From Numbers to Narratives: Dr. Dan Bouk on the U.S. Census and American Democracy

Promising an enlightening journey through the historical and contemporary significance of the U.S. Census, I'm joined by esteemed scholar Dr. Dan Bouk - professor of History at Colgate University and author of the book Democracy's Data: The Hidden Stories in the U.S. Census and How to Read Them. This episode unearths the nuanced power dynamics and biases inherent in the census process. With a focus on the 1940 Census, a task monumental in scale, we uncover the detailed process of transforming raw data into a compelling narrative about who we value as a society.

We investigate the poetic yet complex process of conducting the census, shedding light on the intricacies of data literacy and the potential pitfalls of interpretation. Dr. Bouk offers valuable advice for researchers and analysts, emphasizing the importance of recognizing and conveying uncertainty in statistical data. Moreover, we explore how this counting of people creates narratives about societal importance, resonating particularly when considering marginalized groups and queer communities.

Unmasking the suppression, survival, and blossoming of marginalized communities through the lens of census data, we endeavour to understand how this vital tool impacts the power distribution among states. We analyze the effects on American democracy, considering the challenges and potential of an accurate and inclusive census count. In conclusion, we reflect on the immense potential of the U.S. Census, not just as a data gathering tool, but as a means to shape a more representative and united America.

Recommended:
Democracy's Data: The Hidden Stories in the U.S. Census and How to Read Them - Dan Bouk

Mentioned:
The 272: The Families who were Enslaved and Sold to Build the American Catholic Church - Rachel L. Swarns
Close to the Machine - Ellen Ullman
Thinking Like an Economist: How Efficiency Replaced Equality in US Public Policy - Elizabeth Popp Berman
The Wandering Mind: What Medieval Monks Tell Us about Distraction - Jamie Kreiner

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Episode Transcript

Dan (00:00):
The tragedy.
There is not a tragedy for usnow in which we can see back and
start to recreate these strangeand complex relationships.
The tragedy is to think thatthis was yet another official
statistical manifestation of theerasure of queer life that was
happening constantly at thistime, and that's a tragedy to

(00:23):
understand.
It is also, though, animportant thing for us, a way
that we gain knowledge about thepast.
By thinking about all of theparts of the data, it helps us
really understand exactly how itwas that queerness both
survived, persisted, was honoredby the people who were living
it, and then was suppressed andpretended to be non-existent by

(00:48):
officials with the power to makethe final statistics.

Shawn (00:57):
Welcome to Deep Dive with me, s C Fettig.
Today I'm focusing on data,specifically census data how we
count people, the profoundimplications of this counting
process and how it shapes thevery fabric of our democracy.
The United States Census is avital tool that has evolved over
centuries to offer a snapshotof our nation.

(01:19):
The Census, seemingly just astatistical endeavor, has
far-reaching consequences.
It reveals the story of how we,as a nation, value some groups
and individuals over others, andit holds the key to
understanding representation,funding and the very essence of
our democracy.
The impact of the Censusextends beyond mere numbers,

(01:42):
though.
It informs policy at everylevel, guiding the allocation of
resources and influencing thedecisions of policymakers.
Yet beneath the surface lies anintricate web of challenges and
biases that we must unravel toensure an equitable and just
society.
The goal today is to not onlyuncover these complexities, but

(02:04):
also shed light on how we canimprove our methods of
collecting and utilizing censusdata.
We Americans aspire to harnessthe potential of this invaluable
tool to bolster democraticideals and create a more
inclusive and representativesociety.
It's, in fact, the very reasonwe have a Census.

(02:25):
My guest today is an esteemedscholar in the field, d Dan Bouk
.
Dr.
Bouk is an associate professorin, and chair of, the Department
of History at ColgateUniversity.
With a deep-rooted passion forhistory and expertise in the
quantitative analysis ofhistorical data, d Bauc has made
significant contributions tothe understanding of how we

(02:45):
count and interpret populationsand bridges the gap between
history, data, science andpolicy, offering invaluable
insights into the stepsnecessary to attaining a more
equitable, just andrepresentative future.
His most recent book,democracy's Data the Hidden
Stories in the US Census and howto Read them, anchors much of

(03:07):
our discussion today.
The conversation we haveilluminates the intricacies of
census taking and sheds light onits historical significance and
the way it has and continues toinfluence the lives of
individuals and communities inAmerica, as well as our very
democracy.
If you like this episode, orany episode, please give it a

(03:28):
like on your favorite podcastplatform and or subscribe to the
podcast on YouTube.
And, as always, if you have anythoughts, questions or comments
, please feel free to email meat deepdivewithshawn@gmail.
com.
Let's do a deep dive, d B.

(03:49):
Thanks for being here.

Dan (03:50):
How are you?
I'm great, Shawn.
Thank you for having me.

Shawn (03:53):
Absolutely so.
The catalyst for thisconversation, and why I'm
excited to have you here, isyour book Democracy's Data the
Hidden Stories in the US Censusand how to Read them, which is a
deep dive into the 1940 censusand what we can glean not just
from the data, but how that datawas collected and how it
reflects America and ourpolicies.
And before we jump in, I dowant to say that this book is

(04:15):
really remarkable, and thisisn't just a platitude.
I think this book is a greatdrama in a way, a really
readable peek into history andUS attitudes and policy, and
also a digestible education ondata and why it matters.
So I'm excited to have you here.

Dan (04:30):
I love that you called it a drama.
That's very satisfying.
Thank you for having me.

Shawn (04:35):
Okay, so first things first, though, to help the
listener understand why youfocused on 1940 census data and
not any more recent years, why1940?

Dan (04:44):
So you could perfectly reasonably say that every single
census is in fact its own verysignificant drama.
I mean, if that sounds like astrange thing, actually, you
just need to kind of repurposeor think about this again.
The project is find everysingle American all in 1940,

(05:05):
it's 130, some million, now it's330, some million Find each one
of them and basically try totouch them almost and make sure
that they actually exist, allwithin a space of like a couple
months.
And suddenly you really thinklike whoa, actually that's like
scaling Mount Everest or liketrying to find the North Pole.

