1
00:00:05,440 --> 00:00:08,320
Speaker 1: Late one night in June, Jody was in her car

2
00:00:08,480 --> 00:00:11,879
Speaker 1: driving down a long wooded road in southern Maine. Jodi

3
00:00:11,960 --> 00:00:14,960
Speaker 1: Paglioco drives for Uber and left. She had a passenger

4
00:00:14,960 --> 00:00:17,279
Speaker 1: in the front seat. She picked him up from a

5
00:00:17,280 --> 00:00:20,119
Speaker 1: bar in town and was taking him home. She says

6
00:00:20,160 --> 00:00:23,560
Speaker 1: he seemed drunk. The road was dark and there was

7
00:00:23,600 --> 00:00:28,280
Speaker 1: nobody around. Jody was getting uncomfortable, and he made a

8
00:00:28,360 --> 00:00:32,239
Speaker 1: joke about not meaning a serial killer. But then he

9
00:00:32,280 --> 00:00:36,040
Speaker 1: started talking about sexual stuff and everything, and all I

10
00:00:36,080 --> 00:00:39,680
Speaker 1: could think of what am I going to do if

11
00:00:39,800 --> 00:00:44,120
Speaker 1: he does anything else, or if he tried to grab

12
00:00:44,200 --> 00:00:47,800
Speaker 1: me or touch me. She wanted to let him out

13
00:00:47,840 --> 00:00:49,879
Speaker 1: of the car right then and there, but she had

14
00:00:49,920 --> 00:00:52,920
Speaker 1: to drive to a safe location. I ended up getting

15
00:00:53,000 --> 00:00:56,280
Speaker 1: him to the front gates of his place. I told

16
00:00:56,360 --> 00:00:58,520
Speaker 1: him it was time for him to get out, and

17
00:00:58,600 --> 00:01:00,720
Speaker 1: I told him I wasn't driving him need for alert.

18
00:01:01,720 --> 00:01:04,480
Speaker 1: The man got out of the car and left. Jody

19
00:01:04,520 --> 00:01:07,080
Speaker 1: says she was relieved that nothing else happened to her

20
00:01:07,120 --> 00:01:10,400
Speaker 1: that night, but it left her scared and shaken, and

21
00:01:10,440 --> 00:01:14,080
Speaker 1: then shortly after she had another incident with a passenger.

22
00:01:15,319 --> 00:01:17,920
Speaker 1: You know, I thought the car and everything, doors are unlocked,

23
00:01:18,400 --> 00:01:21,520
Speaker 1: he opens the door and then he reaches and tries

24
00:01:21,760 --> 00:01:28,640
Speaker 1: to grab me and kissed me. So you've only been

25
00:01:28,720 --> 00:01:31,480
Speaker 1: driving for six months. How many times do you think

26
00:01:31,480 --> 00:01:35,440
Speaker 1: you've been put in an uncomfortable or inappropriate situation like that?

27
00:01:36,120 --> 00:01:40,480
Speaker 1: I think she's um. I got almost a thousand rides

28
00:01:40,560 --> 00:01:44,440
Speaker 1: under my belt between Uber and Left. As much as

29
00:01:44,560 --> 00:01:47,279
Speaker 1: I hate to say it, I would say probably about

30
00:01:47,280 --> 00:02:01,040
Speaker 1: two vos. Hi, I'm Brad Stone and I'm Selena Wang.

31
00:02:01,200 --> 00:02:04,640
Speaker 1: And this week on Decrypted, we're exploring the gender dynamics

32
00:02:04,640 --> 00:02:07,920
Speaker 1: of the gig economy. These jobs were supposed to help

33
00:02:08,040 --> 00:02:11,400
Speaker 1: level the playing field in the modern economy, offering away,

34
00:02:11,480 --> 00:02:15,400
Speaker 1: for example, for mother's juggling childcare responsibilities to work with

35
00:02:15,440 --> 00:02:18,760
Speaker 1: flexible hours, and while that promise of flexibility is what

36
00:02:18,880 --> 00:02:21,600
Speaker 1: drew them in, many women like Jody who drive for

37
00:02:21,680 --> 00:02:25,560
Speaker 1: services like Uber and Left say they frequently encountered harassment,

38
00:02:25,919 --> 00:02:29,840
Speaker 1: even assault by male passengers, and that those conditions have

39
00:02:29,960 --> 00:02:32,720
Speaker 1: in turn put them out a disadvantage to mail drivers.

40
00:02:33,200 --> 00:02:35,760
Speaker 1: Later in the episode will dive deep into what the

41
00:02:35,800 --> 00:02:39,040
Speaker 1: economic research says about all this and what Uber and

42
00:02:39,160 --> 00:02:54,799
Speaker 1: Left are doing in response. Stay with us Selena over

43
00:02:54,840 --> 00:02:57,040
Speaker 1: the past few weeks, You've been talking to quite a

44
00:02:57,040 --> 00:02:59,919
Speaker 1: few women in the gig economy. So tell us about Joe,

45
00:03:00,160 --> 00:03:02,200
Speaker 1: who we heard from at the top of the show. So,

46
00:03:02,360 --> 00:03:05,200
Speaker 1: Jody's thirty eight years old, grew up in Arizona and

47
00:03:05,240 --> 00:03:09,320
Speaker 1: has an associates degree. She's divorced with two kids. About

48
00:03:09,320 --> 00:03:12,400
Speaker 1: four years ago, her daughter, who was seventeen at the time,

49
00:03:12,960 --> 00:03:15,919
Speaker 1: was in a terrible accident that paralyzed her from waist down.

