Resumes were sent out to 316 top law firms in the United States, and there were two randomized characteristics of each resume. First, the gender associated with the resume was randomized by assigning a first name of either James or Julia. Second, the socioeconomic class of the candidate was randomly assigned and represented through five minor changes associated with personal interests and other other minor details (e.g. an extracurricular activity of sailing team vs track and field). The outcome variable was whether the candidate was received an interview.
Format
A data frame with 316 observations on the following 3 variables. Each row represents a resume sent a top law firm for this experiment.
- gender
The resume implied the candidate was either
"male"or"female".- outcome
If the candidate received an invitation for an
"interview"or"not".
Source
For a casual overview, see https://hbr.org/2016/12/research-how-subtle-class-cues-can-backfire-on-your-resume.
For the academic paper, see Tilcsik A, Rivera LA. 2016. Class Advantage, Commitment Penalty. The Gendered Effect of Social Class Signals in an Elite Labor Market. American Sociological Review 81:6 p1097-1131. doi:10.1177/0003122416668154 .
Examples
tapply(law_resume$outcome == "interview", law_resume[, c("class", "gender")], mean)
#> gender
#> class female male
#> high 0.03797468 0.16250000
#> low 0.06329114 0.01282051
m <- glm(I(outcome == "interview") ~ gender * class, data = law_resume, family = binomial)
summary(m)
#>
#> Call:
#> glm(formula = I(outcome == "interview") ~ gender * class, family = binomial,
#> data = law_resume)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -3.2321 0.5886 -5.491 0.00000004 ***
#> gendermale 1.5924 0.6621 2.405 0.0162 *
#> classlow 0.5375 0.7483 0.718 0.4726
#> gendermale:classlow -3.2416 1.2903 -2.512 0.0120 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 159.68 on 315 degrees of freedom
#> Residual deviance: 144.49 on 312 degrees of freedom
#> AIC: 152.49
#>
#> Number of Fisher Scoring iterations: 7
#>
predict(m, type = "response")
#> 84 118 180 285 63 280 293
#> 0.16250000 0.03797468 0.01282051 0.06329114 0.16250000 0.06329114 0.06329114
#> 205 194 19 64 54 209 117
#> 0.01282051 0.01282051 0.06329114 0.16250000 0.16250000 0.01282051 0.03797468
#> 233 150 216 297 114 231 277
#> 0.01282051 0.03797468 0.01282051 0.06329114 0.03797468 0.01282051 0.06329114
#> 312 192 37 79 113 4 111
#> 0.06329114 0.01282051 0.16250000 0.16250000 0.03797468 0.16250000 0.03797468
#> 251 98 138 171 141 53 234
#> 0.06329114 0.03797468 0.03797468 0.01282051 0.03797468 0.16250000 0.01282051
#> 188 223 31 202 298 227 178
#> 0.01282051 0.01282051 0.16250000 0.01282051 0.06329114 0.01282051 0.01282051
#> 215 151 145 214 7 129 197
#> 0.01282051 0.03797468 0.03797468 0.01282051 0.16250000 0.03797468 0.01282051
#> 185 128 229 116 65 307 26
#> 0.01282051 0.03797468 0.01282051 0.03797468 0.16250000 0.06329114 0.16250000
#> 83 135 313 105 282 75 303
#> 0.16250000 0.03797468 0.06329114 0.03797468 0.06329114 0.16250000 0.06329114
#> 85 165 263 120 191 21 217
#> 0.16250000 0.03797468 0.06329114 0.03797468 0.01282051 0.06329114 0.01282051
#> 316 206 253 82 264 300 208
#> 0.06329114 0.01282051 0.06329114 0.16250000 0.06329114 0.06329114 0.01282051
#> 94 186 228 103 168 239 76
#> 0.03797468 0.01282051 0.01282051 0.03797468 0.01282051 0.01282051 0.16250000
#> 176 47 164 28 56 33 55
#> 0.01282051 0.16250000 0.03797468 0.16250000 0.16250000 0.16250000 0.16250000