(05:26):
It's like a curriculum thing.
So it could have been any.
I think you could havereasonably done any census.
I started with 1940 because itwas the when I started writing
the book.
It was the most recent censusfor which we had all of the
manuscript material, which is tosay that when a census is
completed, the Census Bureauimmediately, as fast as it can,

(05:50):
starts producing statisticaltables full, you know, all the
reams of numbers explaining howmany people are there in each
state, and then finergeographical levels and then
broken down by a host ofdifferent demographic categories
.
That all happens.
It comes out right away, butwhat each person is supposed to
have said to an enumerator, orwhat they typed into an internet

(06:13):
self-response form.
Now these days, all of that isheld private and completely
confidential from also anyoneelse in the US government for 72
years, and so when I startedthis book, the 1940 census was
the only one that was reallyavailable, and then that meant
that it was both a very moderncensus but one that I could like

(06:33):
read deeply, and I liked theidea that I was starting in the
same place that many people whodo their own family or community
histories also start, becauseif you're beginning a genealogy,
you would also often begin withthat most recent census and
then kind of build back fromthere.

Shawn (06:49):
I mean if this was new information for me.
A lot of governments state,local and federal will protect
certain pieces of information orcertain types of information
for periods of time, so it can'tbe released until X number of
years have passed or a certaindate.
We think about the Kennedyassassination or certain things

(07:10):
related to the Nixoninvestigation etc.
And those kind of make senseright, like to some degree
they're protecting eitherinvestigative methods or they're
protecting investigators orsubjects etc.
It's not entirely clear to mewhy the details of the census
are under lock and key.
I think you said you say in thebook for 72 years, so each

(07:31):
census correct, that's right.
This portion of it is underlock and key for 72 years and
it's not entirely clear to mewhy.

Dan (07:39):
So I think there's a couple of different answers that are
both satisfying Partly yourright to have that instinct that
it's not immediately obvious ornecessary.
So throughout much of the earlyhistory of the US Census that
there was very littleconfidentiality in the making of
the count, in fact, us Marshalsso back then it would be like

(08:01):
essentially the law enforcementwould be in charge and they
would commission other folks tomake these counts and then they
would be posted prominently inpublic areas that people could
check to make sure that thecount had happened properly.
Part of the reason it comes tobe put under lock and key, as
you put it, is that there's asense that the government starts

(08:23):
to ask more and more questionsabout people and to want more
and more information, some ofwhich increasingly might be seen
as something that one doesn'twant to get out.
Or there's a concern,increasing concern that you
maybe don't want to tell thegovernment this thing out of
concern that then it might beused against you in some way.
One really easy way to thinkabout this is in the 1940 census

(08:46):
there was a question aboutincome.
It's trying to be verycontroversial, but there also
was increasingly a move towardstaxing people according to their
income, and so the people whomake these statistics were
reasonably concerned that peoplemight not want to give a
certain kind of information orhonestly disclose their income

(09:10):
if the tax man might then getthat information and use it
against them.
That doesn't even have to be.
This will kind of move into mysecond answer.
You don't have to think aboutthis as like people are liars
and they want to hide theirmoney from the tax man, although
undoubtedly that is true inmany cases.
So in the 1950s there's a seriesof Supreme Court cases and then

(09:31):
a change in legislation thathas to do with whether or not a
company that submits itsinformation to the Census Bureau
about its own profits when itkeeps its records, whether those
records that it keeps it thenhas to give up to the Federal
Trade Commission to determinewhether or not it's part of a

(09:51):
monopoly or to do other kinds ofregulatory things.
And one of the things here isthe idea is that the Census
Bureau wants companies to giveit fast data about its
operations, because it can thenuse all these different
companies fast data in astandardized form to come up

(10:13):
with a reasonably accuratepicture of what the economy
looks like in a region or in anarea, or how what an industry
looks like in a particular place, even if those numbers are not
precise in the way they would befor an IRS statement.
And so because by the time youpay your taxes it's four months
later than the time that thestuff is actually happening.

(10:35):
And so sometimes part of thereason you want confidentiality
is because data that's goodenough to give you a good
statistical picture when it'saveraged out over an entire
population, is not actually agood and accurate representation
of the individual, theindividual person, the
individual company, and soconfidentiality or privacy is
useful, because the data doublethat stands in for you with this

(10:58):
bureaucracy is fine forstatistical purposes, but if it
got out and people used it totry to then do things to you in
the present, that would beinappropriate.
So it's an argument for privacy.

Shawn (11:11):
So reading this book tapped into two perhaps
conflictual reactions, or maybefeelings for me.
And it was gratifying inreading such a careful parsing
of some of the uniquecharacteristics of who we people
living in America are, but itwas also frustrating in
recognizing how limited thatknowledge is based on what you

(11:33):
can get from the census, solimited by a lack of
standardization or limited bythe pieces of information that
fall between the cracks of theinformation collected, or
limited by politics, et cetera.
So I'm wondering how was thisexperience for you, researching
and stitching together thestories and seeing the data as
it was?

Dan (11:50):
All right, so I have an answer for this, but I guess I'm
a little bit curious about yourown explanation of this
frustration.
Did you wish that?
Did it feel like a series offailures, like why don't they,
why do they keep not keeping theright kind of information about
these people?
Or did it just feel too closeto home with your own day to day

(12:10):
work with all this data?

Shawn (12:12):
I think it's hindsight primarily that you know when we
sit down with data and I'm sureyou feel the same that you want
like a perfect data set, right?
And you want especially if it'stime series or if it's data
that's been collected acrossdifferent communities that it's
somehow standardized in a waythat is useful and can be
compared against each other indifferent contexts and different

(12:34):
times, but is also thorough andsensitive.
And I think what came throughis that the census is a battle,
or at least how the census isdesigned and the process is
implemented and how people willbe counted and what are the
types of things that we willcount and how we'll ask those
types of questions.
That's a battle every 10 yearsthat plays out before it ever

(12:56):
lands on our doorsteps, right?
And what that means is thatthere is no standardization.
The questions are different,they're speaking to the time and
in every instance, you know,from decade to decade there's
deterioration.
If the question changes, youknow it might be asking
essentially the same thing, butbecause we've changed a little
bit how we talk about gender,right, we can't compare the

(13:19):
answer to gender today to howpeople might have answered it
100 years ago, and that's justfrustrating, right?