50
00:03:16,639 --> 00:03:18,640
Speaker 1: Last year, Jody moved to Maine to be closer to

51
00:03:18,639 --> 00:03:22,160
Speaker 1: where her daughter will be getting medical treatment. And since

52
00:03:22,200 --> 00:03:25,280
Speaker 1: it's not exactly the easiest thing getting a job out here,

53
00:03:25,320 --> 00:03:28,360
Speaker 1: and especially since I've spent the majority of my marriage

54
00:03:28,360 --> 00:03:32,160
Speaker 1: at home raising kids. I also have a seventeen year

55
00:03:32,160 --> 00:03:36,560
Speaker 1: old son as well, and so yeah, I came out

56
00:03:36,600 --> 00:03:41,960
Speaker 1: here and decided to do the thing. Jody typically drives

57
00:03:41,960 --> 00:03:45,040
Speaker 1: that night from eight pm to three am, times when

58
00:03:45,120 --> 00:03:47,480
Speaker 1: she says she can make the most money. A lot

59
00:03:47,480 --> 00:03:50,040
Speaker 1: of her passengers are going home from bars and events.

60
00:03:50,560 --> 00:03:53,240
Speaker 1: I do try and sing in at least eight hours

61
00:03:53,280 --> 00:03:56,000
Speaker 1: a day, so that way, it's kind of like a

62
00:03:56,120 --> 00:03:58,560
Speaker 1: full time job for me. I met Jody in a

63
00:03:58,600 --> 00:04:02,120
Speaker 1: private Facebook group for mail lift and Uber drivers The

64
00:04:02,160 --> 00:04:05,840
Speaker 1: group has almost ten thousand members. It's a support group

65
00:04:05,840 --> 00:04:09,800
Speaker 1: where women ask questions, give advice, and share their experiences

66
00:04:09,880 --> 00:04:14,080
Speaker 1: driving on the platform, both positive and negative. Now, there's

67
00:04:14,120 --> 00:04:16,640
Speaker 1: many reasons why women might decide to become Lift or

68
00:04:16,720 --> 00:04:19,479
Speaker 1: Uber drivers, but one that I kept hearing how to

69
00:04:19,480 --> 00:04:22,160
Speaker 1: do with childcare. A lot of the women I spoke

70
00:04:22,200 --> 00:04:25,080
Speaker 1: to were mothers. I just needed to make a little

71
00:04:25,080 --> 00:04:27,000
Speaker 1: bit of extra money and I didn't have to find

72
00:04:27,040 --> 00:04:30,200
Speaker 1: childcare if I like just I mean, they're not going

73
00:04:30,240 --> 00:04:32,280
Speaker 1: to the restaurant's not going to judge you for having

74
00:04:32,279 --> 00:04:35,800
Speaker 1: a baby on your hip. That's Hannah Mentor. She lives

75
00:04:35,800 --> 00:04:39,120
Speaker 1: in Savannah, Georgia. She's twenty four and a single mother

76
00:04:39,240 --> 00:04:42,360
Speaker 1: of two kids. She started doing food delivery part time

77
00:04:42,400 --> 00:04:45,520
Speaker 1: to supplement her income, designing things like T shirts that

78
00:04:45,560 --> 00:04:48,480
Speaker 1: she sells on Etsy. And then when you drop off

79
00:04:48,480 --> 00:04:50,880
Speaker 1: with Uber, the majority of the time they're walking up

80
00:04:50,920 --> 00:04:52,800
Speaker 1: to the car and grabbing it from you. So I

81
00:04:53,200 --> 00:04:55,680
Speaker 1: never had to worry about someone watching my kid because

82
00:04:55,680 --> 00:04:57,960
Speaker 1: he was right there with me. You know. After a while,

83
00:04:58,200 --> 00:05:01,280
Speaker 1: Hannah realized she could make more me by driving passengers

84
00:05:01,320 --> 00:05:05,360
Speaker 1: for Uber and Lift, so I started branching out. Even

85
00:05:05,360 --> 00:05:09,839
Speaker 1: though it's a little bit scary to drive strangers in

86
00:05:09,880 --> 00:05:12,960
Speaker 1: your car. I slowly branded out into Uber and Lifts,

87
00:05:13,160 --> 00:05:16,159
Speaker 1: and since the money was just gotten much better. But

88
00:05:16,279 --> 00:05:18,400
Speaker 1: driving for Uber and Lift came but a whole new

89
00:05:18,440 --> 00:05:21,279
Speaker 1: set of risks that Hannah mostly didn't encounter while she

90
00:05:21,360 --> 00:05:24,560
Speaker 1: was delivering food. If you're just dropping off food, you

91
00:05:24,600 --> 00:05:28,080
Speaker 1: don't spend much time with the customers. It's it's pretty

92
00:05:28,279 --> 00:05:30,280
Speaker 1: in and out. They don't really have the time to

93
00:05:31,120 --> 00:05:33,440
Speaker 1: get to a point where they can do something inappropriate

94
00:05:33,560 --> 00:05:37,719
Speaker 1: or something uncomfortable. Whereas with Uber and Lift, the passenger

95
00:05:37,800 --> 00:05:41,120
Speaker 1: is alone with you for at least several minutes. That's

96
00:05:41,240 --> 00:05:44,280
Speaker 1: enough time for things to get weird. For example, just

97
00:05:44,400 --> 00:05:46,760
Speaker 1: days before I spoke with Hannah at the end of November,

98
00:05:46,839 --> 00:05:48,760
Speaker 1: she picked up a man who she said was drunk.

99
00:05:49,880 --> 00:05:54,000
Speaker 1: It slowly progressed to him asking about my love life

100
00:05:54,279 --> 00:05:57,960
Speaker 1: and then telling me about his girlfriend of a month,

101
00:05:58,080 --> 00:06:02,080
Speaker 1: and then him well but surely like trying to ask

102
00:06:02,120 --> 00:06:04,599
Speaker 1: about what things I was into in the bedroom. And

103
00:06:04,640 --> 00:06:07,680
Speaker 1: I finally got him to his destination, and when he

104
00:06:07,720 --> 00:06:09,680
Speaker 1: tried when he went to get out of the car,

105
00:06:09,800 --> 00:06:11,520
Speaker 1: and of course I'm relieved that he's getting out of

106
00:06:11,560 --> 00:06:14,000
Speaker 1: my car at this point, um, he tried to like

107
00:06:15,000 --> 00:06:18,920
Speaker 1: kiss my hand. And this kind of experience isn't unusual.

