WEBVTT - Big Data's Lens Into the U.S. Economy 0:00:01.160 --> 0:00:05.240 Hi, This is Daniel Moss from Bloomberg Opinion. Before this 0:00:05.320 --> 0:00:10.440 week's episode of Benchmark gets under way, an announcement. Often 0:00:10.520 --> 0:00:13.320 more than three years and a hundred and fifty episodes 0:00:13.720 --> 0:00:17.279 will be taking a hiatus. We'd like to thank all 0:00:17.320 --> 0:00:24.200 our listeners. Now enjoy the year's finale. Here's a secret 0:00:24.280 --> 0:00:27.040 about the U. S economy. Most of the data you 0:00:27.120 --> 0:00:31.720 hear about, such as jobs, GDP, consumer spending, and inflation, 0:00:32.080 --> 0:00:36.520 is not actual data. Rather, these numbers are all extrapolated 0:00:36.560 --> 0:00:40.640 from surveys of households and businesses. Now, these surveys tend 0:00:40.680 --> 0:00:44.080 to be several times bigger than say, political polls, but 0:00:44.159 --> 0:00:47.879 they're still samples of the broader population. But what if 0:00:47.920 --> 0:00:51.120 we could measure consumer spending by looking at every single 0:00:51.200 --> 0:00:55.200 purchase that Americans make, or look at every small business's 0:00:55.240 --> 0:00:59.120 bank account to analyze cash flow. One think tank is 0:00:59.160 --> 0:01:02.520 trying to do that and potentially reshaping how we look 0:01:02.520 --> 0:01:15.840 at the economy. Welcome to Benchmark. I'm Start Landman, economics 0:01:15.920 --> 0:01:20.160 editor with Bloomberg used Washington, and I'm Daniel Moss, columnist 0:01:20.240 --> 0:01:24.119 at Bloomberg Opinion in New York. Don't get us wrong. 0:01:24.840 --> 0:01:28.800 US economic data may be based on surveys, but it's 0:01:28.840 --> 0:01:32.920 the gold standard for surveys. Yet they're still subject to 0:01:33.080 --> 0:01:37.040 regular revisions and often face questions over whether they've properly 0:01:37.080 --> 0:01:42.040 adjusted for seasonal events such as holidays, and they often 0:01:42.160 --> 0:01:46.160 don't fully capture what's happening on a week to week, 0:01:46.640 --> 0:01:50.080 let alone day to day basis. Now we have with 0:01:50.160 --> 0:01:52.680 us in our DC studio a person who's using a 0:01:52.720 --> 0:01:56.120 set of big data to explain trends in the economy 0:01:56.400 --> 0:01:59.520 and also to find new ones. Diana Farrell is the 0:01:59.520 --> 0:02:03.080 founding president and chief executive Officer of the JP Morgan 0:02:03.200 --> 0:02:08.079 Chase Institute, a position she's held since Previously, she was 0:02:08.120 --> 0:02:10.880 a senior partner at McKinsey and Company, where she was 0:02:10.919 --> 0:02:14.160 the global head of the McKinsey Center for Government and 0:02:14.440 --> 0:02:18.320 the McKinsey Global Institute. She served in the Obama administration 0:02:18.360 --> 0:02:22.000 as Deputy Director of the National Economic Council and Deputy 0:02:22.040 --> 0:02:25.840 Assistant to the President on Economic Policy. Dianna, thanks for 0:02:25.880 --> 0:02:28.120 being with us on Benchmark. Thank you so much for 0:02:28.200 --> 0:02:30.840 having me so. First of all, can you tell us 0:02:31.160 --> 0:02:36.160 what exactly is the JP Morgan Chase Institute, why did 0:02:36.200 --> 0:02:39.800 the company started, and what we're in are your goals. 