« Freakonomics Radio

317. What Can Uber Teach Us About the Gender Pay Gap?

2018-02-06 | 🔗
The gig economy offers the ultimate flexibility to set your own hours. That's why economists thought it would help eliminate the gender pay gap. A new study, using data from over a million Uber drivers, finds the story isn't so simple.
This is an unofficial transcript meant for reference. Accuracy is not guaranteed.
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goober drivers. The paper it was written by five economists to who are employed by Hubert to Stanford professors and one researcher who's been on economics. Radio several times John with two chairman of the University of Chicago Economics Department and he moonlights as head of the economics team at Goober. Dodd list is one of three economists will be hearing from today: there's Rebecca diamond from Stanford Graduate School of business high. Yes, an Jonathan Hall who leads the public policy in economics team at Goober, yes, M working everywhere for just about three years three hundred sixty days couple hours so talk to me about how you came to be involved, in this crew and in your role, sure you know, work on various issues around the company that have an economics flavour and one
that has arisen recently is the gender pay gap, its generally agreed that it's a terribly important topic in economics, but it's not one. That's while study because of the dearth of data and because people find it uncomfortable so all of us who have read even a little bit about the gender pay gap. Problem were used hearing all different sorts of factors described as determinants. More also used to hearing all different sorts of figures. You know the gap is twelve cents on the dollar, seven, since its twenty four cents and an that's because you know it's hard to decompose and hard to measure. But a lot of the factors that we hear about are things like inflexible our sir temporal flexible inflexibility. So here saying that kind of doesn't exist right in this goober ecosystem. Correct anybody can work whenever they want yeah, exactly that
one reason we were so excited to study this question in the context of Goober data because we could sort of rule out those theories from the beginning. So you re in the paper that, unlike previous studies, you are able to quote completely explain the pay gap. So can you unpack that just a bit sure so over pays drivers based on a relatively simple transport formula that takes into account how long you're right in miles how long the ride takes and potentially I'm a surge multiplier. Where sometimes is excessively high demand so fair itself is determined by an algorithm witches gender blind. The dispatch itself is gender blind and pay structures tied directly to output and not negotiate that transparency and that simplicity of pay is what makes this environment so interesting for studying
a gender pay gap because we were able to work with such excellent detailed data. We believe this is a first of it kinds study in so far as it can actually fully explain the gender pay gap. So before you undertook this project What were you looking for? What did you believe you'd find in the data? Did you think you'd see a gender pay gap here of zero? I think there are reasons to think it could be small, but I went in with a very open mind. I mean we ve, never been able to look at any labour market like this before
but the zero in its hard to forecast any specific number, so my prediction was more or less that men and women would earn the same, but if there was a difference, I think the pay gap with slightly favour women- and this is kind of her two reasons- one. I knew that they had worked fewer hours per week, so they had a chance to cherry pick. The better hours turn the weak point. Number two was: if there was discrimination on the platform, I was thinking that riders would actually prefer female drivers to male drivers. So let's jump right to the number. What kind of pay gap did you actually find if any between male and female goober drivers yeah? You know we ve on something very surprising in the end after you look at the data to look at the raw data, what you find is it?
