A dataset containing the results of a fully randomized conjoint survey of a representative sample of 2000 American adults who were asked to choose between alternative tax rate policies. Variables taxrate1-taxrate6 refer to tax rates for different income brackets and taxrev refers to levels of total tax revenue.

data(taxes)

Format

A data frame (with additional “cj_df” class) with 32000 observations on the following 13 variables. Each row corresponds to a single profile presented to a respondent.

chose_plan

A numeric vector denoting whether the immigrant profile was selected (=1) or not (=0).

taxrate1

An experimental factor with levels “<10k: 0%”, “<10k: 5%”, “<10k: 15%”, “<10k: 25%”.

taxrate2

An experimental factor with levels “10-35k: 5%”, “10-35k: 15%”, “10-35k: 25%”, “10-35k: 35%”.

taxrate3

An experimental factor with levels “35-85k: 5%”, “35-85k: 15%”, “35-85k: 25%”, “35-85k: 35%”.

taxrate4

An experimental factor with levels “85-175k: 5%”, “85-175k: 15%”, “85-175k: 25%”, “85-175k: 35%”.

taxrate5

An experimental factor with levels “175-375k: 5%”, “175-375k: 15%”, “175-375k: 25%”, “175-375k: 35%”, “175-375k: 45%”.

taxrate6

An experimental factor with levels “>375k: 5%”, “>375k: 15%”, “>375k: 25%”, “>375k: 35%”, “>375k: 45%”, “>375k: 55%”.

taxrev

An experimental factor with levels “<75%”, “75-95%”, “95-105%”, “105-125%”, “>125%”.

inequality_aversion

A covariate specifying whether respondent is inequality averse (=1) or not (=0).

taxes_harm_economy

A covariate specifying whether respondent believes taxes harm the economy (=1) or not (=0).

partyid

A factor specifying the respondent's party identification; one of “Independent”, “Democrat”, “Republican”.

ID

A numeric vector indicating the respondent to which the profile corresponds.

weight

A numeric vector containing survey weights.

Source

Ballard-Rosa, Cameron, Lucy Martin, and Kenneth Scheve. 2016. “The Structure of American Income Tax Policy Preferences.” The Journal of Politics 79(1): 1-16. http://doi.org/10.1086/687324