0:02:40.760 --> 0:02:43.520 I'd love to UM. The JP Morgan Chase Institute is 0:02:43.520 --> 0:02:47.280 a relatively new initiative of the Bank UM and the 0:02:47.320 --> 0:02:49.480 best way to think about it is it's a think 0:02:49.520 --> 0:02:53.720 tank and it's trying to do original economics research, but 0:02:53.960 --> 0:02:57.160 with a twist, which is much as you started to 0:02:57.200 --> 0:03:00.880 introduce it. Instead of relying on the typical surveys that 0:03:00.919 --> 0:03:04.840 are designed to answer specific questions on the economy and 0:03:04.880 --> 0:03:07.800 then aggregate up to a view of the economy, we 0:03:07.880 --> 0:03:12.120 start with the actual choices that people make as evidence 0:03:12.200 --> 0:03:18.799 through the financial transactions. So think about the credit card, debit, card, loans, UM, 0:03:19.320 --> 0:03:21.840 any number of finacial transactions that come to the bank 0:03:22.200 --> 0:03:24.600 as the window that we have, and it's a pretty 0:03:24.680 --> 0:03:28.680 extraordinary window. Consider that the JP Morgan chases two and 0:03:28.680 --> 0:03:32.080 a half trillion dollars balance sheet worth of financial transactions. 0:03:32.639 --> 0:03:36.920 That's you know, over half of US household UH, two 0:03:36.960 --> 0:03:40.800 and a half small businesses UM and a very large 0:03:40.880 --> 0:03:43.520 number of institutional investors. If you think about that, is 0:03:43.560 --> 0:03:46.760 the window on economic activity not based on what people 0:03:46.880 --> 0:03:49.760 say they are doing or are going to do, but 0:03:49.920 --> 0:03:52.560 on what they are actually doing. That is the basis 0:03:52.600 --> 0:03:55.640 for us to try to explain some of the things 0:03:55.680 --> 0:03:58.920 that are transpiring in the economy, especially as the economy 0:03:58.960 --> 0:04:02.320 evolves UH compared to the way some of the original 0:04:02.320 --> 0:04:05.640 statistics were developed, so that we can inform what is 0:04:05.680 --> 0:04:09.280 the economic and financial well being of households of small businesses? 0:04:09.600 --> 0:04:12.280 What new developments do we see in labor markets, in 0:04:12.440 --> 0:04:15.600 out of pocket healthcare spend at the city level? Do 0:04:15.640 --> 0:04:18.840 we see, you know, with our very high frequency and 0:04:19.000 --> 0:04:23.560 granular data, patterns of economic vibrancy that cannot be seen 0:04:23.600 --> 0:04:26.640 through some of the other data. UM. Small businesses themselves 0:04:26.720 --> 0:04:30.120 as a sector are really important, and increasingly we're turning 0:04:30.160 --> 0:04:33.400 not just to the Chase platform to understand the economy, 0:04:33.480 --> 0:04:37.280 but the JP Morgan platform, which are more the finacial 0:04:37.320 --> 0:04:42.120 transactions of institutional investors UM. So short view is to say, 0:04:42.200 --> 0:04:45.039 can we take this extraordinary window, which are the financial 0:04:45.120 --> 0:04:49.520 transactions of JP Morgan Chase to inform important discussions that 0:04:49.560 --> 0:04:51.680 the traditional data are not doing a good job of it. 0:04:52.000 --> 0:04:55.720 And I think three years in the answers, Yes, Diana, 0:04:55.800 --> 0:04:58.480 I lived in the Washington area for ten years, and 0:04:58.560 --> 0:05:04.480 there's no shortage of think tanks working modeling the US economy. 0:05:04.640 --> 0:05:08.120 It sounds like your approach is for one of a 0:05:08.160 --> 0:05:14.000 better term disrupting big think tank. Well, that's an interesting question, 0:05:14.160 --> 0:05:17.839 and I would say that it is disrupting certain areas 0:05:17.920 --> 0:05:22.320 of economic research. I would venture to say that to 0:05:22.480 --> 0:05:26.440 have a full understanding of the economy, we're gonna need 0:05:26.560 --> 0:05:28.960 multiple lenses, and no one lens is going to give 0:05:28.960 --> 0:05:31.000 that to us. So even though I think in some 0:05:31.040 --> 0:05:35.400 ways we are informing important economic questions better than say 0:05:35.520 --> 0:05:38.920 the