As Klein points out, the number of cloture motions filed is an imperfect measure of how frequently the filibuster is used. For example, Senators often threaten filibusters and don't have to follow through. Still, the dramatic increase in the number of cloture motions filed over the last four decades must reflect a dramatic increase in the number of times bills have been filibustered in the Senate. There's a hint of an increase in the number of filibusters up to the 91st, but since then the number of filibusters has increased dramatically.

If you're thinking that's a Watergate effect, I'm afraid the timing isn't quite right. The dramatic increase in the number of cloture motions files occurs in the 92nd Congress (1971-1972) - pre-Watergate. I'm sure some political scientist has noticed this before and has a good explanation for why there was such a break with tradition in 1971-1972. It also can't be associated with the change in rules reducing the margin necessary to invoke cloture from 2/3 to 3/5. That rules change happened in 1975. If someone has a good explanation, I'd be delighted to hear it. In fact, the dramatic increase may not be so dramatic after all. Read on for an explanation.

The model I fit included a "change point" where the regression changed. But it turns out that if you fit a much simpler model without a change point, it also fits the data pretty well. A lot of what looks like a break around the 92nd Congress is really just exponential growth taking off. Not all of it, but a lot of it.

Here are a few details on the more complicated model I fit. First, you can see a plot of observed versus expected values in the plot at the left. Second, if you really want to see the details, you can grab the CSV spreadsheet with the data (clotureCounts.csv) and the R code (cloture.R) I used to drive a couple of JAGS scripts (cloture.txt and cloture-fixed-k.txt) and lake them for a testdrive yourself.

If you're not that interested, here's what you'll probably want to know:

If you're not that interested, here's what you'll probably want to know:

- I assumed a Poisson response and used a log link in the regression.
- Although it looks as if there's a break in the relationship around the 92nd Congress, I decided to fit a changepoint model to let the data identify the point at which the slope of the regression changed. It identified the 92nd Congress as the change point with a high posterior probability (ca. 0.997).
- As you can see from the plot at the left, there doesn't seem to be an obvious pattern in the departures from expectation. While I'm sure better models are possible, it doesn't appear that there's any reason to include nonlinear terms involving these covariates.
- I ran 5 independent MCMC chains with a burnin of 5000 iterations, followed by a sample of 25,000 iterations, thinning by 5.
- The figures are produced from an analysis in which I fix the change point at the 92nd Congress.
- The Rhat statistics give strong evidence that the MCMC chains have converged.

`beta.cong`between the two segments is consistent with most of the apparent break being the result of exponential growth taking off. If you're interested in staring at the coefficients and don't want to run the analysis yourself, here they are:Inference for Bugs model at "cloture-fixed-k.txt", fit using jags, 5 chains, each with 30000 iterations (first 5000 discarded), n.thin = 5 n.sims = 25000 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff alpha[1] 0.156 1.150 -2.116 -0.606 0.142 0.943 2.379 1.004 1100 alpha[2] 0.287 0.474 -0.636 -0.032 0.290 0.608 1.209 1.007 460 beta.cong[1] 0.064 0.022 0.022 0.049 0.063 0.078 0.107 1.002 4400 beta.cong[2] 0.083 0.006 0.072 0.079 0.083 0.087 0.094 1.004 1100 beta.pres[1] -0.151 0.335 -0.809 -0.378 -0.150 0.074 0.509 1.002 2400 beta.pres[2] -0.067 0.062 -0.187 -0.109 -0.067 -0.025 0.052 1.003 1700 beta.ratio[1] -0.225 1.697 -3.622 -1.341 -0.177 0.924 3.021 1.004 1100 beta.ratio[2] 1.672 0.590 0.534 1.268 1.671 2.071 2.826 1.007 470 deviance 290.338 3.897 284.591 287.465 289.696 292.551 299.492 1.001 5100 For each parameter, n.eff is a crude measure of effective sample size, and Rhat is the potential scale reduction factor (at convergence, Rhat=1). DIC info (using the rule, pD = var(deviance)/2) pD = 7.6 and DIC = 297.9 DIC is an estimate of expected predictive error (lower deviance is better).

## andrew.latimer

Nice example of how exponential growth is very hard to tell from apparently sudden shift in linear growth rates. It reminds me of plots of invasive species population or distribution, for example, which often show an apparent "latent" phase followed by growth -- but this could simply be exponential growth starting low. I wonder how often it is statistically hard to distinguish these things in ecological data.

But on the politics I was curious if cloture motions would show a correlation with the presence of divided government (President and Senate majority from different parties). If a party's goal in using cloture is to actually pass a bill, you'd expect that more cloture motions would be made when the majority party in the Senate will likely not face a Presidential veto. On the other hand if the primary goal is to embarrass the President by forcing a veto you'd expect more cloture votes in divided government. Apparently the model and data are consistent with the "embarrassment" theory (coefficient for divided government, beta.divided, moderate but positive in the post-1970 era).

On the other hand I feel I may be missing something. The dominant effect is clearly the trend and what is causing that? Partisanship or parliamentary behavior maybe) , but what is the model?

> print(cloture.sim, digits.summary=3)

Inference for Bugs model at "cloture-fixed-k.txt", fit using jags,

5 chains, each with 30000 iterations (first 5000 discarded), n.thin = 5

n.sims = 25000 iterations saved

mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff

alpha[1] 0.636 1.292 -1.954 -0.227 0.663 1.523 3.101 1.004 1100

alpha[2] -0.068 0.511 -1.139 -0.392 -0.061 0.269 0.907 1.012 330

beta.cong[1] 0.069 0.022 0.029 0.055 0.069 0.084 0.112 1.002 2100

beta.cong[2] 0.087 0.006 0.076 0.083 0.087 0.091 0.099 1.007 580

beta.divided[1] -0.626 0.359 -1.341 -0.863 -0.621 -0.382 0.062 1.001 13000

beta.divided[2] 0.217 0.060 0.101 0.178 0.217 0.257 0.335 1.001 6200

beta.pres[1] 0.014 0.373 -0.720 -0.234 0.016 0.263 0.743 1.003 1800

beta.pres[2] -0.077 0.061 -0.194 -0.118 -0.077 -0.036 0.044 1.003 1200

beta.ratio[1] -1.271 1.861 -4.933 -2.559 -1.267 -0.002 2.351 1.004 980

beta.ratio[2] 1.882 0.628 0.675 1.462 1.884 2.296 3.156 1.011 360

deviance 276.004 4.535 269.181 272.695 275.320 278.601 286.590 1.003 1200

For each parameter, n.eff is a crude measure of effective sample size,

and Rhat is the potential scale reduction factor (at convergence, Rhat=1).

DIC info (using the rule, pD = var(deviance)/2)

pD = 10.2 and DIC = 286.3

DIC is an estimate of expected predictive error (lower deviance is better).

## Kent

I wondered about divided government, but didn't take the time to fit the model. Not only is there evidence for more cloture votes when government is divided, but the DIC for the model with that term included is quite a bit better than for the one I showed (delta-DIC ca. 11 points).

As for the trend, maybe there's a simple psychological tendency to escalate conflicts, at least in some circumstances, and those circumstances happen to apply to the United States Senate in the 20th century.

## https://www.google.com/accounts/o8/id?id=AItOawnf7mclyIoYmOIsVCZAuF3tsOgc9joiBxw

i aint a fan of graphing and contingency calculation but this is something i could easily understand minus the technical stuffs around.