108
00:06:19,480 --> 00:06:21,680
Speaker 1: Every day that I drive, every night that I drive,

109
00:06:21,720 --> 00:06:24,479
Speaker 1: there is at least one questionable experience. I hate to

110
00:06:24,520 --> 00:06:27,560
Speaker 1: be the person that's like, I'm scared, but yeah, there's

111
00:06:27,560 --> 00:06:30,000
Speaker 1: always a fear of having the wrong person get into

112
00:06:30,040 --> 00:06:33,640
Speaker 1: your car, or someone that is not respectful, or someone

113
00:06:33,680 --> 00:06:40,840
Speaker 1: that is completely drunk and just does not care. As

114
00:06:40,880 --> 00:06:43,880
Speaker 1: I got deeper into reporting, I noticed that it wasn't

115
00:06:43,920 --> 00:06:47,880
Speaker 1: just that these women had an uncomfortable experience. These experiences

116
00:06:47,920 --> 00:06:51,040
Speaker 1: led to real consequences. A lot of the women said

117
00:06:51,040 --> 00:06:53,279
Speaker 1: that after a bad situation like the one we heard

118
00:06:53,320 --> 00:06:56,240
Speaker 1: from from Hannah, they turned off the app and go home,

119
00:06:56,560 --> 00:06:59,480
Speaker 1: even though they were planning on driving for longer. Some

120
00:06:59,520 --> 00:07:01,479
Speaker 1: women told me it took them even a few days

121
00:07:01,560 --> 00:07:03,960
Speaker 1: before they felt like driving again. Like Jody, who we

122
00:07:04,040 --> 00:07:06,440
Speaker 1: heard from at the start of the show, There's been

123
00:07:07,040 --> 00:07:09,400
Speaker 1: a few times where I've just been like I'm done,

124
00:07:09,640 --> 00:07:11,920
Speaker 1: I'm going to stop doing this. I gotta stop for

125
00:07:11,960 --> 00:07:17,680
Speaker 1: the day because I'm like on edge because I'm thinking, okay,

126
00:07:17,760 --> 00:07:19,600
Speaker 1: what happened. As the next guy that comes in my

127
00:07:19,640 --> 00:07:23,320
Speaker 1: car drives this stuff. You know, I'm driving at night,

128
00:07:23,480 --> 00:07:26,960
Speaker 1: taking people home from the bars and everything, and you

129
00:07:27,000 --> 00:07:30,040
Speaker 1: know it's just I'm I have to take a break

130
00:07:30,200 --> 00:07:32,640
Speaker 1: because I have to get back into, you know, a

131
00:07:32,720 --> 00:07:36,440
Speaker 1: different mindset than what those other people leave me in.

132
00:07:37,240 --> 00:07:39,760
Speaker 1: Turning off the app, even for just a few days,

133
00:07:40,120 --> 00:07:43,760
Speaker 1: definitely results in lost income. Going back to Hannah, the

134
00:07:43,800 --> 00:07:46,400
Speaker 1: twenty four year old in Savannah, she told me it's

135
00:07:46,440 --> 00:07:49,480
Speaker 1: because of those experiences that she isn't earning more money

136
00:07:49,480 --> 00:07:52,960
Speaker 1: by driving more hours. Right now, she only drives between

137
00:07:53,000 --> 00:07:55,679
Speaker 1: six and ten hours a week. I think the only

138
00:07:55,720 --> 00:07:58,640
Speaker 1: thing keeping me from doing that with the it's terrible

139
00:07:58,640 --> 00:08:01,120
Speaker 1: experiences that will turn you off from driving for a

140
00:08:01,160 --> 00:08:05,560
Speaker 1: couple of days, or not even necessarily terrible, just just uncomfortable.

141
00:08:06,280 --> 00:08:08,239
Speaker 1: So these are women who were driving in the first

142
00:08:08,240 --> 00:08:11,400
Speaker 1: place because they're looking for additional income, but now because

143
00:08:11,440 --> 00:08:14,600
Speaker 1: of an experience that made them feel uncomfortable or in safe,

144
00:08:14,720 --> 00:08:17,600
Speaker 1: they're not driving as much. And that means they're missing

145
00:08:17,600 --> 00:08:20,920
Speaker 1: out on the flexibility and opportunity of the gig economy.

146
00:08:21,160 --> 00:08:35,000
Speaker 1: And that's one of the biggest economic trends of this decade. Okay,

147
00:08:35,040 --> 00:08:37,400
Speaker 1: so so far, Selena, we've heard from Jody and Maine

148
00:08:37,559 --> 00:08:41,080
Speaker 1: and Hannah and Georgia about their experiences in the gig economy,

149
00:08:41,320 --> 00:08:43,360
Speaker 1: and many more women I spoke to for this story

150
00:08:43,400 --> 00:08:47,920
Speaker 1: seemed to have gone through similar experiences and made similar observations. Right,

151
00:08:48,000 --> 00:08:50,000
Speaker 1: and if one of the big promises of work in

152
00:08:50,040 --> 00:08:52,880
Speaker 1: the gig economy is that it suits women and men equally,

153
00:08:53,320 --> 00:08:55,679
Speaker 1: and you know, maybe even has advantages for women who

154
00:08:55,760 --> 00:08:58,320
Speaker 1: might value the flexibility so they could juggle work and

155
00:08:58,320 --> 00:09:02,800
Speaker 1: family responsibilities, this kind of widespread harassment does seem to

156
00:09:02,880 --> 00:09:06,000
Speaker 1: undermine the entire promise of the job. And the thing is,

157
00:09:06,040 --> 00:09:09,640
Speaker 1: it's not just the anecdotal evidence that's pointing to these disparities.

158
00:09:09,720 --> 00:09:12,760
Speaker 1: Economists are looking at it too. The labor economists at

159
00:09:12,760 --> 00:09:16,080
Speaker 1: Stanford called Paul Oyer co author to study published earlier

160
00:09:16,080 --> 00:09:19,800
Speaker 1: this year titled the Gender Earnings Gap in the Gig Economy.

