Latest / Trumponomics / Big Data's Lens Into the U.S. Economy

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Big Data's Lens Into the U.S. Economy · Trumponomics

0:01
Speaker 1: Hi, This is Daniel Moss from Bloomberg Opinion. Before this
0:05
Speaker 1: week's episode of Benchmark gets under way, an announcement. Often
0:10
Speaker 1: more than three years and a hundred and fifty episodes
0:13
Speaker 1: will be taking a hiatus. We'd like to thank all
0:17
Speaker 1: our listeners. Now enjoy the year's finale. Here's a secret
0:24
Speaker 1: about the U. S economy. Most of the data you
0:27
Speaker 1: hear about, such as jobs, GDP, consumer spending, and inflation,
0:32
Speaker 1: is not actual data. Rather, these numbers are all extrapolated
0:36
Speaker 1: from surveys of households and businesses. Now, these surveys tend
0:40
Speaker 1: to be several times bigger than say, political polls, but
0:44
Speaker 1: they're still samples of the broader population. But what if
0:47
Speaker 1: we could measure consumer spending by looking at every single
0:51
Speaker 1: purchase that Americans make, or look at every small business's
0:55
Speaker 1: bank account to analyze cash flow. One think tank is
0:59
Speaker 1: trying to do that and potentially reshaping how we look
1:02
Speaker 1: at the economy. Welcome to Benchmark. I'm Start Landman, economics
1:15
Speaker 1: editor with Bloomberg used Washington, and I'm Daniel Moss, columnist
1:20
Speaker 1: at Bloomberg Opinion in New York. Don't get us wrong.
1:24
Speaker 1: US economic data may be based on surveys, but it's
1:28
Speaker 1: the gold standard for surveys. Yet they're still subject to
1:33
Speaker 1: regular revisions and often face questions over whether they've properly
1:37
Speaker 1: adjusted for seasonal events such as holidays, and they often
1:42
Speaker 1: don't fully capture what's happening on a week to week,
1:46
Speaker 1: let alone day to day basis. Now we have with
1:50
Speaker 1: us in our DC studio a person who's using a
1:52
Speaker 1: set of big data to explain trends in the economy
1:56
Speaker 1: and also to find new ones. Diana Farrell is the
1:59
Speaker 1: founding president and chief executive Officer of the JP Morgan
2:03
Speaker 1: Chase Institute, a position she's held since Previously, she was
2:08
Speaker 1: a senior partner at McKinsey and Company, where she was
2:10
Speaker 1: the global head of the McKinsey Center for Government and
2:14
Speaker 1: the McKinsey Global Institute. She served in the Obama administration
2:18
Speaker 1: as Deputy Director of the National Economic Council and Deputy
2:22
Speaker 1: Assistant to the President on Economic Policy. Dianna, thanks for
2:25
Speaker 1: being with us on Benchmark. Thank you so much for
2:28
Speaker 1: having me so. First of all, can you tell us
2:31
Speaker 1: what exactly is the JP Morgan Chase Institute, why did
2:36
Speaker 1: the company started, and what we're in are your goals.
2:40
Speaker 1: I'd love to UM. The JP Morgan Chase Institute is
2:43
Speaker 1: a relatively new initiative of the Bank UM and the
2:47
Speaker 1: best way to think about it is it's a think
2:49
Speaker 1: tank and it's trying to do original economics research, but
2:53
Speaker 1: with a twist, which is much as you started to
2:57
Speaker 1: introduce it. Instead of relying on the typical surveys that
3:00
Speaker 1: are designed to answer specific questions on the economy and
3:04
Speaker 1: then aggregate up to a view of the economy, we
3:07
Speaker 1: start with the actual choices that people make as evidence
3:12
Speaker 1: through the financial transactions. So think about the credit card, debit, card, loans, UM,
3:19
Speaker 1: any number of finacial transactions that come to the bank
3:22
Speaker 1: as the window that we have, and it's a pretty
3:24
Speaker 1: extraordinary window. Consider that the JP Morgan chases two and
3:28
Speaker 1: a half trillion dollars balance sheet worth of financial transactions.
