Party polarization, political alignment, and federal grant spending at the state level

For example, Krasa and Polborn (2015) and Polborn and Synder (2017) discuss the case of Lincoln Chafee, a former Republican US Senator from Rhode Island. While Chafee was looked favorably upon by his Democrat-leaning constituents, he was voted out of office in 2006 because voters felt it was important to make the Democratic party more viable in the Senate (Krasa and Polborn 2015, p. 2).

The political science literature shows evidence that party label has become increasingly important to voters. For example, Kimball (2003) refers to as the “party salience theory of voting (ibid, p. 161).” According to this theory, as the ideological divide between the parties has grown, voters have come to see the party label as an important determinant of how to vote. Supporting evidence from Hetherington (2001) shows that more polarization at the “elite level” has been followed by an increase in polarization at the “mass level.”

See McCarty et al. (1997, 2006), Poole and Rosenthal (2000), and Poole (2005) for more detailed explanations of how DW-NOMINATE scores are constructed.

That is, if in a given year the average Senate delegation’s tenure is 10 years, then a state delegation with an average tenure of 15 years would have a relative tenure of 1.5.

For example, the budget passed by the Congress in year t does not take effect until year \(t + 1\).

Variable normalization has the added advantage of ensuring that our dependent variable is stationary. Results from panel unit root tests for the spending categories analyzed in this paper are presented in Tables A1 and A2 of the “Appendix (ESM)”.

A similar normalization strategy has been employed by Porto and Sanguinetti (2001) and Galiani et al. (2016) in separate analyses of fiscal transfers in Argentina.

Even after normalization at least two of the four panel unit root tests indicate that the variables NINCOME and NELDERLY show evidence of nonstationarity. These results are available in Table A3 of the “Appendix (ESM)”. The potential presence of nonstationarity in these variables warrants further explanation. The normalization procedure calculates each state’s share of elderly and each state’s share of personal income. It is likely that we are observing the fact that some states are gaining a larger share of income and elderly over the observed period. Plotting NINCOME and NELDERLY for each state confirms this suspicion. Since it is not valid to regress a variable integrated to order zero on a variable integrated to order one, the normalized regressors that contain a unit root enter into the regressions in first differences.

For a thorough discussion regarding the proper interpretation of marginal effects when interaction terms are included, see Brambor et al. (2006).

The correct standard error associated with Eq. 3 isThe correct standard error associated with the long-run marginal effect presented in Eq. 4 is computed aswhere \({\varvec{g_{1}^{\prime }}} \equiv \dfrac{\partial \Phi _{1}}{\partial {\varvec{\beta ^{\prime }}}}\), \({\varvec{\beta ^{\prime }}}\) is a vector of parameter estimates, and \(\hat{\sigma }^{2}\) is the squared value of \(\hat{\sigma }\) from the standard error of the short-run marginal effect.

The normalization process makes their inclusion unnecessary. In a level regression the F-test unsurprisingly indicated that year fixed-effects were appropriate.

Of course, spending from the preceding year will capture the effects of other variables as well, thus we cannot say that it is entirely due to what Young and Sobel call a state’s “extraction capital (ibid, p. 458).”

In particular, if a political variable x has the coefficient \(\beta \) and the lagged dependent variable \(y_{it-1}\) has the coefficient \(\gamma \), then the long-run impact on the equilibrium level of spending, \(\bar{y}\), of a permanent increase in the political variable is \(\partial \bar{y}/ \partial \bar{x} = \beta / (1 - \gamma )\).

Moreover, Judson and Owen (1999) show that for longer panels, LSDV regression can outperform methods such as those developed in Anderson and Hsiao (1982) and Arellano and Bond (1991).

We also test the hypotheses of interest using non-normalized data. These results are available in the “Appendix (ESM)”.

The calculation is computed by dividing the coefficient on SMAJOR in column 2 by \((1-0.478)*100\), where 0.478 is the coefficient on \(\textit{NGRANTS}_{t-1}\) from column 2

Statement to the Senate Subcommittee on Rules and Administration Hearing Examining the Filibuster: History of the Filibuster 1789–2008 on April 22, 2010. http://​www.​rules.​senate.​gov/​public/​index.​cfm?​a=​Files.​Serve&​File_​id=​25f59865-abbd-4aa9-80aa-c6ce36e08ad7.