Dan (13:26):
Yeah, Now another.
You put it like that.
I can.
I totally see what you mean.
So I think my primary emotionthroughout actually a lot of the
book, maybe a surprising amountof the book is delight, Like
just that the stuff exists atall, there's.
I see this whenever I bringstudents into like an archive of

(13:49):
any capacity, like just even inour little university here at
Colgate, and you touch documentsfrom 100 years ago.
It really feels like timetravel and it is, in a way,
right Like it is literally thepast, sitting there with you and
every single time I found a newperson, which was a new person
on every single one of thesesheets, and then every time I

(14:11):
thought about what I was reallylooking at.
So I guess one thing that thesenators should be thinking
about is that now many peoplehere who are listening to this,
if they filled out a census in2020, probably submitted their
information online and theyprobably didn't answer a
question asked by any humanperson.
They just like typed in aseries of responses.
In doing so, they undoubtedlyshook their heads at least once

(14:36):
when they're like we're asked aquestion where they're like I
don't know how I fit in here, orlike that's a weird way.
So like that part happened.
But back in 1940, every singleone of these interactions would
have been between two people, orlike almost every single one
where there was a numerator whoknocked on a door and who was

(14:58):
asking a question, getting aresponse and then writing down
the answer.
So when I'm looking at, whenany of us are looking at these
sheets, we're seeing a mediatedresponse in which this
enumerator has heard something,is making judgments, and then is
writing down these things.
So it's like it is such afascinating document that here

(15:19):
I'm seeing from almost 100 yearsago, a conversation that has
been perfectly recreated, butthen like run through this weird
machine that breaks it up intoall of these cells, like on an
Excel sheet, and answers only inlittle pieces that are each one
a little bit suggestive and yetleave just enough ambiguity

(15:41):
that you start to imagine andlike wonder what else is
happening.
So I I cannot look at censussheets for more than five
minutes before I startspeculating wildly, and so a
great deal of the book is tryingto like hold back that in that
instinct.
And yet it's also like that's.
That's just part of the.
What's so delightful about itis the sense that, like these

(16:03):
are real people.
They are super complicated, socomplicated that they don't
really fit inside these simplecells that we've constructed for
them.
And I get this now, this joy ofgetting to like see how they
were stuck in there for thismoment, recognizing it might
have been kind of prettymiserable for them at the time,
but at least something aboutthem was preserved.

(16:25):
And now we get to like sit withthem and try to think like what
was the bigger person, the likefuller person that is
represented by these few marks?

Shawn (16:35):
You know, I think this is kind of fascinating because to
some degree, I think this mightbe highlighting just a
difference in how we look atdata and by we I mean you and I
and how we interpret the rightturns in the data, right?
So what I was mentioning to youas being frustrating was
essentially what flew off thepage to me and not necessarily

(16:58):
in the book, but in what youwere talking about related to
the census was just all of theweaknesses, or all of the breaks
in the data story along the waythat for me, I guess, in the
aggregate, look like justcumulative chaos, right?
And what you're saying is thatthese to you appear to be and
you can correct me if I'mmischaracterizing opportunities

(17:21):
to imagine what was in thosebreaks or what could have
existed there, or how the breaks, the ways that they were
bridged, are somehow tellingmaybe a coherent story.
And then what's in that grayarea?

Dan (17:37):
Yeah, I mean that's a wonderful way of putting it like
that.
In the breaks there's is thechance for story, in the breaks
is the possibility for us tospeculate.
But in the breaks also is just away of acknowledging the way
life really feels to us most ofthe time, which is that we don't
fit well in standardizedcategories, even when they're

(18:00):
really finally parsed.
And so I suppose a lot of thisis getting down to my own
ambivalence aboutstandardization, where I don't
know any other way to run a masssociety or how to make a more
fair society or how to make aplace we can all live in
together than to have statistics.
So I'm actually so I'm forstatistics and at the same time

(18:23):
I really deeply recognize howmuch gets lost and how difficult
it is to actually turn a personinto them.
This is, in a way, why I endedup being such kind of a radical
adherent to principles ofprivacy, because I think the
only way to square those twothat circle for myself is to say
we need to allow people to bemanhandled in a way to be to

(18:47):
turn them into useful statistics, but then we should keep them,
those people, as far away fromthose represent representations
of themselves as possible,because they're not accurate
representations.

Shawn (18:59):
I think, now that we've kind of called this out, the
different lens through which wekind of interpret this, now I
can see how the framing of mynext question is absolutely
through my lens.
So bear with me.
One of the characteristics andperhaps weaknesses, I think, of
the census data so there it is,perhaps weaknesses you maybe

(19:21):
don't see it that way Of thecensus data that comes through
in the book to me is how storiesand experiences and
conceptualizations, which in endrepresent the actual lives and
histories of real people, isembedded in the processes of
census taking.
So the data collected, how it'scollected, who's doing the
collecting, how it's analyzed,all of these things that you've

(19:42):
kind of mentioned already, andthen also the absence of each of
these, so what's not collected,et cetera.
So what are some of theprocesses that generate the
census numbers, or at leastthrough the time periods that
you were interested in, thathave and maybe still do inform
the stories the census tells us,and what other processes, if
you've given us any thought, ifimplemented, could tell a

(20:04):
slightly different story basedon the same numbers?

Dan (20:08):
Yeah.
So one way to answer that is tosay that I think actually, any
data set, particularly like anylarge data set, we can
understand it as having fourbroad phases, and when we
normally think about, like oh,how is data made, we think of
two of them.
So we're, like, are prettycomfortable talking about like

(20:29):
well, probably at some pointthere was somebody who designed
a set of questions, or decidedwhat the categories would be, or
marked out some rows andcolumns, and then, having
decided, like all right, we'regoing to count the number of
apples and oranges, we figure,like all right.
So then what the data lookslike is when we have a number of

(20:50):
apples and a number of orangesin a spreadsheet or printed on a
table.
In between, though, is like andthis is where, like the stuff
that I'm, the other stuff thatI'm really interested in, comes.
I think of those moments.
The moments we think of mostare the places where, like,
there's the most order and themost centralized control.
So, for the census in 1940,that moment of centralized

(21:11):
control meant that a whole bunchof people were drawn together
to the Commerce Department,which is the part of the US
government that runs the census,and a lot of them were working
for government agencies, a lotof them were working for the
Census Bureau, a few other folksrepresenting labor,
representing philanthropy oruniversities.