3:32
Speaker 1: That's you know, over half of US household UH, two
3:36
Speaker 1: and a half small businesses UM and a very large
3:40
Speaker 1: number of institutional investors. If you think about that, is
3:43
Speaker 1: the window on economic activity not based on what people
3:46
Speaker 1: say they are doing or are going to do, but
3:49
Speaker 1: on what they are actually doing. That is the basis
3:52
Speaker 1: for us to try to explain some of the things
3:55
Speaker 1: that are transpiring in the economy, especially as the economy
3:58
Speaker 1: evolves UH compared to the way some of the original
4:02
Speaker 1: statistics were developed, so that we can inform what is
4:05
Speaker 1: the economic and financial well being of households of small businesses?
4:09
Speaker 1: What new developments do we see in labor markets, in
4:12
Speaker 1: out of pocket healthcare spend at the city level? Do
4:15
Speaker 1: we see, you know, with our very high frequency and
4:19
Speaker 1: granular data, patterns of economic vibrancy that cannot be seen
4:23
Speaker 1: through some of the other data. UM. Small businesses themselves
4:26
Speaker 1: as a sector are really important, and increasingly we're turning
4:30
Speaker 1: not just to the Chase platform to understand the economy,
4:33
Speaker 1: but the JP Morgan platform, which are more the finacial
4:37
Speaker 1: transactions of institutional investors UM. So short view is to say,
4:42
Speaker 1: can we take this extraordinary window, which are the financial
4:45
Speaker 1: transactions of JP Morgan Chase to inform important discussions that
4:49
Speaker 1: the traditional data are not doing a good job of it.
4:52
Speaker 1: And I think three years in the answers, Yes, Diana,
4:55
Speaker 1: I lived in the Washington area for ten years, and
4:58
Speaker 1: there's no shortage of think tanks working modeling the US economy.
5:04
Speaker 1: It sounds like your approach is for one of a
5:08
Speaker 1: better term disrupting big think tank. Well, that's an interesting question,
5:14
Speaker 1: and I would say that it is disrupting certain areas
5:17
Speaker 1: of economic research. I would venture to say that to
5:22
Speaker 1: have a full understanding of the economy, we're gonna need
5:26
Speaker 1: multiple lenses, and no one lens is going to give
5:28
Speaker 1: that to us. So even though I think in some
5:31
Speaker 1: ways we are informing important economic questions better than say
5:35
Speaker 1: the government statistics are. Take for example, the work that
5:38
Speaker 1: we've done on the so called gig economy, the online
5:42
Speaker 1: platform economy. I think we have better data as as
5:45
Speaker 1: even these statisticians at BLS at the Bureau of Labor
5:49
Speaker 1: Statistics would would acknowledge. But where it's not a substitute
5:54
Speaker 1: for that, it's a compliment to many of the other
5:56
Speaker 1: data series that have a long shelf life that been
6:00
Speaker 1: around for a while. And I would say the same
6:01
Speaker 1: thing about the think tanks, that we are disrupting the
6:04
Speaker 1: way economic research is being done. I think we're doing
6:07
Speaker 1: that hand in hand with academia. But there's plenty of
6:11
Speaker 1: room for multiple windows and we get a much richer
6:14
Speaker 1: um tapestry of what is in fact a very rich economy.
6:18
Speaker 1: With those various lenses. Well, well, that was actually one
6:21
Speaker 1: of the areas I wanted to ask you about Theanna
6:23
Speaker 1: the research on the online platform economy. It really is
6:27
Speaker 1: a buzzy issue that you've been working on taking a
6:31
Speaker 1: look at how people are using and generating income through
6:34
Speaker 1: platforms like Uber selling things leasing. Can you tell us
6:38
Speaker 1: a little bit about what you discovered in this research
6:41
Speaker 1: how profitable it is for Americans to work this way?