161
00:09:19,920 --> 00:09:22,439
Speaker 1: He worked on it with two other economics professors, as

162
00:09:22,440 --> 00:09:25,320
Speaker 1: well as Uber's chief economist and Uber's data scientist at

163
00:09:25,320 --> 00:09:28,040
Speaker 1: the time, what we are very interested in in the

164
00:09:28,080 --> 00:09:31,440
Speaker 1: gig economy initially was and still are interested in us

165
00:09:32,040 --> 00:09:36,079
Speaker 1: is it seems like a very good place, relatively speaking,

166
00:09:36,120 --> 00:09:39,920
Speaker 1: for women because they might value the flexibility of choosing

167
00:09:39,920 --> 00:09:42,760
Speaker 1: their own hours more. Paul worked with Uber to study

168
00:09:42,760 --> 00:09:45,520
Speaker 1: a sample of more than one million drivers in the

169
00:09:45,600 --> 00:09:51,120
Speaker 1: US between January and March. They found that men earned

170
00:09:51,120 --> 00:09:55,720
Speaker 1: approximately seven percent more than women. So what explains that difference?

171
00:09:56,200 --> 00:09:59,120
Speaker 1: So there were three big factors. One was a difference

172
00:09:59,120 --> 00:10:02,040
Speaker 1: in driving speed. On average, men drive two point two

173
00:10:02,040 --> 00:10:04,960
Speaker 1: percent faster than women, so they finished trips at a

174
00:10:05,000 --> 00:10:09,800
Speaker 1: faster rate, which enlarges the pay gap. It's ironic and

175
00:10:10,000 --> 00:10:13,880
Speaker 1: perhaps misdirected that the men are being compensated for driving

176
00:10:13,960 --> 00:10:17,120
Speaker 1: faster right than perhaps less safely. We don't know for

177
00:10:17,160 --> 00:10:20,000
Speaker 1: sure in the data, but it's certainly boosting their earnings. Okay,

178
00:10:20,000 --> 00:10:22,520
Speaker 1: So what was the second factor. The second factor was

179
00:10:22,559 --> 00:10:25,360
Speaker 1: the difference in where male and female drivers were picking

180
00:10:25,440 --> 00:10:31,760
Speaker 1: up their passengers. One thing that works against women as

181
00:10:31,840 --> 00:10:35,120
Speaker 1: Uber drivers is they live in neighborhoods and artists lucrative,

182
00:10:35,240 --> 00:10:39,520
Speaker 1: and therefore they end up driving in neighborhoods at artists lucrative. Okay,

183
00:10:39,520 --> 00:10:42,480
Speaker 1: I don't understand this. Break this down a little bit.

184
00:10:42,480 --> 00:10:45,560
Speaker 1: Why is that The data doesn't point to exactly why,

185
00:10:45,640 --> 00:10:48,200
Speaker 1: but it did show that women Uber drivers tended to

186
00:10:48,240 --> 00:10:51,640
Speaker 1: live in lower income neighborhoods, which means that the areas

187
00:10:51,640 --> 00:10:54,480
Speaker 1: they're driving in don't earn them as much money. And

188
00:10:54,520 --> 00:10:56,960
Speaker 1: the research also found that men were more willing to

189
00:10:56,960 --> 00:10:59,760
Speaker 1: pick up people in neighborhoods with higher crime rates and

190
00:10:59,840 --> 00:11:03,200
Speaker 1: with more drinking establishments. Okay, and what's the last factor

191
00:11:03,280 --> 00:11:07,640
Speaker 1: that might explain the pay gap? The third factor was experienced, So,

192
00:11:07,840 --> 00:11:10,839
Speaker 1: like any profession, the more experienced someone has, the more

193
00:11:10,880 --> 00:11:13,679
Speaker 1: they make. And Paul found that on average, men were

194
00:11:13,720 --> 00:11:17,200
Speaker 1: driving longer hours per week and they were also less

195
00:11:17,240 --> 00:11:20,960
Speaker 1: likely to stop driving for Uber altogether, and that meant

196
00:11:21,000 --> 00:11:24,200
Speaker 1: they were accumulating more experience as Uber drivers, like learning

197
00:11:24,200 --> 00:11:27,160
Speaker 1: when and where to drive and how to strategically cancel

198
00:11:27,240 --> 00:11:30,200
Speaker 1: and accept trips that allowed them to earn higher rates

199
00:11:30,240 --> 00:11:33,720
Speaker 1: on the platform. Economists call that human capital. Yeah, and

200
00:11:33,720 --> 00:11:37,000
Speaker 1: that reminds me of what Jody and Hannah told us earlier,

201
00:11:37,120 --> 00:11:39,120
Speaker 1: how they would stop driving for the night or even

202
00:11:39,160 --> 00:11:42,160
Speaker 1: a few days after they had a bad experience. They're

203
00:11:42,200 --> 00:11:44,800
Speaker 1: not only earning less because they're working fewer hours, but

204
00:11:44,920 --> 00:11:48,000
Speaker 1: over time, they're missing out on the experiences so that

205
00:11:48,040 --> 00:11:50,600
Speaker 1: they can earn more per hour to begin with, right,

206
00:11:50,679 --> 00:11:55,000
Speaker 1: And that seemed to be paulse hunch too. So, um,

207
00:11:55,040 --> 00:11:58,319
Speaker 1: there's some other things going on on this platform that

208
00:11:58,400 --> 00:12:02,200
Speaker 1: make it more attractive for men for women. UM, I

209
00:12:02,240 --> 00:12:04,440
Speaker 1: don't you know. I don't know if women don't feel us.

210
00:12:04,480 --> 00:12:06,800
Speaker 1: I can't speak to this. I don't see that data.

211
00:12:06,920 --> 00:12:09,319
Speaker 1: But you know, you could imagine they don't feel it's safe.

