709 (-2.0612) **

Swing_1 -2.8037 (-0.2804)

Electvotepercap 1519.709 (0.4606) 2034.438 (0.5836)

Pop -5.19E-06 (-2.7674) *** -6.03E-06 (-2.446) **

Pop517 -193.3773 (-2.7928) *** -717.2793 (-2.2035) **

Pop65 -17.83 (-0.6693) -22.8065 (-0.7946)

Realincomepercap 0.0002 (0.9965) 0.0002 (0.9539)

Unemployment -0.2283 (-0.4093) 0.2351 (0.356)

Obs 719 719

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R-squared 0.9275 0.926

OLS regressions; T-statistics in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1%). The following controls are included in all regressions: State Fixed Effects, Year Fixed Effects and constant term. White Cross-section coef covariance method used in all regressions.

Table 5: Alignment bias

Dependent variable: Real percapita Federal Grants, 1960-1974

Dependent variable (1) Fedgrants (2) Fedgrants

Gov-Pres 0.2948 (0.1873) 1.1071 (0.6044)

Gov-Congress -2.2855 (-1.0936) -1.9442 (-1.0331)

Govstatehouse -6.8745 (-5.1761) ***

Govstatesenate 6.1348 (3.5517) ***

Housep -10.7671 (-5.2633) ***

Senatep 1.5093 (0.8184)

Housestatemajor 10.7939 (4.8324) ***

Senatestatemajor 4.4309 (1.3742)

Pop -4.99E-06 (-3.2705) *** -8.57E-06 (-5.8766) ***

Pop517 -135.2823 (-1.6751) * -158.5019 (-1.9698) *

Pop65 -6.2287 (-0.1953) 6.3611 (0.1828)

Realincomepercap 0.0002 (0.6754) 0.0002 (0.9036)

Unemployment 0.9734 (0.9485) 1.4957 (1.2859)

Obs 719 719

R-squared 0.9209 0.9276

OLS regressions; T-statistics in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1%). The following controls are included in all regressions: State Fixed Effects, Year Fixed Effects and constant term. White Cross-section coef covariance method used in all regressions.

Table 6: VIF relative to the coefficients in table 4 & 5

Table & Column 4.1 4.2 5.1 5.2

Sharevote 1.0856 0.8886

Closeness 1.2688

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Swing_1 1.0201

Electvotepercap 1.4813 1.4669

Gov-Pres 1.0085 1.0568

Gov-Congress 1.0129 1.0452

Govstatehouse 1.2516

Govstatesenate 1.308

Housep 1.38

Senatep 1.1976

Housestatemajor 1.4943

Senatestatemajor 1.4102

Pop 1.4454 1.3973 0.9876 1.0532

Pop517 0.1985 0.2081 0.1053 0.1182

Pop65 0.9381 0.9361 0.936 0.9537

Realincomepercap 1.0165 1.0136 1.0067 1.0264

Unemployment 0.98 0.9711 0.9499 1.0315

Table 7: Auto- or serial correlation estimation relative to the specifications in table 4

Dependent variable (1) Resid (2) Resid

Sharevote -0.0058 (-0.0782) -0.02 (-0.2961)

Closeness 0.0119 (0.1926)

Swing_1 -2.1507 (-0.4409)

Electvotepercap 474.2798 (0.8188) 520.0619 (0.8977)

Pop 3.64E-07 (1.7085) * 3.22E-07 (1.5279) *

Pop517 68.8239 (1.5272) 58.1756 (1.2814)

Pop65 -25.0903 (-1.1495) -23.9135 (-1.0894)

Realincomepercap -9.91E-06 (-0.0662) -1.26E-05 (-0.084)

Unemployment 1.5305 (2.7147) *** 1.3575 (2.3801) **

Resid t-1 0.4699 (9.6603) *** 0.4883 (10.0347) ***

Resid t-2 0.047 (0.8492) 0.057 (1.0212)

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Resid t-3 0.1667 (2.7914) *** 0.169 (2.8059) ***

Resif t-4 -0.1508 (-2.2412) ** -0.1696 (-2.4968) **

Resid t-5 -0.1029 (-1.7121) * -0.1117 (-1.8582)

obs 479 479

R-squared 0.3097 0.3269

OLS regressions; T-statistics in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1%). The following control is included in all regressions: constant term.