government statistics are. Take for example, the work that 0:05:38.960 --> 0:05:42.360 we've done on the so called gig economy, the online 0:05:42.360 --> 0:05:45.680 platform economy. I think we have better data as as 0:05:45.720 --> 0:05:49.159 even these statisticians at BLS at the Bureau of Labor 0:05:49.720 --> 0:05:54.360 Statistics would would acknowledge. But where it's not a substitute 0:05:54.400 --> 0:05:56.760 for that, it's a compliment to many of the other 0:05:56.839 --> 0:06:00.160 data series that have a long shelf life that been 0:06:00.200 --> 0:06:01.680 around for a while. And I would say the same 0:06:01.720 --> 0:06:04.560 thing about the think tanks, that we are disrupting the 0:06:04.600 --> 0:06:07.159 way economic research is being done. I think we're doing 0:06:07.200 --> 0:06:10.960 that hand in hand with academia. But there's plenty of 0:06:11.040 --> 0:06:14.120 room for multiple windows and we get a much richer 0:06:14.680 --> 0:06:18.000 um tapestry of what is in fact a very rich economy. 0:06:18.200 --> 0:06:21.560 With those various lenses. Well, well, that was actually one 0:06:21.600 --> 0:06:23.800 of the areas I wanted to ask you about Theanna 0:06:23.960 --> 0:06:27.919 the research on the online platform economy. It really is 0:06:27.960 --> 0:06:31.440 a buzzy issue that you've been working on taking a 0:06:31.440 --> 0:06:34.080 look at how people are using and generating income through 0:06:34.440 --> 0:06:38.279 platforms like Uber selling things leasing. Can you tell us 0:06:38.320 --> 0:06:41.520 a little bit about what you discovered in this research 0:06:41.920 --> 0:06:45.880 how profitable it is for Americans to work this way? 0:06:46.080 --> 0:06:48.320 I would love to um, As you say, it is 0:06:48.360 --> 0:06:49.919 an area that we've done a lot of work in 0:06:50.560 --> 0:06:54.120 and um what I would say about it that is 0:06:54.160 --> 0:06:57.680 important is that we got into a view of what 0:06:57.800 --> 0:07:02.200 was happening in the online platform economy by understanding that 0:07:02.480 --> 0:07:06.760 if you observe households on a high frequency basis, you know, monthly, weekly, 0:07:07.040 --> 0:07:09.720 what we observe is that most hustles are facing high 0:07:09.760 --> 0:07:12.800 levels of income and spending volatility. That's not something you 0:07:12.800 --> 0:07:17.760 would capture with annual surveys, for example. And the conventional 0:07:17.760 --> 0:07:20.760 wisdom of three years ago or so, as this economy 0:07:20.800 --> 0:07:24.200 was getting started these gig jobs and otherwise is that's 0:07:24.200 --> 0:07:26.840 why we have so much income volatility because people are 0:07:26.880 --> 0:07:30.640 doing multiple gigs and and that explains it all, and 0:07:30.760 --> 0:07:33.040 we thought, well, what kind of data exists on that, 0:07:33.200 --> 0:07:36.200 And you know, there's the Contingent Worker Survey, but that's 0:07:36.240 --> 0:07:39.320 only done every three years. There's the pieces of this, 0:07:39.440 --> 0:07:41.480 some of those were discontinued as a matter of fact, 0:07:41.520 --> 0:07:43.600 and we said, we can answer that question because we 0:07:43.640 --> 0:07:49.160 can go and understand who is participating on these platforms 0:07:49.200 --> 0:07:52.440 by observing who's receiving income from them and then linking 0:07:52.480 --> 0:07:56.600 those to their overall economic and financial kind of outcomes. 