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Jeremy Ney Visualizes American Inequality
559 Plays1 year ago
Jeremy is the author of American Inequality, a biweekly newsletter that uses data visualization to highlight U.S. inequality topics and to drive change in communities. His work has been published in TIME, Bloomberg, and the LA Times. He was a dual-degree masters student at MIT Sloan and the Harvard Kennedy School and was formerly a macro policy strategist at the Federal Reserve. He now works at Google and lives in Brooklyn.
Episode Notes
Jeremy on Twitter | Op-ed in Time
American Inequality newsletter: americaninequality.substack.com
Federal Reserve Bank of New York
Technology and Disability: The Relationship Between Broadband Access and Disability Insurance Awards
Some coverage of the map:
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Transcript
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Speaker
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00:01:46
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Welcome back to the Policy Vis Podcast. I'm your host, John Schwabisch. I hope you're well. Spring is well underway here in Virginia. The pollen is everywhere. We tend to call it the Great Pollening here.
00:01:58
Speaker
Eyes are itchy, noses are running, but we soldier on. I'm excited for this week's episode of the show. Before we get into that, I want to let you know that my next book, Data Visualization in Excel, is about to hit bookshelves. This is a step-by-step book to help you create better and more effective graphs in the Microsoft Excel tool. I'm a big believer that any data visualization tool
00:02:22
Speaker
can be used for different purposes. It's not one tool to rule them all. There's another sort of Lord of the Rings data visualization tool out there. So I use Excel for a lot of my work. I don't use it for all of my work because it's not going to work for certain types of visualizations or certain products or certain use cases.
00:02:42
Speaker
But if you want to expand how you use Excel, you want to create better different types of graphs, I hope this book will be the book for you. So I've put a link to both the CRC Press page, which is the publisher of this book, and to the Amazon U.S. site. And if you'd like to go and preorder it, of course, as you probably know, preorders really help.
00:03:05
Speaker
get the word out to more and more people so that they can use the book in their own works. I hope you will check it out. That's Data Visualization in Excel, hitting bookshelves any day now.
Guest Introduction: Jeremy Nay
00:03:15
Speaker
Having said that, on this week's episode of the show, I chat with Jeremy Nay from americaninequality.io
00:03:24
Speaker
to talk about a recent map he created on disparities in life expectancy across the United States. And it was a map that got picked up in lots of different places, including the New York Times, including the Washington Post. And so Jeremy came on the show to talk about why he thought the map did as well as it did. His ongoing work and the different tools that he uses in his own process to analyze, collect, and visualize his data.
00:03:52
Speaker
So I hope you'll enjoy this week's conversation with Jeremy Nay here on the Policy This podcast. Hey Jeremy, good afternoon. How are you doing? Hey John, I am doing very well. How about you? I'm doing great. Thanks for getting in touch. I'm excited to talk about this work you've been doing and all the places you've had to follow up. It sounds like you've been busy with this particular map that we'll focus on.
00:04:17
Speaker
Yes, very busy. It's been a lot of interest in life expectancy and yeah, yeah. It's really interesting. And we'll get to it. But really interesting how like, you know, you make that like one thing that kind of just like takes off all of a sudden. So I thought we would start maybe you could just tell folks a little bit about yourself and what you've been doing. And then we can segue into the the current work that you're up to.
00:04:43
Speaker
Yeah, absolutely. So I am the author of American Inequality, which is a newsletter and data portal that uses data visualization to highlight US inequality topics that are often left in the dark.
00:05:01
Speaker
life expectancy, internet access, food deserts, debt, things like that. This work really emerged from a lot of research that I was doing when I was at the Federal Reserve in New York, mainly looking at U.S. income inequality. And I, through that, recognized that inequality was about way more than just income. It's tied up in
00:05:25
Speaker
education and health care and taxes and race and gender and location. Went off to do more research at MIT and Harvard, where I kind of started building out more of this, you know, these data visualizations, was sitting on them and decided to turn it into this publication, which is sort of, you know, what we've now been digging into. And then I also work at Google doing tech policy work there as well. Cool. So let's talk about the map.
