Republicans go for a moderate

For those who are interested in milestones, this is our 400th post.

When US House of Representatives majority leader Eric Cantor was defeated in his primary last week, it looked as if Republican voters were telling their leadership that they wanted less pragmatism and more ideological purity. If so, the caucus appears not to have heard, since they’ve gone ahead and replaced Cantor with the next in line, the relatively moderate Republican whip, Kevin McCarthy.

McCarthy, who has been whip since the beginning of 2011, previously served as Republican leader in the California state assembly. As one might expect from a Californian, he’s used to working with Democrats and is regarded as a deal-maker rather than an ideologue. The New York Times says that “lawmakers are likely to see a more pragmatic and inclusive leadership than Mr. Cantor preferred.”

McCarthy’s only opponent was Raul Labrador, a conservative Republican from Idaho who is aligned with the “tea party”. Voting figures are not released, but the Times says that McCarthy won “overwhelmingly”.

But pragmatism wasn’t victorious across the board. To replace McCarthy as whip, the GOP caucus chose Steve Scalise, a hardline conservative from Louisiana. Again the margin is not known, but since Scalise won on the first ballot against two opponents – one of them the establishment favorite, Peter Roskam, who was McCarthy’s deputy – he clearly had strong support.

Scalise now becomes the highest-ranking southerner among the Republicans. Although it’s unusual for the south not to be represented in the top two, his presence is a reminder that the conservatives still hold the upper hand, backed as they are by the sort of grassroots anger that toppled Cantor. And that extreme conservatism in turn is the major handicap the party faces in trying to improve on its Congressional position in November’s elections.

For more on Cantor, don’t miss Nate Silver’s comparison last week of primary upsets and earthquakes. As he points out, both are subject to overfitting – “a fancy term for picking up on coincidences that apply to a few incidents but that don’t generalize well enough to explain a system’s underlying dynamics.”

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