6:46
Speaker 1: I would love to um, As you say, it is
6:48
Speaker 1: an area that we've done a lot of work in
6:50
Speaker 1: and um what I would say about it that is
6:54
Speaker 1: important is that we got into a view of what
6:57
Speaker 1: was happening in the online platform economy by understanding that
7:02
Speaker 1: if you observe households on a high frequency basis, you know, monthly, weekly,
7:07
Speaker 1: what we observe is that most hustles are facing high
7:09
Speaker 1: levels of income and spending volatility. That's not something you
7:12
Speaker 1: would capture with annual surveys, for example. And the conventional
7:17
Speaker 1: wisdom of three years ago or so, as this economy
7:20
Speaker 1: was getting started these gig jobs and otherwise is that's
7:24
Speaker 1: why we have so much income volatility because people are
7:26
Speaker 1: doing multiple gigs and and that explains it all, and
7:30
Speaker 1: we thought, well, what kind of data exists on that,
7:33
Speaker 1: And you know, there's the Contingent Worker Survey, but that's
7:36
Speaker 1: only done every three years. There's the pieces of this,
7:39
Speaker 1: some of those were discontinued as a matter of fact,
7:41
Speaker 1: and we said, we can answer that question because we
7:43
Speaker 1: can go and understand who is participating on these platforms
7:49
Speaker 1: by observing who's receiving income from them and then linking
7:52
Speaker 1: those to their overall economic and financial kind of outcomes.
7:56
Speaker 1: And so what we learned is that counter to that
7:59
Speaker 1: conventional whiz them, this is not really the future of
8:02
Speaker 1: work as as cast glibly, because in fact, even now
8:07
Speaker 1: that we've updated these numbers, at you know, at best,
8:10
Speaker 1: it's one point six percent of the population is participating
8:14
Speaker 1: in these platforms, and we can document that with very
8:17
Speaker 1: large samples, and most of them are not working on
8:20
Speaker 1: more than one platform, although that is increasingly a phenomenon.
8:24
Speaker 1: The only link we had therefore to the income volatilities
8:26
Speaker 1: that we noticed that for some of the participants, say
8:29
Speaker 1: those that were participating in what we call the labor platforms,
8:34
Speaker 1: think of the transportation services in their full kind, and uh,
8:38
Speaker 1: there are now many many of those, but also all
8:41
Speaker 1: the other services that walka dogs and the shop for
8:44
Speaker 1: people and all that. There's a labeled platforms as distinct
8:48
Speaker 1: from capital platforms like the airbnb s or other places
8:51
Speaker 1: you can rent an asset or buy a good. That
8:54
Speaker 1: for those participating in labor platforms, this was a way
8:58
Speaker 1: of offsetting the income ball is tility from their traditional job.
9:02
Speaker 1: But most of them actually had traditional jobs, so we
9:04
Speaker 1: could observe that for most people this is supplemental income
9:08
Speaker 1: to the tune of um, not the whole reality. In
9:13
Speaker 1: any given month where they're participating, say driving or or
9:17
Speaker 1: renting an Airbnb room or otherwise, it can be significant
9:22
Speaker 1: for that month, but over the course of the year
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Speaker 1: it's really just supplemental income, often designed to with offset
9:29
Speaker 1: in effect, the volatility from the traditional source of income. Diana.
9:33
Speaker 1: The official unemployment rate in the US is three point
9:38
Speaker 1: seven percent, and it's been below four for some months now,
9:43
Speaker 1: and yet it isn't generating a surge in inflation. C
9:47
Speaker 1: p I is more or less around the Federal reserves
9:50
Speaker 1: target of two percent. What do those figures miss? Well?