212
00:12:09,400 --> 00:12:13,000
Speaker 1: You could imagine. Paul's big conclusion was this, even though

213
00:12:13,040 --> 00:12:16,160
Speaker 1: it's great that these big economy jobs offer flexible work

214
00:12:16,200 --> 00:12:20,040
Speaker 1: arrangements for women, that flexibility on its own isn't enough

215
00:12:20,080 --> 00:12:22,439
Speaker 1: to close the gap between what women earn and what

216
00:12:22,480 --> 00:12:33,360
Speaker 1: men earn. So Selena, I guess the next question is

217
00:12:33,520 --> 00:12:36,000
Speaker 1: what are Uber and Left doing to tackle this well.

218
00:12:36,000 --> 00:12:38,160
Speaker 1: First of all, Uber and Left both allowed drivers to

219
00:12:38,200 --> 00:12:42,280
Speaker 1: report incidents in the app or through call line. For example,

220
00:12:42,280 --> 00:12:44,560
Speaker 1: in that story we heard from Jody at the beginning,

221
00:12:44,640 --> 00:12:47,319
Speaker 1: where a man was making her uncomfortable on a night drive.

222
00:12:47,960 --> 00:12:50,640
Speaker 1: She reported that to Uber afterwards, and she told me

223
00:12:50,679 --> 00:12:54,960
Speaker 1: that she's reported many many other incidents for being solicited

224
00:12:55,000 --> 00:12:58,079
Speaker 1: for sex or for drugs. And what happens after a

225
00:12:58,160 --> 00:13:00,760
Speaker 1: driver reports someone Joe He has said that in the

226
00:13:00,800 --> 00:13:03,040
Speaker 1: past when she's made a report, the company told her

227
00:13:03,040 --> 00:13:06,960
Speaker 1: she wouldn't be paired with the person again, and supposedly

228
00:13:07,400 --> 00:13:11,280
Speaker 1: they will kick them off the flat, they'll investigate. It

229
00:13:11,320 --> 00:13:14,920
Speaker 1: is what they always tell me, and usually I don't

230
00:13:14,960 --> 00:13:17,720
Speaker 1: hear anything else from that except for you you won't

231
00:13:17,760 --> 00:13:22,280
Speaker 1: be paired from with them again. However, I have ran

232
00:13:22,360 --> 00:13:26,120
Speaker 1: into those people that I've been told I won't be

233
00:13:26,200 --> 00:13:30,280
Speaker 1: paired with again, and still seeing them ordering Uber rides

234
00:13:30,440 --> 00:13:33,320
Speaker 1: being picked up by other Uber drivers. Do you think

235
00:13:33,480 --> 00:13:37,880
Speaker 1: Uber and Lift do enough to help women who are

236
00:13:37,880 --> 00:13:43,240
Speaker 1: put in situations like this? Absolutely not. I don't. I

237
00:13:43,679 --> 00:13:47,560
Speaker 1: really wholeheartedly believe that they've done slowly, and I find

238
00:13:47,559 --> 00:13:49,360
Speaker 1: this to be outrageous. I mean, don't you think that

239
00:13:49,400 --> 00:13:52,400
Speaker 1: there should be just a zero tolerance policy that these

240
00:13:52,440 --> 00:13:56,040
Speaker 1: apps have towards passengers who act this way? Well, certainly,

241
00:13:56,080 --> 00:13:58,040
Speaker 1: the sentiment from the women I spoke to is that

242
00:13:58,080 --> 00:14:00,960
Speaker 1: there's a very high bar for conduct it comes to drivers,

243
00:14:01,000 --> 00:14:03,880
Speaker 1: but for passengers, since they want as many writers as possible,

244
00:14:04,160 --> 00:14:05,760
Speaker 1: the bar is pretty low, and you have to do

245
00:14:05,800 --> 00:14:08,800
Speaker 1: something pretty terrible to get permanently. But in this case,

246
00:14:08,840 --> 00:14:12,240
Speaker 1: the customer is not always right exactly. And I mean,

247
00:14:12,280 --> 00:14:15,240
Speaker 1: even though Jodie has reported a lot of these incidents,

248
00:14:15,440 --> 00:14:18,000
Speaker 1: many women don't. A lot of the ones I spoke

249
00:14:18,040 --> 00:14:20,560
Speaker 1: to said they're worried that if they report their passengers

250
00:14:20,560 --> 00:14:24,160
Speaker 1: for grasping them or even just rebuffing their advances in person,

251
00:14:24,600 --> 00:14:27,640
Speaker 1: the passengers will retaliate by giving them a bad rating,

252
00:14:28,120 --> 00:14:31,480
Speaker 1: report them for a serious infraction like drunk driving, and

253
00:14:31,520 --> 00:14:35,239
Speaker 1: that has serious consequences for the drivers, like getting suspended

254
00:14:35,240 --> 00:14:38,320
Speaker 1: from driving for multiple days while they conduct an investigation.

255
00:14:38,800 --> 00:14:41,480
Speaker 1: They could even get kicked off the platform altogether. And

256
00:14:41,560 --> 00:14:44,160
Speaker 1: many drivers depend on that income to pay their bills.

257
00:14:44,560 --> 00:14:46,360
Speaker 1: So what do Uber and Left have to say about

258
00:14:46,360 --> 00:14:48,760
Speaker 1: all this? So I spoke to Uber's head of safety

259
00:14:48,800 --> 00:14:53,360
Speaker 1: and consumer protection policy, Stephanie Bryson. One of the most

260
00:14:53,400 --> 00:14:56,520
Speaker 1: impactful things is that really the tone from the top

261
00:14:56,600 --> 00:15:00,400
Speaker 1: has changed. She's referring to Uber's change in management dar

262
00:15:00,520 --> 00:15:03,640
Speaker 1: Causra Shah. He became CEO last year, taking over from

263
00:15:03,640 --> 00:15:06,880
Speaker 1: Travis Kalinik, and when Dara came in, he did a

264
00:15:06,880 --> 00:15:09,200
Speaker 1: full review of the business, as you would expect any