(21:33):
They all would show up some bigpeople from big business and
they would hash out togetherthese are going to be our
questions.
And then and today I'm sureyou've encountered this limit
you can only ask so manyquestions, like the fundamental
limitation for the folks in 1940was there was literally only so

(21:54):
much room on the page, but youalso just have only so much
patience and so much of thepeople you're going to be asking
questions of.
So you do have to make choices,and so you hash this out, you
come up with questions.
That's the first process.
Then, having put those sheetstogether that are blank, you
send them off with all theseenumerators to go into all these
different communities.
And this is one of thesemoments where that chaos breaks

(22:17):
loose.
And that chaos is.
It's dramatic in the sense thatit does create an enormous
problem for the people whoactually want to do the counting
, because they're going to getall kinds of weird responses and
enumerators even well-trainedenumerators dealing with
complicated situations, aregoing to end up putting down on

(22:37):
these pieces of paper stuff thatdoesn't fit into any of the
established categories.
So, having gone out, youcollect data about all the world
.
It's handwritten on pieces ofpaper, sometimes crawling over
the edges and often with thingsthat just don't quite fit, and
all of that then gets sent backto Washington DC, to a warehouse

(22:57):
in this case, that was rentedfor the purpose because there
wasn't enough space, and theneventually they were able to
build a new building.
They take all this material andthis is the third phase, and
this is the phase where itultimately leads to the printing
of statistics, and we can talkabout this as data cleaning or
we talk about it as editing,going through and making sure
all of the responses actuallyfit.

(23:18):
One of the acceptable answersputting them on punch cards,
pieces of paper where you couldpunch in the responses, using
those to then make tabulations,which literally just means
adding things up to producetables of numbers, and that
produces the official formalnumbers.
But even then, I don't thinkthat the story of data is done.
I think each one of thesephases is the data, just in a

(23:40):
different phase or differentform.
The final stage is thoseprinted tables go off into the
world and they too, live a verychaotic life, as people choose
to use these numbers andinterpret them in a wide variety
of different ways.

Shawn (23:57):
There's almost poetic quality to the way that you
explain this process in the book, because it could be quite dry
or you could imagine, to somepeople this is all stuff that
they're not interested in.
They're interested in the endproduct, and the way that we get
to that end product is not ofinterest to them.
I could imagine in certaincontexts to some people,

(24:18):
depending on what they'reinterested in their eyes, just
gloss over right.
But I think you do some reallyinteresting things here, and
explaining this in the book isbecause you could have skipped a
lot of that.
You could have skipped theprocess and then just jumped to
where I want to go next, whichis how we actually talk about
people.
But before we get there, I dowant to say that I think there's
a poetic quality to the waythat you talk about this in the

(24:42):
book, such that when I wasreading it actually, like I said
at the outset, it felt verydramatic, like I could imagine
this happening, the interactionbetween the numerator and the
citizen or the person right andI could imagine the cleaning
stage.
I just it was something thatwas very tangible to me.
So that's one thing that I'mreally impressed with, but the

(25:02):
other is that in doing so, bynot glossing over it and really
focusing on it and parsing outsome of this nuance, I think you
did an amazing job ofexplaining why it's so important
to pay attention to those partsof the process that might seem
like just wrote componentsnecessary to collecting
information, that they actuallyare part of the census in their

(25:25):
own weird way, even though wedon't see that in the end data.

Dan (25:30):
Yeah, I mean there's so much.
We lose so much when we onlylook at the final tabular data.
But I wouldn't even go that far.
Like I looked the way you saidit was poetic.
I think of it as what I want todo in the book because this is
how it works in my life is to beable to look at a table of data

(25:54):
and start to have some of theappreciation that, like someone
has when they've been taught howto read a poem for the first
time, or like how to look at apiece of artwork.
I write about this in the book,but it's for real, Like I.
Then, whenever I see tabulardata I mean a lot of times when
I see a table of data, I startjust like drawing conclusions or

(26:16):
making critiques about itsmethodology.
But I am also capable of thenalso thinking like oh, there's
like a lot of sweat and energyand creativity that went into
trying to take the world andinvent from it the set of
numbers.
And I can learn a lot by tryingto figure out like.

(26:39):
I can learn a lot about thevalues of the people who made it
.
I can learn a lot about the waythe society is set up that
decided that this data needed tobe gathered.
I can learn a lot about howpeople with power or the power
to count at least see the world,by what they decide to ask and

(27:01):
how they decide to theninterpret this and turn this
into numbers.
And then, if I then happen tohave access to those
intermediary phases, the placeswhere I see, like people's
responses and how they gettranslated, of course, like the
person whose job it is to makethe tables, their job is not to
rap poetic about sorry, to waxpoetic about what it is that

(27:22):
they're doing.
Their job is to clean the data.
Someone like me on the outsidecan then sit there and look at
that and say, actually, thatperson who's doing this work,
which can be mind numbing attimes, is also its own kind of
dramatic activity, as they'recharged with taming the chaos
and producing from it somethingthat our society needs to make

(27:44):
good decisions.

Shawn (27:46):
So let's follow this line of thought, because I think
you're making a compellingargument that if we study only
the numbers in end and that indoing so we come to incomplete,
maybe inaccurate, conclusions,and this has implications across
not just the count and how wetalk about the people in the
United States, but a lot ofresearch relies on census data

(28:08):
across a lot of fields.
I've been in a lot of spaceswhere, in at one point or
another, people question whetheror not the census might be a
good place for us to get some ofthe data we're looking for.
So what are some examples ofwhat we might be missing or
getting wrong?
And then, what suggestions doyou have for researchers and
analysts when they do mine thisdata or use this data to

(28:30):
mitigate the damage that mightbe done by just extrapolating a
story or a conclusion from justthe raw numbers?

Dan (28:37):
Yeah.
So one thing I would say for Imean maybe the first thing I
should say, because for thosepeople who are listening to this
and are maybe folks who dospend a lot of time using census
data is that actually often thepeople who use census data have
thought about this stuff a lot.
So I don't want to feel likeyou're hearing this and being

(28:58):
like, of course I've thoughtabout the fact that this is not
precise.

Shawn (29:01):
We aim to offend here.

Dan (29:04):
Oh, oops, I didn't read the memo carefully.
The people who what do I mean?
Again, I think about this islike the people who most know
the weaknesses of a data set arethe people who constructed it.
So this is not the census, butI think about this in my last
book, which is about lifeinsurance companies.

Shawn (29:21):
Also interesting.
Believe it or not, you takesomething that's seemingly dry
and it is.
You make it interesting.
I have read it.

