[Music]
i do that is the logo of our center here
the salem center for policy that's
that's
what we do we focus on these issues any
issues
that's a great quote to think by remind
us of that for sure
um i haven't sent you a team i'll send
you a t-shirt on that if you if you
okay thanks excellent all right i think
that is probably enough time to let
folks come in
um hi everyone and welcome to another
salem center
event we're happy today to have bruce
meyer from the university of chicago
where he is the mccormick foundation
professor
at the harry school of public policy
bruce studies poverty and inequality tax
policy
government safety net programs as an
employment insurance compensations food
stamps medicaid etc
and he's joining us today to talk about
his recent work on developing
um new poverty measures and you explain
to us where that work is coming from and
and and anyway bruce it's a pleasure to
have you here thanks for joining us
thank you carlos thank you for having me
let me
share my screen and
i'm gonna
get this started so can you
um see the presentation
yes okay good um
so the work that i'm going to talk about
is the work of over 25 people on a
federal working group spread across
11 agencies the
background research that i'm also going
to talk about
is the work of the comprehensive income
data set
project staff we call it the
kid and
also many co-authors what i'm going to
say is not the views
of the census bureau
so um you might
wonder why a university of chicago
professor is going to tell you
about a federal committee that he
co-chaired
but for three years i was a part-time
census employee starting in february
2018 i had
served on the evidence-based policy
making commission
which produced a bill called the
evidence act of 2018 that was
signed into law in 2019 and
the former director of the census bureau
called it the
most important uh
thing that has happened for data for the
common good in his lifetime
it's kind of not not widely known but it
makes
data more available for research
and it protects data better and
encourages
federal agencies to evaluate their
their programs a fellow
commissioner on this
federal commission was nancy potock
who had been appointed as chief
statistician
in the obama administration i know this
is getting involved and
you might wonder what
the chief statistician is or even might
one
wonder um uh about
why we even have one you might not have
known that we
do when i told my uh brother
that we have a chief statistician he
asked
why it's not statistician general
or statistician laureate
eos likes to tease but
the chief statistician is responsible
for coordinating
the many statistical agencies
and the u.s has 130
statistical agencies at the federal
level
13 are considered principal including
the
census bureau and the bureau of labor
statistics
well i went to see the chief
statistician
in her office in the executive office
building
and suggested that we could measure
poverty
better than we currently do about
six months later no good deed goes
unpunished and i was asked to
co-chair this committee that was
evaluating how we measure poverty
and suggesting new alternatives
the report that i'm going to describe
really has recommendations
but in the ways of the federal
government
these recommendations are likely to be
implemented because they fall in a
gray area between recommendations and
decisions
the um people who would be responsible
for
implementing the recommendations were
part of the committee
in particular uh representatives from
the census bureau and the bureau of
labor statistics
so um the background
for this uh
report is that there
is a lot of evidence that the surveys
that we rely on
to measure poverty have a lot of
under-reporting
of various kinds of income in particular
transfers so
social insurance and welfare payments
and
surprisingly even pensions private
pensions
are badly reported in most of our
household surveys with about
half of pension income not reported
there's also been some other trends in
recent years
improved availability and understanding
of administrative data
and some evidence that
poverty could be well measured by
consumption data
and that the
measures using consumption often
are better validated in terms of
um corresponding more to other
indicators of
material deprivation than than income
measures
so let me show you some of the evidence
on what i just said so here is a figure
that shows you the proportional
bias in reports
of four different transfer programs
tanf being our main cash
welfare program snap another name for
food stamps
ssdi is disability insurance
and ui is unemployment insurance
this figure shows that
the surveys and
let's focus on the current population
survey or cps
bars in pink
those indicate
say in the case of tanf that half of
tanf is not recorded in the survey
and in the case of snap 42
of benefits are recorded in in the
survey
uh so this is pretty dramatic
understatement
of certain kinds of income in our
main surveys the cps being the source of
official poverty statistics
now
you might wonder if there are other
problems with our household surveys
and the biggest problem really seems to
be
under reporting of income that our
surveys tend to do a pretty good job of
capturing the population
that would want to be interviewing
to measure poverty so
what this figure shows and focusing say
on the cps
results in green says that
if you take a food stamp or snap
records and link them
to the current population
survey and
you do that
to see if food stamp recipients
show up in the survey and you
need to apply the appropriate weights
that come along with the survey
you find that 95
of the people that you
expect should show up in the survey do
in fact