00:05:55
Speaker
to start the most recent map. I don't want to call it the map because you've done a lot of a lot of different things. But I think our conversation will branch back into some things you just mentioned because I'm curious about
00:06:07
Speaker
your experience both at the Fed and then at school and how data is sort of played a part in all the different roles. But maybe we'll get to start on your life expectancy map. So this is a Coropleth map, county level of the US looking at disparities in life expectancy. And that's all I'm going to say about it. And I'm going to let you explain it for folks who maybe haven't seen it. And of course, I'll put it on the notes page so they can take a look in more detail, but I'll let you take a spin. Sure thing.
Understanding U.S. Life Expectancy Disparities
00:06:34
Speaker
Yeah.
00:06:36
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Biggest thing that we kind of found in this map, and I think why I caught so many folks' attention is what the data shows is that the US is actually experiencing the greatest divide in life expectancy across regions in the last 40 years. So what you can see from some of these red spots or blue spots is that if you are born in certain counties in Mississippi or Florida, you may die at 67 on average.
00:07:06
Speaker
But if you're born in certain parts of Colorado or California, you may live to 87 on average, so a 20-year gap in life expectancy. From no fault of your own, you may live there or be subject to a lot of these challenges of that region. And so I think that that was what was really quite striking to folks in this, is that
00:07:29
Speaker
when we talk about averages or the average experience of America, we're actually not digging into those communities. We actually see much bigger divides and not everyone is really experiencing those same outcomes. That can be driven by a whole host of factors within those communities.
00:07:45
Speaker
I'm curious because there's been a lot of discussion about changing life expectancy, obviously across nationally, but also within the United States, you've seen real dramatic shifts in mortality, particularly among middle aged white males, which sort of reversed the decades long trend. And I'm curious why you think this particular map struck such a chord. Yeah, so I think there were
00:08:13
Speaker
three things that were kind of happening all at once to create this perfect storm of why this really took off. So I think first and foremost was, you know, John Bernd Murdock at the Financial Times published this piece talking about US versus UK life expectancy, basically showing that Americans die far younger than British people do. I think, you know, a lot of the data visualizations that John presented were quite striking to folks.
00:08:43
Speaker
And in particular that the average American actually has the same life expectancy as the worst region in the UK in this area, you know, Blackpool. And so I think that, you know, really drew this interest in life expectancy kind of into the fold. I think as people started to dig into it, they're like, hey, the average of the US might not be quite right because there's such different experiences that we can have in
00:09:06
Speaker
you know, Alabama versus Washington State, for example. So I think that was number one. I think number two was new data just came out basically around the same time. And so the new data there was that the US actually hit the largest
00:09:24
Speaker
decline in life expectancy in the last hundred years. Much of that was COVID driven, but some of it was these other factors as you had mentioned. But basically not since 1921 to 1923, had we seen this same terrible decline. And on top of that, new data showed also that the US is far, far, far worse than any other country when it comes to some of these declines.
00:09:51
Speaker
And then third and finally on kind of this perfect storm was that
00:09:56
Speaker
there's so much that we talk about in the US about these different divides that we have, whether it's political divides or healthcare divides or race-based divides. And so I think there was something about life expectancy that really allowed everybody to come together and sort of have this appreciation for this common challenge. We all want to spend many years with our loved ones. We want to celebrate birthdays with each other.
00:10:26
Speaker
And even as the map was blowing up, this one woman reached out to me, this woman, Amy, in Kentucky, who talked about how her father died at 46 from
00:10:38
Speaker
cancer and how the only doctor that he ever saw was the doctor that he saw in prison. Now she was so worried that she herself wouldn't live to 40. And so I think it's these types of challenges that everyone is so worried about, about their longevity. And Kentucky too has the fifth
00:10:57
Speaker
I think it's the fifth worst life expectancy of any state in the US, too. And so I think it's, you know, this new work that is being done comparing the US across countries. It was this new data and it was this new, you know, like this continued sense of feeling that a life is really precious. And I'm concerned about it that really allowed. I think this this data is in particular of the many that we have to really take off.
00:11:21
Speaker
Yeah. I think personally I would add a fourth to that and that the structure of the map itself is really interesting because if you said to someone create someone in the data, this community said, create a map for me. Here's the data, create a map that shows life expectancy. The range is from, you know, about 66 to 90. I think most people, when they create that map, they'd create it using a sequential color palette. It would go from like light blue to dark blue.
00:11:47
Speaker
But the way you created it was you did it as a diverging palette where the center point is the national average. And so you've got this above average in shades of blue and below average in shades of red. And so I think in a lot of ways that draws is pretty stark difference that is really visual and sort of, you know, instinctual on the map rather than just going from like a light color to a dark color.
