Who's Poor Today?

Uploaded by serviceresources on 30.01.2012

Amy: If you have a question, feel free to type it into the chat.
So at this point we're recording.
And I would like to know, and I know Stephen's really interested in it as well,
how many of you were on the first webinar in this series?
And you can use the icons over here, the check if you were in the webinar and the X if you did not attend.
So I attended, so I'm going to put a checkmark.
So go ahead and vote and tell us how many of you attended the last webinar,
and maybe if you did not, you could just participate by clicking the X button.
So quite a few people were on the last webinar, and then we have several new folks.
And a few people haven't chimed in yet, but it looks like a lot of folks are new to this series.
So, Stephen, I'm sure you're seeing this as well.
It looks like most of our folks that have answered so far did not attend the first webinar in the series.
And I just want to remind people, you may notice already, but you can watch the recording of the first webinar,
which talks about the historical perspectives of poverty on the same page that you registered on the VISTA Campus.
And we will send that link out later on after the webinar so you know how to access it.
And as you know, we are also recording this session, so if for some reason you want to go back and look at it again
or share it with colleagues, it will also be posted on the VISTA Campus as soon as possible.
So we had over 100 people registered for this webinar, and we're delighted that we were able to open it up
and get everyone in the session.
So if you were on the waiting list at one point, I'm so glad that you were able to get into the session.
We have about 75 people logged on and it's 11:00, and I know Stephen has a lot he wants to tell you,
so let's go ahead and get started.
So, again, my name is Amy Cannata and I'd like to welcome everyone.
We're very excited that you're able to join us for our second webinar in the series,
Conversations about Poverty with Dr. Stephen Pimpare.
Once again, Stephen is joining us today from New York, and I'm hosting the webinar here from Portland, Oregon.
My colleagues Nicky Martin and Mary Rosenthal are joining me, and they'll be assisting with the webinar today.
So there we are, our shining faces.
And some housekeeping items that I want to cover with you.
First of all, as you know, the phones are in listen-only mode,
which means you're all muted so we don't have to worry about background noise.
If you have a question or would like to share something, please use the chat panel to type your questions,
and that's over here on the side.
And make sure that you send your question or comment to all participants,
and you can choose who to send it to by using that dropdown menu there in the chat panel.
Stephen will be getting to questions when he's at a good stopping point,
and there will also be time at the end to answer questions.
The other thing that we'll be doing today is using annotation tools to write directly on some of the slides
and to get your input on some of the slides.
And so what we'll do when we use the annotation tools -- we have a lot of folks --
so use this pointer tool which I'm pointing on to stake your claim on the page,
and then you can use the text tool, which is located up here to add your comments next to your name.
So there's mine right there.
So you'll see -- we'll prompt you when it's time to use these.
All right.
So at this point I am going to go ahead and turn it over to Stephen so we can get started with the presentation.
So take it away, Stephen.
Stephen: Terrific.
Thank you, Amy.
Hello, everyone, and welcome.
It's nice to see that we've got more than a handful of folks returning us who had been here for our first webinar,
and lots of new participants too.
And we're all very glad that we've got all of you here today.
I thought that I might begin by telling you just a little bit about myself.
I currently teach courses on social policy, mostly to graduate social work students at the moment,
at Columbia University, NYU, and The City University of New York, and I write books and articles,
mostly about poverty, inequality, and welfare.
But most relevant perhaps for our purposes is that before going off to graduate school some number of years ago now,
I worked for over a decade in New York City not-for-profit organizations,
and most of that time was with small community-based, mostly faith-based soup kitchens
and food pantries helping them better manage and expand their programs.
So I've got lots of experience with literally hundreds of different kinds of small
and mid-sized organizations for the most part throughout New York City.
So I likely have at least some sense of the kinds of organizations that many of you are likely to be placed in,
and the kinds of challenges that you're facing.
And what I hope we'll be able to show over the course of the hour
or so we have together today is some of the ways in which having some basic information about poverty data at hand can
help you in your day-to-day work.
So here's what the goals we have for us today.
So we hope that by the end of the webinar you'll be able to do the following.: Now as you can see from this list of
objectives here, our mission for today is mostly description: to understand poverty rates in the United States,
how they're constructed, and how they vary from group to group and place to place so that in our next webinar,
which we're thinking of as sort of a continuation of this one,
we can then turn our attention to explaining why people are poor;
and in that one we'll look specifically at the effects of the Great Recession and why so many people are poor today.
but we'll use that as a way to try to explain other moments of American poverty too.
But those explanations are going to be mostly for later, so we'll need to be patient.
And today, what we want you all to be able to do is simply to describe the population of people living in poverty in
the United States.
So here's one place to start,
By the end of the session we hope that at minimum you'll know the importance of each of these numbers.
Now at this point you might want to write them down or call up a notepad, say, on your computer
and make a list of them there.
And while you're looking at that dull list of numbers and pondering maybe what they might refer to,
I want to offer something of a very gentle warning and some reassurance:
we'll be reviewing a lot of data today.
We'll be looking at charts and graphs with lots of numbers.
Now most of them you'll find, I hope, are fairly basic, but some of them are going to be a bit more complicated.
Now, I will, for each of those slides,
highlight what I think is most important for the purposes of our discussion today,
and you shouldn't feel as if you need to take in everything all at once.
We'll post the webinar to the VISTA Campus soon after it's concluded so you'll have the slides available for reference
if you want to go back.
And you'll be able, on at least a couple of points over the course of the session today, to ask some questions.
So we'll be looking at many numbers in addition to the ones above, but if we do our jobs right,
and I do my job right, it should all make sense.
All righty then.
So let's begin with this: by way of a little bit of larger context.
Now what you're looking at here is American poverty rates compared to poverty in other rich democracies,
for the most part for the year 2004 or for the closest year for which we have reliable data,
and that's that number in parentheses next to the country name over on the left
is the year from which those data are taken.
Now the pattern that you see here has been very consistent for a very long period of time.
The United States has higher rates of poverty than do most, but not all, but most other comparable nations,
and we've got much higher rates than some, right.
Compare the United States rate of 12.4% in 2004, look all the way down to the bottom to Denmark and Sweden at 5.6%.
All right, almost twice as high, almost twice as much poverty as some other countries.
So we'd like to just sort of keep this in the back of your mind,
particularly for when we turn our attention to possible explanations for poverty in the United States since we'll want to
ultimately figure out not just why some Americans live in poverty,
but why so many more do than in other rich democracies.
Now let's turn just to the United States.
So you're looking here at two different sets of numbers at two different trends.
That top chart is showing you the number of people in poverty from 1959;
that's that number all the way to the left at the bottom, all the way through to 2010;
that's the most recent year for which we have census data.
We won't have numbers on 2011 until somewhere in the fall of this current year of 2012.
So now look at that top, that number of poverty.