do appear there
so that's not perfect but it's
only a five percent
um share of
snap recipients which are a good proxy
for the poor snap being our largest
transfer program in terms of the number
of
uh low-income people
reached um
and uh
this just shows that the survey does a
pretty good job of capturing the
relevant population the problem is
once you interview these
uh individuals a lot of people aren't
willing to tell you about their income
and you
understate their their income quite
dramatically um so
if you um
instead of just relying on the survey
reports
you bring in administrative data
and you link it at the individual level
to the survey records which
we can do um
at the census bureau or at a research
data center
um and the records that we analyze
have been anonymized so that we're not
actually
looking at any names or addresses the
administrative data and the survey
reports are
are joined using anonymous codes
when we do that we
see that using almost any measure
of poverty in this figure shows you
various measures looking just at
pre-tax cash income accounting for
taxes in the next set of
bars and then accounting for in-kind
transfers like housing benefits and
staff and then
finally in this figure we
account for a flow value of income
for those who own a home
or a car if you own a home
you don't have to pay rent um
most relevantly if you own it outright
so you're not even
paying a mortgage you essentially get
the flow of
resources that's akin to a
rental value in the form of income
so this figure shows
various measures of income
poverty and the
maroon uses just survey data the
gray combines the administrative data
with the survey data and you can see
that
the share of people below official
poverty cut-offs
is often much lower when
you use the combined
survey in administrative data so
administrative data which has become
more available
can be successfully incorporated with
our surveys and it
changes our understanding of the poverty
level
now it also
changes our understanding of the poverty
reduction
of government programs policy makers and
researchers often use poverty measures
to assess the effects and targeting
of government benefits the poverty
reduction of many programs it turns out
is sharply understated by existing
income poverty measures when we
in this figure look at how much higher
poverty
would be without various programs we see
much larger effects of many programs
with the combined
survey and administrative data than we
do
with the survey data alone so the maroon
bars here
come from a census bureau report
the gray ones
are what we get when we combine
the survey and administrative data that
i
described just a few minutes ago
and you can see that many
programs have their poverty reduction
sharply understated
with existing survey data alone
for example snap
the survey data alone say the poverty
reduction
is about 11 so 11
of people would be poor 11
more would be poor if we
did not have the staff program but
if you um use the uh
administrative data combined with the
survey data it's
25 more people would be
poor um if you
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didn't have the staff program and that's
because
snap benefits are so underreported
in our surveys about half of them aren't
registered in our surveys and
the administrative data in this
analysis here is also used to figure out
who's originally poor in the first place
before you do the experiment of
potentially
removing the stat program so
both of those changes to the calculation
affect the number here and you can see
that
it's not just snap that is understated
in terms of its poverty reduction
housing benefits and ssi
short for supplemental security income
for the
aged and disabled
is also dramatically understated in its
effect
understated using survey data alone
the an additional motivation
for this uh
committee was the finding
that consumption data seems to better
identify
the most deprived
in the population than income data
and what we did in a paper
um jim sullivan and i almost 10 years
ago
we looked at a number of measures of
deprivation
and we looked at whether
when you classify people as poor using
consumption data you got a more
deprived population
of poor individuals than when you define
poverty based on income and we didn't
find
uniform support for consumption
doing a better job of capturing
well-being but
a majority of the indicators that you
can
see in this table like
um uh measures
of uh education
or health or characteristics
of a person's home
um those tended to be
uh
worse um for
those defined as consumption poor those
defined as income poor and we
think of the goal of poverty measurement
as
identifying those who are most deprived
so
this analysis tended to
indicate that consumption did a better
job of capturing
poverty than than income
um so
there were other motivations for
revising poverty measurement
it's been 25 years since a national
academy of sciences report that's
considered a
landmark report on poverty measurement
came out and that report
was an important advance at the time
and it eventually led to
a new poverty measure that the census
bureau produces called the
supplemental poverty measure which most
people
take to be our
best available measure of uh
poverty these days rather than the
official measure which
doesn't account for taxes doesn't
account for
in-kind transfers which are the main
things that we've done to reduce
poverty over the last 30 or 40 years
um and um
there are however um
drawbacks to this measure
alternative measure that the census
bureau produces
it's fairly complicated and hard to
understand
it has um some
quite arbitrary elements to its
uh cut-offs under which you're you're
called poor
