Speaker 1: Hi, This is Daniel Moss from Bloomberg Opinion. Before this
Speaker 1: week's episode of Benchmark gets under way, an announcement. Often
Speaker 1: more than three years and a hundred and fifty episodes
Speaker 1: will be taking a hiatus. We'd like to thank all
Speaker 1: our listeners. Now enjoy the year's finale. Here's a secret
Speaker 1: about the U. S economy. Most of the data you
Speaker 1: hear about, such as jobs, GDP, consumer spending, and inflation,
Speaker 1: is not actual data. Rather, these numbers are all extrapolated
Speaker 1: from surveys of households and businesses. Now, these surveys tend
Speaker 1: to be several times bigger than say, political polls, but
Speaker 1: they're still samples of the broader population. But what if
Speaker 1: we could measure consumer spending by looking at every single
Speaker 1: purchase that Americans make, or look at every small business's
Speaker 1: bank account to analyze cash flow. One think tank is
Speaker 1: trying to do that and potentially reshaping how we look
Speaker 1: at the economy. Welcome to Benchmark. I'm Start Landman, economics
Speaker 1: editor with Bloomberg used Washington, and I'm Daniel Moss, columnist
Speaker 1: at Bloomberg Opinion in New York. Don't get us wrong.
Speaker 1: US economic data may be based on surveys, but it's
Speaker 1: the gold standard for surveys. Yet they're still subject to
Speaker 1: regular revisions and often face questions over whether they've properly
Speaker 1: adjusted for seasonal events such as holidays, and they often
Speaker 1: don't fully capture what's happening on a week to week,
Speaker 1: let alone day to day basis. Now we have with
Speaker 1: us in our DC studio a person who's using a
Speaker 1: set of big data to explain trends in the economy
Speaker 1: and also to find new ones. Diana Farrell is the
Speaker 1: founding president and chief executive Officer of the JP Morgan
Speaker 1: Chase Institute, a position she's held since Previously, she was
Speaker 1: a senior partner at McKinsey and Company, where she was
Speaker 1: the global head of the McKinsey Center for Government and
Speaker 1: the McKinsey Global Institute. She served in the Obama administration
Speaker 1: as Deputy Director of the National Economic Council and Deputy
Speaker 1: Assistant to the President on Economic Policy. Dianna, thanks for
Speaker 1: being with us on Benchmark. Thank you so much for
Speaker 1: having me so. First of all, can you tell us
Speaker 1: what exactly is the JP Morgan Chase Institute, why did
Speaker 1: the company started, and what we're in are your goals.
Speaker 1: I'd love to UM. The JP Morgan Chase Institute is
Speaker 1: a relatively new initiative of the Bank UM and the
Speaker 1: best way to think about it is it's a think
Speaker 1: tank and it's trying to do original economics research, but
Speaker 1: with a twist, which is much as you started to
Speaker 1: introduce it. Instead of relying on the typical surveys that
Speaker 1: are designed to answer specific questions on the economy and
Speaker 1: then aggregate up to a view of the economy, we
Speaker 1: start with the actual choices that people make as evidence
Speaker 1: through the financial transactions. So think about the credit card, debit, card, loans, UM,
Speaker 1: any number of finacial transactions that come to the bank
Speaker 1: as the window that we have, and it's a pretty
Speaker 1: extraordinary window. Consider that the JP Morgan chases two and
Speaker 1: a half trillion dollars balance sheet worth of financial transactions.
Speaker 1: That's you know, over half of US household UH, two
Speaker 1: and a half small businesses UM and a very large
Speaker 1: number of institutional investors. If you think about that, is
Speaker 1: the window on economic activity not based on what people
Speaker 1: say they are doing or are going to do, but
Speaker 1: on what they are actually doing. That is the basis
Speaker 1: for us to try to explain some of the things
Speaker 1: that are transpiring in the economy, especially as the economy
Speaker 1: evolves UH compared to the way some of the original
Speaker 1: statistics were developed, so that we can inform what is
Speaker 1: the economic and financial well being of households of small businesses?
Speaker 1: What new developments do we see in labor markets, in
Speaker 1: out of pocket healthcare spend at the city level? Do
Speaker 1: we see, you know, with our very high frequency and
Speaker 1: granular data, patterns of economic vibrancy that cannot be seen
Speaker 1: through some of the other data. UM. Small businesses themselves
Speaker 1: as a sector are really important, and increasingly we're turning
Speaker 1: not just to the Chase platform to understand the economy,
Speaker 1: but the JP Morgan platform, which are more the finacial
Speaker 1: transactions of institutional investors UM. So short view is to say,
Speaker 1: can we take this extraordinary window, which are the financial
Speaker 1: transactions of JP Morgan Chase to inform important discussions that
Speaker 1: the traditional data are not doing a good job of it.