In 2010 there were over 46 million Americans living in poverty - that's the most ever
and quite a bit more than that 39.5 million who were living in poverty in 1959,
and 1959 is the first year for which we have data that are roughly consistent produced by the Census Bureau.
But now, at some level that's not necessarily a fair comparison, because, sure,
we have more people living in poverty today than we did in 1959, but we have more people in almost every category than
we did in 1959 since we have so many more people overall, which is why, in most instances,
when you're looking at poverty data, and other kinds of data for that matter,
you're much more likely to be looking at percentages -
what percentage of the population is living in poverty?
because that's a number that's comparable from year to year.
So as you see here, that 15.1% of all Americans were living in poverty in 2010 is
when we look at the percentage, in fact, lower than the number was in 1959 at 22.4%; but also notice,
higher than it was in 1973.
1973 is the lowest poverty rate for any year for which we have official data - 11.1% in 1973, 15.1% in 2010.
All right.
So there are all sorts of things that we might pay attention to here.
I think there's all sorts of useful and, for me, very interesting information here.
So now I just want to draw your attention to a couple of things.
First of all, you'll be seeing this in lots of the data that we look at.
See these shaded areas? those are periods of recession.
So when the economy slows down or when the economy declines,
you'll see a little bar in the chart that shows you those periods.
That's useful information:
If you see a spike in poverty in particular periods,
you'll see very often that corresponds with periods of recession when the overall economy is slowing down.
Notice also: here's the period that we've been referring to as the Great Recession.
Notice the very large spike in the numbers of people who are poor.
(Now I'm officially cluttering up with too much stuff here.
Let me see if I can erase some things.
Oh, good.)
And you'll see an increase here, but the percentage increase -- (whoops, I grabbed the wrong tool,
there we go) -- the percentage increase, not quite as steep as the number increase, but, again,
you see a spike in the most recent period thanks to the recession.
All right.
And the last thing that I want to point out here is notice that -- this is 1959, remember,
all the way here to the left --
notice that this sharp decline in poverty bringing us eventually
to 1973 precedes the War on Poverty programs of the mid-1960s.
Now, notice that poverty continues to decline at a fairly quick rate after those War on Poverty programs, but,
in fact, this decline precedes the War on Poverty suggesting, perhaps,
that there's something else going on in the larger economy that may be affecting poverty rates in the 1950s and 1960s
then accelerated until we get to the 1970s.
And then, finally, this is that 15% on your list.
15% is the overall poverty rate -- (whoops, did I just do that?
I think I just did that.
Thank you, Amy, for saving me from myself.)
Of that 15% poverty rate of 2010, you should know that off the top of your head, right?
How many people are poor for the most recent year we have Census data? 15% of the population overall.
Now let's turn our attention to the next slide.
And here, instead of looking at how poverty has varied over time and what poverty was last year overall,
we're looking at how poverty rates differ geographically.
Notice poverty is much more prevalent in the South than it is in the North
with the exception of Michigan and Appalachia.
Appalachia, a region that in many ways was the --
whose poverty jumpstarted the war on poverty itself under the Kennedy
Administration and has historically been one of the poorest areas in the United States,
and Michigan in large part thanks to the collapse of the larger economy
and secondarily to the auto industry in Detroit.
Too often poverty is thought of as a problem of the North and particularly of those large industrial cities
or post-industrial cities of the Northeast.
I think the first thing that you should know is that that's by and large not true.
Poverty rates are deeper in the South.
And, in fact, if we look at census data and what they call "non-metro poverty" - poverty in suburban areas,
of which in 2000 was about 8%, excuse me, was 12% -- up from 8% in 2000. --
if we look at non-metro poverty in suburban areas and rural areas,
you'll see much higher incidences of poverty than you will in the North.
This, I think, comes as a surprise to lots of people who have learned to associate poverty
with big cities in all sorts of kinds of ways.
While we're looking at this map here (and let me get rid of the mess that I've made,)
just to get a sense as to where you all are coming from,
what we'd like you to do is to simply use that pointer tool that Amy was pointing to earlier -- and thank you, Amy,
she's now pointing to it again -- and simply point to us where you are in the nation.
Terrific, Rebecca, thank you, is in Oregon.
Oh, nice.
Oh, terrific.
A lot in the West, a lot in the East it looks like.
A little bit in the South, not too much in the Midwest.
We've got Latasha representing Texas.
Two in Hawaii, Jacobeth and Ashley in Hawaii.
That's great.
Melissa in Alaska, that's terrific.
That really is mostly just to satisfy all of our pure interest into seeing where it is that everybody's come from,
and nice to see such geographic variety.
We've clearly got good representation, especially for the East and West Coasts.
Now what I'd like you all to think about -- and we're not going to do anything with this information,
but I hope that you on your own will do something with it after the webinar is over -- to ask yourself whether you
know what the poverty rate is in your particular area or your particular state.
We know it's 15% nationwide.
We know that it's deeper in the South.
Those dark-colored areas are higher than 16%.
We know that in some places in the North it's lighter.
Do you know what the poverty rate is in your particular area?
After the webinar if, in fact, you go to our resource page you'll see a few sites that you can visit
to get more information, more detailed information in poverty, and some will, in fact,
give you information if you plug in your zip code it will give you very particular kinds of information about where it
is that you're located.
And I think that that's something that you should know as VISTAs.
You should know what the poverty is not just in your region, not just in your state,
but in the very particular community that you're working in and that you're serving.
You should know that simply because that's useful, important information to have at your disposal.
But it also might come in handy if you are charged with, say, helping put together grant applications,
providing very particular kinds of data in order to justify the need for particular new funding for your agency,
or communicating with the community or others about the work that your agency is doing.
If you can talk with specifics about the kind of need that is present in your area,
you may have a much better chance of enlisting people whether it's donating time or energy
or money to the organizations that you're working with for some point later.
Now we've seen the ways, very briefly, the ways in which that 15% overall national poverty rate obscures variation.
We've seen that it obscures variation regionally.
Some parts of the country, in fact, are poorer than 15%.
Others have rates higher than 15%.
Here, notice how much variation there is by race.
Again, we're looking at the same kinds of trends over time: 1959 right up through 2010.
Here's the rate for African Americans.
(Oh, what have I done wrong?)
27.4% for African Americans; 26.6%, also, essentially 27% for Hispanics; poverty rate of 12% for Asian Americans;
and 9.9%, essentially 10%, for Whites.
Notice again, look at the long term trend.
This dotted line, when you see a dotted line in data like this,
it tells you that either they're data that come from a different source --
because the Census Bureau wasn't tracking consistently by race in these earlier periods,
so these data were pulled from another source -- it's a combination of other data and projections of data.
But this is a good number.
It's put together by people who are really thoughtful about these kinds of questions.
The rate for African Americans, highest among all ethnic
or racial groups in the United States today has nonetheless declined by almost 50%, by half, from 55% to 28%.
