This post continues the series on social and health factors associated with Trump’s vote in PA. Subsequently, this post will look at counties in Pennsylvania with severe housing cost burden and Trump’s vote in that county. County Health Rankings defines severe housing cost burden as “the percentage of households that spend 50% or more of their household income on housing.” The regression model states that for every 1% increase in the housing burden %, there is a predicted 1.15% decrease in Trump’s county vote. The univariate graph is below.
The graph above shows a steep straight line sloping downward. Altogether, the r2 statistic of 0.563 means that 56.3% of the variability in Trump’s vote is explained by the variability in housing cost burden. If 100% of the variance were accounted for, all of the counties would fall on the straight line. The most obvious outlier on the graph is Pike County with 15.8% housing cost burden and 61.6% of the vote for Trump. Pike county is a northern county on the border with New Jersey.
This pattern is analogous to the one found with median household income and Trump’s vote. Similarly, Counties with higher income levels tend to have less support for Trump. The most glaring exception for this trend is Philadelphia County with a lower income level and the least support for Trump. With the lower income and high housing cost burden, a higher cost of living is in Philadelphia.
The effect of housing cost (56.3%) is stronger than that of median income (33.3%). This is why housing cost remained in the model while median income did not. Counties with lower housing cost burden tended to have higher percentages of the vote for Trump. This is at the same time when concerns about inflation was a major factor among Trump voters.
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This is fascinating, thank you.