um it has an ad hoc
treatment of housing status there's no
um it doesn't put people
in in different housing statuses
on the same um playing field
and it also um
this supplemental poverty measure it
has an odd treatment of health insurance
it mostly ignores
health insurance and medical costs but
um for someone who gains health
insurance
but pays a positive amount
for the uh insurance when they when they
gain that
uh health insurance this
measure um would
say that such a person is worse off
even if they gained wonderful health
health insurance if they paid a penny
for it
um this measure would consider them to
be worse off
so that's um a real drawback
in a period
like the past 10 years and maybe going
forward where
um the extent to which people are
covered by
government health insurance changes
dramatically
so the charter of this working group
was to consider whether alternative
measures of poverty
could be produced and should be produced
and what those would look like
the idea was that these new measures
would supplement not replace existing
measures
the group was established by the
chief statistician that i mentioned
earlier
and there were subject matter experts
from 11
agencies on this working group
everybody was a career agency member not
a
political appointee
the co-chairs were carrie leslie
from the statistical policy office
in the office of information and
regulatory affairs
that's where the chief statistician sits
and myself as a representative from
census we had 46
meetings of at least two hours in length
over
a two-year period it was fairly grueling
we
got to know each other very well
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the interim report came out
last year in february we received about
20 000 comments um
now most of those comments and
anyone who's worked on um
regulatory uh actions in the
in the federal government will tell you
that
you get a lot of form comments and
a lot of um comments that aren't
substantive
and that was true with our
report so most of the comments were
um that people wanted more time
people were were given um 60 days
to comment but this
report came out just before the start of
the
pandemic so
people were
not surprisingly busy with
adjusting to all the changes that the
pandemic wrought
so we let people submit comments beyond
the 60-day
deadline and we did get a lot of
substituted comments which
are uh summarized in in the report
and we also um describe what we
did relative to those comments our final
report
came out earlier this year
and here's the table of contents it's
um almost 130 pages long
and covers a lot of ground
let me tell you what we recommended
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we were able to agree on many things
the report was a consensus report
so that we all signed on to it
but there were some details that we
couldn't fill out
so there was uh a process
described in the report to
complete some of the details of the
poverty measures in particular
exactly what the cutoffs would be
below which you're declared poor
we spent most of our time on what are
called the resource measures or the
often those be just one resource measure
and the resource measure is what you
compare
to a threshold and when the resource
measures below the threshold
you're you're called poor and we
recommended
that there be two
different resource measures one
that would be based on income but would
combine administrative data
with the survey reports
and this is a a new thing because there
aren't federal statistics that
use surveys that combine um
their information with administrative
data in this way
and so
an important aspect of this
report is that it moves the
use of administrative data which
is a way of correcting a lot of errors
in
in survey reports it moves it to the
fore
so that was our first resource measure
that we've recommended one that combines
income reports from surveys
with administrative data from program
welfare and social insurance programs
second resource measure was
a consumption measure based on the
consumer expenditure survey
combining um
the information in the expenditure
survey with
a much more limited uh array of
administrative data
we also had a number of recommendations
that applied to both of these
income and consumption resource measures
we talked briefly about thresholds and
and future research so let me
um talk about the
income measure so um
we recommended that we continue to use
the
current survey that's used to measure
income for poverty and income
distribution purposes in the
us the current population survey annual
social
and economic supplement we recommended
adjusting for taxes
and including in-kind benefits because
it's disposable income that really
generates well-being you can't
spend what is taxed away
and credits like the earned income tax
credit
and the child tax credit are major
anti-poverty
um efforts that we want to account for
um there also are a lot of in-kind
benefits
like food stamps and housing benefits
that are are key
programs for reducing poverty and you'd
want to
account for those as well
we recommended linking administrative
data
to replace survey values with
administrative values
when there's evidence that the survey
reports
tend to um badly
capture often understate
the receipt of
um programs or um
earnings or various other kinds of
income like
pension income
we recommended
that the default approach
should be to just substitute the
administrative data for the survey data
when
supported by research but more
complicated
approaches will often be needed
in part because current
administrative data are
often incomplete not reaching
um
we currently don't have data for
all states for some programs like
snap and tanf so one would have to