Dan (29:28):
Wow, all right, I appreciate that there's this.
One of the things that happensin that book is I'm looking at
how private modes of thinkingabout insurance are then used as
a means of trying to constructthe US social security system.
And there, early on, congressis holding hearings trying to
determine how to set up thesystem, and they are starting to

(29:50):
think about this as having anactuarial basis, which is to say
that theoretically there wouldbe this pot of money that's
being drawn from people's wages,stored somewhere and invested
to gather interest and use tothen pay those people some years
down the line.
That's how it happens ininsurance company.
You pay premiums, it gathersinterest over many, many years,

(30:10):
hopefully, hopefully, decadesand decades, and then when you
die, that money goes to yourbeneficiaries, or when you
retire, it goes to you as anannuity.
So as they're doing this, theone of the actuaries shows up
into Congress and they arereally concerned with how big
that reserve fund is going to be, not because they're concerned
about having enough money downthe line.

(30:32):
They're actually reallyconcerned about having too much,
too big a reserve fund thatthen gives the government too
much power to invest this money.
It's funny to think about thethings that were driving their
concerns.
But remember the actuary givingtestimony and he says all right,
I'm going to give you a numberabout the size of this reserve,
but I need you to know, before Itell you this number, that it's

(30:53):
wrong.
You, congress, can change itinstantly because and you
probably are like in 10 minutesyou're going to like pass a law
which is going to change someprocedure about how this is paid
or who's can be a beneficiary,and suddenly this number is
going to go out the window.
But you insist on me giving youa number.
So I'm going to give you anumber, but don't take it
seriously.
Having done this, he gives themthe number and it's printed on

(31:15):
the front on all thesenewspapers the next day as, like
this is the size of the reserve, all of the caveats having been
lost.
And I think this is thefundamental problem that people
who produce data sets face,which is that they are often
called upon and required bylater data users to produce
precise numbers, and they areaware of the fact that sometimes

(31:39):
there's measurable uncertainty,and then there's all these
different forms of unmeasurableuncertainty that lead in and
feed into producing their data.
And the question, the realquestion, I think, is so how do
you find a way to try to getdata users to take seriously
those forms of uncertainty?
How can you try to break thisup?

(32:00):
And since Spiro has methods, Ithink we can probably talk about
some of those methods, abouthow it talks about uncertainty,
but I don't think that I'm thebest person to give the answer.
How you fix that.
I think that's identifying.
The problem we need to identifyis how can we not just
communicate uncertainty butbuild systems that understand
the precision that we seem tohave with our data is not so

(32:23):
precise as it looks like it is.

Shawn (32:26):
I want to pivot to talking a little bit about
United States people and how wecount them and how the ways that
we count them create theirstory in a way.
So let me start here.
You in the book use the phrasestatistical margins of society,
and I'm not sure why this feltso profound to me, but it really

(32:48):
was, and I think that thisphrase statistical margins of
society captures a lot in justthose four words.
It captures the idea thatsociety has a hegemony, that
some people live on the fringesand that how we count these
people both reflects andinfluences how we think about
these people and people insociety, and that the narrative
that translates from that isthat some people are maybe not

(33:10):
important, at least at certainpoints in time.
So in your research for thebook, I guess I'm wondering how
was this showing up, or how wereyou seeing this or ingesting
this?

Dan (33:20):
So I came up with that phrase.
I mean part of it's just evokedby like, literally, you look at
these pieces of paper andthere's some answers that fit
inside, that are allowed.
Like there are eight allowableI've just made up that number, I
can't have to count likeallowable racial categories that

(33:40):
can be included, and so youknow that there's going to be
people who in fact aren't goingto find listed a race that's
going to properly describe them,and so like there's literally,
like sometimes you will seespilling off into the margins of
the paper people trying toexplain or offer more

(34:01):
information, and so, like, whenI think of the statistical
margins, I literally mean themargins of the paper, but also
like we talk about marginalizedpeople or marginalized groups,
and one of the reasons we likeusing that kind of frame is that
it makes it clear that somebodywas the marginalizer or like

(34:22):
society has produced a norm, asyou put it, a hegemony that then
makes it such that others falloutside of it.
One way that this manifestedwould be like with the
homeowner's loan corporationmaps.
So this is not from the censusdata, but it's stuff I was using
to try to think about who mightexist on the statistical
margins.
So your listeners have probablyseen these maps before.

(34:43):
When they hear the phrase redlining, probably what flashes in
their minds is one of thesemaps in which there are some
communities that are literallypainted in red on these old
1930s maps of communities.
And that then these?
We understand that these wereused as a means of trying to
decide who would get access tocredit for mortgaging their

(35:07):
houses and who didn't.
It turns out it's actually apretty complicated story about
how these maps actually directedpeople's decisions, so we'll
leave that to the side.
What we know for sure, orwhat's easy to say, is that
those homeowner's loancorporation maps were a
representation of how elitebankers, elite real estate

(35:31):
agents in communities saw thosecommunities.
So when they colored in an areared or yellow, they were
indicating we believe that thisarea is a higher risk space.
It's a space where we thinkthere's either more poor people,
there's more people of color,explicitly racialized.
We think it's a place that thebuildings are of lesser quality.

(35:54):
It is a literal judgment and ameans that we can figure out.
Okay, so if I was an elite inthe city, what did I think the
good neighbors, what's where?
What did I think the badneighborhoods were.
This map tells me that, that'sthe thing it tells me most
directly, and so I thought ofthat like all right, good, this
is the thing that shows me.
This is what the statisticalmargins of the society are.
I guess the final thing I'llsay is the margins can go the

(36:16):
other way too.
Right you can be.
This isn't a bad kind ofmarginalization, maybe, but like
one can exist on the margins oroutside of the site because of
being exceptionally powerful orexceptionally wealthy.
So for the 1940 census, like Isaid, there was an income
question and it was meant torecord each person's income

(36:39):
precisely to the dollar.
Unless they made more than$5,000 a year, which at that
time was quite a lot of money.
Someone who made more than$5,000 would just have it marked
down as $5,000 plus.
So in that case this isn't asign that people weren't
important.
It's a sign that they arereally important and in fact so
important and powerful that theycan manage to make themselves

(37:01):
disappear and not have to giveprecise information about their
income to the census taker.

Shawn (37:07):
So one of the things that I think that in discussing
marginalization that we as asociety struggle with, and that
I struggle with, is threadingthe needle in that conversation
in such a way as to determinewhat is marginalization by
harmful intent and what ismarginalization as a byproduct

(37:32):
of something benign, benignignorance, or just a benign
byproduct of a behavior that wasin no way considering
marginalization.
The reason I'm mentioning thisis because this is something
else that I think that camethrough in the book in focusing
on each of the different actorsat each different stage and how
critical their role was incollecting this data and then

(37:54):
producing this data about peoplein the United States, and that
in some way reflects thesepeople that live in the margins
of society.
That there are certain stagesthat are simply byproducts of
the process that createmarginalization or enhance

(38:15):
marginalization, and there arecertainly some stages and
processes that have that intent,and just how complicated it is
to have a true conversationabout marginalization without
considering these two potentialinfluencers, and I'm wondering

(38:39):
if you have thoughts about that.