0:07:56.680 --> 0:07:59.200 And so what we learned is that counter to that 0:07:59.280 --> 0:08:02.720 conventional whiz them, this is not really the future of 0:08:02.840 --> 0:08:07.520 work as as cast glibly, because in fact, even now 0:08:07.720 --> 0:08:10.800 that we've updated these numbers, at you know, at best, 0:08:10.840 --> 0:08:13.840 it's one point six percent of the population is participating 0:08:14.080 --> 0:08:17.000 in these platforms, and we can document that with very 0:08:17.080 --> 0:08:20.080 large samples, and most of them are not working on 0:08:20.120 --> 0:08:23.640 more than one platform, although that is increasingly a phenomenon. 0:08:24.120 --> 0:08:26.880 The only link we had therefore to the income volatilities 0:08:26.960 --> 0:08:29.720 that we noticed that for some of the participants, say 0:08:29.720 --> 0:08:33.679 those that were participating in what we call the labor platforms, 0:08:34.120 --> 0:08:38.400 think of the transportation services in their full kind, and uh, 0:08:38.440 --> 0:08:41.240 there are now many many of those, but also all 0:08:41.240 --> 0:08:44.800 the other services that walka dogs and the shop for 0:08:44.880 --> 0:08:48.000 people and all that. There's a labeled platforms as distinct 0:08:48.000 --> 0:08:51.440 from capital platforms like the airbnb s or other places 0:08:51.480 --> 0:08:53.960 you can rent an asset or buy a good. That 0:08:54.080 --> 0:08:58.240 for those participating in labor platforms, this was a way 0:08:58.280 --> 0:09:01.520 of offsetting the income ball is tility from their traditional job. 0:09:02.040 --> 0:09:04.680 But most of them actually had traditional jobs, so we 0:09:04.720 --> 0:09:08.360 could observe that for most people this is supplemental income 0:09:08.400 --> 0:09:13.040 to the tune of um, not the whole reality. In 0:09:13.080 --> 0:09:17.080 any given month where they're participating, say driving or or 0:09:17.760 --> 0:09:22.400 renting an Airbnb room or otherwise, it can be significant 0:09:22.480 --> 0:09:24.440 for that month, but over the course of the year 0:09:24.600 --> 0:09:29.280 it's really just supplemental income, often designed to with offset 0:09:29.320 --> 0:09:33.319 in effect, the volatility from the traditional source of income. Diana. 0:09:33.440 --> 0:09:38.000 The official unemployment rate in the US is three point 0:09:38.080 --> 0:09:42.760 seven percent, and it's been below four for some months now, 0:09:43.200 --> 0:09:47.520 and yet it isn't generating a surge in inflation. C 0:09:47.720 --> 0:09:50.720 p I is more or less around the Federal reserves 0:09:50.760 --> 0:09:56.319 target of two percent. What do those figures miss? Well? 0:09:56.440 --> 0:09:59.600 I think there are many aspects of the economy that 0:10:00.040 --> 0:10:05.200 are new and interact with inflation in wages in ways 0:10:05.240 --> 0:10:08.360 that we are only beginning to understand. So if we 0:10:08.440 --> 0:10:14.640 come back to these online platform economy businesses, particularly those 0:10:14.720 --> 0:10:18.560 that are involving assets, whether it's driving a car or 0:10:18.600 --> 0:10:21.880 renting a house or an apartment or a room, one 0:10:21.880 --> 0:10:24.520 of the effects of that, if you think about it, 0:10:24.640 --> 0:10:27.800 is that you are delivering a service or a good 0:10:28.240 --> 0:10:32.560 without requiring more capital in the system. So before the 0:10:32.640 --> 0:10:35.400 car would sit in a parking lot, now it's being 0:10:35.520 --> 0:10:37.520 used at a much higher rate by those who are 0:10:37.679 --> 0:10:42.160 driving folks around or delivering things for them. So to 0:10:42.640 --> 0:10:44.960 that house that used to sit empty is now being 0:10:45.080 --> 0:10:47.320 used without having to build a new house, and so 0:10:47.400 --> 0:10:50.280 that in effect it has a deflationary impact on the 0:10:50.280 --> 0:10:53.440 economy that we're not