predict values for those
states that don't have administrative
data to substitute for the survey
reports
we recommended strengthening data
sharing across government
agencies to facilitate the production of
these income statistics and other
statistics that the federal government
produces
in terms of consumption
recommended using the
consumer expenditure survey produced by
the bls
as a source of expenditure data
and constructing a poverty measure
that's based on instead of income what
people are in fact able to spend on food
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housing clothing and other goods and
services
um and that provides a
more direct way of
looking at people's uh living standards
um we
recommended that eventually the
bls produced such a consumption-based
poverty measure at the state level
but in the interim produced one at
the census division level there are
four census
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divisions and
and recommendation was to produce
um uh
poverty measures at the division level
i'm sorry i think it's
it's actually nine divisions four
regions
um and
uh um we also recommended that
administrative data be incorporated when
appropriate
for example we have good administrative
data
on public and subsidized housing
benefits
that can be incorporated with the
consumer expenditure survey
rather than relying on individuals
reports
of um their uh
the value of their housing benefits
um we had
several recommendations for
how to measure um
resources when
people are
potentially getting health benefits or
health insurance
and we recommended that
the federal government construct
measures with and without health
insurance
that's because we we had to get everyone
to agree this was a
consensus working group
and everybody recognized
that you want to
account for health insurance because
it's a very valuable in-kind benefit
when either provided by your employer
or provided by the federal government in
the form of medicaid or medicare
or veterans health insurance benefits
and ignoring health insurance
seems like an important admission but
there was also a lot of recognition that
low-income individuals
may not value health insurance at its
full cost
so what we settled on
is that the
federal government should produce
measures including
and excluding so that you can see the
effect
in both cases and that
the value of health insurance should be
capped at a share
of total resources so that someone
couldn't be
made non-poor lifted above the poverty
threshold
just from uh having health insurance
so it was kind of a middle ground a
compromise
to include the value of health insurance
but
cap it at a share of
of total resources
um we
recommended that um
in the interim um before
everyone could um
uh agree on a set of cut-offs below
which you were
declared poor um we would use some
interim thresholds because we felt that
the group knew enough
about how to
measure resources and that would lead to
a
substantial improvement in how we
measure poverty and we could just use
interim thresholds that
might not be
what everyone was completely happy with
so we argued that final thresholds would
require
additional research and additional
discussion
but we felt that we
had come up with measures of
consumption and income using
administrative data as well as survey
data
that would make a substantial
improvement in identifying who's
poor so that those should be
applied immediately one
issue that we
failed to agree on and that is
whether or not you should adjust
thresholds
for local prices um
and there's um
a very strong strong intuitive
uh argument that you would want
cutoffs to be poverty threshold cutoffs
to be
higher in areas with say high housing
costs like new york or
san francisco than you would in
rural areas in the south say
but
there's also the standard economic
argument that you get what you pay for
and part of the reason um
housing may be more expensive in
california or new york is people want to
live there
for other uh characteristics other
amenities
of the area that they uh
get um and
um we also
um and this is work out of the
comprehensive income data set project um
we also looked at some evidence
that um indicates that
when you geographically adjust
poverty thresholds so when you make them
higher in new york and california than
in
rural areas for example um
you um tend to
identify a group of
people as being poor who are
um better off so
that's the opposite of what you would
hope to do
with a change to the
um poverty thresholds and so
what this figure shows is that
it shows different
domains to well-being that
we looked at and
it shows the number of indicators that
um i did that
show that a um
geographically adjusted poor population
would be um
more deprived and the number that showed
that
a geographically adjusted poor
population would be
less deprived and you can see that more
are to the left which
indicates less deprived which is the
opposite of what you would hope the
geographic
adjustment would do
so
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so we had um sort of conflicting
intuitive arguments and
some evidence that suggested that um
geographic adjustments did not do
what you would hope them to do so
we were unable to recommend
a specific
approach there so
we also made some recommendations on
future research
there were a lot of people on the
working group who recognized that
one indicator
income or consumption might not be
enough
to really characterize who is deprived
so we recommended that
future um work and and that
more um
a later group of federal officials
examined the possibility of the u.s
government producing a multi-dimensional
poverty measure that would look at