wait a minute: this is a big juicy new piece of research, with a huge and detailed data set drawn from an economic ecosystem that practically made for this kind of analysis. So, let's not get ahead of ourselves. There will be plenty of time to hear about the fine, and the ramifications right after this I'm happy from W and Y see studios. This is freakin comics, radio, the that explores the hidden side of everything. Here's your host Stephen Gardner
now for people who don't know what you were is or haven't used. It just give us a quick explanation of how it works, and you no kind of what it is over is a platform that connects drivers to writers, have people on a ride from HIV, and you put that into the upper app and it or match you with a driver who will happily give you that ride when Ober receives a call from a customer. Like me what they say, they do? Is they put out a dispatch to drivers that our near by and ask them to accept the trip, and then a driver who's on the system can decide whether or not to accept your request and if she can come pick you up. and take you really need to get so we we ultimately word Nate drive prison riders, we're talking today
a new working paper that uses Goober data to explore the gender earnings gap. It was written by five economists, Cody Cook Goober, Paul lawyer of Stanford, and the three economists were speaking with today: Rebecca Diamond Jonathan Hall and journalists. explain if you would why these data are particularly useful and trying to answer this question. So we have mounds and minds of data. We have millions of drivers We have millions of observations and twenty five million driver weeks across a hundred and ninety six cities suggest the depth of the data in the understanding of both the compensation function and the production function of drivers gives us a chance once we observe if there is indeed a gap which gives us a chance to unpack
the features. I can explain that gap are so described. The data I wanna know both the overall universe of uber driver data in the U S and then which subset your data comprises of that. We look at driver weeks for or Ober drivers from January of twenty fifteen to March of twenty seventeen that is over. We point, eight million drivers. During this time, and over seven hundred and forty million the trip, so we have really alive data to work with and for part of the paper we focus on one city. So we pick one city to go deep on four, very practical reasons that the work they are doing is very data. Intensive saw it'll, be in French, a cargo. I said: let let's do it go now, how much of that was because you're from Chicago in how much was it? Was your
is he in it's gonna be easier for the work they do not and I'm gonna none. So the team is out in San Francisco and increased. It's done not, you know, we ve done Detroit Houston in Boston and we find similar results and give us just dumb tinder breakdown in Chicago in how representative? That is, if the rest of the? U S So in the nation wide sample, twenty seven percent of drivers are female and the Chicago Data set. Thirty percent of drivers are female, so it's a slightly more, but it's pretty similar. Let me ask you this: how big, if you know, is what we call these days, the gig economy. Some estimates suggest that up to fifteen percent of people are full time employed in the gig economy, and other estimates tell you that up to thirty percent of people are employed.
At least part time in a gig economy, and does the gig economy tend to lean more male or female. In writing. To gig economy looks a lot like What we have on over, which is about a third of female drivers in two thirds of male drivers, is the uber albert. gender blind, the algorithm is gender blind, both in the literal sense that it doesn't that gender is not fed into it. It does not increase. Great gender into the into the calculation at all and innocent. but it doesn't facilitate discrimination by the users, the human users, who are more clever than the algorithm right, but that does not guarantee that the platform couldn't facilitate some kind of nefarious discrimination. There are two kinds of discrimination that might actually occur on olbers platform. The first is from the dispatches earth or from set in the wages and that's what members job is and of course there is, discrimination there
but on the other hand there could be customer side discrimination. It could be the case that We as riders prefer men or prefer women as our drivers, so did you find nation on behalf of the users of Albert. Did riders tend to prefer male drivers to women find: no evidence of discrimination on the customer side, meaning that riders dont prefer for men to women or women to mandate. They view men and women the same when it comes to be in their driver. That's right and we don't see overall differences and rejection rates. Between male and female drivers, and if you were to put that in the regression, it doesn't contribute to a gender gap right. So let me just make I'm clear, you're, saying: there's no discrimination on the upper side on the supply side, because the Algarve MS gender blind and the prices, the price and you're saying, there's no
discrimination on the passenger sides. It does that mean that discrimination accounts for zero percent of whatever pay gap you find or don't find between male and female labour drivers. That's correct are so You are telling us that you're prediction was at there'd, be either zero or positive pay gap for women. What kind of pay gap did you actually find it between male and female uber drivers yeah. You know we fall something very surprising, what you find men, make about seventy percent more per hour on average, which is pretty substantial for doing the exact same job in setting were
Work. Assignments are made by a gender blind algorithm and pay structures tied directly to output and not negotiate so seven percent gap. How does I compare to the best research in other occupations? So there's been some previous work that has looked at within firm gender pay gap, and seven percent is not very different than the overall average. We see across all firms, even in the traditional labour market. Sadly, so were you despondent or depressed, or a little sad when you saw the size doctor? I just wanted to know more. I want to know where it was coming from and what were the cause is ok, so I want to get into what are the factors in the paper? You rate that there are three number one so after reaching the dead end of do no discrimination doesn't seem to be a determinant. We then decided to ask