entirely measuring it because the way 0:10:53.480 --> 0:10:58.439 we think about GDP growth or even unemployment UM is 0:10:58.600 --> 0:11:02.720 through the category worries of employment and GDP that we 0:11:02.880 --> 0:11:07.600 used to think we're the norm. Uh, new forms are 0:11:07.600 --> 0:11:10.360 being introduced that are sort of challenging that. So I 0:11:10.360 --> 0:11:14.080 think that that's one of the reasons we're not seeing UM. 0:11:14.160 --> 0:11:18.920 The pressure on inflation or pressure on wages is that 0:11:19.120 --> 0:11:21.800 assets are being utilized better and that keeps costs down 0:11:21.840 --> 0:11:24.920 in other ways, and your data tells us about that 0:11:25.080 --> 0:11:28.120 car and the garage and that house that's being used 0:11:28.360 --> 0:11:32.240 very clevely, very clearly, and we really are note seeing 0:11:32.280 --> 0:11:36.120 that it's it's assets sort of being utilized that would 0:11:36.120 --> 0:11:39.120 otherwise be laying fallow, and so you're not creating new 0:11:39.160 --> 0:11:42.840 production to make those available and therefore not driving the 0:11:42.880 --> 0:11:46.559 inflation that would that might go with that. That's really interesting, Diana. 0:11:46.720 --> 0:11:50.120 Let's talk a little bit more about the ways that 0:11:50.160 --> 0:11:53.640 you measure cash flow in Americans bank accounts and their 0:11:54.000 --> 0:11:57.439 day to day spending. One of the most interesting things 0:11:57.520 --> 0:12:00.640 that I thought you put out was looking at out 0:12:00.679 --> 0:12:04.880 of pockets spending on healthcare and and the financial burden, 0:12:05.400 --> 0:12:08.320 uh it places on Americans where we have this kind 0:12:08.320 --> 0:12:12.200 of crazy health care system in this country that people 0:12:12.240 --> 0:12:15.320 have to kind of really manage their expenses and be 0:12:15.440 --> 0:12:21.520 careful about. What have you found about about Americans healthcare 0:12:21.880 --> 0:12:25.920 spending and in something you know, really interesting things that 0:12:25.960 --> 0:12:29.520 stand out about that. Um. Yes, So let me um 0:12:29.800 --> 0:12:32.080 zoom out a little bit to understand the out of 0:12:32.080 --> 0:12:35.760 pocket healthcare send because it's important. I mentioned earlier that 0:12:35.880 --> 0:12:40.160 we observe through this high frequency lens at the individual 0:12:40.160 --> 0:12:44.080 household level, very high levels of income and spending volatility 0:12:44.600 --> 0:12:47.600 and um when you double click on what is driving 0:12:47.720 --> 0:12:52.320 that volatility on the spending side, not surprisingly, one of 0:12:52.360 --> 0:12:56.520 the big buckets is healthcare. So the three are healthcare, 0:12:56.559 --> 0:13:00.959 auto repairs, and tax payments. Those three events will be 0:13:01.040 --> 0:13:05.160 an extraordinary expense for nearly one out of four Americans 0:13:05.200 --> 0:13:09.360 every year. But healthcare is important because even for those 0:13:09.400 --> 0:13:12.800 who have insurance, if they have a high deductible plan, 0:13:13.120 --> 0:13:16.400 which most of us have, UH, that will require a 0:13:16.480 --> 0:13:19.760 cash outflow. Now, some of that cash outflow may eventually 0:13:19.800 --> 0:13:22.560 be reimbursed, and by insurance, some of it will not 0:13:22.600 --> 0:13:25.959 be reimbursed, but it creates a real cash flow event um. 0:13:26.040 --> 0:13:28.800 And we know from our data, but also other data 0:13:29.400 --> 0:13:32.320 the Federal Reserve and others have put out that most 0:13:32.320 --> 0:13:35.720 Americans don't have a financial buffer to sort of withstand 0:13:35.800 --> 