Speaker 1: And I think three years in the answers, Yes, Diana,
Speaker 1: I lived in the Washington area for ten years, and
Speaker 1: there's no shortage of think tanks working modeling the US economy.
Speaker 1: It sounds like your approach is for one of a
Speaker 1: better term disrupting big think tank. Well, that's an interesting question,
Speaker 1: and I would say that it is disrupting certain areas
Speaker 1: of economic research. I would venture to say that to
Speaker 1: have a full understanding of the economy, we're gonna need
Speaker 1: multiple lenses, and no one lens is going to give
Speaker 1: that to us. So even though I think in some
Speaker 1: ways we are informing important economic questions better than say
Speaker 1: the government statistics are. Take for example, the work that
Speaker 1: we've done on the so called gig economy, the online
Speaker 1: platform economy. I think we have better data as as
Speaker 1: even these statisticians at BLS at the Bureau of Labor
Speaker 1: Statistics would would acknowledge. But where it's not a substitute
Speaker 1: for that, it's a compliment to many of the other
Speaker 1: data series that have a long shelf life that been
Speaker 1: around for a while. And I would say the same
Speaker 1: thing about the think tanks, that we are disrupting the
Speaker 1: way economic research is being done. I think we're doing
Speaker 1: that hand in hand with academia. But there's plenty of
Speaker 1: room for multiple windows and we get a much richer
Speaker 1: um tapestry of what is in fact a very rich economy.
Speaker 1: With those various lenses. Well, well, that was actually one
Speaker 1: of the areas I wanted to ask you about Theanna
Speaker 1: the research on the online platform economy. It really is
Speaker 1: a buzzy issue that you've been working on taking a
Speaker 1: look at how people are using and generating income through
Speaker 1: platforms like Uber selling things leasing. Can you tell us
Speaker 1: a little bit about what you discovered in this research
Speaker 1: how profitable it is for Americans to work this way?
Speaker 1: I would love to um, As you say, it is
Speaker 1: an area that we've done a lot of work in
Speaker 1: and um what I would say about it that is
Speaker 1: important is that we got into a view of what
Speaker 1: was happening in the online platform economy by understanding that
Speaker 1: if you observe households on a high frequency basis, you know, monthly, weekly,
Speaker 1: what we observe is that most hustles are facing high
Speaker 1: levels of income and spending volatility. That's not something you
Speaker 1: would capture with annual surveys, for example. And the conventional
Speaker 1: wisdom of three years ago or so, as this economy
Speaker 1: was getting started these gig jobs and otherwise is that's
Speaker 1: why we have so much income volatility because people are
Speaker 1: doing multiple gigs and and that explains it all, and
Speaker 1: we thought, well, what kind of data exists on that,
Speaker 1: And you know, there's the Contingent Worker Survey, but that's
Speaker 1: only done every three years. There's the pieces of this,
Speaker 1: some of those were discontinued as a matter of fact,
Speaker 1: and we said, we can answer that question because we
Speaker 1: can go and understand who is participating on these platforms
Speaker 1: by observing who's receiving income from them and then linking
Speaker 1: those to their overall economic and financial kind of outcomes.
Speaker 1: And so what we learned is that counter to that
Speaker 1: conventional whiz them, this is not really the future of
Speaker 1: work as as cast glibly, because in fact, even now
Speaker 1: that we've updated these numbers, at you know, at best,
Speaker 1: it's one point six percent of the population is participating
Speaker 1: in these platforms, and we can document that with very
Speaker 1: large samples, and most of them are not working on
Speaker 1: more than one platform, although that is increasingly a phenomenon.
Speaker 1: The only link we had therefore to the income volatilities
Speaker 1: that we noticed that for some of the participants, say
Speaker 1: those that were participating in what we call the labor platforms,
Speaker 1: think of the transportation services in their full kind, and uh,
Speaker 1: there are now many many of those, but also all
Speaker 1: the other services that walka dogs and the shop for
Speaker 1: people and all that. There's a labeled platforms as distinct
Speaker 1: from capital platforms like the airbnb s or other places
Speaker 1: you can rent an asset or buy a good. That
Speaker 1: for those participating in labor platforms, this was a way
Speaker 1: of offsetting the income ball is tility from their traditional job.