265
00:15:09,240 --> 00:15:12,640
Speaker 1: CEO to do, and coming out of that review, he

266
00:15:12,760 --> 00:15:15,520
Speaker 1: said that we should commit to safety being the top

267
00:15:15,520 --> 00:15:19,280
Speaker 1: priority of the company, and the company has really fallen

268
00:15:19,280 --> 00:15:22,720
Speaker 1: in line behind that new safety features include this emergency

269
00:15:22,760 --> 00:15:25,080
Speaker 1: button that connects drivers to nine one one, and a

270
00:15:25,120 --> 00:15:27,400
Speaker 1: feature that makes it easier to share trips with friends

271
00:15:27,400 --> 00:15:32,840
Speaker 1: and family. The company also eliminated forced arbitration agreements for employees, riders,

272
00:15:32,920 --> 00:15:35,880
Speaker 1: and drivers who make sexual assault or harassment claims against

273
00:15:35,920 --> 00:15:38,920
Speaker 1: the company, So that means that instead of resolving any

274
00:15:39,000 --> 00:15:42,280
Speaker 1: legal claims in an arbitration hearing, people can actually take

275
00:15:42,320 --> 00:15:45,400
Speaker 1: those claims to court. Stephanie also explained to me how

276
00:15:45,480 --> 00:15:49,800
Speaker 1: Uber evaluates reports on inappropriate behavior from passengers. When a

277
00:15:49,960 --> 00:15:53,440
Speaker 1: report does come in, UH, that is, it goes into

278
00:15:53,440 --> 00:15:59,240
Speaker 1: our reporting flow and UH those instances that are safety

279
00:15:59,360 --> 00:16:03,240
Speaker 1: related are flagged to a special team, and that team

280
00:16:03,280 --> 00:16:09,320
Speaker 1: has received special training on trauma informed techniques as well

281
00:16:09,320 --> 00:16:16,479
Speaker 1: as other training on handling sensitive or high sensitivity incidents,

282
00:16:17,560 --> 00:16:21,080
Speaker 1: and it goes through a process of follow up. If

283
00:16:21,120 --> 00:16:27,760
Speaker 1: it is a high severity incident, we will usually undertaken

284
00:16:27,800 --> 00:16:34,280
Speaker 1: investigation two, speak with both sides of the encounter and

285
00:16:34,400 --> 00:16:38,440
Speaker 1: trying to discern the facts and then make make a

286
00:16:38,480 --> 00:16:43,240
Speaker 1: call on what next steps need to be taken. Finally,

287
00:16:43,320 --> 00:16:46,040
Speaker 1: I asked her about the fears from sub women drivers

288
00:16:46,040 --> 00:16:48,960
Speaker 1: that they feel like they were being given unfair ratings.

289
00:16:49,600 --> 00:16:51,800
Speaker 1: If a driver feels that they were given an unfair

290
00:16:51,920 --> 00:16:55,200
Speaker 1: rating as some kind of retaliation, it's really important that

291
00:16:55,240 --> 00:16:57,240
Speaker 1: they report that to Uber so that we can take

292
00:16:57,240 --> 00:17:00,480
Speaker 1: steps to make that right. Lift has similar features to Uber,

293
00:17:00,600 --> 00:17:03,080
Speaker 1: though the company declined to offer anyone from the company

294
00:17:03,080 --> 00:17:05,399
Speaker 1: to speak with me for the podcast, but in a

295
00:17:05,440 --> 00:17:08,080
Speaker 1: written statement, the company said that safety is a top

296
00:17:08,119 --> 00:17:11,600
Speaker 1: priority and that harassment isn't tolerated, and that that kind

297
00:17:11,600 --> 00:17:14,840
Speaker 1: of behavior can lead to a permanent ban. Lift also

298
00:17:14,880 --> 00:17:17,359
Speaker 1: said that it's announcing fifteen new features by the end

299
00:17:17,400 --> 00:17:20,640
Speaker 1: of this year, many of which focus on driver's safety. Selena,

300
00:17:20,680 --> 00:17:23,080
Speaker 1: are they doing anything to tag all those more nuanced

301
00:17:23,080 --> 00:17:25,720
Speaker 1: forces at work that are keeping women in a disadvantage,

302
00:17:26,040 --> 00:17:28,720
Speaker 1: the kind of factors that Paul Oyle told us about,

303
00:17:28,760 --> 00:17:31,800
Speaker 1: like women driving slower and this searning less money, or

304
00:17:31,840 --> 00:17:34,920
Speaker 1: the higher turnover rates for female drivers. So I did

305
00:17:34,920 --> 00:17:37,639
Speaker 1: pose that question to Stephanie from Uber, and she mentioned

306
00:17:37,640 --> 00:17:40,520
Speaker 1: that they have used that to help shape product decisions

307
00:17:40,560 --> 00:17:42,680
Speaker 1: at a high level and they aren't done thinking about

308
00:17:42,720 --> 00:17:45,280
Speaker 1: the studies findings, but she didn't have any specifics to

309
00:17:45,320 --> 00:17:48,840
Speaker 1: tell me. So, Selena, is there anything that surprised you

310
00:17:48,840 --> 00:17:52,119
Speaker 1: about your reporting. I was definitely surprised by the frequency

311
00:17:52,240 --> 00:17:55,840
Speaker 1: of inappropriate experiences that these women faced. I mean, by

312
00:17:55,880 --> 00:17:59,639
Speaker 1: and large, these women enjoy the flexibility, they enjoy the money,

313
00:18:00,160 --> 00:18:03,720
Speaker 1: they chose to start on this platform, and they realize

314
00:18:03,760 --> 00:18:06,480
Speaker 1: that they have to deal with this issue and that

315
00:18:06,600 --> 00:18:08,720
Speaker 1: is just a nature of doing the job. And it's

316
00:18:08,760 --> 00:18:11,640
Speaker 1: almost so sort of reality check for them, and they

317
00:18:11,640 --> 00:18:13,560
Speaker 1: feel like it's no choice, but it's a reality they

318
00:18:13,560 --> 00:18:15,399
Speaker 1: have to face, just like in day to day life.