#> 14 144 196 173 177 101 90
#> 0.03797468 0.03797468 0.01282051 0.01282051 0.01282051 0.03797468 0.03797468
#> 221 132 142 302 58 212 259
#> 0.01282051 0.03797468 0.03797468 0.06329114 0.16250000 0.01282051 0.06329114
#> 45 265 100 193 124 278 273
#> 0.16250000 0.06329114 0.03797468 0.01282051 0.03797468 0.06329114 0.06329114
#> 73 88 30 3 224 248 89
#> 0.16250000 0.16250000 0.16250000 0.16250000 0.01282051 0.06329114 0.16250000
#> 127 195 97 256 34 272 87
#> 0.03797468 0.01282051 0.03797468 0.06329114 0.16250000 0.06329114 0.16250000
#> 287 40 43 112 107 15 270
#> 0.06329114 0.16250000 0.16250000 0.03797468 0.03797468 0.03797468 0.06329114
#> 315 170 109 220 95 232 219
#> 0.06329114 0.01282051 0.03797468 0.01282051 0.03797468 0.01282051 0.01282051
#> 121 106 42 211 126 78 245
#> 0.03797468 0.03797468 0.16250000 0.01282051 0.03797468 0.16250000 0.06329114
#> 268 18 294 236 92 305 74
#> 0.06329114 0.06329114 0.06329114 0.01282051 0.03797468 0.06329114 0.16250000
#> 243 182 244 12 44 240 222
#> 0.06329114 0.01282051 0.06329114 0.16250000 0.16250000 0.01282051 0.01282051
#> 139 69 250 134 295 10 50
#> 0.03797468 0.16250000 0.06329114 0.03797468 0.06329114 0.16250000 0.16250000
#> 108 149 93 122 207 237 226
#> 0.03797468 0.03797468 0.03797468 0.03797468 0.01282051 0.01282051 0.01282051
#> 169 91 104 246 200 189 261
#> 0.01282051 0.03797468 0.03797468 0.06329114 0.01282051 0.01282051 0.06329114
#> 119 67 242 25 99 308 275
#> 0.03797468 0.16250000 0.01282051 0.16250000 0.03797468 0.06329114 0.06329114
#> 70 218 49 13 115 35 72
#> 0.16250000 0.01282051 0.16250000 0.16250000 0.03797468 0.16250000 0.16250000
#> 225 314 38 252 32 213 59
#> 0.01282051 0.06329114 0.16250000 0.06329114 0.16250000 0.01282051 0.16250000
#> 279 199 271 62 267 210 77
#> 0.06329114 0.01282051 0.06329114 0.16250000 0.06329114 0.01282051 0.16250000
#> 102 11 80 190 286 247 288
#> 0.03797468 0.16250000 0.16250000 0.01282051 0.06329114 0.06329114 0.06329114
#> 179 269 276 123 16 291 48
#> 0.01282051 0.06329114 0.06329114 0.03797468 0.03797468 0.06329114 0.16250000
#> 86 299 24 5 310 255 161
#> 0.16250000 0.06329114 0.16250000 0.16250000 0.06329114 0.06329114 0.03797468
#> 159 152 283 175 143 81 158
#> 0.03797468 0.03797468 0.06329114 0.01282051 0.03797468 0.16250000 0.03797468
#> 157 167 172 292 68 257 254
#> 0.03797468 0.01282051 0.01282051 0.06329114 0.16250000 0.06329114 0.06329114
#> 181 166 301 162 306 51 61
#> 0.01282051 0.01282051 0.06329114 0.03797468 0.06329114 0.16250000 0.16250000
#> 284 39 133 184 71 266 22
#> 0.06329114 0.16250000 0.03797468 0.01282051 0.16250000 0.06329114 0.06329114
#> 230 296 235 130 174 203 9
#> 0.01282051 0.06329114 0.01282051 0.03797468 0.01282051 0.01282051 0.16250000
#> 290 52 155 8 201 198 17
#> 0.06329114 0.16250000 0.03797468 0.16250000 0.01282051 0.01282051 0.01282051
#> 140 137 281 204 41 262 29
#> 0.03797468 0.03797468 0.06329114 0.01282051 0.16250000 0.06329114 0.16250000
#> 2 183 146 260 36 156 154
#> 0.16250000 0.01282051 0.03797468 0.06329114 0.16250000 0.03797468 0.03797468
#> 110 147 6 27 148 274 309
#> 0.03797468 0.03797468 0.16250000 0.16250000 0.03797468 0.06329114 0.06329114
#> 160 60 163 46 1 57 136
#> 0.03797468 0.16250000 0.03797468 0.16250000 0.16250000 0.16250000 0.03797468
#> 20 304 153 125 249 187 23
#> 0.06329114 0.06329114 0.03797468 0.03797468 0.06329114 0.01282051 0.16250000
#> 241 238 66 96 131 311 289
#> 0.01282051 0.01282051 0.16250000 0.03797468 0.03797468 0.06329114 0.06329114
#> 258
#> 0.06329114