Speaker 1: But most of them actually had traditional jobs, so we
Speaker 1: could observe that for most people this is supplemental income
Speaker 1: to the tune of um, not the whole reality. In
Speaker 1: any given month where they're participating, say driving or or
Speaker 1: renting an Airbnb room or otherwise, it can be significant
Speaker 1: for that month, but over the course of the year
Speaker 1: it's really just supplemental income, often designed to with offset
Speaker 1: in effect, the volatility from the traditional source of income. Diana.
Speaker 1: The official unemployment rate in the US is three point
Speaker 1: seven percent, and it's been below four for some months now,
Speaker 1: and yet it isn't generating a surge in inflation. C
Speaker 1: p I is more or less around the Federal reserves
Speaker 1: target of two percent. What do those figures miss? Well?
Speaker 1: I think there are many aspects of the economy that
Speaker 1: are new and interact with inflation in wages in ways
Speaker 1: that we are only beginning to understand. So if we
Speaker 1: come back to these online platform economy businesses, particularly those
Speaker 1: that are involving assets, whether it's driving a car or
Speaker 1: renting a house or an apartment or a room, one
Speaker 1: of the effects of that, if you think about it,
Speaker 1: is that you are delivering a service or a good
Speaker 1: without requiring more capital in the system. So before the
Speaker 1: car would sit in a parking lot, now it's being
Speaker 1: used at a much higher rate by those who are
Speaker 1: driving folks around or delivering things for them. So to
Speaker 1: that house that used to sit empty is now being
Speaker 1: used without having to build a new house, and so
Speaker 1: that in effect it has a deflationary impact on the
Speaker 1: economy that we're not entirely measuring it because the way
Speaker 1: we think about GDP growth or even unemployment UM is
Speaker 1: through the category worries of employment and GDP that we
Speaker 1: used to think we're the norm. Uh, new forms are
Speaker 1: being introduced that are sort of challenging that. So I
Speaker 1: think that that's one of the reasons we're not seeing UM.
Speaker 1: The pressure on inflation or pressure on wages is that
Speaker 1: assets are being utilized better and that keeps costs down
Speaker 1: in other ways, and your data tells us about that
Speaker 1: car and the garage and that house that's being used
Speaker 1: very clevely, very clearly, and we really are note seeing
Speaker 1: that it's it's assets sort of being utilized that would
Speaker 1: otherwise be laying fallow, and so you're not creating new
Speaker 1: production to make those available and therefore not driving the
Speaker 1: inflation that would that might go with that. That's really interesting, Diana.
Speaker 1: Let's talk a little bit more about the ways that
Speaker 1: you measure cash flow in Americans bank accounts and their
Speaker 1: day to day spending. One of the most interesting things
Speaker 1: that I thought you put out was looking at out
Speaker 1: of pockets spending on healthcare and and the financial burden,
Speaker 1: uh it places on Americans where we have this kind
Speaker 1: of crazy health care system in this country that people
Speaker 1: have to kind of really manage their expenses and be
Speaker 1: careful about. What have you found about about Americans healthcare
Speaker 1: spending and in something you know, really interesting things that
Speaker 1: stand out about that. Um. Yes, So let me um
Speaker 1: zoom out a little bit to understand the out of
Speaker 1: pocket healthcare send because it's important. I mentioned earlier that
Speaker 1: we observe through this high frequency lens at the individual
Speaker 1: household level, very high levels of income and spending volatility
Speaker 1: and um when you double click on what is driving
Speaker 1: that volatility on the spending side, not surprisingly, one of
Speaker 1: the big buckets is healthcare. So the three are healthcare,
Speaker 1: auto repairs, and tax payments. Those three events will be
Speaker 1: an extraordinary expense for nearly one out of four Americans
Speaker 1: every year. But healthcare is important because even for those
Speaker 1: who have insurance, if they have a high deductible plan,
Speaker 1: which most of us have, UH, that will require a
Speaker 1: cash outflow. Now, some of that cash outflow may eventually
Speaker 1: be reimbursed, and by insurance, some of it will not
Speaker 1: be reimbursed, but it creates a real cash flow event um.