what about where and when. So what I'm thinking about here is a time of day day of weak in where in Chicago they actually drive in here we had some success. So what we fear and is that after you explore the wares and wines, we find that we can explain roughly twenty percent of the gender pay gap by choices over where to drive and win to drive and an important contributor to the gap is particularly where the ride started. So different neighborhoods are gonna defer and the types of rights in Europe I got and also catch with the frequency of rides you're going to get called for so men and women tend to target different neighborhoods of where they're, driving and men Our targeting more lucrative pay pay areas than women and incentive to do with lake
three o clock in the morning on Saturday and I want to go out and world bars- are there might be a surge or is it more Bonanno early morning, airport ships? Can you characterize the nature of those most lucrative trips? Men seem to be doing a little better. So what is more important than when you drive, is exactly which trips or roots do tend to focus on. So what particularly salient example here is that dumb airport trips tend to be the most profitable trips Anubis platform. So what you have is that men tend to complete more airport ships than women complete? There are differences between when men and women dry. Men are much more likely to drive the graveyard overnight shift, which could have more people coming home from bars or what not but women are actually dramatically more likely to derive the Sunday afternoon shift, and that is all,
so a very lucrative driving time. So it's not so much that there aren't differences about when many women drive it just doesn't seem to be super related to a pay gap, because they're, both driving lucrative times are just different times in Sunday afternoon. That's when footballs on may be women more willing to go. Dr Gruber then, and why is Sunday afternoon a more lucrative time to drive the consumer
men: are male drivers are watching football, said: they're, not flooding the market with supply of drivers and therefore that prices up I mean that's a theory. We haven't unpacked what so magical about Sunday afternoon, but the pay tends to be high then, and women work disproportionate hours, then ok, but for all those potential differences, the absolute amount is still relatively small. Using twenty percent of a gap of seven percent can be explained by time and location right now. I think that's right, but now, after looking at Timon and location, that analysis actually hinted at a deeper, a fact which I will a driver experience yes, so there are pretty large returns to what we call experience, which is literally the number of ships that you have done, This is an area that is pretty well studied and economic sense, learning by doing So we estimate that the more
Epps. You do as a driver, the more you earn about how to make money on the platform. So obviously this is not getting a rays from goober in the sense that the formula of pay is changing. Drivers just getting better figuring out when and where to drive a little bit about how fast to drive and also how to strategically accept or cancel rides that they think are bad and we estimate that men and women learn identically quickly in trips, so a man or a woman in the data who have in the same number of trips will have it
We waited the same amount of learning. However, when you look at the experience of our drivers are the average ten year. This is heavily tilted and man's direction. What you have in your men are far more likely to have been driving on offer for over two years. Women are likely to have just joined in recent months, and this is because women leave the platform much more often than men. What is the overall driver attrition re Anna, whether its measured in six months or a year, whatever? Yes, six months as what we ve been looking at? More than sixty percent of those who start driving are no longer active on the platform six months later
the six month, attrition rate for the whole? U S for men is about sixty three percent and four women. It's about. Seventy six percent wow, so that would connote to me an amateur. At least that may be this. Gender pay gap among goober drivers is reflected in the fact that women leave it somewhat, or maybe just a job that, on average women really dont lake? Is that measurable? That's a good question I like to think about people like him to be a ride, share partner rather than disliking fit, but it is measurable when you look at the attrition rates, it is true that women to fall off the platform more, but there also earning less. So it's not clear whether it's because of preferences for not liking to drive as much as men like to drive,
or if it's simply in earnings effect right. You know it's likely. The combination about those two yet, but does this higher female attrition rate- mean that the average female is likely to be less experience in the average male driver and therefore will earn less ya know. That's right when you look at experience, really manner more experience and women because of two primary reasons. One women drop off the platform more often than men, but to even for those who are on the platform for the same amount of time. Since the average man tribes about fifty percent more trips per week than the average woman, you still have their dicks. Fancy fact for those who have been on the platform the same in a number of months, so at any given day or time, the men driving for
We have a higher level of experience under their belt than women, and that plays an important role in compensation, and that explains about thirty percent of the pay gap that we measure. A third of the gap can be explained by returns to experience he said about. Twenty percent of the gap can be explained by time of location and work, but that leaves almost half the can be explained by the third factor. What is that that's right? So after we account for experience now, we're left scratching our head, so we're thinking. Well, we fried discrimination, we you ve done where, when we ve done experience, you know what possibly could be and then what we notice in the data is that men are actually completing more trips per hour than women, so this is sort of our Eureka moment, their drive and faster. I may yet so the third factor, which explains the remaining fifty,
percent of the gap is speed. So men happened to just drive a little bit faster and because driving a little bit faster gets you to finish your trips that much quicker and get onto the next trip you can fit more trips in an hour and you end up with a higher amount of pay. Now, how did these goober driver data for male female speed? Compare to male female driver speed generally, do we know for a fact that men generally drive faster than women, yet what you find is it in the general population, Ten men actually drive faster than women, okay, so male goober drivers drive faster than female goober drivers and therefore that helps to make more money. Is that, however, more dangerous, the faster driving?