00:12:14
Speaker
Yeah, I think the two differences in colors, the blue versus red is really striking not only for drawing your attention to those areas in red and kind of that signaling of when we do see red, we tend to think of some sort of calamity going on in some ways.
00:12:33
Speaker
But what we have been working on quite a bit more at American inequality is this concept of of opportunity mapping is how can these areas in red that are particularly struggling with a challenge like learn from those areas in blue as well, and so I think when you have those
00:12:50
Speaker
at Birken Pallets, it's easier for you to see like, hey, I'm actually in one of these struggling regions. How can I better identify someone else who is doing better with this challenge? And so it's finding those comparable regions to say, hey, I'm maybe challenged with this particular inequality. Can I find some other area that can help me work through it? Yeah, you mentioned the politics of the kind of our divided country too. I wonder, do you think like the red, blue,
00:13:20
Speaker
Do you think it sort of harkens to that Democrat, Republican color palette? Do you think people try to like mentally make that link?
00:13:28
Speaker
I think people definitely make that Lincoln in particular, you know, the day or so after, you know, the, the map really blew up. Paul Krugman wrote a op-ed in the New York Times talking about exactly that, where he talked about, you know, this actually relationship between Democratic and Republican states and in particular counties that, you know, swung one way or another.
00:13:55
Speaker
in 2016 and in 2020 as well and their relationship as well with life expectancy and so I think that does, I think you're right that that, you know, color choice in some ways does work into that and then Paul Krugman basically went further to say, hey in fact we actually do see a bit of this. Right, right.
00:14:15
Speaker
you grab new data and the map is made in data wrapper.
Tools for Data Visualization
00:14:21
Speaker
And so I'm curious about your toolkit. I mean, you've talked about working at Google, working at the Fed of New York, MIT, Harvard. And so I'm curious about your toolkit, both from the data extraction and cleaning process all the way through the end, through the visualization. So for you, what does that look like? And has your toolkit sort of changed over time?
00:14:45
Speaker
The toolkit has definitely changed over time. And I will say Data Wrapper is an absolutely fantastic tool. I cannot recommend it highly enough. I think particularly for folks who are starting out earlier in their data vis journeys, it's such an easy tool to create those interactive tools to help start building narratives, to help give you prompts as well about what works. I think it's really quite helpful there. And it's also been nice for
00:15:12
Speaker
helping to embed your work in other tools like a sub-stack or things like that, where we have our newsletter. But our process is really, we often will either start with an idea like, hey, we really want to write about childcare and inequality or something, or we'll start off, someone will send us an interesting data set.
00:15:33
Speaker
And so some of the other tools that we'll use depending on how big the data set is, there's a lot of Python and R as well to access those from large government agencies like the CDC or the EPA or the FBI to try and pull that in and clean that data.
00:15:50
Speaker
But we also always really try and focus on these these county level maps in particular because inequality happens in communities right and so having you know state level or national level data is helpful, but it actually can obfuscate what's really happening on the ground, particularly if you look at some of these.
00:16:09
Speaker
you know, bigger states like, you know, California or Texas or Florida, where what's happening in the north or the south can really be, you know, quite different. And so, whenever we're going to those big government agencies we try and find that that county level, you know, insights.
00:16:28
Speaker
And then depending as well on what we're trying to build out, Tableau has also been a really fantastic tool for creating some of those dashboards to say, hey, I am a state or local politician or policymaker. I really want to understand what is this factor in my community that's most strongly correlated with life expectancy, for example. And so you create all these interesting toggles that we built a tool around that as well to say,
00:16:57
Speaker
you know, it might turn out that it's, you know, cancer is one of your biggest factors or gun violence or something like that. Tableau is quite helpful for understanding some of that.
00:17:08
Speaker
And to your point about the county level, there's another graph in the original, I guess the original medium posts that maybe hasn't got as much play, but is it interesting visualization that really, I think takes advantage of the fact that you have a lot of data. I mean, you have county level data for, I don't know what it is, like, you know, maybe about like 10 different years since 1980.
00:17:37
Speaker
Could you talk about that? Like what was the impetus of, and I'll let you describe it, like what was the impetus behind that second graph and the process of creating that one?
