Outline

This markdown contains table 01s for elections with updated MTurk labels. These now come from a side-by-side survey which is much closer to the survey conducted in Todorov et.al. We also updated the CNN to take care of the trainval-test split errors we found last week. All other variable definitions are the same as before. There are two tables here:

  1. Table 01 Configuration 4 (using own-party-vote-share-jacknife in the election_lm) with updated Mturk features in the same model

  2. Table 01 Configuration 4 but with each MTurk feature entred as a univariate model to check for each individually

Definitions

Before jumping into regression output, I will put all definitions of important terms here:

  1. Vote-Share : In all tables below, when vote-share is a LHS variable this means the vote-share of a specific candidate election. I.e. how much of the total vote did a specific candidate get in their election.

  2. Own-Party-Vote-Share-Jacknife : This is a feature and computed as the “average vote share of my party in this state over all years excluding the current”

Table 1 - Configuration 4

Replacing own_party_win_rate with own_party_vote_share_jacknife in the election lm. This now includes:

  1. Party (limited to Democrat vs. Republican)
  2. Vote-share

Own Party Vote-share-Jacknife is computed as: “average vote share of my party in this state over all years excluding the current”

Table 01 - Version 04 - Election Regressions
Fit measured in adjusted R squared and AUC
Model Configuration Election Outcome Vote Share
Adjusted R Squared ROC AUC Adjusted R squared
Single Variable Model
Election LM 0.0667 0.6421 0.0742
Lower 95% C.I. 0.0468 0.6115 0.0537
Upper 95% C.I. 0.0920 0.6726 0.0986
Sex 0.0037 0.5183 0.0048
0.0005 0.4962 0.0007
0.0098 0.5404 0.0127
Skine-Tone −0.0077 0.5412 −0.0022
−0.0032 0.5098 0.0009
0.0172 0.5726 0.0262
MTurk Features 0.0127 0.5581 −0.0003
0.0057 0.5264 −0.0018
0.0290 0.5898 0.0098
P_hat_cnn 0.1011 0.6858 0.0804
0.0774 0.6566 0.0578
0.1289 0.7149 0.1076
Combined Variable Model
Election LM + P_hat_cnn 0.1579 0.7323 0.1469
0.1299 0.7047 0.1172
0.1915 0.7598 0.1783
Election LM + Sex 0.0683 0.6443 0.0780
0.0476 0.6139 0.0563
0.0933 0.6748 0.1040
Election LM + Sex + P_hat_cnn 0.1594 0.7342 0.1504
0.1317 0.7068 0.1242
0.1932 0.7616 0.1845
Election LM + Sex + Skin-Tone 0.0613 0.6577 0.0758
0.0532 0.6276 0.0644
0.1013 0.6878 0.1185
Election LM + Sex + Skin-Tone + P_hat_cnn 0.1540 0.7394 0.1492
0.1353 0.7123 0.1333
0.1987 0.7666 0.1998
Election LM + Sex + Skin-Tone + MTurk 0.0731 0.6699 0.0742
0.0652 0.6403 0.0674
0.1172 0.6995 0.1222
Election LM + Sex + Skin-Tone + MTurk + P_hat_cnn 0.1575 0.7432 0.1465
0.1424 0.7161 0.1300
0.2047 0.7702 0.1964

Table 01 - Configuration 04 - Including Individual MTurk Features

In this table I decide to include the different MTurk features individually in the single variable model section. We can see that the significant signal is coming from Competence and Trustworthiness. Of note here is that Competence is the feature isolated as significant in Todorov et.al (2015). This is very exciting news, since our CNN is still able to isolate significant signal above these features.

Table 01 - Version 04 - Election Regressions - Individual MTurk Features
Fit measured in adjusted R squared and AUC
Model Configuration Election Outcome Vote Share
Adjusted R Squared ROC AUC Adjusted R squared
Single Variable Model
Election LM 0.0667 0.6421 0.0742
Lower 95% C.I. 0.0463 0.6115 0.0529
Upper 95% C.I. 0.0888 0.6726 0.1003
Sex 0.0037 0.5183 0.0048
0.0007 0.4962 0.0006
0.0099 0.5404 0.0127
Skine-Tone −0.0077 0.5412 −0.0022
−0.0038 0.5098 0.0004
0.0162 0.5726 0.0262
Attractiveness −0.0005 0.5143 −0.0007
−0.0008 0.4824 −0.0008
0.0035 0.5463 0.0022
Competence 0.0027 0.5368 0.0008
−0.0006 0.5050 −0.0008
0.0105 0.5687 0.0066
Dominance −0.0008 0.4912 −0.0008
−0.0008 0.4592 −0.0008
0.0021 0.5232 0.0021
Trustworthiness 0.0051 0.5425 0.0006
0.0001 0.5107 −0.0008
0.0152 0.5743 0.0060
P_hat_cnn 0.1011 0.6858 0.0804
0.0750 0.6566 0.0596
0.1302 0.7149 0.1085
Combined Variable Model
Election LM + P_hat_cnn 0.1579 0.7323 0.1469
0.1277 0.7047 0.1189
0.1891 0.7598 0.1800
Election LM + Sex 0.0683 0.6443 0.0780
0.0490 0.6139 0.0570
0.0946 0.6748 0.1036
Election LM + Sex + P_hat_cnn 0.1594 0.7342 0.1504
0.1324 0.7068 0.1230
0.1910 0.7616 0.1837
Election LM + Sex + Skin-Tone 0.0613 0.6577 0.0758
0.0523 0.6276 0.0643
0.1027 0.6878 0.1195
Election LM + Sex + Skin-Tone + P_hat_cnn 0.1540 0.7394 0.1492
0.1369 0.7123 0.1306
0.2012 0.7666 0.1996
Election LM + Sex + Skin-Tone + MTurk 0.0731 0.6699 0.0742
0.0662 0.6403 0.0680
0.1185 0.6995 0.1195
Election LM + Sex + Skin-Tone + MTurk + P_hat_cnn 0.1575 0.7432 0.1465
0.1421 0.7161 0.1298
0.2057 0.7702 0.1963