so the gap is small men drive about two percent faster than women said doesn't suggests that thus leading to big differences in in risk, but also Did you see that the University of Michigan Transportation Research unit? They looked at a big national representative sample of police reported crashes, and they did seem to find that females on average on a per mile driven basis have more ashes, then males in your data. Certainly you could you have all of that data right. You have miles driven, you have crashes. Presumably, could you look if you wanted? We will see if, on a per mile basis, women crash more or less than men. I haven't worked with that data, leaving just working with the Labour market data Buber. Maybe could look at that, but that hasn't been something with we ve worked at your we don't have.
Answer to that is something that we would like to study. But we do not have an answer to it. I think, on the flip side of their feelings of them, female having women having more accidents. I think men have more fatal accidents, so their sort of her quality quantity, trade off. So it's not clear who is actually driving safer. One thing I can say is we ve looked at like the ratings of customers on faster slow arrives and, if anything, it's marginally correlated with a higher rating. So it looks like writers you value getting their faster So in summary, this is The labour ecosystem goober drivers that would seem to remove all gender discrimination, and yet women earn seven percent less for doing essentially the same work.
I mean I think, they're not doing the same right wheels were showing their doing different there, making different choices in the labour markets. I think it's really the whole point. they're not doing the same, and once you control for the differences, they are paid the same. That's right. We ve stripped away all of the factors that we face odd were underline determinants of the gender pay gap and we go to this new vibrant gig economy that promises worker flexibility in labour flexibility and equal pay for equal work. You know, when you analyze amounts and mounds of data, it ends up that we have a seven percent difference now. What's interesting, an intriguing is that after you unpack those differences, what you find is at their there perfectly reasonable explanations,
for what's happening on the Gruber platform, perfectly reasonable explanations, maybe, but the bottom line is that women are still making less money so coming up on economics, radio, what? If anything, be done about that work. Allow our policy experts to answer that question and if you want to keep up with all things for economics, subscribed to our free email newsletter, just dropped by free economic stock. Commons up. We will be right back
We ve been speaking with three economists: Jonathan Hall of Goober, Rebecca Diamond of Stanford in John List of the University of Chicago and Goober, about their new working paper, which uses Goober data to explore. The gender gap in earnings found that even in the labour market, where discrimination can be ruled out, women still earn seven percent less than men. In this case roughly mean dollars an hour versus twenty one. The difference is due to three factors: time and location of driving driver experience, an average speed so via enlarge. these three things are causing men to be more productive on the platform and in return earn their getting a higher wage?
years ago, we put out for economics, radio episode called the true story of the gender pay gap. The labour economist, Claudia Golden argued that the gender gap does it seemed to be driven primarily or even much by discrimination. This new over research very much confirms that, according to gold, in the main culprit is economists speak female demand for temporal flexibility. also known as a preference for flexible working hours, the preference, often driven by the fact Women have more family care obligations than men. Here's a golden laid it out. I like to think about an individual who gets hit degree. Let's say a large degree. so woman and now I have an individual, a man who gets the law to green. They graduate from law school. They both equally brilliant and they both get jobs in approximately the tape
the firm by and large they're going to earn approximately the same amount when they start things Wolf. Can new in their lives there, both perhaps find partners get married, have kids. It's often the case that women will leave the very large law firms that put a lot of time, demands on them and go to small firms or become corporate council become part time. Corporate council, perhaps for a while, they will vote,
two small firms where the workload is somewhat different. They may work in fact the same number of hours, but they may work ours that are their hours rather than the hours imposed on them by the firm. The woman will then begin to make if she's the one who did this, she will make considerably less than than the man and a lot of what we see not all of it. But a lot of what we see is this choice: to go in to occupations that have less expensive temporal flexibility that allow individuals to do their work on their own time. Earlier John List told us what he thought be: Goober Data would show, so my prediction was more or less than men
women would earn the same, but if there was a difference, I think the pay gap with slightly favour women that prediction turned out to be wrong. Sir John, one of the explanations that is often given for the gender pay gap coming from Claudia Golden at Harvard is that it has to do with temporal flexibility inflexibly. How did that factor into your prediction of what these uber data would show, because I would assume, but maybe I'm wrong, that if ever there was a job that offered totalled temporal flexibility, it would be a Neuber driver. Now I think that's exactly right. A plot is the world's expert in this area, and she's argued, I think, quite persuasively