9:56
Speaker 1: I think there are many aspects of the economy that
10:00
Speaker 1: are new and interact with inflation in wages in ways
10:05
Speaker 1: that we are only beginning to understand. So if we
10:08
Speaker 1: come back to these online platform economy businesses, particularly those
10:14
Speaker 1: that are involving assets, whether it's driving a car or
10:18
Speaker 1: renting a house or an apartment or a room, one
10:21
Speaker 1: of the effects of that, if you think about it,
10:24
Speaker 1: is that you are delivering a service or a good
10:28
Speaker 1: without requiring more capital in the system. So before the
10:32
Speaker 1: car would sit in a parking lot, now it's being
10:35
Speaker 1: used at a much higher rate by those who are
10:37
Speaker 1: driving folks around or delivering things for them. So to
10:42
Speaker 1: that house that used to sit empty is now being
10:45
Speaker 1: used without having to build a new house, and so
10:47
Speaker 1: that in effect it has a deflationary impact on the
10:50
Speaker 1: economy that we're not entirely measuring it because the way
10:53
Speaker 1: we think about GDP growth or even unemployment UM is
10:58
Speaker 1: through the category worries of employment and GDP that we
11:02
Speaker 1: used to think we're the norm. Uh, new forms are
11:07
Speaker 1: being introduced that are sort of challenging that. So I
11:10
Speaker 1: think that that's one of the reasons we're not seeing UM.
11:14
Speaker 1: The pressure on inflation or pressure on wages is that
11:19
Speaker 1: assets are being utilized better and that keeps costs down
11:21
Speaker 1: in other ways, and your data tells us about that
11:25
Speaker 1: car and the garage and that house that's being used
11:28
Speaker 1: very clevely, very clearly, and we really are note seeing
11:32
Speaker 1: that it's it's assets sort of being utilized that would
11:36
Speaker 1: otherwise be laying fallow, and so you're not creating new
11:39
Speaker 1: production to make those available and therefore not driving the
11:42
Speaker 1: inflation that would that might go with that. That's really interesting, Diana.
11:46
Speaker 1: Let's talk a little bit more about the ways that
11:50
Speaker 1: you measure cash flow in Americans bank accounts and their
11:54
Speaker 1: day to day spending. One of the most interesting things
11:57
Speaker 1: that I thought you put out was looking at out
12:00
Speaker 1: of pockets spending on healthcare and and the financial burden,
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Speaker 1: uh it places on Americans where we have this kind
12:08
Speaker 1: of crazy health care system in this country that people
12:12
Speaker 1: have to kind of really manage their expenses and be
12:15
Speaker 1: careful about. What have you found about about Americans healthcare
12:21
Speaker 1: spending and in something you know, really interesting things that
12:25
Speaker 1: stand out about that. Um. Yes, So let me um
12:29
Speaker 1: zoom out a little bit to understand the out of
12:32
Speaker 1: pocket healthcare send because it's important. I mentioned earlier that
12:35
Speaker 1: we observe through this high frequency lens at the individual
12:40
Speaker 1: household level, very high levels of income and spending volatility
12:44
Speaker 1: and um when you double click on what is driving
12:47
Speaker 1: that volatility on the spending side, not surprisingly, one of
12:52
Speaker 1: the big buckets is healthcare. So the three are healthcare,
12:56
Speaker 1: auto repairs, and tax payments. Those three events will be
13:01
Speaker 1: an extraordinary expense for nearly one out of four Americans
13:05
Speaker 1: every year. But healthcare is important because even for those
13:09
Speaker 1: who have insurance, if they have a high deductible plan,
13:13
Speaker 1: which most of us have, UH, that will require a
13:16
Speaker 1: cash outflow. Now, some of that cash outflow may eventually
13:19
Speaker 1: be reimbursed, and by insurance, some of it will not
13:22
Speaker 1: be reimbursed, but it creates a real cash flow event um.