things like
health or education besides income
and consumption we
noted that current measures of poverty
do not incorporate the homeless
and that
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it would be good to one
make that more clear in government
reports
and then do other analyses
to um uh
document the the plight of the of the
homeless
um we also noted that current
poverty statistics are not timely at all
um we won't have any official
numbers on poverty during the
pandemic during last year even
until september of this year
and that means that we
won't have any official measures that
can be
used to guide policy
in a situation like the pandemic
so we recommended that
future work at the federal level
include how to come up with timely
poverty measures
so let me conclude
um just to give a little bit of
commentary um
the recommendations by
encouraging um the use of
administrative data by the
federal government and producing poverty
measures
would in general make administrative
data
more available for other statistical
analyses
and for research
which is a hugely important side benefit
of a new poverty measure that combines
survey and administrative data
because it's not only for poverty
measurement
that the um
decline in the quality of survey
evidence in our in our main surveys
um leads to um
bad bad evidence and bad policy making
um so let me uh
stop there and take any any questions
all right thanks bruce uh let me uh uh
make sure that everybody here has a
chance to type either in the q
a or uh if you want to ask a question
raise your hand i see who it already has
his end up
and he would wait a second let me ask a
question first to to bruce here
uh and i'm dying to ask this question
i've been following your work for a
long time bruce just because i i teach a
lot um
to our students and especially
undergraduates how to look at data to
understand the effect of policies and
how difficult it is
for measure measurement is a super
difficult thing right so i re
use a lot of your your examples and
papers in those classes
and one thing that you know correct me
if i'm wrong um
in the measures that you're proposing
right now you showed us a slide in the
beginning
that basically shows that there's a
disparity in the way we think about the
percentage of people that are poor in
the country
that if you use surveyed only data you
could say it's twelve percent
or all the way down to six percent
which you know this is a a difference of
what uh something like 50 million people
right so so this is a huge difference
between
the way definition of poverty uh will
take place depending on
on how one measures things so this is
not saying what's right or wrong just
telling
how difficult it is for us to make
decisions based on data given that it's
so hard to measure some of those things
the question i want to ask is that we've
seen in economics in the past maybe 10
or 15 years
an enormous emphasis on the idea of
inequality
and again to talk about inequality we
need to measure and measurement that is
also very difficult
uh what is your sort of take on on the
impact of this consumption based
measures that you that you work with in
in
sort of like uh modifying or or coloring
a little bit
the sort of narrative that we have
around inequality in in in the past in
the profession in the past let's say
you know 10 years or so well i think
it's clear that when you use
consumption data um
and look at what people are able to
buy in terms of food and clothing and
housing
that inequality
has not increased nearly as much
over the past 40 years
and you really don't see much of an
increase
in the last 30 years at all
in the bulk of the population
now we don't have consumption data that
can allow you to look at the top tenth
of one percent or really even the top
one percent and um
so the consumption data don't really
um bear on uh the very
top tail of the distribution
but when you look at consumption data
you see much less increase in
inequality and significant part of that
is because um there's
so much under reporting of
income at the bottom
and you know it's not because people are
trying to um be devious
or hide um
illegal behavior or it's it's that
um people are just over surveyed
and income is a sensitive topic and they
just
say when asked if they get income from a
given source they just say i didn't get
any from that source
and and in truth um
the interviewers are also very
um busy and
um often go through the surveys
quickly and don't take the time to
elicit complete responses
so um the result is that
particularly at the bottom
it appears that
there's much more inequality if you rely
on
income but if you look at what people
are able to actually
purchase there's less inequality
at a point in time and it's gone up uh
much less over the past 40 years
than um income inequality
professor ron go ahead
thank you bruce my question follows up a
little bit on carlos's
um and maybe above your purview but let
me ask it anyway
if we were to use supplemental poverty
measures
or consumption-based measures how would
federal policies
actually change
sure um so at this point
um the um
the proposal is to just
produce these measures
uh for um
uh to understand
the extent of poverty and to understand
who is who is poor um
not to use these measures to
determine eligibility for programs
um i would argue that
we would realize that
our programs are doing
a better job of reducing
poverty than they are
given credit for doing and
we would conclude that fewer people
are falling through holes in the safety
net
so that um uh
for example um
they've been you know uh i've seen
reports saying that
a very low share of the
unemployed in the pandemic received
unemployment insurance