Table 8: Auto- or serial correlation estimation relative to the specifications in table 5

Dependent variable (1) Resid (2) Resid

Gov-Pres 0.2548 (0.1599) -1.107 (-0.5437)

Gov-Congress 1.3877 (0.7629) 1.4686 (0.4341)

Govstatehouse 2.2336 (1.0039)

Govstatesenate -0.9238 (-0.3909)

Housep 2.1244 (0.8483)

Senatep 0.75 (0.2638)

Housestatemajor -3.8184 (-0.9007)

Senatestatemajor 0.8132 (0.2923)

Pop 2.75E-07 (1.4679) -3.22E-07 (-0.0662)

Pop517 66.3218 (1.4695) 149.1523 (1.6047)

Pop65 -30.0728 (-1.3482) -56.7751 (-3.148) ***

Realincomepercap 4.62E-05 (0.3016) 1.80E-05 (0.0667)

Unemployment 1.4913 (2.6212) *** 3.1342 (2.5812) **

Resid t-1 0.5058 (10.1743) *** 0.4376 (4.2194) ***

Resid t-2 0.0997 (1.6553) * 0.0908 (0.7441)

Resid t-3 0.1719 (2.6099) *** 0.1734 (2.5622) **

Resid t-4 -0.1772 (-2.5296) ** -0.1063 (-1.3396)

Resid t-5 -0.1338 (-2.132) ** -0.0447 (-0.9526)

obs 479 479

R-squared 0.3683 0.3716

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OLS regressions; T-statistics in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1%). The following control is included in all regressions: constant term.

Table 9: Robustness Check

Dependent variable: Real percapita Federal Grants, 1960-1974

Dependent variable (1) Fedgrants (2) Fedgrants (3) Fedgrants

Sharevote -0.4737 (-5.43) *** -0.4898 (-5.6784) *** -0.4898 (-5.4414) ***

Closeness -0.222 (-1.801) * -0.2298 (-1.8122) * -0.222 (-1.8378) *

Govstatehouse -7.013 (-4.3839) *** -6.7366 (-4.1585) *** -6.9236 (-4.4654) ***

Govstatesenate 5.588 (3.3641) *** 5.2859 (3.3571) *** 5.0328 (3.1103) ***

Housep -6.2541 (-3.1391) *** -6.2043 (-2.9475) *** -6.0757 (-2.7348) ***

Housestatemajor 7.974 (3.7257) *** 8.1388 (3.8238) *** 7.6359 (3.3321)***

Gov-Pres 3.9899 (1.7964) * 3.4633 (1.5967)

Govelectionyr 0.1082 (0.02518) -0.4912 (-0.115)

Gov-Pres*Govelectionyr -7.1946 (-1.6223) -6.5376 (-1.6153)

Senatorpercap 8896.908 (1.4606)

Turnout 0.000814 (0.0164)

Pop -4.22E-06 (-2.39) ** -4.42E-06 (-2.5969) ***

-4.99E-06 (2.6269) ***

Pop517 -1.70E-05 (-4.7992) ***

-1.72E-05 (-5.0571)*** -1.75E-05 (-5.0298) ***

Pop65 1.81E-06 (0.5585) 1.88E-06 (0.5688) 1.54E-06 (0.4624)

Realincomepercap 0.0003 (1.1019) 0.0003 (1.0722) 0.0002 (1.0372)

Unemployment -0.2486 (-0.3739) -0.3545 (-0.5432) 0.1133 (0.1313)

Obs 719 719 719

R-squared 0.931 0.9314 0.9319

OLS regressions; T-statistics in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1%). The following control is included in all regressions: Country Fixed Effects, Year Fixed Effects and constant term. White Cross-section coef covariance method used in all regressions.

Table 10: More on Swing & Loyalty bias

45

Dependent variable: Real percapita Federal Grants, 1960-1974

Dependent variable (1) Fedgrants (2) Fedgrants (3) Fedgrants

Sharevote -0.7487 (-5.7114) *** -0.7524 (-7.5868) *** -0.555 (-3.66) ***

Swing_1 14.0927 (0.9427)

Swing_2 124.6313 (6.8) ***

Lastelecswing -1.311 (-0.5749)

Statepreswon 0.8348 (0.2974)

Statepreswon*Swing_1 -18.358 (-2.1263) **

Electvotepercap 2061.545 (0.6082) 3135.15 (0.9924) 2574.54 (0.7236)