Dan (38:43):
So I'm led again back to the way we've been talking about
our different ways ofconceiving of the holes in the
data.
I guess, as we talk about thatdistinction in terms of how and
why someone is marginalized, orhow in my system, produces
marginalization, and so maybethis is like.

(39:04):
Maybe I will partly dodge andpartly answer your question by
pointing to the chapter aboutpartners, which is certainly one
of my favorite pieces.
So early on in my research,somebody asked me.
A colleague said so.
I found these two people in thecensus and one of them is
labeled head in the householdand then the next person with

(39:28):
whom they're living is labeledpartner.
There's two women.
I think they're probably twowomen, harlem although the
person who asked me was a littlebit cagey about it because they
wanted to write about itthemselves.
But they said what does it meanthat these are partners?
And I had never run into anypartners.
It was very early on in theproject once I started to
investigate it and going throughtheir head, my head, everyone's

(39:50):
heads, it was like all right,well, we know that these days a
partner can mean lots of things,but it often means kind of like
an intimate partnership.
Is this what was happening, andI think that we see queer
communities or queer couplesshow up in these census records
in ways that we wouldn't expectthem to show up.
Well, I mean, yes, the answeris yes, but partly the answer is

(40:11):
yes, and this is where I thinkI am still getting to.
This question you're asking isbecause we have to take queer
history, or looking at kind ofqueerness, and use a very
expansive definition of what itmeans to be queer as explicitly
not just looking at for gay orlesbian couples, but really

(40:31):
looking for people who existoutside of what the straight
norms are, who form householdsthat don't fit what the straight
norm is intended to be a manand a woman with children
together living in a nuclearfamily Anything that's outside
of that suddenly counts as beingessentially a queer

(40:52):
relationship.
And what I, what could be, youknow again, like frustrating in
some ways, is that, like wedon't know in many cases why a
person is listed as partner.
There are, like some againfascinating characters full of
mysteries here.
My favorite two are two womenliving together in Greenwich
Village in Manhattan named LeeLusgarden and Tessie Finger, and

(41:17):
Tessie Finger was, of course, astenographer, like the perfect
name for the perfect name, ofcourse, and Novelis too, made
this stuff up.
You'd be like that was a littlebit too heavy handed.

Shawn (41:27):
Too brute force yeah.

Dan (41:31):
But I don't know anything about Lee Lusgarden and Tessie
Finger really, so like that iskind of frustrating.
But while I don't know anythingabout their relationship, what
I did determine was this is areally interesting queer
community here in GreenwichVillage.
You're like oh thanks, dan, Ididn't realize the Greenwich
Village was a queer community.
Well, this can be expanded toother places too.

(41:52):
Like you start to find partnersshowing up in the sense of
manuscript census in lots ofdifferent places and part of
what you figure from this islike all right, it's showing me
not just a place in which peoplecould have gay relationships,
but it's also showing us a placewhere all kinds of other
different family formationscould exist and often did exist

(42:14):
and could be counted.
Because my kind of sense isthat probably an enumerator in a
queer community wanderingaround pretty soon is disabused,
or pretty quickly is disabusedof the idea that they're going
to find a bunch of readilycountable nuclear families in
the traditional straight senseand thus, having their eyes
opened, need other ways tofigure out how to make people

(42:35):
count, and so they use thispartner category to do this, and
so, yeah, it produces a lot ofholes.
It shows us people who areliterally marginalized.
It's like the marginalizationis not just these people get
counted, all the folks living inwhat we're going to call a
queer community broadly but thenthey are ultimately erased from
the numbers.
So we know they exist now, afterthe manuscript data is released

(42:57):
, but at the moment of thecounting, because the Census
Bureau didn't see queerness orqueer couples as a viable
counting option all of thosepeople who had been labeled
partners were edited, revised,punched in paper cards as
lodgers and showed up,ultimately, at the time, only as

(43:19):
categories of the lodgers.
So the tragedy there is not atragedy for us now in which we
can see back and start torecreate these strange and
complex relationships.
The tragedy is to think thatthis was yet another official
statistical manifestation of theerasure of queer life that was

(43:40):
happening constantly at thistime, and that's a tragedy to
understand.
It is also, though, animportant thing for us, a way
that we gain knowledge about thepast by thinking about all of
the parts of the data.
It helps us really understandexactly how it was that
queerness both survived,persisted, was honored by the

(44:03):
people who were living it, andthen was suppressed and
pretended to be non-existent byofficials with the power to make
the final statistics.

Shawn (44:13):
Well, I'm glad you brought us here because this is
a good segue.
I don't think this was theintent.
This wasn't the main theme ofyour book, but I noticed in
reading it.
As a queer person, I'm alwaysinterested in uncovering queer
history and I think that whatyou did in a way highlights how

(44:34):
difficult it can be formarginalized communities to
document their histories andstories because of the ways that
we as society choose to countand acknowledge and memorialize
non-dominant communities.
I think you just outlined veryeloquently and you do so in the
book by way of example the useof the word partner on the
census.
But I know, as a queer personI'm really hungry for

(44:56):
understanding how the communityhas evolved, how the community
has shown up in history and howthat might inform the community
now, and I don't think I'm alonein having that kind of hunger
or that interest.
Do you think this means thatfor some of us queer folks and
folks of color, immigrants,women have just maybe lost

(45:18):
critical parts of our stories tohistory?