Nonetheless, poverty rate for African Americans and Hispanics is significantly higher than it is for Whites
and higher again for Asians, right.
And that, by the way, that's the 27 on your list: an overall poverty rate of 15%, but it's 27% for African Americans,
it's 27% for Hispanics.
A theme that I will be hammering home a bit, in the time that we have left is that any time you see an overall number,
an average, ask yourself; what kind of variation is that obscuring?
So if someone tells you - and they're right - that poverty in the United States in 2010 was 15% you know now to ask,
"Well wait a minute, but how does that differ regionally and does it mean that it's 15% in my area?"
And you also now know that it varies enormously by things like race.
But let's look at the next slide here.
It doesn't just vary from racial group to racial group, but, in fact,
poverty rates can vary enormously even within racial groups.
So for the moment I want to look at just the part of this chart that's below that red line that I've just drawn.
We saw earlier in that previous slide that while poverty rates were 27% for African American
and Hispanics, 10% for Whites, was 12% for Asian Americans,
and you'll hear people refer to the relatively low poverty rates among Asian Americans.
But, again, that 12% is also obscuring important variation:
it was 23%, not 12%, among Cambodian, Hmong, and Laotian Americans, and yet only 7% among Filipinos.
And while we're here, notice also how high the rate is for Native Americans: 25%. Again,
how much lower for Whites, --
and these are 2000 census data, which is why these numbers are slightly different than the ones we've just seen.
But, again, the lesson here: that 15% overall number obscures a lot,
and even that 12% poverty rate from Asians obscures a lot.
Now among the questions that a number of you posed to us before the webinar,
more than a handful asked for some tips and some suggestions about simple ways to communicate poverty data.
This, in some ways, points to part of the problem because when all is said and done, it's not necessarily so simple.
Poverty rates vary. They vary over time, they vary from place to place, they vary from group to group,
and they even vary within groups.
And just to make things more complicated, the variation within those groups changes over time.
Keep that complexity in mind as you think about the people who your agencies are serving
and the people living in poverty in your communities.
Now poverty varies by age as well.
And just to get a sense of the room, of the virtual room,
we're going to open a poll here -- Amy -- I shouldn't say "we," Amy is doing all the hard work here --
Amy is going to open a poll here --
and we want simply for you to click one of those boxes in response to this question: which age group do you think
currently has the highest poverty rate in the United States: people 18 and under, children; people 18-64, adults;
or people 65 and older, the elderly?
So take just a moment and click on one of those boxes
and let us know which of those groups you think has the highest poverty rate.
All right, and, Amy, whenever you think that we've reached sort of a threshold of people weighing in,
feel free to go ahead and just throw those results up there and we can all take a look at them.
Amy: Okay.
30 second warning, folks.
Go ahead and vote if you haven't done so yet.
Stephen: I feel like, I don't know, I should be singing or something in order to pass the time,
but I won't subject people to that.
That seems cruel.
Amy: Okay.
So WebEx is putting together the results.
So just a couple more seconds and I'll publish so that everyone can see the results.
So there we go.
Stephen: So what do we have?
So 42% think children have the highest poverty rate of any age group, 20% thought adults,
and 30% thought those over the age of 65, 8%, much to their credit, are willing to admit that they're not sure
and actually said so.
So let's go on to the next slide and take a look at what, in fact, the answer to that question is.
And the answer is, in fact, children.
Again, this should start to look very familiar.
We've got 1959 as the first year over here. 2010 is the most recent year for which we have data.
We're seeing trend lines starting over here.
Here's the child poverty rate today: 22%.
More than one in five children in the United States of America today lives in poverty.
By contrast, however, under 10% of the elderly, by official Census Bureau measures, live in poverty.
Now, again, there are any number of things that are interesting in this chart that we might pay attention to.
I want, for now, just to draw your attention to this trend, and that is -- notice that red line,
that's the elderly poverty rate, 35% in 1959, higher than any other group --
even significantly higher than the poverty rate for children.
Look at the poverty rate now, today, 2010, for those over the age of 65: 9%;
Sharper decline than for any other group.
And notice here -- this is the thing that's fascinating I think,
although I tend to be easily fascinated by these sorts of things --
notice that for -- (now I'm clouding this up with too much) --
notice that for every other group in the current recession, this recessionary line,
that poverty spiked up -- and we expect to see that in periods of recession -- except for the elderly.
In fact, in this period of recession, the poverty rate for those over the age of 65 continued to decline --
not as sharply as it had been in the 1950s and '60s, but nonetheless continued to decline.
And that's only true for that group.
Now, again, I said the explanations we'd reserve for the most part for the next webinar.
I do want to point your attention to one sort of large set of things that's going on.
There are a number of kinds of explanations for why the poverty rate of the elderly is so much lower
and why it's continued to decline.
The shortest answer to that is Social Security and Medicare; and, in fact,
there are much more elaborate kinds of income support
and healthcare support programs available to those over the age of 65,
which have been effective not only at reducing the poverty rate of older Americans,
but even in periods of recession, of reducing the poverty rate of older Americans.
And, by the way, that 22% and that 9% are both in your list of numbers.
So you've got the overall poverty rate of 15%, you've got a poverty rate of 27% for African Americans and Latinos,
22% for children, 9% for the elderly.
However -- and let's skip on to the next slide -- we can see -- I'm sorry?
Nicky: Hi, Stephen, this is Nicky.
I'm sorry to interrupt you, but we had a question that was quite specific to the last slide,
and I was wondering if you --
Would be willing to entertain it?
Stephen: Yes, absolutely.
Go ahead.
Nicky: Thanks. Tom is wondering why the poverty rate for children tends to fluctuate much more than for other groups?
Do you have any thoughts about that?
Stephen: I have many thoughts about that, and it's a terrific question.
I'm going to defer it,
either until maybe later in the webinar when we know we've got a block of time for me to answer that a little bit,
or make sure that we pay attention to that in the subsequent webinar.
It's a terrific question, it's just -- it requires a little bit of elaboration and lengths
and groundwork to make it make sense, and I don't want to lose the thread of what it is that we're doing right now.
So if Tom can be patient, I promise that, if not later today, then we'll get to it in the next webinar.
Nicky: Sounds great.
Stephen: All righty.
So we've seen that poverty varies: it varies geographically, it varies by race, it varies by age.
Now we're looking at the confluence of two of those factors.
We're looking at age and race.
So we've seen that children have much higher poverty rates than any other group in the United States.
But if we start to look at how that breaks down by race, we see as much
or more variation as we've seen in any other categories.
Look at how much higher the child poverty rate is for African Americans and Hispanics,
some 30 percentage points higher than it is for White children in many years.
39% -- (I've done it again) -- 39% versus 12%.
Note, again, what averages can obscure.
Think of it in human terms.