Pop -5.98E-06 (-2.7042) *** -6.86E-06 (-3.2694) *** -5.56E-06 (2.3658) **

Pop517 -185.9608 (-2.7575) ** -281.0084 (-3.1807) ***

-183.0171 (-2.1822) **

Pop65 -22.7586 (-0.7795) -19.4389 (-0.6607) -22.9995 (-0.8237)

Realincomepercap 0.0002 (0.9253) 0.0002 (0.7172) 0.0002 (0.8755)

Unemployment 0.2357 (0.4072) 0.4323 (0.6273) 0.6205 (0.9236)

Obs 719 719 719

R-squared 0.9266 0.9329 0.9274

OLS regressions; T-statistics in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1%). The following control is included in all regressions: Country Fixed Effects, Year Fixed Effects and constant term. White Cross-section coef covariance method used in all regressions.

Table 11: More on Alignment bias

Dependent variable: Real percapita Federal Grants, 1960-1974

Dependent variable (1) Fedgrants (2) Fedgrants (3) Fedgrants

Govstatehouse -20.7544 (-4.6876) ***

Govstatesenate 1.5833 (0.3938)

Housep -35.403 (-2.8333) ***

Senatep 2.8774 (0.8937)

Housestatemajor 6.6324 (1.8778) *

Senatestatemajor -6.526 (-1.9914) **

Pop 2.87E-06 (-5.9892) *** -2.80E-06 (-5.7281) *** -3.18E-06 (6.5312)***

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Pop517 48.552 (0.1281) 90.9214 (0.2789) 43.5726 (0.1134)

Pop65 340.2933 (1.8373) * 322.7808 (1.8905) * 370.7329 (1.9675) **

Realincomepercap 0.0015 (1.636) 0.0014 (1.6871) * 0.0014 (1.5019)

Unemployment 3.1831 (0.6989) 3.7301 (0.9131) 3.3976 (0.6989)

Obs 719 719 719

R-Squared 0.0975 0.1352 0.0812

OLS regressions; T-statistics in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1%). The following control is included in all regressions: Country Fixed Effects, Year Fixed Effects and constant term. White Cross-section coef covariance method used in all regressions.

► C: Autoregressive model analysis

Table 12: Swing & Ideological bias

Dependent variable: Real percapita Federal Grants, 1960-1974

Dependent variable (1) Fedgrants (2) Fedgrants

Fedgrants t-1 1.0152 (19.8149) *** 1.0153 (20.2901) ***

Sharevote -0.2992 (-1.8299) * -0.2868 (-1.6723) *

Closeness 0.0304 (0.4769)

Swing_1 1.3985 (0.3139)

Electvotepercap -98.433 (-0.0878) -126.3348 (-0.1162)

Pop 1.43E-07 (0.5274) 1.22E-07 (0.4638)

Pop517 -54.3359 (-0.8168) -55.7709 (-0.8326)

Pop65 -29.544 (-0.6426) -28.9606 (-0.6348)

47

Realincomepercap 0.0005 (1.7702) 0.0005 (1.8294) *

Unemployment -0.3061 (-0.2719) * -0.3527 (-0.3177)

Obs 672 672

R-squared 0.9187 0.9187

OLS regressions; T-statistics in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1%). The following controls are included in all regressions: Country Fixed Effects, Year Fixed Effects and constant term. White Cross-section coef covariance method used in all regressions.

Table 13: Alignment bias

Dependent variable: Real percapita Federal Grants, 1960-1974

Dependent variable (1) Fedgrants (2) Fedgrants

Fedgrants t-1 1.0118 (19.2846) *** 1.0082 (19.2327) ***

Gov-Pres -1.2736 (-0.5028) -1.3718 (-0.5265)

Gov-Congress 0.3339 (0.1046) 0.5084 (0.1619)

Govstatehouse -0.9876 (-0.4584)

Govstatesenate 2.1715 (1.7982) *

Housep -1.3249 (-0.8449)

Senatep 1.6337 (0.6055)

Housestatemajor 1.0647 (0.5815)

Senatestatemajor -2.7416 (-1.182)

Pop 1.74E-07 (1.2323) 1.37E-07 (1.0398)

Pop517 -27.1625 (-0.3508) -17.7669 (-0.2163)

Pop65 -19.768 (-0.3781) -18.485 (-0.3515)

Realincomepercap 0.0004 (1.5948) 0.0004 (1.5918)

Unemployment 0.0732 (0.0654) 0.0741 (0.0671)

Obs 672 672

R-squared 0.9175 0.9179

OLS regressions; T-statistics in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1%). The following controls are included in all regressions: Country Fixed Effects, Year Fixed Effects and constant term. White Cross-section coef covariance method used in all regressions.