that once we take off the table, this idea that, if you labor long hours or work specific hours during the week, once we take those off the table, then it's much more likely that this gender pay gap might entirely vanish so kind of my intuition. Actually, a rose from Claudia's work. This type of job is at the extreme of temporal flexibility that this allows you to work anytime anywhere you want and why the observers at. Even when you give a lot of flexibility, you don't see a really tiny or non existent gender pay gap, but in a can definitely be through that there exist occupations outside of Cuba in the labour market that do exhibit this hours. Earnings relationship that compensates long hours of work, let's Rebecca diamond again talking about Claudia,
goldens research. If you look at her previous work, for example, on workers in the financial industry, the pay gap, there is enormous and bigger than seven percent and in the financial industry. There's this huge compensation for work in extreme. Long hours and never having like interruptions in your career, but goober shows that Even when you strip away all of this stuff, you deftly don't go to a gender gap of zero and you still have this important experience component. Where you work more on you learn about how to do the job better, you get better doing the job, so you can't
hey it's all, gonna, be perfect. In this new gig economy, the we won't have total equality, but the reasons we gonna total equality are not because of discrimination or problems in how we compensate workers. It really is about working more hours and gaining knowledge on the job and differences in gender preferences and win an economist like you says, differences and gender. preferences, can you unpack that little bit, because I think you guys mean something a little bit different by the word preferences and the lay person does shore. For example, women preferred a work. Fewer hours per week than men on over ended and the broader economy, that's a choice by men and women, given the other aspects of their life, they're, taking into account when deciding how much to work. Women are choosing to drive slower, which
The choice likely just based on preferences of just how they learn to drive, ah in their broader life. Those aren't aspects of labour. Those are just differences between men and women outside of labour force. That happened to lead to differences and compensation in the Labour force right, but then one if I say well, I know they may look like preferences on paper. They choose to work, twenty. So we can still thirty, but the reason I'm doing. That is because I am the one who needs to let's say: take care of my elderly mother, or First, my mother in law, my husband's mother, cause he's not gonna. Do that so yeah. It shows up in the data as a preference, but in fact it's much more of a structural component that I really can't do anything about. So are those to the same thing or is something being lost in the data. So there's a separate question about
Why in the broader were that we live in men and women have different roles in the household and and life does is obviously those differences have gotten the lot smaller as time progressed? Obviously, women are still the ones to be pregnant and bear children that we're gonna change, don't say: never. and those differences can lead to labour supply differences, but labour market there could be. inches in how women and men supply hours over time. As our broader says, Eighty evolves bit, we shouldn't NASA, early be worried about a problem in the labour market because of these differences and gender choices? We may be worried or interested and understanding why broader society,
Leads men and women to make different choices outside the labour market and that can lead to different outcomes in the labour market. But it's not a problem in the labour market. So I think when you look at our data, I think it's actually a mixture of preferences new driving fast, but I also think it's a mixture of, constraints and what I mean by that is men, work more hours and take more trips than the average woman. So why is that part of? It is because women have more constraints I take the kid to school. In the morning and be responsible for taken Johnny to the soccer game, and I think those constraints then led women to actually receive less experience in less learning. By doing so, I think it's actually a mixture of
France's and constraints now's policymakers. What we want to do is make sure that we can alleviate those constraints as much as possible. So Ober could just, I guess, increase its baseline pay to female drivers by seven percent. But I guess I would be discussed the story when against men, where we do not want to allow our policy experts to answer that question in the literal sense of the word, that would be discriminatory. That again is Jonathan Hall who directs public policy at goober. But what I ve learned as an economist is a practitioner at over. Is that simple ending solutions, don't always work what I would call for as a scientific approach that not just do about lots and lots of people and lots of company should take two trying things and testing scientifically. Ideally, we academic collaborators, whether or not those things work. So this is brand new research. The fine
there are just out. Goober has had some well publicized issues with sexual harassment, discrimination recently in including in the headquarters. So you know for some people might look at this and think. Well, maybe this is a kind of a reputational fix for goober, or some people might think that well effected women drivers who were earn less is indicative of the problem. Getting goober is weak reader was worried about publishing this, finding On the one hand, you could praise the company for inventing. Essentially a gender blind labour ecosystem ray on the other, you could imagine some headline writer saying you know: Goober under pays women drivers by seven percent, there's some risk. I'm curious how Ober approach that, Nobody denies that that Hoover's made mistakes in the past and that we need to improve one thing