Speaker 1: And we know from our data, but also other data
Speaker 1: the Federal Reserve and others have put out that most
Speaker 1: Americans don't have a financial buffer to sort of withstand
Speaker 1: that extraordinary expense or withstand a drop and income of
Speaker 1: some sort, and so the result is large levels of
Speaker 1: out of pocket healthcare spend that can either translate into
Speaker 1: deferred care, people not seeking the care they need until
Speaker 1: they have that cash flow, say a tax refund or
Speaker 1: something to do it, or just a real impact on
Speaker 1: their financial outcomes, meaning they go into debt through credit
Speaker 1: card or a need to harness other resources. UM. And
Speaker 1: we do find that to the tune of over a
Speaker 1: thousand dollars. You know, families are going to face that
Speaker 1: kind of out of pocket hit, which is extraordinary given
Speaker 1: that they don't have the financial buffer to withstand that. Deanna,
Speaker 1: you've also sat on the other side of the table.
Speaker 1: You are a policymaker in an Obama administration, governments and
Speaker 1: central banks inching closer to your model. What are you
Speaker 1: hearing from them? Well, let me I'm glad you mentioned that,
Speaker 1: because one of the inspirations for the institute was this
Speaker 1: recognition that at times of crisis in particular, but maybe
Speaker 1: at other times too, policymakers aren't always equipped with the
Speaker 1: real time, granular, high frequency information that they need to
Speaker 1: make the best possible decisions. And so part of what
Speaker 1: I think that the bank excited certainly got me excited,
Speaker 1: was imagine if we could begin to bring that into
Speaker 1: the decision making that policymakers have, that that other people
Speaker 1: who are making decisions have, and and so that is
Speaker 1: a bit of the inspiration behind this UM. I think
Speaker 1: many are moving close to that direction, and one as
Speaker 1: an example, one of the data series that we have
Speaker 1: now put up for two and a half years is
Speaker 1: what we call local consumer Commerce, and this is a
Speaker 1: view of sales if you like, that transpire in a
Speaker 1: given city as a measure of the vibrancy of that city.
Speaker 1: The inspiration behind that was a set of conversations we'd
Speaker 1: had with the statistical agencies that said, we don't have
Speaker 1: good enough data at the city level. If we take
Speaker 1: our national data and then bring it down to the
Speaker 1: city level, it would be very helpful to have a
Speaker 1: corroborating view of what's happening at the city level that
Speaker 1: will improve what they do. So I think that's a
Speaker 1: good example. Another example that that we are very keen
Speaker 1: on is you recall and we may be entering a
Speaker 1: period like this, this very steep drop in gas prices
Speaker 1: that transpired from it's a very dramatic drop in gas
Speaker 1: prices and very vaccine question for traditional surveys and others
Speaker 1: is what are people doing with the savings at the
Speaker 1: gas them And if you went out to survey people,
Speaker 1: which many people did, they pretty much categorically said, well,
Speaker 1: we're saving it. Of course we are because that's what
Speaker 1: we all intend to do. And and it's very hard
Speaker 1: to know what you're really doing with what amounts to
Speaker 1: five percent of total spend over the course of the year,
Speaker 1: even though for some people it's a lot and for
Speaker 1: other people's small amount. And our data lent itself very
Speaker 1: very powerfully to saying, well, at the household level, what
Speaker 1: decreases do we see in gas spending and what corresponding
Speaker 1: meaning causal that we can really map to that dropping
Speaker 1: gas spending can we link to different kinds of spending
Speaker 1: and we find out in fact people were spending most
Speaker 1: of that they were spending it on groceries, on restaurants.
Speaker 1: Now that matters a lot to central banks. At the time,
Speaker 1: the FED was and you know, it's constant deliberations as
Speaker 1: to whether it should start raising rates or not. And
Speaker 1: it was important to that conversation to know whether that
Speaker 1: gas savings was in effect still a buffer that could
Speaker 1: bring the economy forward or whether it was already built
Speaker 1: into the numbers as a way of understanding whether it
Speaker 1: was time to move or not. And um and I
Speaker 1: think this kind of microwork, of course, with many other
Speaker 1: things complementing it. To my earlier point was important to
Speaker 1: consider that maybe they should wait at least one more
Speaker 1: quarter before moving forward, since in fact that was already
Speaker 1: built into the GDP numbers and we couldn't reasonably expect
Speaker 1: that it would be a wind in the sales of
Speaker 1: the economy in the future. So, with the gasoline prices
Speaker 1: again declining in recent weeks, as the price of oils dropping,
Speaker 1: fuel prices at the pump are probably going to go
Speaker 1: down even further, has that earlier work inform the approach
Speaker 1: that policymakers should be taking now, given that they're already
Speaker 1: uh in this interest rate well into this interest rate
Speaker 1: hiking cycle. Well, it's a good question. And frankly, after
Speaker 1: we concluded that work, we did it once as a
Speaker 1: snapshot peak to trough, and then we did it again
Speaker 1: over the course of the whole year. And you know,
Speaker 1: people do habituate. You know, once they're used to a thing,
Speaker 1: they're more likely to keep doing it. And so we thought, okay,
Speaker 1: we think we have an understanding of how people change
Speaker 1: their behavior when gas prices go down significantly. Um, we're
Speaker 1: going to wait and see what happens when prices go up.