No one you meet is an average.
No one in your community is an average.
No one your agency serves is an average.
Each of them will have their own particular kinds of experiences.
So you should be able to describe the poverty rates in your area and how that varies
from neighborhood to neighborhood, from race to race, from gender to gender, from age to age.
But don't make assumptions that everybody in need of assistance is going to neatly fit into what you identify to be
their prescribed category.
So let's look at still more variation.
And here -- and this was a particular request that came from a couple of people in the questions beforehand
looking at how poverty rates vary now by family structure, by family type --
here you can see that poverty among female-headed households is some 40% -- 41% if we're rounding up;
9% for married couple families.
Again, we've seen fairly significant declines over the period -- and another one right here --
but, nonetheless, a fairly consistent and very large difference.
We're, for the most part, saving explanations for the next time,
but I do want to say just a couple of things about what seems to be going on here.
Why is it that female-headed households in particular have such high poverty rates?
Some of this is basic sort of income maps.
Any single-parent household, all else equal,
is going to be poorer than a two-parent household because it increases the likelihood that there's only one adult
working, and if there's only one adult working it means there's only one income coming into the household which means,
right, there's less money.
Some of this is just basic.
Two adults in a household, all else equal, is likely to lead to two incomes,
that's going to reduce the likelihood that you're going to be poor.
But there's something else that factors into a single-parent household,
and that is: you don't have another adult in the household with whom you can share childcare responsibilities.
So you might have to work less, which is going to reduce the income in your household
in order to make yourself available to stay home with a sick child or to pick a child up from school
or to stay home when the babysitter doesn't show,
or... I forgot what the or was, isn't that awful? ...childcare responsibilities, two incomes
and adults in the household, shared childcare responsibilities...
And then lastly, not only do you have one less income in a single-parent household,
you don't have anyone with whom you can share child-giving responsibilities, but if that's a female-headed household,
still to this day, women in the United States doing exactly the same jobs as men
on average, earn $0.77 for every dollar that men are paid.
So they've got less money coming into the household,
potentially fewer hours worked because they've got no one to share caregiving responsibilities.--
oh, and here's the other thing I forgot about: childcare.
Childcare is expensive.
It's the obvious things sometimes you forget, right?
So not only do you have to stay home more and lose income from work to care for a child,
but you're spending money on childcare that you might otherwise not spend because you've got a spouse
or a partner who's taking care of that. So, enormous variation, right?
Again, there's more going on here than just that,
but you can see the ways in which some of these sorts of things start to play out.
There's your 40% on your list.
You should now probably be able to do this off the top of your heads.
15% overall poverty rate, 27% for African Americans, 27% for Hispanics, 22% for children,
9% for those over the age of 65, 40% -- 41% really -- for female-headed households.
And now let's look at yet another way to look at poverty.
Here we're looking at trends, not just in poverty, although that's that blue line here --
you can all see that sort of marker I'm pointing to here -- 15.1%.
But here we're looking at people who live in what's often called "deep poverty."
As we'll see shortly and as you likely remember from your PSO, there's an official threshold;
if you have income above the poverty line, you're not poor; if you have income below the poverty line, you are poor.
This is looking at -- this second line down here, the red one --
is looking at, not people who have income below the poverty line, just below it, which is that 15.1% number,
but people whose incomes are at half of the poverty line or below.
So what we can see here is that not only are more Americans poorer lately --
about as poor as they were in the 1980s if you look at those trends --
but more of those people who are poor are very poor
In this case it means a family of three with annual income below about $8,200.
Or to put that another way, poverty in the United States is increasingly as deep as it is broad.
If you turn your attention to the resource page, you can see areas of concentrated
and deep poverty as the census has identified, and then look at that kind of variation.
So let's turn on to the next.
So the assumption, I think, is often that poverty -- while it may fluctuate over time --
poverty in the United States is generally confined to a relatively small minority,
and many think of that minority as being in the inner cities.
Well we've seen that people in the South are actually poorer,
but here we can see that many, many Americans experience poverty over time.
That normal measure, the data that we've been looking at,
those census data from 2010, give us data in what people call a snapshot.
It tells us how many people were poor at the moment that that survey was conducted.
What you're looking at here, by contrast, is a three-year period.
So you can see over here it says "percent of people" -- "percent of spells in interval, 2004 to 2007," --
it's just saying that it's telling us we're not just looking at this sort of snapshot moment in time in 2010,
but we're looking here at a three-year period over the course of 2004 to 2007.
Remember, that snapshot number in 2010 was 15%.
If we look here we can see that from 2004 to 2007, 46% of Americans were poor at least once for two to four months.
46% of Americans were poor at least once over a three-year period for two to four months.
And you can see 20% poor for five to eight months, 9% poor for 9 to 12 months.
I want to especially draw your attention to these numbers over here:
this is what you might think of as long term poverty, which some people refer to as "permanent poverty"
or "intractable poverty" or "cyclical poverty." We've got all sorts of names for it.
You can see that over that three-year period, fewer than 7% were actually poor the entire time.
Poverty in the United States is common.
Over this three-year period, almost half of all Americans were poor at least once.
Long-term poverty in the United States is relatively uncommon.
The line between poor, working poor, working class, and middle class in the United States is thin.
It's permeable.
Over time, people move in and out of poverty --
Sometimes poor one month, not poor for the next six months, poor again for three months, not poor again, et cetera--
which is, I think, hugely important
and something that we very often miss when we think about poverty because we're focusing so much of our attention on
those snapshot numbers, that 15% in 2010.
Let's move on to the next slide.
Because we can step back even further.
We saw that 46% of all Americans over that three-year period were poor at least once.
If we look at the course of people's lives, adults from the age of 20 to 75,
59% of Americans will be poor for a year or more over the course of their adult lives.
Now, you don't see this here on this slide, but 75% of Americans will spend a year
or more below 150% of the poverty line, right, so if we adjust the poverty line upwards by a little bit --
a number that a lot of people think still qualifies as poor -- 75% of all Americans are "poor
or low income" -- that's how that's often characterized. 75% of Americans.
So two lessons for today: poverty is varied -- by age, by race, by geography, by family structure, and more --
but it's exceptionally common in the United States.
All righty.
So here's where we've been.
With a little luck all of those numbers should be working at the very front of your head.
If you find yourself at all the wrong kinds of cocktail parties and that qualifies as good cocktail party chatter,
you'll have those data readily available to share with other people, and they'll, of course,
be instantly impressed and intrigued.
What we want to do at this point, since I've been doing all of the talking for about 40 minutes now,
is we want to sort of formally open up the chat.
We want to pause for a bit and ask you to think about how knowing this basic data about poverty,
this basic information, might matter.
We, as you might imagine, and I, as you might imagine, have some thoughts on that question,
but we want you to sort of think for yourselves,
begin to explore what you do with this information
instead of us just sort of dumping our own ways of thinking about this.