Dan (45:23):
Oh, yes, I mean I don't want to laugh like it's funny
this is like the laughter ofgreat tragedy, Like yes, no.
The answer is yes.
Queer folks, people of color,immigrants, any group that has
been pushed to the margins, hugeamounts have been lost.
I mean that can still be partof the story, right?
Like often, then, one of thethings we do is we try to

(45:47):
explain that story ofmarginalization, because then
that's part of a means ofthinking about all that those
who came before us endured andthe way in which, like you know
to be queer today, we get to totrod on paths that were opened
up for us by the hard work ofthe queer people who came before

(46:08):
us.
At the same time, there arealso methods we can use to try
to help fill in some of those,some of those gaps.
Say, D Hartman, scholar ofAfrican American history and
literature, talks about this andshe uses the phrase critical
fabulation, and this is a kindof method I felt I was sometimes
using as well, which is to saythat, faced with records that

(46:30):
have been, that have erasedpeople systematically, we're
left only to use ourimaginations as an act of
retrieval and a means of trying,when impossible.
You write, you work as hard asyou can, and then you we always
in history run up to a placewhere we run out of facts.
And then the question is likedo we just simply give up, or is

(46:54):
it reasonable?
And I think we answer many ofus has.
It is yes, it is reasonable tostart speculating precisely when
we're dealing with communitieswho have, when so much work has
been done to erase them or tomake them make it not visible,
to have lost this history.
We, we tried to do works ofrepair, to like find a way
towards those stories.

Shawn (47:16):
So it strikes me that we have come all this way and we
haven't mentioned one of thewords in your book's title, and
that word is democracy.
So let's fix that.
I think a central tenet of yourbook is that our American
democracy is only as good as thedata, but I'm wondering if you
could maybe put some flesh onthat bone.
So how is our democracyinformed by census data, and

(47:39):
could we make it better if wedid things differently, and how
we implement and conduct thecensus?

Dan (47:45):
The census exists because the United States Constitution
built its theory ofrepresentation on population.
So it said that one branch ofgovernment, the House of
Representatives, and then themeans of electing the president,
the electoral college, would beapportioned, would be like,

(48:06):
would decide how many, how muchpower each state had according
to its size.
And so, because theConstitution had that in it,
this whole operation had toexist and we had to start
counting people.
So that's why it seemsreasonable to call this
democracy's data, like theentire democratic system of
allocating power in thisparticular federal society is

(48:27):
dependent on getting account.
Ideally that's an accuratecount, but at the very least it
has to be a count thateveryone's willing to agree on
and use.
And then, for most of thehistory of the United States,
congress would get from theCensus Bureau, or from the
Census Bureau didn't exist formost of this time.
So we'd get from the Secretaryof State, whoever was in charge

(48:48):
of this, a count of the numberof people living in each state,
and then their job was to figureout what do we do with this,
and for most of the time, theywould pass a new law, and that
new law would say when the nextCongress starts, this is how
many seats there will be in theHouse of Representatives, and
they usually increased it.
And then this is how many seatseach state will get, and there

(49:10):
would be a list.
And that apportionment amongsteach of the states was meant to
be proportional Turns out themath of breaking that down is
complicated.
There's a number of differentways to do this to make it
proportional, because you alwaysend up with fractional pieces.
They go through a bunch ofdifferent techniques.
That's not quite so important.
One of the things thatsurprised me in doing this
research was I realized after alittle while that after 1910 or

(49:37):
1920 on, the size of the Househas been constant at 435 seats,
and this maybe isn't surprisingreally, when you just put it
like that.
But what had been surprisingwas that before the normal thing
was that the House, the size ofthe House, would increase with
the size of the population.
Ever since it's been frozen in1920, we've had a way in which

(49:59):
this apportionment ofrepresentation happens breaking
up, taking seats away fromstates that are shrinking or
growing more slowly, givingseats instead to states that are
growing more quickly, and thatcan be like a pretty serious
thing to take away a seat or togive a seat to new states and at
the same time, that we'vetripled the population of the

(50:21):
United States.
So we have this thing where wehave a very high stakes
calculation happening how thecount of the population matters,
and sometimes it can be like afew hundred seats, a few hundred
people counted, who decidewhether or not a seat goes to
one state or another.
High stakes decision and it'sbeing based on numbers that we

(50:43):
know.
The Census Bureau is going tocome out with a coverage
measurements survey a coupleyears later.
That's going to say, yeah, thisis a great count and our
uncertainty is in the order of100 to 200,000 people in every
state.
So the uncertainty is muchlarger than the differences

(51:04):
we're often talking about inmaking these decisions about
allocating representation.
So it's like that was for me ofthe sense that like, oh, this is
so crucial to how we set up ourdemocracy, but it shows that
because we've frozen the size ofthe house, we're relying on the
data to make decisions for usat a precision that it just
doesn't have, and then the finalcost is just that we've made it

(51:27):
so that we, as each individualperson, have so much less
representation and so much moredistance from our actual
Congress people.
I didn't know that I thoughtthe House should expand before I
started this book, but then,having worked in the book, I
suddenly thought oh, this isamazing.
This is like very bad that thehouse has been frozen for so
long and we need a lot morerepresentatives.

Shawn (51:49):
One of the arguments whenever this conversation comes
up with some amount ofsincerity in Congress, one of
the arguments against it is thatCongress is already so large
that it would just becomeunwieldy.
But that's ignoring the factthat many democracies around the
world have much largerlegislative bodies that function
seemingly at leastcontemporarily more efficiently.

(52:12):
But we're nearing the end ofour conversation and I guess I
want to ask you a question thatyou lead with in the book and
that is here we could cue likesome swelling symphony what is
our American democracy if thisis its data?

Dan (52:29):
So I want to answer in two ways.
One way I just want to quote myfriend, mita Anand, who is kind
of like the head of all thingsdata and data equity at the
Leadership Council for HumanRights, and Mita has this way
where she talks about the censusas a report card for the

(52:50):
country and what she means.
I think in particular she'sthinking about the way in which
we know that there's apersistent undercount of people,
particularly of people of colorand also of other kind of
marginalized groups, by thecensus, and part of what she's
getting at is that there areways, there are distinct ways in

(53:12):
which we can do things thatwould decrease the undercount,
but part of the best way to getrid of an undercount would be to
build a society in whicheveryone wanted to be counted
and was in a stable situation tobe counted, because it's not
even a lot of it like wanting tobe counting it.
When we think about who doesn'tget counted, it's often those

(53:33):
who are have the least resourcesor who don't have a stable home
or who don't have access tomany of the systems that make
counting something easy to do.
Right, you might just be nothaving high speed internet these
days and so, like the census,in a truly equitable society
we'd probably have a bettercount, precisely because people

(53:56):
would all live in such a fashionthat they would be easy to
count and readily countable.
And to the degree that this isdifficult, it can show us some
of the ways in which there arepersistent and difficult
inequities in the way oursociety runs.
But the other answer I mean, Ithink there's some hope in that
one but then even more hopefulanswer is to say when we can
look at the system right in thebook I talk about some pretty