Speaker 1: Do you have a symmetrical effect the other way, Do
Speaker 1: people spend less because they now have to spend more
Speaker 1: on gas, you know, other things go down or do
Speaker 1: they not? And so we were eagerly awaiting. But now
Speaker 1: what we have is the probability of a pretty significant
Speaker 1: continued decreases at least as as summer predicting it and um,
Speaker 1: so the question is do we have the same level
Speaker 1: impact in the increase in spending other things or are
Speaker 1: we at a different point in that curve? And you know,
Speaker 1: my guesses will turn back to that question when we
Speaker 1: have a few months of data to answer it. That
Speaker 1: that's an interesting thing. I think most of us know
Speaker 1: that that's not a linear curve, meaning that a ten
Speaker 1: percent increase in gas prices doesn't at at gas price
Speaker 1: two dollars is not the same as a ten percent
Speaker 1: increase at gas price four dollars um. And so we're
Speaker 1: interested to see what that curve looks like. The microwork
Speaker 1: we're doing, I think over time will help inform that
Speaker 1: both on the way up and on the way down
Speaker 1: as gas prices go up or down. Thank you for
Speaker 1: mentioning cities. The term urban rural divide is a popular
Speaker 1: one and it relates mainly to politics. Are you seeing
Speaker 1: a divide in your bottoms up economic data? I would
Speaker 1: say that getting a much better understanding of what's happening
Speaker 1: at the city level is important, even before we start
Speaker 1: informing the city to rural divide. I would argue, there's
Speaker 1: actually quite a variation in the experience of even what
Speaker 1: we would consider cities, and then that variation gets even
Speaker 1: wider when you include rural areas. The work that we've
Speaker 1: been doing on cities has been primarily focused on at
Speaker 1: this point fourteen larger cities than not. We have some
Speaker 1: that are larger, some of that are smaller, and that's
Speaker 1: the what we are mostly getting a window on. Partially,
Speaker 1: it's it's that we have enough representation, enough sort of observation,
Speaker 1: so to speak, to feel very confident that we're saying
Speaker 1: something important about those cities. And partly, as you can imagine,
Speaker 1: is that the bank's footprint is much stronger in cities
Speaker 1: than it is in the rural area. So I suspect
Speaker 1: that just as we're seeing significant variation across the city
Speaker 1: samples that we have, we would just increase that variation
Speaker 1: significantly if we had rural observations. But we have so
Speaker 1: far not actually done a rural urban divide. To inform
Speaker 1: that question, well, Diana, your former White House colleague Larry
Speaker 1: Summers has popularized the term from the nineteen thirties Alvin
Speaker 1: Hansen's secular stagnation. What does your data tell us about
Speaker 1: that term? Well, to try to mainstream the concept a
Speaker 1: little bit for those who may not be as familiar
Speaker 1: with it, I think the best way to phrase that
Speaker 1: kind of line of thinking is that we should be
Speaker 1: expecting a new normal, so to speak, that we're not
Speaker 1: likely to to see the kind of growth rates that
Speaker 1: we experienced it at peak levels. And that's not the
Speaker 1: only aspect of that theory. But but I think as
Speaker 1: we look into that one in particular, UM, there are
Speaker 1: some things that are incontrovertible that would correspond to that,
Speaker 1: which are, for example, that we are aging society, and therefore,
Speaker 1: as there's some growth that comes UM strictly from population
Speaker 1: growth that we have less of, and if we go
Speaker 1: down the path of UM limiting immigration more, that's one
Speaker 1: other source of demographic sort of weight down. And so
Speaker 1: in that sense, I think most people would say that
Speaker 1: stands to reason. I think in the other sense, we
Speaker 1: have seen even as you know, imperfectly measured as it is,
Speaker 1: GDP growth um pick up quite significantly in the last while,
Speaker 1: which suggests that there are some things that can be
Speaker 1: done to move in that direction, certainly some of the
Speaker 1: fiscal stimulus that was put in place or otherwise, although
Speaker 1: what's yet to be seen is how long lived any
Speaker 1: of that is. Our data are not the best to
Speaker 1: inform that question, because really that is a macro economic
Speaker 1: question and the strength of our data are that we're