So think a little bit about how this matters, what you do with this information, what you might do with it,
maybe you want to think about it as offering suggestions to your colleagues who are online.
If you'd prefer, feel free to comment on anything that we've seen.
And lastly, this is a good time if you want to ask any substantive questions you might have.
I'm going to pause here for a moment or two.
Nicky, in particular, is going to sort of scroll through the chat that's been going on while I've been talking,
and what's going on now and help me sort of pick out questions.
So let's pause for just a moment, let you guys think for a minute and weigh in for a minute.
Amy: And just a technical reminder, folks, the chat panel may have collapsed when we did the poll,
so be sure to click on that little arrow next to the word "chat," on the right hand side of your screen,
to open up the chat panel.
Remember to send your reflections or questions to all participants.
Nicky: Thanks, Amy.
Hi, everyone.
This is Nicky.
Stephen, while folks are kind of thinking and writing to your prompt,
we did have one interesting question from Elizabeth Lawrence a few minutes ago,
and it was regarding that slide that indicated that 46% of Americans were poor at one time for two to four months.
And her question was whether those are probably more likely one time events, or would those be families
or people who fluctuate in and out of poverty?
Stephen: It's a terrific question.
The answer is complicated, and I'm going to stay away from sort of the depths of the complexity.
Part of it is just that sort of we, as social scientists,
have a really hard time tracking, sort of, those kinds of patterns over time because really the only way you can do
that is -- you're actually tracking the same families over the course of their lives --
there are a couple of national studies that try to do that, but you can imagine how hard that is.
So sort of the data on that still needs some work.
With that -- by way of sort of footnote and caveat, if you had to lay money
then you should expect that a family who has experienced poverty is, all else equal,
more likely to experience poverty again.
And if you sort of think about that 46% number in the context of that 59% number,
in order for us to get to the point where 60% of Americans are poor at least once,
you can imagine that there's a lot of, sort of, going in and out that happens over time.
So, yes, it's typical for families in poverty to slip in and out of poverty over time.
The other thing to say here is -- and this relates to what we talk about as "mobility,"
the likelihood that people who are born poor will die poor
and the likelihood that people who are rich will die rich -- all else equal, the United States, in fact,
has less of that kind of mobility than do other rich democracies, than do those other nations of Western Europe
and the Nordic states, which I think surprises lots and lots of people, right.?
We've assumed for years and years and years that we have higher mobility rates.
We don't.
So all else equal, if you were born poor in the United States,
you are much less likely to escape poverty than if you were born in other kinds of nations.
Those kinds of trends have gotten worse over the last five years or so.
So to bring this back around to the question, all else equal,
a family that experiences poverty is likely to escape it, but is also likely to be poor again.
In the current period, those families are even more likely to slip back into poverty after they manage to escape it.
Nicky: Thanks, Stephen.
We have a lot of very interesting reflections popping up from folks about how they might use
and apply this information, and I appreciate that.
I'll let folks scan those on their own.
We also had a few interesting questions looking for statistics about specific group breakdowns that maybe haven't been
addressed, and I'm not sure if you'll be able to answer these or if we can provide some follow-up information.
But, in particular, some folks asked about single-parent households that are headed by males and single fathers
and what kind of data we might have on those.
Also, about military veterans and their poverty rates among that population.
And, finally, some questions about the breakdown of female single headed households by race.
And I realize you probably can't rattle all those off off the top of your head, but --
Stephen: Why don't we do this, Amy, because, I mean, I'd be happy to sort of talk about that,
but I'm also mindful of the time
and want to make sure that folks who have only allotted an hour are able to escape in an hour,
and the clock is ticking by.
So, Amy, why don't we make a point, we'll scroll through all of those chats
and we'll look at all of those kinds of requests, and then we'll make a point of providing citations
or resources for answers to those questions when we put together the final resource page on the Campus, how's that?
Amy:That sounds great.
Stephen: We'll make sure that we offer data information about each of those.
They're great questions and, as you might imagine, they're a little complicated.
But I'll be happy to point you in the right direction if those are particular kinds of interests.
Nicky: I know you want to keep moving ahead, so I won't ask you to answer this, Stephen,
but I also just wanted to point out to participants that there's an interesting conversation going on in the chat
about the fact that most of the VISTAs on that map were not located in what we recognize
as the highest poverty areas, so.
Stephen: Yes.
Nicky: ...Folks can keep sharing their thoughts about that.
I think that's a lively discussion.
Stephen: Yes, and I think an important observation.
And, you know, some of this may be selection bias, right?
You know, it's not necessarily true that the 104 of you who are joining us today
are a representative sample of all VISTAs,
but it raises some interesting questions about how well the program succeeds
in targeting people to the areas in greatest need.
And, you know, that's, as you might imagine, a complicated kind of coordination problem, among other things.
So, Amy, how about -- I think we should probably move on because I see that the clock is ticking away,
and I do want to try to keep us on time here.
So if we could -- oh, you're way ahead of me. Right.
So we've done a tour of poverty rates.
We've looked at it for some key groups of Americans.
At this point, we should talk about what it is that poverty is by that official standard,
and a little bit about how it's calculated.
So here are the official Census Bureau poverty thresholds for 2010.
Let's pay particular attention for our purposes today, to the number for a family of four: $22,314.
And this should, I am hoping, be familiar from your PSO, right.
And the way this works is fairly simple.
If your income -- if you're a family of four and your income is above $22,314 you're not poor,
even if your income is $22,315.
If your income is $22,314 or below, you are poor.
That's the official standard.
So let's talk a little bit about how it's calculated:
how the Census Bureau gets the numbers of people whose income is above or below those lines.
This is a very old method.
Again, this is probably familiar to most of you.
Devised in the 1960s by a woman called Mollie Orshansky who was working for the Social Security Administration at the
time, and she was using data from the 1950s,
looking to build a crude calculation for figuring out how many people were living in poverty.
And at the time, in the early 1960s
when Orshansky made this calculation, the typical American family spent about a third of its budget on food.
So she went to the USDA and used what they called their "thrifty food plan,"
which is an estimate of what it costs to buy the minimum nutrition necessary to sustain life, for all intents
and purposes -- so a very bare bones diet.
She figured out what's the cost of that.
She saw other data that said food is about a third of the total budget.
So if I figure what the cost of food is, if I multiply it by three, adjust it for family size --
because it's going to need to be a little bit higher for larger families -- and we adjust it for inflation,
well that's your poverty measure. That's how we get the threshold.
So it's the cost of food multiplied by three, adjusted for family size, adjusted for inflation.
We add up all of your income: it's above the line, you're not poor. If it's below the line, you're poor.
Cash income in that measure is what counts, right?
So it's earnings, it's unemployment, social security checks, supplemental security income, welfare
or public assistance, pension benefits, interest incomes, dividend incomes if you've got any, royalties;
it's only cash income that counts.