Income and Life Expectancy Correlation
00:17:47
Speaker
Yeah, so I'm very glad you brought that up. So there's a handful of other visualizations that we did in there. One is this one that basically shows how life expectancy has changed over time, because I think it's also important to acknowledge that the US has made a lot of really great strides in improving life expectancy over the last decade and even decades before that, even going back as far as 1940 when we had some of this data from.
00:18:16
Speaker
But when we look, you know, on the county level, we actually see some regions that really aren't making as much strides as one would expect when we talk about like the overall American experience, you know, and so I think that's why it's, you know, helpful to look at that. And then one of the other, you know, kind of critical pieces that we found in this work is the high
00:18:37
Speaker
correlation between life expectancy and income in America. And that this is really one of those driving forces too. I think that county level data really shows that if you happen to be born in one of these counties like Loudoun County in Virginia that has the highest median household income, you are far more likely to live much longer.
00:19:02
Speaker
than being born in Oglala, Lakota County in South Dakota, where a huge portion of folks there live in poverty, median household income closer to 35,000 or so. And so really this relationship that we see between wealth and life expectancy is quite strong. And having all of these county level data points really allowed us to show that relationship.
00:19:27
Speaker
What is your approach to this generally of visualizing and talking about inequality? Because there are huge literatures, right? I mean, economists and sociologists and political scientists have made careers
00:19:40
Speaker
on talking about inequality and figuring out different ways to decompose inequality into different factors and controlling for, you know, age distribution and education distribution. So how do you think about that? Because the way that you are describing it and the way that you are presenting it is really here's the data. It's sort of you've broken down to
00:20:03
Speaker
I mean, I don't know if it's the smallest level of geography, maybe the smallest meaningful level of geography at the county level, but so how do you sort of try to maybe think about that or thread the needle when it comes to these different aspects of data and modeling?
00:20:20
Speaker
Yeah, I think it's at least two parts to it. One is we really try and just let the data speak for itself and capturing it from these large government agencies and presenting it almost as is. We do some data cleaning or sometimes we'll show these relationships, but
00:20:39
Speaker
In my experience, both from the Fed and in some of the grad research as well, a lot of this fancy econometric work can sometimes feel inaccessible to a larger audience. And while it's really important to make sure you're not falling into some of these fallacies of base rates and things like that, you do want to make sure you're controlling for certain things.
00:21:05
Speaker
presenting the data as is, I think, allows folks to really say like, okay, this is, you know, I'm looking at it from the source, and I can understand it, and this hasn't been, you know, manipulated. And I think at a time where we're all like, really, you know, searching for, for truth, and understanding what's out there, having just presenting the facts as is, I think really goes a long way. And it's been part of the reason this has really caught on with a lot of folks.
00:21:27
Speaker
And then I think the second piece, you know, why we also try and present the data in that way is it also helps us understand the stories behind the data, right? So once we visualize it and we see this county in red, you know, we'll say like, hey, what's going on there? Why is this region really struggling with this challenge, you know, instead of doing, you know, a lot of manipulation?
00:21:49
Speaker
So for example, you know when we wrote a piece on cancer and inequality, we found this one county Elkhart County in Indiana was you know dying of cancer much higher rates than in the rest of the US couldn't really understand what was going on you know like pretty racially diverse area relatively high income levels.
00:22:10
Speaker
fairly high educated, but once we basically started doing this research, we found that Elkhart County is the RV capital of the world. It's where basically air streams are produced and all these RVs. And so all the folks who are working and living around there are basically breathing in all of this fiberglass all the time from these RVs that are being produced. And as a result, folks there are dying of lung cancer at four or five times the rate.
00:22:38
Speaker
of the US and so I think that process also allows us to not like come with too many priors about the data but just to see what it is and then start digging in and finding those stories of the communities where the individuals are really struggling with that data because that's also a really important part right like recognizing that you know these challenges aren't just about the statistic like there's very real people who struggle with these very real challenges.
00:23:05
Speaker
So how do you go about doing that then? Do you start making phone calls? Do you talk to journalists? What is the next step in that? Because the storytelling piece, I think we could have a whole other discussion about data vis and storytelling and how those words match up. But you've mentioned stories and community a bunch in this discussion. So what is that next step for you? So you identify this county, and in the end, do you start making phone calls? Do you start talking to people? What is that next step for you?
00:23:33
Speaker
Yeah, it's a handful of things. I mean, part of it will be from other work that I've done in the past, either through startups that I have worked on or areas where I know teachers in the area or folks who are working on startups in that particular area of inequality will reach out to them to try and get stories either from them or from clients or something that they've worked with has been one area.