Speaker 1: taking a micro view and then taking it up to
Speaker 1: a macro view. So I would argue that that is
Speaker 1: probably not the best question we can inform. All Right,
Speaker 1: last question, Dianna, what can we expect from JP Morgan
Speaker 1: Institute in the rest in terms of big insights projects
Speaker 1: or anything else exciting that you're planning. Well, thank you
Speaker 1: for asking, because this is my opportunity to say please
Speaker 1: follow us. And all our research is out in the open,
Speaker 1: available to all at our website. So um things that
Speaker 1: are coming up are will continue on some of these
Speaker 1: main themes, but with new twists. So I mentioned the
Speaker 1: financial economic well being of household. We want to keep
Speaker 1: understanding this um notion of what makes households resilient, and
Speaker 1: you know, besides having a saving buffer, what do we
Speaker 1: know about the behavior of those households that thrive versus
Speaker 1: those that not That might be good lessons for folks
Speaker 1: in the future. We are have been doing, but will
Speaker 1: continue to do, significant work on mortgages to understand how
Speaker 1: well does kind of that that portion of household debt
Speaker 1: interact with other financial outcomes, and that becomes important as
Speaker 1: we think about very significant changes in mortgage rate deductions
Speaker 1: and otherwise interest rate deductions on mortgages. We're starting and
Speaker 1: launching a new segment on student loans, which we think
Speaker 1: is very important. Many of you will have seen how
Speaker 1: much of an increase in student loan debt burdens there
Speaker 1: has been in the last while, and we're going to
Speaker 1: try to understand what does that mean in terms of
Speaker 1: consumption patterns, other decisions that households with that debt are
Speaker 1: taking on or not. Will continue our work on healthcare
Speaker 1: and very proud of the work we just put out,
Speaker 1: but we'll continue to update that shows the out of
Speaker 1: pocket healthcare spending. We talked about um by county, by
Speaker 1: demographic group. What are the levels of spend, what are
Speaker 1: the burdens as a share of income, and how can
Speaker 1: and how does that interface with the overall economic picture
Speaker 1: in the city. Escape Since you mentioned it, the first
Speaker 1: foray we had was on let's understand what's happening with
Speaker 1: purchases at local merchants. We've now done a companion view
Speaker 1: that will come out soon on what about local residents?
Speaker 1: And the reason that's kind of an interesting view is
Speaker 1: that we know increasingly people are spending not just at
Speaker 1: local merchants, but online and at places other than their
Speaker 1: own UM city, and we really want to start mapping
Speaker 1: that out. There are some data series that commerce and
Speaker 1: others do on that, but what we're learning is that
Speaker 1: as the economy evolves in that way, we're not capturing
Speaker 1: that as well as we think we can because we
Speaker 1: really know where exactly that purchase took place and by whom.
Speaker 1: So we'll do a lot of work on the online
Speaker 1: economy and and the health of residents in key cities.
Speaker 1: The small business area is one that we will continue
Speaker 1: to further understand, and we really want to bring um
Speaker 1: a demographic lens into that so that we understand better
Speaker 1: the performance of women and minority small businesses. Will have
Speaker 1: some of that view into households as well, which we're
Speaker 1: excited about. And then I mentioned very briefly, we haven't
Speaker 1: talked about it, but for those of you who might
Speaker 1: be interested on the financial market side, we've done some
Speaker 1: interesting work to understand institutional investor behavior, So what actually
Speaker 1: happens minute by minute, hour by hour when big events
Speaker 1: like Brexit, the US election, uh, the Swiss Bank moving
Speaker 1: the floor on the Swiss franc Um And we're mapping
Speaker 1: those kinds of events out and we plan to do
Speaker 1: much more of that next year. All right, well, it
Speaker 1: sounds like you have plenty to keep you busy for
Speaker 1: quite a while. Diana Farrell, President of the JP Morgan
Speaker 1: Chase Institute, Thank you so much for taking the time
Speaker 1: with us on Benchmark, and thank you for having me.
Speaker 1: Thanks for listening to Benchmark. You can find all our
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