Here's what we'd like you to do, and we realize we've got lots of people joining us today,
so this is going to be sort of messy, but we'd like you to stake a place using that pointer on the screen
and just sort of walk through what it is that's wrong with that calculation.
All right.
So you'll see Amy has marked on a territory there.
She's now going to choose the text tool and will give us a phrase or a sentence thinking about what you don't like,
what you think could be improved, what is insufficient about this measure.
So we got folks sort of marking out territory.
Yes, it's going to be very hard to read, isn't it?
That's all right, we'll be fine.
So when you're ready just offer some thoughts.
And, Amy and Nicky, I'm going to count on you guys to help me sort of see things as things get cluttered.
People pay more on rent or heat, right, great, two really great points.
One of the things that the official measure doesn't do: it doesn't account for variations in the cost of living,
and the greatest variation is in rent, right?
I live in Manhattan, I live in New York City, the cost of living here or in Boston
or in San Francisco is significantly higher than the cost of living in rural Mississippi or North Dakota, for example.
The measure doesn't take that into account.
What else do we have here?
Amy: Folks, I want to remind you, you may not know this, but after you type your reflection,
click somewhere else on the page to post it.
I see a lot of claims, but not as many reflections.
So be sure to click elsewhere which will then post your text.
Go ahead, Stephen.
Stephen: I see "50s nutritional ideas are what we now know to be healthy eating" Yes, absolutely.
I mean, the way the measure is calculated, it's not concerned with healthy eating,
it's concerned with mostly calories, not health, but the sustenance of life.
So it's a very minimal threadbare kind of threshold.
"It doesn't account for medical costs," I see.
Yes, absolutely, right.
And that can be huge.
Remember, the $22,314 for a family of four is a cutoff; if you have income of $25,000 and you're a family of four,
hey, you're above the poverty line, you're not poor.
But if you don't have insurance and you get sick and it costs you $5,000 in an emergency room visit,
well your income is not $25,000, it's $20,000, theoretically you're poor.
The official measure doesn't take that into account.
Nicky: It looks like some other folks, Stephen, are mentioning the increase in transportation costs
or perhaps the lack of counting transportation costs.
Stephen: Yes.
Which, again, can be huge.
Right, I mean, it's -- you know, it's -- again, sort of use me as an example,
I live in the middle of Manhattan,
all else equal, my transportation expenses can be fairly minimal.
If I have a New York City Subway pass for the month, I'm done.
And those can be not trivial, but, again, if you live somewhere where you're commuting an hour-and-a-half back
and forth to work, you need to own a car, you need to maintain it, you need to put gasoline in it,
you need to repair it when it breaks down, and since none of that gets calculated in. Those are the kinds of events,
however, that can move a family from just getting by to not really getting by at all.
The official measure doesn't take those sorts of things into account.
Nicky: Someone else has shared medical costs, I think that's a big one for many families.
Stephen: Medical cost is huge.
I also see "modern age families are not spending a majority of each month on food, it's housing." That's exactly right.
Part of the problem with the formula is that when Orshansky built it people really did spend a third of their income
on food.
It's something like an eighth of their income now. The largest single expense for most Americans today is housing,
and that can be as much as two-thirds of their total expenses.
That measure doesn't take that into account.
So there's a great selection there.
Let's move on just in the interest of time.
And, in fact, I see that most of you in some ways are way ahead of the game:
the realization that this measure really is insufficient across a lot of dimensions.
Starting in the 1980s or so, a group of mostly academics associated with an organization
called the National Association of Scholars --
social scientists for the most part -- started putting together alternative ways of calculating poverty.
Just this past year, for the very first time, the Census Bureau -- separate from its normal report,
just as sort of an experiment -- adopted one of its own alternative measures for poverty.
What it did was added in things like food stamps, because the official measure only counted cash income.
It didn't count the value of food stamps.
Well, that's going to affect your household income and determine whether you're poor or not poor.
They added in the value of the School Lunch Program, and breakfast program, for that matter,
on the assumption that money that's going through those programs to children
is money that doesn't have to come out of the household.
The WIC program, which is a supplemental nutrition program for pregnant women
and women with children under the age of five.
Housing subsidy, section eight housing vouchers, which can be hundreds of dollars a month sometimes,
are now counted in terms of income.
And the LIHEAP program --
this is a subsidy that mostly goes to elderly Americans living in places where it gets cold in the winter to help them
buy heating oil for their houses.
So all of those kinds of income were added into this new "Supplemental Poverty Measure" they call it, the SPM,
you see it here.
And on the other side of the ledger, they factored in things like taxes.
Tax policy, believe it or not, isn't counted.
The taxes you pay doesn't count in the official poverty measure.
So, as we all know, that's going to affect how much income you have.
Plus, on the other side of that, the Earned Income Tax Credit, you could get a tax refund;
that is counted in the SPM, it wasn't counted in the official measure.
Work-related expenses, as some folks pointed out, are now factored in.
Childcare expenses, which I made reference to earlier, are factored into the SPM.
Medical out-of-pocket expenditures, the unfortunate "MOOP" acronym, can be enormous for lots of households.
50 million Americans don't have health insurance.
This can be enormous if someone gets sick.
Child support paid and child support received, for that matter.
There are other things,
but you can see that this supplemental measure does a decent job of capturing a lot of what you all pointed out as
flaws, both on the revenue side -- income coming into the household that we should count --
and on the expenditure side:
expenditures beyond your control that are going to reduce the available money you have to do things like buy food
and support your family.
So here's what this does to the poverty rate.
That's the official rate in dark blue, and that's that supplemental poverty measure in the lighter blue.
Right now, what you don't see here is that what the new measure does is it changes that threshold.
It raises it by about $185 a month for a family of four.
So it's a relatively modest difference, surprisingly enough.
Now look at the overall difference here: relatively small.
This 15.2 is different than the 15.1 we've been looking at for sort of technical,
statistical reasons that I'm not going to bore us with at the moment.
It doesn't matter for our purposes.
So it goes from 15.2 to 16: less than one full percentage point does the poverty measure change.
So it goes up when we use this more comprehensive measure, but you'll see it doesn't go up by much.
But notice what happens to age groups, for example.
The poverty rate goes down something like four percentage points for children, goes up a little bit for adults,
but look what happens to that 9% poverty rate we were patting ourselves on the back for earlier among those over the
age of 65 and older.
Look what happens to the poverty rate among those over the age of 65.
Now, we're still sort of working with these data.
Notice the children still have the highest rates.
But the best data available to us right now suggests that what's going on here is calculating medical out-of-pocket
expenditures, that while those over the age of 65 have access to Medicare,
so they're guaranteed access to healthcare, Medicare is not free to the beneficiary.
You have to pay monthly premiums and you pay supplemental premiums, and there's still a deductible.
