Where will voters have the most political influence in 2020?

Published

In a previous post, I calculated the voting power index for every state and federal election over the last election cycle. But not all of these seats will be contested next year, so I thought it would be interesting to select out just the 2020 elections for a new analysis. As a reminder, the voting power values are calculated using this equation:

voting_power_index = seat_potential_power/percent_absolute_margin

This calculation is explained more in the previous post, but the main point is that it allows me to combine both the importance and margin of an election into a single metric. These values can then be aggregated and compared across different states. All the code for this project is available on GitHub here.

So, without further ado, here are the results for 2020:

And here’s a table of the results, note that the table is sorted by the 2020 voting power value.

state_abbr capita_spend spend_ratio voting_power voting_power_2020 rank_change
0 NC 14.873 0.744 4.105 4.025 1
1 MI 21.483 0.869 3.661 3.418 1
2 NH 23.242 1.349 1.524 1.485 2
3 PA 25.173 1.436 1.575 1.421 0
4 FL 29.879 0.707 6.552 1.394 -4
5 WI 22.068 0.677 1.193 0.738 0
6 TX 26.893 0.523 0.68 0.511 1
7 MN 23.256 1.052 0.514 0.464 2
8 GA 18.276 0.505 1.001 0.416 -2
9 CA 43.097 2.15 0.521 0.392 -1
10 NY 68.74 2.593 0.365 0.294 2
11 AZ 17.845 0.729 0.29 0.242 3
12 UT 11.393 0.88 0.209 0.202 6
13 NV 101.142 0.476 0.306 0.173 0
14 IL 46.466 1.212 0.241 0.167 2
15 CO 29.497 1.198 0.272 0.167 0
16 OH 24.516 0.42 0.431 0.166 -6
17 VA 61.918 0.705 0.422 0.163 -6
18 WA 26.133 2.129 0.174 0.159 4
19 IN 17.374 0.531 0.182 0.148 2
20 MO 22.602 0.656 0.207 0.141 -1
21 ME 25.096 2.268 0.124 0.107 6
22 NJ 23.654 1.571 0.231 0.1 -5
23 WV 13.427 0.619 0.102 0.097 8
24 IA 14.155 0.866 0.186 0.082 -4
25 NM 19.158 2.064 0.105 0.082 5
26 MT 31.36 0.92 0.087 0.08 8
27 OR 15.198 2.376 0.132 0.075 -3
28 KS 25.679 0.431 0.128 0.072 -3
29 SC 10.081 0.43 0.127 0.072 -3
30 CT 51.359 1.544 0.164 0.065 -7
31 TN 16.678 0.455 0.084 0.055 5
32 KY 14.579 0.605 0.106 0.05 -4
33 OK 20.827 0.322 0.076 0.048 4
34 MA 47.482 2.886 0.065 0.044 4
35 LA 22.462 0.288 0.094 0.036 -3
36 NE 26.392 0.549 0.044 0.032 4
37 MS 11.292 0.209 0.055 0.032 2
38 ND 20.396 0.532 0.042 0.029 3
39 AR 34.187 0.563 0.042 0.028 3
40 DE 19.605 1.604 0.037 0.027 4
41 AL 12.857 0.328 0.087 0.026 -6
42 MD 42.382 3.127 0.105 0.026 -13
43 AK 21.693 0.793 0.092 0.025 -10
44 RI 24.2 2.57 0.033 0.025 1
45 ID 12.434 0.541 0.029 0.019 1
46 VT 22.729 6.363 0.021 0.014 1
47 SD 20.033 0.318 0.039 0.013 -4
48 HI 17.087 4.348 0.02 0.01 0
49 WY 70.562 0.443 0.012 0.009 0

So although Florida leads in the previous voting_power calculation, it ranks four spots lower in the voting_power_2020 numbers. This is because although there were many close elections in Florida over the past cycle, fewer of those seats are up for reelection in 2020.

North Carolina on the other hand has elections at every level of government in 2020. Many of those elections will have close margins, resulting in a higher voting power value. I think this underscores the importance of considering every election in an analysis instead of just thinking about the presidency. An approach like this allows you to make the best use of limited resources by focusing on places where your effort helps more than one campaign. It’s not reflected in this analysis, but North Carolina has additional appeal for Democrats in 2020 because its maps for congress and state legislature will have to be redrawn due to an unconstitutional gerrymander. This gives Democrats an additional incentive to focus on this state.

Note that I also included per capita spending and the D:R ratio of spending in the table above. I find this data useful because it gives me an idea of the marginal benefit of investing in a state. For example, if a place already has really high per capita spending or if your party already outspends your opponent 2:1, it probably doesn’t make sense to spend more resources there. These numbers are courtesy of the Center for Responsive Politics over the 2014-2018 election time period. In the future, I might try to incorporate the per capita spending directly into the index but it adds too much complication for now.

Here’s the breakdown by office for the top ten states:

office_voting_power
state_abbr state_voting_power office
NC 4.025 governor 3.637
president 0.191
ushouse 0.114
statehouse 0.038
statesenate 0.023
ussenate 0.022
MI 3.418 president 3.334
ushouse 0.035
statehouse 0.03
ussenate 0.019
NH 1.485 ussenate 0.908
president 0.506
statesenate 0.041
governor 0.015
statehouse 0.01
ushouse 0.006
PA 1.421 president 1.283
statehouse 0.066
ushouse 0.049
statesenate 0.023
FL 1.394 president 1.124
statehouse 0.123
ushouse 0.112
statesenate 0.035
WI 0.738 president 0.608
statesenate 0.111
ushouse 0.011
statehouse 0.008
TX 0.511 president 0.196
ushouse 0.151
statehouse 0.09
ussenate 0.049
statesenate 0.024
MN 0.464 president 0.306
ushouse 0.086
statesenate 0.04
statehouse 0.021
ussenate 0.01
GA 0.416 ushouse 0.231
president 0.145
ussenate 0.018
statehouse 0.014
statesenate 0.008
CA 0.392 ushouse 0.127
statesenate 0.111
president 0.085
statehouse 0.069

One thing that concerns me about this analysis is that it may overfit to the results of past elections. Instead, I could use aggregated polling data for each election to predict the future margin, then combine this with the seat_potential_power to make a live-updating index. Maybe I’ll start doing this once 538 or another polling aggregator starts publishing predictions for the 2020 elections.

References

[1] The Center for Responsive Politics, opensecrets.org. https://www.opensecrets.org/overview/statetotals.php

[2] Where do voters have the most political influence?. https://pstblog.com/2019/03/05/voting-power-comprehensive

[3] Source code, voting-power-comprehensive https://github.com/psthomas/voting-power-comprehensive