13:26
Speaker 1: And we know from our data, but also other data
13:29
Speaker 1: the Federal Reserve and others have put out that most
13:32
Speaker 1: Americans don't have a financial buffer to sort of withstand
13:35
Speaker 1: that extraordinary expense or withstand a drop and income of
13:39
Speaker 1: some sort, and so the result is large levels of
13:43
Speaker 1: out of pocket healthcare spend that can either translate into
13:46
Speaker 1: deferred care, people not seeking the care they need until
13:49
Speaker 1: they have that cash flow, say a tax refund or
13:52
Speaker 1: something to do it, or just a real impact on
13:55
Speaker 1: their financial outcomes, meaning they go into debt through credit
13:59
Speaker 1: card or a need to harness other resources. UM. And
14:03
Speaker 1: we do find that to the tune of over a
14:05
Speaker 1: thousand dollars. You know, families are going to face that
14:09
Speaker 1: kind of out of pocket hit, which is extraordinary given
14:11
Speaker 1: that they don't have the financial buffer to withstand that. Deanna,
14:39
Speaker 1: you've also sat on the other side of the table.
14:43
Speaker 1: You are a policymaker in an Obama administration, governments and
14:49
Speaker 1: central banks inching closer to your model. What are you
14:54
Speaker 1: hearing from them? Well, let me I'm glad you mentioned that,
14:57
Speaker 1: because one of the inspirations for the institute was this
15:01
Speaker 1: recognition that at times of crisis in particular, but maybe
15:06
Speaker 1: at other times too, policymakers aren't always equipped with the
15:10
Speaker 1: real time, granular, high frequency information that they need to
15:15
Speaker 1: make the best possible decisions. And so part of what
15:18
Speaker 1: I think that the bank excited certainly got me excited,
15:21
Speaker 1: was imagine if we could begin to bring that into
15:25
Speaker 1: the decision making that policymakers have, that that other people
15:30
Speaker 1: who are making decisions have, and and so that is
15:33
Speaker 1: a bit of the inspiration behind this UM. I think
15:36
Speaker 1: many are moving close to that direction, and one as
15:40
Speaker 1: an example, one of the data series that we have
15:42
Speaker 1: now put up for two and a half years is
15:44
Speaker 1: what we call local consumer Commerce, and this is a
15:48
Speaker 1: view of sales if you like, that transpire in a
15:52
Speaker 1: given city as a measure of the vibrancy of that city.
15:55
Speaker 1: The inspiration behind that was a set of conversations we'd
15:58
Speaker 1: had with the statistical agencies that said, we don't have
16:02
Speaker 1: good enough data at the city level. If we take
16:06
Speaker 1: our national data and then bring it down to the
16:08
Speaker 1: city level, it would be very helpful to have a
16:10
Speaker 1: corroborating view of what's happening at the city level that
16:12
Speaker 1: will improve what they do. So I think that's a
16:14
Speaker 1: good example. Another example that that we are very keen
16:19
Speaker 1: on is you recall and we may be entering a
16:22
Speaker 1: period like this, this very steep drop in gas prices
16:26
Speaker 1: that transpired from it's a very dramatic drop in gas
16:31
Speaker 1: prices and very vaccine question for traditional surveys and others
16:36
Speaker 1: is what are people doing with the savings at the
16:39
Speaker 1: gas them And if you went out to survey people,
16:42
Speaker 1: which many people did, they pretty much categorically said, well,
16:46
Speaker 1: we're saving it. Of course we are because that's what
16:47
Speaker 1: we all intend to do. And and it's very hard
16:50
Speaker 1: to know what you're really doing with what amounts to
16:53
Speaker 1: five percent of total spend over the course of the year,
16:56
Speaker 1: even though for some people it's a lot and for
16:58
Speaker 1: other people's small amount. And our data lent itself very
17:02
Speaker 1: very powerfully to saying, well, at the household level, what
17:05
Speaker 1: decreases do we see in gas spending and what corresponding
17:09
Speaker 1: meaning causal that we can really map to that dropping
17:12
Speaker 1: gas spending can we link to different kinds of spending
17:15
Speaker 1: and we find out in fact people were spending most
17:17
Speaker 1: of that they were spending it on groceries, on restaurants.