You still pay out-of-pocket for drug expenditures.
All else equal, older people are sicker than younger people.
The suspicion is that when we start to count the cost of medical care,
we start to see a significantly higher poverty rate among elderly Americans than we thought we had.
So I think that's enormously interesting.
Time will tell.
With a little luck, the Census Bureau will continue to publish these experimental numbers.
These aren't affecting the way the government programs distribute benefits or anything yet -- it's just a research tool --
but with a little luck, they'll continue to produce this and we'll gain some more insight into variation.
We can turn to the next slide.
Here's, however, what I think is really useful and interesting about the new supplemental measure,
because it allows us to count all of the income that's coming in from all of these different sources.
We can see the particular effect of particular government programs.
So this is the poverty line, right across here, and what this shows,
let's look at food stamps, SNAP.
What this shows is that food stamps lowers poverty by about one-and-a-half percentage points.
So without the food stamp program we can assume that that 16% would probably be 17.5%.
That's the effect of food stamps on overall poverty.
On the other end of the equation,
we can see that medical out-of-pocket expenditures add in three percentage points to the poverty rate.
So that 16% supplemental rate would be 13% if it weren't for all the money that people were spending out-of-pocket on
medical care.
So this becomes, I think, really,
really useful in helping us evaluate the impact of specific government programs so that we can see which ones are more
effective and which ones are less effective and how that varies over time.
I mean, this could be really helpful for people interested in designing strategies that effectively reduce poverty.
So as we wind our way toward a conclusion here, I want to move a little bit away from data
and want to talk about a very, very different kind of way of thinking about poverty --
aAnd this is poverty not as an absolute measure, which is what we've been talking about --
but to think about poverty as a relative one.
This is a way of thinking about poverty that comes from Adam Smith, who wrote a book you've probably heard about
called The Wealth of Nations.
Adam Smith is one of the founders of modern economics, no less.
And Smith wrote the following -- and this is 1776, remember,
this will become important for the context -- "Every man is rich
or poor according to the degree to which he can afford to enjoy the necessaries, conveniences,
and amusements of human life.
By necessaries, I understand not only the commodities which are indispensably necessary for the support of life,
but whatever the custom-of-the-country renders it indecent for creditable people, even of the lowest order,
to be without.
A linen shirt is, strictly speaking, not a necessary of life.
The Greeks and Romans lived, I suppose, very comfortably, though they had no linen.
But in the present times, through the greater part of Europe,
a creditable day laborer would be ashamed to appear in public without a linen shirt,
the want of which would be supposed to denote that disgraceful degree of poverty, which it is presumed nobody can well
fall into without extreme bad conduct."
I think that's a lovely counterpoint to all of the data that we've looked at is to think about this custom-of-the-
country standard.
not to think about, sort of, "how much income do people have in the household" and that sort of crude binary measure:
poor/not poor.
To think about, "what does it mean to be a full economic citizen, a full social citizen, a full member of the culture?"
What does the custom-of-the-country, in Smith's language, dictate about what one needs in order to be a full member of
the society?
Think of it as either the custo- of-the-country standard or the linen shirt standard.
I think that, in part, because we've thrown so much data
and thrown so many numbers at you that it's useful to step back a bit
and think more broadly about what it is that we mean when we talk about poverty.
What does it mean to be poor?
What does it mean to live in poverty?
So we've seen official poverty rates.
We've seen how they vary: 15%, 27%, 27%, 9%, 40%, 59%.
And writing this last segment, you should have had a bit of a refresher about how the official measure is calculated,
and now, although we went through that a bit quickly,
should have a good sense as to what that supplemental poverty measure, that SPM that comes out of the Census Bureau,
how that differs.
Having done all of that, what we'd now like to do -- I know that we're just a little bit over --
and for those of you who need to sign off, we hope that you'll come
and you'll listen to the rest of the discussion at a time when you're able to because this will be posted on the
website in relatively short order, we hope.
But for those of you who can stay around, we want to go maybe another ten minutes or so to do two things.
First we want to turn this over to you, and ask you to share your thoughts on what you've seen,
but we'd like you to do it with specific attention to this question -- and that's, going back to what we asked earlier:
So what? What does this matter to you?
What can you do with this information? And, related, what more information what you want?
So let's do this in two separate ways:
On the whiteboard, let's have people offer their thoughts on how this matters. Llet's do the same thing:
stake your claim with the pointer tool and then type in.
And then in the chat, if you've got questions, comments, queries, any of the sort of stuff that's been going on.
I'll ask Nicky in particular to sort of scroll through that.
I have a hard time focusing on both areas of that screen at once.
And if there's conversation that's been going on that you think is useful for me to draw my attention to,
I'm happy to do that.
Then we'll take a look at what's going on in the whiteboard in terms of what matters.
That's the harder one too, and we puzzle with this as well.
So, with that, let me stop talking for a bit and we'll try to turn it over to you guys for a bit.
Nicky: Thanks, Stephen.
This is Nicky.
While people are writing I just want to point out, there has been a lot of lively discussion going on in the chat,
and in particular, a lot of back and forth about how VISTAs
and we as citizens, in general, can do to affect real change at the federal level,
both in the way poverty is measured and also in terms of the federal programs that aim to bring people out of poverty.
So I think there's a lot of great discussion about that.
Stephen: Yes.
I mean, of course, you know, your mission as VISTAs is not to affect change at the federal level,
it's to affect change at the community level, not that those aren't important questions to be thinking about,
but they're very different kinds of missions, you know,
and what I would say is that right now given the work that all of you are doing in your communities,
I would say, focus your attention on what's going on there: soak yourself,
immerse yourself in that world as thoroughly and completely as you can.
Gain as much firsthand knowledge as you can of the real, lived experience of Americans who are living in poverty,
and at the same time read reports that come out of congress and the congressional budget office
and the Census Bureau and the general accountability office.
See statements about the data that government produces and what's going on and how things change over time,
and begin, for yourself, to try to sort of reconcile what we talk with social workers
about as the "micro" and the "macro."
How do you make sense of what you see going on in that smaller world where you're doing your work with that
larger, "macro" world with all the data and statistics? That's easier said than done, right?
That's a complicated kind of endeavor
and arguably it's something that I'm still constantly engaged in on a regular basis.
But I think that's -- in some ways it can -- both of those things, I think, can productively inform each other.
Nicky: Thanks.
We've got a lot of folks populating the whiteboard too,
and I'm happy to see a lot of comments from folks that this presentation has deepened their understanding of poverty
and some of the nuances and different aspects of both the demographic breakdowns.
And some folks also talking about application,
that they'll be better able to make presentations about these topics
and work effectively with their individual community based on some of the knowledge that they gained today.
Stephen: So I guess I'll direct this question to both Amy and Nicky: what do you think we should do in terms of time?
I'm happy to ask -- excuse me, answer -- particular questions.