00:24:03
Speaker
As you had mentioned earlier, there's so much written on so many of these inequalities as well, and so if we can reach out to some of the local newspapers in a region that have been really covering
00:24:18
Speaker
in an area, nobody really seemed to be like digging into these stories, except for these local newspapers, you know, we'll talk to them and try and use some of their stories about, you know, the gas station that ended up getting, you know, flooded in a hundred year flood that happened, you know, five times in the last 10 years, you know, that type of thing. Yeah. And my guests, so correct me if I'm wrong, I'm going to guess here that throughout your education, being primarily a quantitative person, you probably haven't had a lot of qualitative methods training formally.
00:24:48
Speaker
Yeah, I think that's largely true. There was, I worked at IDEO actually, you know, for a bit as well. And so that I think also helped give me, you know, kind of this deeper insight into like, you know, user design and user research as well. And that's also part of the reason that
00:25:04
Speaker
you know, at American equality, we tend to really try and bridge those two gaps of not always being so quantitative in the work, but trying to drive some of the qualitative as well. And we see that also, you know, like kind of flipped in many scenarios too, where, you know, in the early days of the pandemic, for example, you know, policymakers knew qualitatively that they had this internet access issue in Oregon, for example, but they didn't know quantitatively.
00:25:30
Speaker
you know, where to actually, you know, be making change. And so for folks who can help us like kind of balance that, like we have the data, they have the stories, we're able to actually create these great mixes. And so in Oregon, we're able to work with those folks, you know, and distribute, you know, hundreds of Wi-Fi hotspots with, you know, with a local nonprofit across the state to really drive that impact. But as you mentioned, you know, it takes both of those sides for sure. And I ask because I think
00:26:00
Speaker
I think a lot of people who are in the database field or maybe quantitative researchers, for those who don't just like poo-poo qualitative work, who actually want to do qualitative work, it sounds interesting. I think it's interesting to me that your approach is to call journalists and call newspapers and find those stories. So if there was someone else on this call right now lurking behind me, which hopefully no one's lurking behind me,
00:26:29
Speaker
another quantitative person lurking behind me and wanted to do something similar, what would your one or two pieces of advice be to go find those deeper stories?
00:26:41
Speaker
Yeah, I have really found like, you know, Twitter is surprisingly fantastic at that, you know, for like, you know, putting these stories out there, Twitter and Medium too. People really, you know, the maps resonate with them and they'll, you know, write me back things saying like, hey, I'm from the only, you know, Blue County, you know,
00:27:03
Speaker
my county doing better you know in my state like I'm so happy that I'm going to be like not you know struggling with this inequality or you know someone else being like oh darn I now realize that I'm going to be dying you know 10 years younger than like my peers you know right across the border or something like that.
00:27:19
Speaker
And it's also been quite helpful to engage folks. The article that we wrote on internet access, someone in North Carolina reached out saying, hey, this article really resonated with me because I only have one telco in my region. They charge me exorbitant prices, and I get terrible internet service. And so putting the work out there, trying to allow that stuff to come in,
Engaging with Interactive Maps
00:27:44
Speaker
But having that inbound only works after a certain point that outbound as well. I think in particular, there's those local newspapers, but I think Vox and The Atlantic in particular do tremendous storytelling work around a lot of these issues.
00:28:01
Speaker
Before I let you go, I want to get back to one more database thing. And there's a kind of a two-part question here, a technical question and sort of a general question. So the general question is, the graphs in the medium post, and again, I'll link to these for folks who want to dive in more, and they obviously should. They are native data wrapper graphs, so they are interactive.
00:28:23
Speaker
And then on the sub-stack newsletter they are static. And so this is kind of a two-part question. The first question is more conceptual is,
00:28:32
Speaker
Why interactivity? And is that sort of like, is that a priority for you when you go in to create these denser visualizations to enable the interactivity? And then second, I don't exactly know how to ask this question, but it's kind of like on the technical challenge. Like, do you wish you could put an interactive graph in the substack and do you see that as being, would that be valuable or is it just
00:28:56
Speaker
It's too heavy for email for a newsletter and it's better just push people back to the original where they can interact with it. So a variety of pieces in there, but it's really focusing on this the interactivity versus the static.