17:20
Speaker 1: Now that matters a lot to central banks. At the time,
17:24
Speaker 1: the FED was and you know, it's constant deliberations as
17:28
Speaker 1: to whether it should start raising rates or not. And
17:31
Speaker 1: it was important to that conversation to know whether that
17:35
Speaker 1: gas savings was in effect still a buffer that could
17:40
Speaker 1: bring the economy forward or whether it was already built
17:43
Speaker 1: into the numbers as a way of understanding whether it
17:46
Speaker 1: was time to move or not. And um and I
17:48
Speaker 1: think this kind of microwork, of course, with many other
17:51
Speaker 1: things complementing it. To my earlier point was important to
17:55
Speaker 1: consider that maybe they should wait at least one more
17:57
Speaker 1: quarter before moving forward, since in fact that was already
18:00
Speaker 1: built into the GDP numbers and we couldn't reasonably expect
18:03
Speaker 1: that it would be a wind in the sales of
18:05
Speaker 1: the economy in the future. So, with the gasoline prices
18:09
Speaker 1: again declining in recent weeks, as the price of oils dropping,
18:13
Speaker 1: fuel prices at the pump are probably going to go
18:15
Speaker 1: down even further, has that earlier work inform the approach
18:20
Speaker 1: that policymakers should be taking now, given that they're already
18:23
Speaker 1: uh in this interest rate well into this interest rate
18:26
Speaker 1: hiking cycle. Well, it's a good question. And frankly, after
18:29
Speaker 1: we concluded that work, we did it once as a
18:32
Speaker 1: snapshot peak to trough, and then we did it again
18:35
Speaker 1: over the course of the whole year. And you know,
18:38
Speaker 1: people do habituate. You know, once they're used to a thing,
18:42
Speaker 1: they're more likely to keep doing it. And so we thought, okay,
18:46
Speaker 1: we think we have an understanding of how people change
18:48
Speaker 1: their behavior when gas prices go down significantly. Um, we're
18:52
Speaker 1: going to wait and see what happens when prices go up.
18:56
Speaker 1: Do you have a symmetrical effect the other way, Do
18:58
Speaker 1: people spend less because they now have to spend more
19:01
Speaker 1: on gas, you know, other things go down or do
19:03
Speaker 1: they not? And so we were eagerly awaiting. But now
19:07
Speaker 1: what we have is the probability of a pretty significant
19:10
Speaker 1: continued decreases at least as as summer predicting it and um,
19:14
Speaker 1: so the question is do we have the same level
19:17
Speaker 1: impact in the increase in spending other things or are
19:22
Speaker 1: we at a different point in that curve? And you know,
19:24
Speaker 1: my guesses will turn back to that question when we
19:27
Speaker 1: have a few months of data to answer it. That
19:29
Speaker 1: that's an interesting thing. I think most of us know
19:32
Speaker 1: that that's not a linear curve, meaning that a ten
19:35
Speaker 1: percent increase in gas prices doesn't at at gas price
19:39
Speaker 1: two dollars is not the same as a ten percent
19:42
Speaker 1: increase at gas price four dollars um. And so we're
19:45
Speaker 1: interested to see what that curve looks like. The microwork
19:48
Speaker 1: we're doing, I think over time will help inform that
19:50
Speaker 1: both on the way up and on the way down
19:52
Speaker 1: as gas prices go up or down. Thank you for
19:55
Speaker 1: mentioning cities. The term urban rural divide is a popular
20:00
Speaker 1: one and it relates mainly to politics. Are you seeing
20:04
Speaker 1: a divide in your bottoms up economic data? I would
20:09
Speaker 1: say that getting a much better understanding of what's happening
20:13
Speaker 1: at the city level is important, even before we start
20:17
Speaker 1: informing the city to rural divide. I would argue, there's
20:20
Speaker 1: actually quite a variation in the experience of even what
20:24
Speaker 1: we would consider cities, and then that variation gets even
20:28
Speaker 1: wider when you include rural areas. The work that we've
20:31
Speaker 1: been doing on cities has been primarily focused on at
20:35
Speaker 1: this point fourteen larger cities than not. We have some
20:38