and that's based on survey reports of
asking people who are unemployed did you
receive unemployment insurance
but we we know that people
um do not do a good job of answering
that kind of question
um and um
so we would conclude that um
unemployment insurance in the pandemic
is
reaching a larger share of people
than um
many papers seem to report
and we
would i think be probably less
worried about potential failures of the
program
but also realize that
they are more important because they're
reaching more people than
um than
is stated at face value by
our our statistics
all right anybody has any other question
i i have another one here um
how difficult it is to make the the
adjustments of
of this the demonstrative data to bring
in consumption-based measures to measure
poverty
um to go back in time and think about
the the time series of it is it
something that how far back can we go
in other words right a lot of this
effort has started with lyndon johnson's
war on poverty
a lot of the measures of poverty line
started around that time right
can we go back and think about the
poverty line and and re-measure things
all the way back or it's difficult to do
that
well it's fairly difficult to do it
accurately our consumption data
was irregular until
the early 1980s so
we have a couple um
uh there were there was a survey in the
early 60s and another one
in the early 70s um
but not every year so
and there were some changes in how the
survey was done and what information was
collected
so um it can't be done
as accurately as one would like
and for income we're not going to be
able to
correct old surveys
for misreporting of various sources of
income
but um there was less misreporting
back in the 60s and 70s
so it's it's less of a problem but
you know it's still there so um
we can do it but not as reliably
as we would like now
um i'd argue that poverty back in the
60s was
a completely different thing
back then you know you
worried about people not having running
water
and not full um
[Music]
uh um
bathroom facilities and
you worried about people not having
sufficient calories
today
obesity is a bigger issue than
malnutrition
and 90 percent of those
in the bottom 20 percent of the income
distribution
have air conditioning uh either rumor
central air
so what it means to be poor is
completely different
now than it was in the 60s and
it's not clear you'd want to
um apply the same
measure all the way back then um
so i i think that that that's
a drawback that these new measures could
not be used
all the way back then but it's not clear
that you'd want to
um do that because uh
poverty was quite different back in 1964
when the war on poverty was
was started yeah i think
that that is an interesting aspect of it
because the the notion of
talking about the poor it's a abstract
concept that is
also relative and also uh uh
i'm from brazil originally and 20 years
ago when i moved here
the notion of poverty that i had in my
mind is very different the notion of
poverty that maybe
folks here would have right and and so
so so
not only back in time but today even if
i go back and visit my family
what is poverty around them is something
very different than what's poverty
around us so it's definitely not an
absolute thing right
which makes it even harder to to to to
measure appropriately and to think
uh are we addressing the right things or
not okay so see anybody else in the
audience
um and so
when you one of the plots you had that i
found interesting was the one that
reduction in poverty
per program when looking at the
different um
the different measures so you had like i
think you mentioned snap
going down something like 25 reduction
in property as opposed to like 15
as currently based on survey data um
do you from that from that analysis
uh is is is it did you find anything in
terms of
relative sense of the effectiveness of
the programs is that any program that
that that that shows up as being after
adjusting for the consumption
uh scale being more effective than we
previously thought
well quite a few programs um
turn out to be more effective so snap
and housing benefits reach many more
people than um you would get from the
survey data alone
it's also true for some of the tax
credits that are in the news
the earned income tax credit and the
child tax credit
reach more people than the survey data
indicate so um
you know there's some um
news here that i think um
liberals would like and some news here
that conservatives would like
i think it's clear that there are
fewer people uh who have income
below official poverty cutoffs
but one of the main reasons why that's
true
is that our poverty reduction efforts
have been
more effective than is currently
advertised yeah i was thinking more in a
sense that if you were to prioritize
them if you were to
focus more efforts uh which ones are you
know have the highest derivative i
suppose that's the
the if you know uh uh in reducing things
that is a good question and we
haven't put um the programs on the
on a common scale to do that
to say um and that's a good idea we
should do
that whether um a dollar spent on snap
uh is more effective than a dollar
spent on an earned income tax credit
or ssi um
and that that really would be a good
next step
i guess that only applies in the world
where we have trade-offs and currently i
guess we have no budgetary constraints
so
let's put money everywhere right it does
seem to be
um the current approach that's right
that's right
bruce getting to five o'clock um thank
you so much again this was wonderful
and i'm definitely gonna make my
students watch this in this coming
semester so thank you for helping me
teach you my classes in the near future
it's been a pleasure um