What do you think we should do at this point? Given that we're already the tiniest bit over,
we're about ten minutes over.
Amy: So, Stephen, this is Amy.
I propose at this point that we go ahead and formally wrap up.
And then I know you mentioned you had a little bit of time,
so we can stay on the line afterwards for folks that still have questions.
Stephen; Sure, I'm happy.
Yes, I'm happy to.
Amy:So I just, again, want to thank everyone for attending.
And you can visit the VISTA Campus to continue this conversation on the VISTA forum.
There's been a lot of great discussion.
Also visit the Campus to sign up for the third webinar in this series titled "Causes of Poverty: The Economic
Crisis and Beyond." And I'm looking forward to that one.
Also, just a couple other housekeeping items:
please complete the webinar survey that will appear in your browser once you've closed WebEx.
First you'll see a WebEx survey, you can complete that if you want,
and then once you close that you'll see our survey for this webinar.
So please take a minute to fill that out.
As we've mentioned several times, the recording of this will be posted on the Campus soon --
we'll work on that as soon as possible --
and you will be able to register for the third webinar coming up very soon as well.
So it's all on that same page that you registered for this one.
So this concludes the formal presentation.
And, as I mentioned, Stephen's available for more questions if folks can stick around.
And, if not, we just want to thank you for joining us.
So at this point let's go ahead and use the chat panel to provide any other questions that you might have for Stephen.
Nicky: And I'll keep scanning those for you, Stephen.
This is Nicky.
Stephen: Great.
Thank you, Nicky.
Some additional, nice reflections here while we wait for questions.
Folks talking about how they can use these statistics to take them to potential donors
to raise awareness about some of these widespread issues.
There was another comment about the fact that, you know,
some folks had been operating on slightly outdated information
and that this helps bring them up to the current point in time.
And also a nice comment, this is from Danielle, "this reminds me to investigate statistics further
and not take them at face value because there are many ways to break down generalizations
like the overall poverty rate."
So I think that was an important part of what you presented.
So we do have some questions coming in.
"Will the resources that address the issues raised in this webinar be made available?"
Stephen: Yes.
Nicky: Okay.
I mean, I know the webinar resources will be made available.
This might mean some additional resources, and I think, you know,
we talked about gathering some of that demographic data that folks were interested in.
Stephen: So, yes.
Yes, I mean, it's easy enough.
We'll take a look through, it will take a little time, but we'll look through the chat that's taken place,
and I'll put together a short bibliography and try to give, you know, sort of some of the best
and most accessible discussions of those kinds of questions, of the fatherhood question,
sort of the fluctuation in child poverty question.
There are a couple of good sources to go for a good overview,
and then people on their own can sort of burrow down and look for more detailed information,
beyond there.
Nicky: Well, and that's a great segue to our next question from Tom,
"do you have any recommendations about the best places for folks to find local statistics about poverty?"
Stephen: I would start with local organizations in your area.
Odds are that if you identify the city agencies
or the not-for-profit organizations that are serving people living in poverty,
that they have been gathering data on poverty, as I referred to earlier,
in order to make arguments to potential donors and things.
They've probably got really good resources.
The state legislature often will track data across the state.
For all the different kinds of state programs serving different kinds of populations it's usually important for all of
those kinds of agencies to have good information.
So, I would go: city agencies, state agencies, and then look at local organization,
service delivery organizations. Look on their website.
And if you've got good relationships with them, call them up and say you're a VISTA working with so
and so and you're interested in more detailed information about poverty and hunger or homelessness, or what have you,
in the area, and you thought that they might be a good resource for those sorts of things.
My experience is that most organizations are perfectly happy to share that information because they want people to
know in order to draw attention to the issues they care about.
Nicky: Great.
Thank you.
So we've still got a good number of folks on the line or on the webinar, but I'm not seeing any more questions.
We can maybe give it another minute or so if anyone's in the midst of typing, and then maybe we'll wrap things up.
Stephen: It can be hard too because, I mean, it's -- all these data, all those numbers, all that information,
it can be a bit overwhelming.
So sometimes it's hard -- you don't know what you think about it until you've had some time to sort of reflect
and ponder for yourself what it means.
Iif you don't sort of feel an urgent, compelling question, there's nothing wrong with that at all.
Nicky: Absolutely.
So here's another question.
We've had the fluctuation of child poverty rates reposited as a question, if there is time.
If there's not, I think we are going to send resources to address that.
And then an accompanying question about how child poverty is calculated.
And I don't know if that one might be easier to answer more succinctly.
Stephen: Curiously, it's actually harder to answer succinctly because of the nature of these things.
We will post some more information.
The very short and insufficient answer as to why child poverty rates tend to vary more
is that children have fewer resources consistently available to them.
But we'll talk a little bit about this in the final webinar.
The principal source of income for all Americans, poor and not poor, is work.
This is not true of six-year-olds,
so you see much more variation because they are much more susceptible to variations in changes in the household
and variations in government and not-for-profit programs that deliver services specifically to them.
There's less that they have available to them, so they're more dependent upon fewer kinds of sources,
and you see that, iin periods of crisis, if programs don't respond effectively,
you tend to see sort of child poverty sometimes as a leading indicator.
I know that's terribly insufficient, but, yes.
Nicky: Well I think that at least is a good start in addressing Tom's question and also speaks to Kristen's question about,
you know, doesn't -- "don't child poverty rates depend on their providers,"
and I assume by "providers" you mean either the family structure -- Stephen: The adults in the household, yes.
Yeah, well, I mean, it's the very nature of being a child, right? You are dependent.
It's -- you know, that's the very reason we have to make a distinction in the law between minors and adults.
Again, we don't live in a world -- I'm going to say thankfully -- in which we expect six-year-olds to go out and work.
We used to. In 19th century they used to go out and work in the mines
or work in mills up in, outside, Lowell, in Massachusetts.
We don't live in a world in which we expect that anymore,
but it means that those children are not now bringing an income, so it makes them dependent upon their parents
and upon programs that might provide support for them.
Nicky: Thank you.
So I think we're getting close to a little more than 20 minutes past time.
And, again, I'm not seeing anymore questions.
So I think maybe we just want to thank you very sincerely, Stephen, for this presentation.
And I don't know, Amy, if you have any parting words or comments?
Amy: Just to echo what Nicky said, thank you all for attending.
This was the largest group that we've had so far,
and we look forward to hopefully seeing all of you again on the next webinar.
So check back on the VISTA Campus next week
and there should be registration information available for the third webinar in the series.
You will receive a follow-up email from me, pointing you to some of these links
and the recording once it's ready.
And I'll also be reminding folks to fill out the evaluation if you didn't do that.
So you'll hear a little bit more from us, but for the most part we're going to sign off now.
And everyone have a great day.
And thanks, again, for attending our webinar.
Thank you, everyone.