00:29:09
Speaker
Yeah, the interactivity is really important. I think for this work for helping people understand, right, the challenges in their communities. People love to say like, hey, I'm from King's County. I really want to understand, you know, what is going on in this region in Brooklyn.
00:29:28
Speaker
Because people, the map is such an easy way that's going to be really accessible for folks because everybody knows where they live or where they grew up and it allows you to actually like, you know, feel like there's this work is really touching you in a way that a, you know, bar chart or a doc bot doesn't.
00:29:48
Speaker
And for us, where we talk across such a huge range of topics as well, the map is that uniting force that connects a lot of that research and writing. So the interactivity is really important, I think, for helping folks understand what's actually happening in their communities. Two, on the substat part, do I wish that they had the ability to do this?
00:30:13
Speaker
For sure, I think, you know, email, it's this interesting time where I think folks are like figuring out these, you know, best ways to be communicating with each other. And particularly in the, you know, policy vis and data vis world and following a lot of, you know, writers and journalists now on, on Substack 2. I see lots of folks experimenting with different ways to communicate this, like whether it's through video embeds or GIFs or static images.
00:30:40
Speaker
But in every, you know, sub-stack post that we do, you know, we put this button below every chart that says, you know, explore the data here, in part because it helps achieve that goal of allowing people to interact with it, but two, because we are also, you know, really big proponents of open source data in a lot of this work, right? So not only do we allow you to access it there, but on our website, you know, americaninequality.io,
00:31:07
Speaker
we allow you to basically download everything, you know, as a already cleaned, you know, CSV Excel file. So you can access it yourself because navigating a lot of these, you know, government portals, for example, can be quite challenging. And we don't want that to be a barrier for folks for unpacking inequality or understanding what's happening in their community. And so, you know, that way of trying to lead folks to those areas, I think also helps with that that open source ethos. Yeah.
00:31:37
Speaker
Absolutely. And it is worth noting that on your data wrapper charts, folks can go get the data directly in the data wrapper chart, which should be known for those who don't know. A producer can turn that off in data wrapper. You have the ability to turn that off, but you have fortunately left that available. So you go look at this really cool bubble chart of income and life expectancy. You want to download the data, explore it on your own. You're able to do so, which is great.
00:32:04
Speaker
Jeremy, so our last question is where can folks find you? Where can they sign up for your materials and get more of the inequality content?
Further Resources and Closing
00:32:15
Speaker
Yeah, so as we chatted about the newsletter, you can sign up for it there at americaninequality.substack.com. We send about battery every two weeks or so. Our next one coming up will be on child care and inequality, which we're quite excited about. The Department of Labor for the first time ever just released county-level data. So we're excited to be putting work forward on that.
00:32:44
Speaker
americanequality.io, as I mentioned, is where we host kind of all of the maps and the open source data as well. And then folks are also interested in following along on Twitter with a lot of this work on that, Jeremy Binet on Twitter too. Perfect. Jeremy, thanks so much for coming on the show. Appreciate it. Great work. Love it. And I hope folks will sign up. So thanks a lot. Appreciate it. Thanks for having me, John.
00:33:12
Speaker
And thanks to everyone for tuning into this week's episode of the show. I hope you enjoyed that. I hope you will head over to Substack and sign up for Jeremy's newsletter. I also hope you will check out the new book page for my new book, Data Visualization in Excel on policyvis.com.
00:33:27
Speaker
Also, you can go pre-order the book at Amazon, CRC Press, or your favorite book seller. The book comes out May 26th is when we'll start shipping. So I hope you enjoyed this week's episode of the show. Be sure to check back for our next episode coming up in a couple of weeks, where we will once again help you think about ways to improve the way you create and communicate your data. So until next time, this has been the Policy Vis Podcast. Thanks so much for listening.
00:33:54
Speaker
A number of people help bring you the policy of this podcast. Music is provided by the NRIs. Audio editing is provided by Ken Skaggs. Design and promotion is created with assistance from Sharon Satsuki-Ramirez. And each episode is transcribed by Jenny Transcription Services. If you'd like to help support the podcast, please share it and review it on iTunes, Stitcher, Spotify, YouTube, or wherever you get your podcasts.
00:34:16
Speaker
The Policy Vis podcast is ad-free and supported by listeners. If you'd like to help support the show financially, please visit our PayPal page or our Patreon page at patreon.com slash policyvis.