Speaker 1: that are larger, some of that are smaller, and that's
20:40
Speaker 1: the what we are mostly getting a window on. Partially,
20:45
Speaker 1: it's it's that we have enough representation, enough sort of observation,
20:50
Speaker 1: so to speak, to feel very confident that we're saying
20:53
Speaker 1: something important about those cities. And partly, as you can imagine,
20:56
Speaker 1: is that the bank's footprint is much stronger in cities
21:00
Speaker 1: than it is in the rural area. So I suspect
21:03
Speaker 1: that just as we're seeing significant variation across the city
21:06
Speaker 1: samples that we have, we would just increase that variation
21:09
Speaker 1: significantly if we had rural observations. But we have so
21:13
Speaker 1: far not actually done a rural urban divide. To inform
21:17
Speaker 1: that question, well, Diana, your former White House colleague Larry
21:22
Speaker 1: Summers has popularized the term from the nineteen thirties Alvin
21:27
Speaker 1: Hansen's secular stagnation. What does your data tell us about
21:33
Speaker 1: that term? Well, to try to mainstream the concept a
21:38
Speaker 1: little bit for those who may not be as familiar
21:40
Speaker 1: with it, I think the best way to phrase that
21:43
Speaker 1: kind of line of thinking is that we should be
21:46
Speaker 1: expecting a new normal, so to speak, that we're not
21:49
Speaker 1: likely to to see the kind of growth rates that
21:52
Speaker 1: we experienced it at peak levels. And that's not the
21:54
Speaker 1: only aspect of that theory. But but I think as
21:57
Speaker 1: we look into that one in particular, UM, there are
22:01
Speaker 1: some things that are incontrovertible that would correspond to that,
22:05
Speaker 1: which are, for example, that we are aging society, and therefore,
22:09
Speaker 1: as there's some growth that comes UM strictly from population
22:14
Speaker 1: growth that we have less of, and if we go
22:17
Speaker 1: down the path of UM limiting immigration more, that's one
22:22
Speaker 1: other source of demographic sort of weight down. And so
22:25
Speaker 1: in that sense, I think most people would say that
22:28
Speaker 1: stands to reason. I think in the other sense, we
22:30
Speaker 1: have seen even as you know, imperfectly measured as it is,
22:35
Speaker 1: GDP growth um pick up quite significantly in the last while,
22:39
Speaker 1: which suggests that there are some things that can be
22:42
Speaker 1: done to move in that direction, certainly some of the
22:46
Speaker 1: fiscal stimulus that was put in place or otherwise, although
22:49
Speaker 1: what's yet to be seen is how long lived any
22:51
Speaker 1: of that is. Our data are not the best to
22:53
Speaker 1: inform that question, because really that is a macro economic
22:57
Speaker 1: question and the strength of our data are that we're
23:00
Speaker 1: taking a micro view and then taking it up to
23:03
Speaker 1: a macro view. So I would argue that that is
23:07
Speaker 1: probably not the best question we can inform. All Right,
23:10
Speaker 1: last question, Dianna, what can we expect from JP Morgan
23:14
Speaker 1: Institute in the rest in terms of big insights projects
23:21
Speaker 1: or anything else exciting that you're planning. Well, thank you
23:23
Speaker 1: for asking, because this is my opportunity to say please
23:26
Speaker 1: follow us. And all our research is out in the open,
23:30
Speaker 1: available to all at our website. So um things that
23:33
Speaker 1: are coming up are will continue on some of these
23:35
Speaker 1: main themes, but with new twists. So I mentioned the
23:39
Speaker 1: financial economic well being of household. We want to keep
23:42
Speaker 1: understanding this um notion of what makes households resilient, and
23:47
Speaker 1: you know, besides having a saving buffer, what do we
23:51
Speaker 1: know about the behavior of those households that thrive versus
23:55
Speaker 1: those that not That might be good lessons for folks
23:57
Speaker 1: in the future. We are have been doing, but will
24:01
Speaker 1: continue to do, significant work on mortgages to understand how
24:04
Speaker 1: well does kind of that that portion of household debt
24:08
