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.
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.
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 0.156 1.150 -2.116 -0.606 0.142 0.943 2.379 1.004 1100 alpha 0.287 0.474 -0.636 -0.032 0.290 0.608 1.209 1.007 460 beta.cong 0.064 0.022 0.022 0.049 0.063 0.078 0.107 1.002 4400 beta.cong 0.083 0.006 0.072 0.079 0.083 0.087 0.094 1.004 1100 beta.pres -0.151 0.335 -0.809 -0.378 -0.150 0.074 0.509 1.002 2400 beta.pres -0.067 0.062 -0.187 -0.109 -0.067 -0.025 0.052 1.003 1700 beta.ratio -0.225 1.697 -3.622 -1.341 -0.177 0.924 3.021 1.004 1100 beta.ratio 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).