(54:18):
terrible stuff that happens withthe census, including the means
by which the census data isturned against Americans,
american citizens, even duringthe Japanese incarceration
during World War II, but in itslike, absolute best form.
Right, if you think about like,how does the democracy's data
tell us something about thisplace?
I think the most fundamentalvalue and one of the reasons I

(54:40):
wrote the book in the end, likewas there's something really
valuable in the idea that everyperson is supposed to count.
It doesn't always happenperfectly, but every person is
supposed to matter.
They're supposed to then becounted.
They were supposed to put a lotof energy and resources into
counting them and then we'regoing to preserve their privacy

(55:00):
for 72 years but hold on totheir records so that, when
everything is said and done,they have a place in history,
especially the marginalized andthose who about whom there's
seldom going to be records kept.
They are kept in this place.
So there's reasons to not be tolook at the data, the census,

(55:21):
and say like, oh, america has along way to go and that's right.
There's also reasons to look atand say like there are some
fundamentally powerful valuesthat are instantiated in their
being a census, and that'spretty cool.

Shawn (55:35):
All right.
Final question Are you readyfor it?
I'm ready.
What's something interesting?
You've been reading, watching,listening to or doing lately.

Dan (55:44):
I mean I can't just say one good book that I was listening
to, so I was thinking about this, and there's a few really great
ones.
One I want to call out RachelSwarance is a New York Times
journalist and she's got thisbook I would call the 272, the
272, the families who wereenslaved and sold to build the
American Catholic Church, andshe's doing some of the stuff we

(56:04):
were just talking about.
She is at one level.
She's trying to explain why itwas that Jesuit leadership
running Georgetown decided thatthey needed to sell enslaved
people in a mass sale in the1830s, and she tells that story
really, really well.
At the same time, she's alsodoing the work of trying to

(56:26):
reconstruct the lives of anentire enslaved family and then
their descendants, with a realemphasis on the extent to which
they were fighting for theirfreedom and asserting their
freedom throughout the entiretime that they were enslaved and
really honoring their owndesires and the lives that they
were building for themselves.

(56:47):
And she just does a great job.
She sits on the shoulders ofthe members of this family and
she puts us on the shoulders ofthe folks who are trying to run
Georgetown and really asks us totry to understand both of them.
And that's just such animportant thing to do at this
moment because a lot of us aretied to institutions that are

(57:08):
both thinking about how to takecare of and actually do some of
the reparative work from thelong histories of exploitative
systems like slavery, and alsobecause we're also in
institutions or belong tonations that are right now tied
up with and invested in reallymessy system of exploitation or

(57:30):
systems of global climate changethat need to be changed, and I
think Swarons is getting us tothink about that.
I'll say more briefly the otherones.
People should read EllenAlman's Close to the Machine.
It's just my favorite memoirfrom a tech writer.
It's now 20 years old, but shereally tries to again help
people to understand why it isthat allure of being the

(57:53):
computer programmer is, andespecially the allure of using
that as a way to escape from themessiness of humanity.
And then two books that areseemingly about thinking, but in
very different ways.
Death Pop Berman wrote the bookThinking Like an Economist,
which tries to explain why it isthat economists seem to do so
much public policy these days,and it's just brilliant.
It's really good, she's right.

(58:13):
And then the other one is byhistorian Jamie Kreiner.
It's called the Wandering Mind,what medieval monks tell us
about distraction, and it's justa hoot and she really gets us
to think about how long and thenhow many different ways people
have been trying to understandand think about what it means to

(58:34):
think and what it means to bedistracted.
This is not like a new problemthat we face today.

Shawn (58:39):
Typically, I've heard of one book someone mentions and
I'm always looking for books toread.
You just mentioned four that Ihave to add to my list and I
haven't heard of these, but youmake them all sound so
fascinating, I have to sayrelated to the 272, so there's a
bit of history, the CatholicChurch here and I have to say

(59:00):
the churches really have in amoment right now and it ain't
all good right.

Dan (59:04):
No.
So Warren's is interestingAgain, just like she's such a
brilliant book that sheconstructs, because part of what
she's doing then.
The book, though, is also lateron explaining how and why for
formerly enslaved people, it wasreally important to be able to
hold on to their Catholic faith,and how they dissociated that
from the reality of the means bywhich the power structure had

(59:27):
enslaved them.
So I mean, as you probablypicked up from, and listeners
will pick up from this like I'mall about my ambiguities, and
she just does it really well.

Shawn (59:37):
Dr Bouk, it's truly been a pleasure.
Thanks for taking the time tostop by this is wonderful,
thanks for having me.
Census data and its method ofcollection are the linchpins of
a thriving democracy At its core.
Census data represents morethan just numbers.

(59:58):
It reflects the heartbeat of anation, the diversity of its
people and the power of theirvoices.
It's not just a count.
It's a vital reflection of oursociety, revealing the diverse
tapestry of voices that cometogether to form this great
nation.
As Dr Bauch has highlightedboth in his book Democracies
Data and again today in ourconversation, how this data is

(01:00:22):
collected directly impacts thevery essence of democracy and
its core ideals.
And if we see census datathrough that lens, then it isn't
merely a snapshot of a numberof people in a specific place on
a certain day.
It's a compass guiding ustoward a more equitable future.
It informs policies that touchevery aspect of our lives, from

(01:00:43):
education and healthcare toinfrastructure and social
programs.
A complete and accurate censusempowers policymakers to make
informed decisions, ensuringthat no community is left behind
and every voice is considered.
The challenges we face inachieving an accurate and
inclusive census count aresubstantial.
Overcoming historical barriers,fostering trust and embracing

(01:01:08):
modern methodologies are crucialsteps toward enhancing the
accuracy and completeness ofthis essential data set.
By addressing these challengeshead on, we pave the way for a
more representative democracy,one that respects and
acknowledges the rich diversityof its populace.
So, keeping this in mind, let'scarry forward the understanding
that the census is a reflectionof who we are as a nation and a

(01:01:31):
testament to our commitment todemocratic ideals.
As Dr Bauch suggested, let'sengage, educate and advocate for
a more inclusive, accurate andrepresentative census, because
it's through this sharedconviction that we can build a
stronger, more united America.
Alright, a final update I willbe out of country for the next

(01:01:53):
few weeks, so deep dive will beon a short hiatus.
In the meantime, though, youwon't be left hanging each
Sunday while I'm away.
A deep dive greatest hit willrelease, so keep coming back.
Chat soon, folks.