See also

Examples

# \donttest{ data("taxes") f1 <- chose_plan ~ taxrate1 + taxrate2 + taxrate3 + taxrate4 + taxrate5 + taxrate6 + taxrev cj(taxes, f1, id = ~ ID, weights = ~ weight)
#> outcome statistic feature level #> 1 chose_plan amce Tax rate for <$10,000 <10k: 0% #> 2 chose_plan amce Tax rate for <$10,000 <10k: 5% #> 3 chose_plan amce Tax rate for <$10,000 <10k: 15% #> 4 chose_plan amce Tax rate for <$10,000 <10k: 25% #> 5 chose_plan amce Tax rate for $10,000-$35,000 10-35k: 5% #> 6 chose_plan amce Tax rate for $10,000-$35,000 10-35k: 15% #> 7 chose_plan amce Tax rate for $10,000-$35,000 10-35k: 25% #> 8 chose_plan amce Tax rate for $10,000-$35,000 10-35k: 35% #> 9 chose_plan amce Tax rate for $25,000-$85,000 35-85k: 5% #> 10 chose_plan amce Tax rate for $25,000-$85,000 35-85k: 15% #> 11 chose_plan amce Tax rate for $25,000-$85,000 35-85k: 25% #> 12 chose_plan amce Tax rate for $25,000-$85,000 35-85k: 35% #> 13 chose_plan amce Tax rate for $85,000-$175,000 85-175k: 5% #> 14 chose_plan amce Tax rate for $85,000-$175,000 85-175k: 15% #> 15 chose_plan amce Tax rate for $85,000-$175,000 85-175k: 25% #> 16 chose_plan amce Tax rate for $85,000-$175,000 85-175k: 35% #> 17 chose_plan amce Tax rate for $175,000-$375,000 175-375k: 5% #> 18 chose_plan amce Tax rate for $175,000-$375,000 175-375k: 15% #> 19 chose_plan amce Tax rate for $175,000-$375,000 175-375k: 25% #> 20 chose_plan amce Tax rate for $175,000-$375,000 175-375k: 35% #> 21 chose_plan amce Tax rate for $175,000-$375,000 175-375k: 45% #> 22 chose_plan amce Tax rate for >$375,000 >375k: 5% #> 23 chose_plan amce Tax rate for >$375,000 >375k: 15% #> 24 chose_plan amce Tax rate for >$375,000 >375k: 25% #> 25 chose_plan amce Tax rate for >$375,000 >375k: 35% #> 26 chose_plan amce Tax rate for >$375,000 >375k: 45% #> 27 chose_plan amce Tax rate for >$375,000 >375k: 55% #> 28 chose_plan amce Tax revenue <75% #> 29 chose_plan amce Tax revenue 75-95% #> 30 chose_plan amce Tax revenue 95-105% #> 31 chose_plan amce Tax revenue 105-125% #> 32 chose_plan amce Tax revenue >125% #> estimate std.error z p lower upper #> 1 0.0000000000 NA NA NA NA NA #> 2 -0.0213657909 0.01153916 -1.85159027 6.408468e-02 -0.043982123 0.001250541 #> 3 -0.1085089681 0.01345414 -8.06509750 7.317744e-16 -0.134878602 -0.082139334 #> 4 -0.2417205854 0.01756500 -13.76149232 4.344651e-43 -0.276147349 -0.207293822 #> 5 0.0000000000 NA NA NA NA NA #> 6 -0.0216654811 0.01529549 -1.41646250 1.566401e-01 -0.051644081 0.008313119 #> 7 -0.1028048639 0.02277710 -4.51351823 6.376097e-06 -0.147447163 -0.058162565 #> 8 -0.2194266402 0.02894688 -7.58032053 3.447023e-14 -0.276161485 -0.162691795 #> 9 0.0000000000 NA NA NA NA NA #> 10 0.0001812976 0.01151841 0.01573981 9.874420e-01 -0.022394376 0.022756971 #> 11 -0.0650669525 0.01446934 -4.49688323 6.895683e-06 -0.093426346 -0.036707558 #> 12 -0.1260348655 0.01762314 -7.15166921 8.572896e-13 -0.160575585 -0.091494146 #> 13 0.0000000000 NA NA NA NA NA #> 14 0.0109438886 0.01084215 1.00938387 3.127906e-01 -0.010306330 0.032194107 #> 15 -0.0155419502 0.01134766 -1.36961717 1.708064e-01 -0.037782955 0.006699055 #> 16 -0.0126122794 0.01190376 -1.05952103 2.893626e-01 -0.035943212 0.010718653 #> 17 0.0000000000 NA NA NA NA NA #> 18 0.0332708646 0.01162503 2.86200317 4.209727e-03 0.010486230 0.056055499 #> 19 0.0425251595 0.01305320 3.25783404 1.122660e-03 0.016941357 0.068108961 #> 20 0.0580764422 0.01212647 4.78922984 1.674226e-06 0.034309002 0.081843883 #> 21 0.0327895914 0.01402496 2.33794557 1.939007e-02 0.005301176 0.060278007 #> 22 0.0000000000 NA NA NA NA NA #> 23 0.0558225384 0.01369058 4.07744003 4.553427e-05 0.028989485 0.082655591 #> 24 0.0896621795 0.01494775 5.99837302 1.993043e-09 0.060365128 0.118959231 #> 25 0.1139343820 0.01551745 7.34233988 2.098916e-13 0.083520742 0.144348022 #> 26 0.1099558977 0.01729319 6.35833663 2.039501e-10 0.076061876 0.143849919 #> 27 0.0838214633 0.01874157 4.47248772 7.731484e-06 0.047088652 0.120554275 #> 28 0.0000000000 NA NA NA NA NA #> 29 0.0297942004 0.01755028 1.69764821 8.957418e-02 -0.004603715 0.064192116 #> 30 0.0692814207 0.02344570 2.95497381 3.126956e-03 0.023328699 0.115234143 #> 31 0.0911924955 0.03000830 3.03890950 2.374362e-03 0.032377316 0.150007675 #> 32 0.1354902736 0.03885990 3.48663428 4.891398e-04 0.059326261 0.211654286
# }