319
00:18:15,520 --> 00:18:19,200
Speaker 1: What do you think about these companies potentially using technology

320
00:18:19,240 --> 00:18:22,000
Speaker 1: to solve one of the problems of their technology, So

321
00:18:22,040 --> 00:18:25,720
Speaker 1: for example, making it easier for female drivers at night

322
00:18:25,800 --> 00:18:28,560
Speaker 1: perhaps who might be feeling vulnerable in a in a

323
00:18:28,560 --> 00:18:32,560
Speaker 1: certain neighborhood to solicit rides only from female passengers or

324
00:18:32,600 --> 00:18:36,720
Speaker 1: female passengers, uh, from from just female drivers or like

325
00:18:36,920 --> 00:18:39,720
Speaker 1: you know, a group that includes at least one female

326
00:18:39,720 --> 00:18:42,760
Speaker 1: as are the companies talking about anything like that. So

327
00:18:42,840 --> 00:18:45,280
Speaker 1: that's actually something that d D the Uber of China,

328
00:18:45,359 --> 00:18:49,560
Speaker 1: tried so after several incidents where female passengers were allegedly

329
00:18:49,560 --> 00:18:53,080
Speaker 1: assaulted by male drivers, they only wanted to match women

330
00:18:53,520 --> 00:18:56,520
Speaker 1: with women in late hours. But the problem with that

331
00:18:56,600 --> 00:18:58,840
Speaker 1: was that it cost a shortage of drivers that were

332
00:18:58,880 --> 00:19:01,399
Speaker 1: available to women, since d D obviously doesn't have as

333
00:19:01,480 --> 00:19:04,240
Speaker 1: many female drivers, and this was something I thought was

334
00:19:04,280 --> 00:19:07,639
Speaker 1: really interesting. So on Uber, only about fifteen percent of

335
00:19:07,680 --> 00:19:09,880
Speaker 1: the drivers are female, and on Lift is only about

336
00:19:11,400 --> 00:19:13,760
Speaker 1: And if you look at the overall transportation industry, women

337
00:19:13,800 --> 00:19:16,560
Speaker 1: actually make up a super small percentage of that and

338
00:19:16,600 --> 00:19:19,720
Speaker 1: it's actually largely a result of safety concerts. So that's

339
00:19:19,760 --> 00:19:22,040
Speaker 1: why there are so few women in the industry overall.

340
00:19:22,960 --> 00:19:25,440
Speaker 1: That reminds me a sleen of some Uber history. Back

341
00:19:25,440 --> 00:19:29,040
Speaker 1: in two fifteen, Uber had announced this partnership with with

342
00:19:29,119 --> 00:19:33,000
Speaker 1: the United Nations Right to create a million jobs for

343
00:19:33,080 --> 00:19:37,240
Speaker 1: women by which of course is now basically a year away.

344
00:19:37,280 --> 00:19:39,920
Speaker 1: So remind us what happened with that partnership. Yeah, what's

345
00:19:39,960 --> 00:19:41,919
Speaker 1: interesting is that I think a lot of what I

346
00:19:42,000 --> 00:19:44,639
Speaker 1: spoke to with these women's about are reflected in the

347
00:19:44,720 --> 00:19:46,879
Speaker 1: ultimate decision of the U n which was to cancel

348
00:19:46,960 --> 00:19:49,880
Speaker 1: that partnership. I mean, just days after it was announced

349
00:19:49,920 --> 00:19:53,920
Speaker 1: back in the International Transportation Federation published a letter saying

350
00:19:54,040 --> 00:19:57,240
Speaker 1: Uber actually does not empower women, that it makes the

351
00:19:57,320 --> 00:19:59,879
Speaker 1: labor market more and equal by denying people basic for

352
00:20:00,040 --> 00:20:03,440
Speaker 1: tections like fair wages, job security and safety at work.

353
00:20:06,160 --> 00:20:07,880
Speaker 1: So one of the women you talked to, I think

354
00:20:07,960 --> 00:20:10,680
Speaker 1: was Hannah, said she was delivering food because of course

355
00:20:10,680 --> 00:20:13,320
Speaker 1: then she doesn't need to put a passenger uh in

356
00:20:13,600 --> 00:20:16,160
Speaker 1: the backseat. She maybe just has a pizza back there.

357
00:20:16,640 --> 00:20:19,119
Speaker 1: Is that Is that a reasonable solution for women who

358
00:20:19,240 --> 00:20:21,840
Speaker 1: might feel unsafe driving for Uber and left to just

359
00:20:22,160 --> 00:20:25,160
Speaker 1: do another take another job in the gig economy. It's

360
00:20:25,200 --> 00:20:27,560
Speaker 1: true that a lot of the women did like food

361
00:20:27,560 --> 00:20:29,840
Speaker 1: delivery for the fact that they could actually bring their

362
00:20:29,920 --> 00:20:32,560
Speaker 1: kids with them, but by and large, pretty much every

363
00:20:32,600 --> 00:20:34,760
Speaker 1: woman I spoke to you said it wasn't as lucrative

364
00:20:35,000 --> 00:20:37,520
Speaker 1: and they weren't making as much money and that that's

365
00:20:37,520 --> 00:20:40,080
Speaker 1: why they were returning to Uber and left. Yeah, it's

366
00:20:40,119 --> 00:20:42,240
Speaker 1: kind of a shame that the largest part of the

367
00:20:42,240 --> 00:20:44,280
Speaker 1: gig economy would be off limits to some of the

368
00:20:44,280 --> 00:20:47,399
Speaker 1: people who really need the flexibility and the earning opportunity

369
00:20:47,480 --> 00:20:50,520
Speaker 1: the most. But I'm curious from your perspective, I mean,

370
00:20:50,560 --> 00:20:53,959
Speaker 1: with Travis being out of the company back when he

371
00:20:54,000 --> 00:20:57,600
Speaker 1: was really criticized for not dealing with sexual craftsman internally