now that we have done recently is as review, pay, equity across levels, jobs and dumb geographies, so something that were very keen to continue to improve on. But I take your point that this project could be. cast in a similar way. So I can't guarantee that nobody will write a bad headline, but my perspective as much longer term. I feel very strongly that where we as a company need to do is develop a long run, credible perspective and difficult problems, I think we should really be happy and excited that Goober wants to take on these really important quest. Dense that, as you pointed out, if you're not careful and understanding, what's real, going on, could lead people to misinterpret the data and they were willing to. That risk and show how data creed,
goodbye, goober and their business model. Has these super interesting benefits to the academic community and to just now allege overall on their willing to take that Riskin put knowledge. First Rank Sue. Ok, the million dollar question based on what you ve learned from this research. How should goober and the rest of the world respond? I think, This is showing that the gender pay gap is not likely go away completely anytime soon. And last somehow things our broader society really change about how men and women are making choices about their broader lives than just the labour market, but it's not also o worry that the labour market is not functioning correctly. It makes sense to compensate people who are doing more productive,
work. It makes sense to pay people more if they work more hours and you don't think those are things that we would ever consider. Thinking should be changed because there are problems just real reasons that productivity can differ. between men and women, and we should compensate people based on productivity. We want to dive much deeper into this to understand what the Space of potential interventions looks like in order to reduce the gender gap. So, for example, I mean we're not committing tenant simply because I feel john- that we do not have an understanding of this yet but, for example, you Imagine that if we make our software easier to use and weaken steepen up the learning curve. Then, if people learn more quickly on the system than that portion of the gap could be resolved visa, some kind of intervention-
but that's just an example and were not there yet with their depth of understanding to simply write off the gender gap as a preference. Now, as I understand it, these data were gathered before goober allow. Writers to Tipp drivers, I'm curious to know what you think tipping will do to the gender pay gap we ve just been Compiling some data on tipping, not the tipping algorithm, has just started in the past year, so we don't have the exact dataset. We need to look at for this issue, but what you do find is that women do receive more tips on the platform compared to men,
in fact, ten to twenty percent more than in the Tipp category. I'm curious. Do female walls get higher tips in restaurants and so on than men? Yet I think when you look at the tipping data in general, you do find a killed in favour of women compared to men in general. Will but tipping paper for you in a few months, because the economics of tipping in a sort of wide open and will have a paper just like this one called something like I've been a nation wide experiment on tipping and wool. Will the tipping roll out and show you how earnings change with the introduction of tipping? You know their earnings actually
go down a little bit. They don't go up after introduce tipping now. How can that be? What happens? Is the supply curve shifts out enough to compensate the higher tips and when the supply curve shifts out the organic wages go down, and what you have is Travers, are under utilised. So what I mean by that is typically, though they sit in their car empty, no thirty, five percent of the time with tipping maybe of up to thirty eight percent of the time so, in other words the wage declines, because more drivers think they're gonna make more money since tips are now included, but that in This is a supply of drivers, which means there is less demand to go around exactly of perfect
it would be interesting if one form of gender imbalance, higher tips for women wound up closing the gender pay gap at goober again the paper we ve been talking about today. called the gender earnings gap in the gig economy. Evidence from over a million ride share drivers. We spoke with John List of University of Chicago in Goober Jonathan Hall of Over and Rebecca Die and abstained for the other Co. Authors are Cody Cook of Goober and Paul lawyer of Stanford. Thanks for checking the special episode we will be back soon, with our next regular episode, the latest instalment in our secret life of a ceo series when we talk about what happens when things go wrong, lower, maybe fifteen or twenty thousand people who were very upset, but next time on freedom
bring them with radio is produced by W and my c studios and doubly productions. This episode is produced by Gregg resolve scheme. Are staff also includes Allison Hockenberry Stephanie Tam MAX Miller Merit Jacob Beer, Carruthers Harry Huggins and Branca Tara's. The music you here throughout the absurd is composed by Luis Scare. You can subscribe to phenomena, radio and apple podcast or where ever you get your punk ass. We can also be heard on NPR stations across the country and on serious exe
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Transcript generated on 2021-01-22.