andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-187 knowledge-graph by maker-knowledge-mining
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Introduction: Nick Obradovich saw our graphs and regressions showing that the most popular governors tended to come from small states and suggested looking at unemployment rates. (I’d used change in per-capita income as my economic predictor, following the usual practice in political science.) Here’s the graph that got things started: And here’s what Obradovich wrote: It seems that average unemployment rate is more strongly negatively correlated with positive governor approval ratings than is population. The unemployment rate and state size is positively correlated. Anyway, when I include state unemployment rate in the regressions, it pulls the significance away from state population. I do economic data work much of the day, so when I read your post this morning and looked at your charts, state unemployment rates jumped right out at me as a potential confound. I passed this suggestion on to Hanfei, who ran some regressions: lm (popularity ~ c.log.statepop + c.unemployment)
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1 Nick Obradovich saw our graphs and regressions showing that the most popular governors tended to come from small states and suggested looking at unemployment rates. [sent-1, score-1.218]
2 (I’d used change in per-capita income as my economic predictor, following the usual practice in political science. [sent-2, score-0.213]
3 ) Here’s the graph that got things started: And here’s what Obradovich wrote: It seems that average unemployment rate is more strongly negatively correlated with positive governor approval ratings than is population. [sent-3, score-0.869]
4 The unemployment rate and state size is positively correlated. [sent-4, score-0.718]
5 Anyway, when I include state unemployment rate in the regressions, it pulls the significance away from state population. [sent-5, score-0.869]
6 I do economic data work much of the day, so when I read your post this morning and looked at your charts, state unemployment rates jumped right out at me as a potential confound. [sent-6, score-0.904]
7 I passed this suggestion on to Hanfei, who ran some regressions: lm (popularity ~ c. [sent-7, score-0.446]
8 29 Also some other versions including lots of non-statistically-significant interactions. [sent-66, score-0.066]
9 We centered each predictor to allow easy interpretation of each main effect in the presence of interactions. [sent-72, score-0.314]
10 The punch line is that, in these models, state-level unemployment is highly predictive of lower popularity for the governor. [sent-73, score-0.751]
11 Even after controlling for these economic predictors, governors of smaller states remain more popular, but this trend is no longer statistically significant. [sent-74, score-0.899]
12 The next step is to look at other years and other statewide offices. [sent-75, score-0.1]
13 I think there are some articles on this in the political science literature that Shigeo found when we last looked at the topic. [sent-76, score-0.144]
14 In the meantime, I’ll take the position that the most popular governors tend to be from smaller states, and some of this pattern appears to be explained by economic factors. [sent-77, score-0.73]
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Introduction: Nick Obradovich saw our graphs and regressions showing that the most popular governors tended to come from small states and suggested looking at unemployment rates. (I’d used change in per-capita income as my economic predictor, following the usual practice in political science.) Here’s the graph that got things started: And here’s what Obradovich wrote: It seems that average unemployment rate is more strongly negatively correlated with positive governor approval ratings than is population. The unemployment rate and state size is positively correlated. Anyway, when I include state unemployment rate in the regressions, it pulls the significance away from state population. I do economic data work much of the day, so when I read your post this morning and looked at your charts, state unemployment rates jumped right out at me as a potential confound. I passed this suggestion on to Hanfei, who ran some regressions: lm (popularity ~ c.log.statepop + c.unemployment)
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