372
00:20:57,800 --> 00:21:00,280
Speaker 1: and in terms of drivers safety, do you think is

373
00:21:00,320 --> 00:21:03,320
Speaker 1: really ushering this new way that the company is telling

374
00:21:03,320 --> 00:21:05,719
Speaker 1: me about, well, he you know, he's certainly presenting a

375
00:21:05,760 --> 00:21:08,040
Speaker 1: friendlier public face. But it goes back to one of

376
00:21:08,119 --> 00:21:10,639
Speaker 1: the things that that some of our drivers told us about,

377
00:21:10,680 --> 00:21:13,280
Speaker 1: which is that when they report bad behavior, they're not

378
00:21:13,359 --> 00:21:17,280
Speaker 1: certain that it's being punished. And the great promise not

379
00:21:17,440 --> 00:21:19,840
Speaker 1: of the gig economy, but of these kind of feedback

380
00:21:20,080 --> 00:21:23,560
Speaker 1: rating mechanisms that are ubiquitous on the internet, you know,

381
00:21:23,760 --> 00:21:26,320
Speaker 1: is that actions have consequences, right, and if you get

382
00:21:26,320 --> 00:21:29,080
Speaker 1: a bad rating because you're a passenger and you try

383
00:21:29,119 --> 00:21:32,280
Speaker 1: to hold your driver's hand, there should be zero tolerance.

384
00:21:32,359 --> 00:21:34,679
Speaker 1: You know, that should be recorded on your record, and

385
00:21:34,720 --> 00:21:37,159
Speaker 1: a future driver should know about it. And and you know,

386
00:21:37,200 --> 00:21:39,800
Speaker 1: either you're out or drivers see it to see it

387
00:21:39,840 --> 00:21:41,480
Speaker 1: in the form of a low rating and have the

388
00:21:41,520 --> 00:21:44,720
Speaker 1: opportunity to decide not to pick you up. What it

389
00:21:44,840 --> 00:21:49,240
Speaker 1: sounds like is that these companies are still so desperate

390
00:21:49,280 --> 00:21:51,000
Speaker 1: for growth then they don't want to lose drivers, and

391
00:21:51,040 --> 00:21:53,359
Speaker 1: they don't want to lose passengers that they're they're not

392
00:21:53,520 --> 00:22:00,520
Speaker 1: enforcing their own enforcement mechanisms. So, Selena, before we finish up,

393
00:22:00,680 --> 00:22:03,119
Speaker 1: what are Jody and Hannah's plans for the future. Do

394
00:22:03,200 --> 00:22:05,640
Speaker 1: they want to keep driving. Hannah told me she will

395
00:22:05,720 --> 00:22:10,400
Speaker 1: keep on driving. It's not bad money, it's good money. Um,

396
00:22:10,640 --> 00:22:14,040
Speaker 1: direct up quickly. You can have some really pleasant experiences.

397
00:22:14,160 --> 00:22:16,520
Speaker 1: I'm gonna work from home mom. I'm around my children

398
00:22:16,520 --> 00:22:19,160
Speaker 1: all the time. If you get social interaction with adults

399
00:22:19,960 --> 00:22:21,800
Speaker 1: and not necessarily people that you would meet on a

400
00:22:21,840 --> 00:22:26,480
Speaker 1: day to day basis. But Jody was less certain. Honestly,

401
00:22:27,040 --> 00:22:30,199
Speaker 1: I'm at the point where as I liked it. I

402
00:22:30,280 --> 00:22:34,560
Speaker 1: love the flexibility and everything, but the money just isn't

403
00:22:34,640 --> 00:22:40,600
Speaker 1: consistent in us, and I feel, you know, there's no benefits.

404
00:22:41,560 --> 00:22:45,000
Speaker 1: You know, some days, yeah, I'm doing great by making hour,

405
00:22:45,480 --> 00:22:49,440
Speaker 1: but some days I'm not doing so great and I'm

406
00:22:49,480 --> 00:22:53,720
Speaker 1: making fries. I mean, I would much rather have a

407
00:22:53,920 --> 00:22:58,560
Speaker 1: full time jobs, even if it means me having to

408
00:22:58,560 --> 00:23:00,720
Speaker 1: give a notice three weeks at a time for a

409
00:23:00,840 --> 00:23:16,480
Speaker 1: day off. And that's it for this week's Decrypted. Thanks

410
00:23:16,520 --> 00:23:18,960
Speaker 1: for listening. Are you a woman who drives for a

411
00:23:19,040 --> 00:23:21,560
Speaker 1: lift or Ubert? We want to hear your story. You

412
00:23:21,600 --> 00:23:25,119
Speaker 1: can email us at Decrypted at Bloomberg dot net or

413
00:23:25,160 --> 00:23:29,159
Speaker 1: I'm on Twitter at Selena underscore Y Underscore Way and

414
00:23:29,200 --> 00:23:31,680
Speaker 1: I'm at brad Stone. If you're a fan of the show,

415
00:23:32,040 --> 00:23:34,240
Speaker 1: please take a moment to rate and review us. It

416
00:23:34,320 --> 00:23:37,560
Speaker 1: helps new listeners find the show. This episode was produced

417
00:23:37,560 --> 00:23:40,879
Speaker 1: by Pio Gadkari and Liz Smith. Our story editor was

418
00:23:40,960 --> 00:23:44,760
Speaker 1: Aki Edo. Thanks also to add Vandermay, Emily Busso, and

419
00:23:44,840 --> 00:23:50,120
Speaker 1: Magnus Hendrickson. Francesca Levi is head of Bloomberg Podcasts. This

420
00:23:50,200 --> 00:23:52,639
Speaker 1: is our last episode for the season. We're taking the

421
00:23:52,680 --> 00:23:54,960
Speaker 1: next few weeks off to work on new episodes, but

422
00:23:55,000 --> 00:23:57,119
Speaker 1: we'll be back again in the spring. See you in

423
00:23:57,160 --> 00:23:57,680
Speaker 1: the new year.