Speaker 1: interact with other financial outcomes, and that becomes important as
24:13
Speaker 1: we think about very significant changes in mortgage rate deductions
24:18
Speaker 1: and otherwise interest rate deductions on mortgages. We're starting and
24:22
Speaker 1: launching a new segment on student loans, which we think
24:24
Speaker 1: is very important. Many of you will have seen how
24:28
Speaker 1: much of an increase in student loan debt burdens there
24:31
Speaker 1: has been in the last while, and we're going to
24:33
Speaker 1: try to understand what does that mean in terms of
24:36
Speaker 1: consumption patterns, other decisions that households with that debt are
24:39
Speaker 1: taking on or not. Will continue our work on healthcare
24:43
Speaker 1: and very proud of the work we just put out,
24:45
Speaker 1: but we'll continue to update that shows the out of
24:48
Speaker 1: pocket healthcare spending. We talked about um by county, by
24:52
Speaker 1: demographic group. What are the levels of spend, what are
24:55
Speaker 1: the burdens as a share of income, and how can
25:00
Speaker 1: and how does that interface with the overall economic picture
25:04
Speaker 1: in the city. Escape Since you mentioned it, the first
25:06
Speaker 1: foray we had was on let's understand what's happening with
25:10
Speaker 1: purchases at local merchants. We've now done a companion view
25:15
Speaker 1: that will come out soon on what about local residents?
25:19
Speaker 1: And the reason that's kind of an interesting view is
25:21
Speaker 1: that we know increasingly people are spending not just at
25:24
Speaker 1: local merchants, but online and at places other than their
25:28
Speaker 1: own UM city, and we really want to start mapping
25:31
Speaker 1: that out. There are some data series that commerce and
25:34
Speaker 1: others do on that, but what we're learning is that
25:37
Speaker 1: as the economy evolves in that way, we're not capturing
25:40
Speaker 1: that as well as we think we can because we
25:43
Speaker 1: really know where exactly that purchase took place and by whom.
25:46
Speaker 1: So we'll do a lot of work on the online
25:48
Speaker 1: economy and and the health of residents in key cities.
25:53
Speaker 1: The small business area is one that we will continue
25:56
Speaker 1: to further understand, and we really want to bring um
26:00
Speaker 1: a demographic lens into that so that we understand better
26:04
Speaker 1: the performance of women and minority small businesses. Will have
26:09
Speaker 1: some of that view into households as well, which we're
26:11
Speaker 1: excited about. And then I mentioned very briefly, we haven't
26:14
Speaker 1: talked about it, but for those of you who might
26:16
Speaker 1: be interested on the financial market side, we've done some
26:19
Speaker 1: interesting work to understand institutional investor behavior, So what actually
26:23
Speaker 1: happens minute by minute, hour by hour when big events
26:27
Speaker 1: like Brexit, the US election, uh, the Swiss Bank moving
26:31
Speaker 1: the floor on the Swiss franc Um And we're mapping
26:34
Speaker 1: those kinds of events out and we plan to do
26:36
Speaker 1: much more of that next year. All right, well, it
26:38
Speaker 1: sounds like you have plenty to keep you busy for
26:41
Speaker 1: quite a while. Diana Farrell, President of the JP Morgan
26:45
Speaker 1: Chase Institute, Thank you so much for taking the time
26:48
Speaker 1: with us on Benchmark, and thank you for having me.
26:54
Speaker 1: Thanks for listening to Benchmark. You can find all our
26:57
Speaker 1: past episodes on the Bloomberg terminal, bloom work dot com,
27:00
Speaker 1: our Bloomberg app, as well as podcast destinations such as
27:04
Speaker 1: Apple Podcasts, Spotify, or wherever you listen. We'd love it
27:08
Speaker 1: if you took the time to rate and review the
27:10
Speaker 1: show so more listeners can find us. And you can
27:13
Speaker 1: find us on Twitter, follow me at scott Landman Dan,
27:17
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27:25
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27:30
Speaker 1: Thank you for listening for the past three years. We
27:32
Speaker 1: wish everyone a happy holiday season and New Year.