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Table 14: VIF relative to the coefficients in table 12 & 13

Table & Column 4.1 4.2 5.1 5.2

Fedgrants t-1 1.2094 1.2661 1.153 1.2594

Sharevote 1.2984 1.0989

Closeness 1.3391

Swing_1 1.1262

Electvotepercap 1.5919 1.5622

Gov-Pres 1.0738 1.112

Gov-Congress 1.0337 1.0675

Govstatehouse 1.2607

Govstatesenate 1.3089

Housep 1.5061

Senatep 1.2282

Housestatemajor 1.5989

Senatestatemajor 1.4442

49

Pop 1.691 1.6501 1.2477 1.3001

Pop517 1.2472 1.2572 1.2171 1.2985

Pop65 1.0722 1.0723 1.065 1.0763

Realincomepercap 1.0283 1.026 1.0286 1.0417

Unemployment 1.0808 1.0669 1.0224 1.093

Table 15: Auto- or serial correlation estimation relative to the specifications in table 12

Dependent variable (1) Resid (2) Resid

Fedgrants t-1 0.0183 (1.045) 0.0195 (1.074)

Sharevote 0.0466 (0.6143) 0.0306 (0.4432)

Closeness -0.01824 (-0.287)

Swing_1 -1.1401 (-0.2101)

Electvotepercap -183.4001 (-0.2792) -149.6184 (-0.2288)

Pop 3.24E-07 (1.4753) 3.20E-07 (1.4634)

Pop517 51.957 (1.0998) 49.7892 (1.0424)

Pop65 2.2479 (0.0944) 4.3024 (0.18)

Realincomepercap 5.68E-06 (0.0377) 3.05E-06 (0.0202)

Unemployment 0.8643 (1.339) 0.7191 (1.1126)

Resid t-1 -0.2038 (-3.7122) *** -0.1987 (-3.6312) ***

Resid t-2 -0.1158 (-2.0788) ** -0.1124 (-2.0187) **

Resid t-3 0.0844 (1.46) 0.091 (1.5798)

Resif t-4 -0.0827 (-1.3285) -0.0856 (-1.3677)

Resid t-5 -0.086 (-1.355) -0.0926 (-1.4548)

obs 432 432

R-squared 0.0659 0.0639

OLS regressions; T-statistics in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1%). The following control is included in all regressions: constant term.

50

Table 16: Auto- or serial correlation estimation relative to the specifications in table 13

51

Dependent variable (1) Resid (2) Resid

Fedgrants t-1 0.0129 (0.7522) 0.0153 (0.8624)

Gov-Pres 1.232 (0.7016) 1.3504 (0.7578)

Gov-Congress 1.7794 (0.9199) 1.7851 (0.8979)

Govstatehouse -0.2278 (-0.1224)

Govstatesenate 1.114 (-0.6007)

Housep 1.3011 (0.6982)

Senatep -0.9564 (-0.546)

Housestatemajor 1.5682 (0.8273)

Senatestatemajor 0.7701 (0.4249)

Pop 4.39E-07 (2.1467) ** 4.04E-07 (1.9194)

Pop517 70.3113 (1.4695) 88.3027 (1.7785)

Pop65 2.8637 (0.1172) 2.9901 (0.1224)

Realincomepercap -9.55E-06 (-0.0617) -1.37E-05 (-0.0884)

Unemployment 1.1235 (1.7447) * 1.101 (1.6762)

Resid t-1 -0.2049 (-3.6644) *** -0.2027 (-3.5585)

Resid t-2 -0.0913 (-1.5248) -0.0928 (-1.5063)

Resid t-3 0.113 (1.8522) * 0.1202 (1.9347)

Resid t-4 -0.0756 (-1.1942) -0.091 (-1.4189)

Resid t-5 -0.073 (-1.1223) -0.0986 (1.5025)

obs 430 430

R-squared 0.0695 0.0764

OLS regressions; T-statistics in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1%). The following control is included in all regressions: constant term.