0:13:39.520 that extraordinary expense or withstand a drop and income of 0:13:39.600 --> 0:13:42.800 some sort, and so the result is large levels of 0:13:43.120 --> 0:13:46.840 out of pocket healthcare spend that can either translate into 0:13:46.880 --> 0:13:49.720 deferred care, people not seeking the care they need until 0:13:49.760 --> 0:13:51.920 they have that cash flow, say a tax refund or 0:13:52.000 --> 0:13:55.080 something to do it, or just a real impact on 0:13:55.120 --> 0:13:59.040 their financial outcomes, meaning they go into debt through credit 0:13:59.080 --> 0:14:03.560 card or a need to harness other resources. UM. And 0:14:03.640 --> 0:14:05.520 we do find that to the tune of over a 0:14:05.559 --> 0:14:09.040 thousand dollars. You know, families are going to face that 0:14:09.120 --> 0:14:11.840 kind of out of pocket hit, which is extraordinary given 0:14:11.840 --> 0:14:39.720 that they don't have the financial buffer to withstand that. Deanna, 0:14:39.960 --> 0:14:42.760 you've also sat on the other side of the table. 0:14:43.320 --> 0:14:49.400 You are a policymaker in an Obama administration, governments and 0:14:49.600 --> 0:14:54.040 central banks inching closer to your model. What are you 0:14:54.080 --> 0:14:57.040 hearing from them? Well, let me I'm glad you mentioned that, 0:14:57.200 --> 0:15:01.480 because one of the inspirations for the institute was this 0:15:01.640 --> 0:15:05.880 recognition that at times of crisis in particular, but maybe 0:15:06.000 --> 0:15:10.680 at other times too, policymakers aren't always equipped with the 0:15:10.720 --> 0:15:15.240 real time, granular, high frequency information that they need to 0:15:15.280 --> 0:15:17.880 make the best possible decisions. And so part of what 0:15:18.600 --> 0:15:21.080 I think that the bank excited certainly got me excited, 0:15:21.160 --> 0:15:24.880 was imagine if we could begin to bring that into 0:15:25.440 --> 0:15:30.600 the decision making that policymakers have, that that other people 0:15:30.600 --> 0:15:33.280 who are making decisions have, and and so that is 0:15:33.480 --> 0:15:36.000 a bit of the inspiration behind this UM. I think 0:15:36.480 --> 0:15:40.000 many are moving close to that direction, and one as 0:15:40.040 --> 0:15:42.760 an example, one of the data series that we have 0:15:42.880 --> 0:15:44.840 now put up for two and a half years is 0:15:44.880 --> 0:15:48.120 what we call local consumer Commerce, and this is a 0:15:48.240 --> 0:15:52.160 view of sales if you like, that transpire in a 0:15:52.240 --> 0:15:55.120 given city as a measure of the vibrancy of that city. 0:15:55.880 --> 0:15:58.960 The inspiration behind that was a set of conversations we'd 0:15:58.960 --> 0:16:02.240 had with the statistical agencies that said, we don't have 0:16:02.280 --> 0:16:05.960 good enough data at the city level. If we take 0:16:06.000 --> 0:16:08.320 our national data and then bring it down to the 0:16:08.360 --> 0:16:10.200 city level, it would be very helpful to have a 0:16:10.200 --> 0:16:12.560 corroborating view of what's happening at the city level that 0:16:12.600 --> 0:16:14.560 will improve what they do. So I think that's a 0:16:14.560 --> 0:16:19.840 good example. Another example that that we are very keen 0:16:19.960 --> 0:16:22.760 on is you recall and we may be entering a 0:16:22.800 --> 0:16:26.440 period like this, this very steep drop in gas prices 0:16:26.520 --> 0:16:31.760 that transpired from it's a very dramatic drop in gas 0:16:31.760 --> 0:16:36.800 prices and very vaccine question for traditional surveys and others 0:16:36.920 --> 0:16:39.120 is what are people doing with the savings at the 0:16:39.160 --> 0:16:42.880 gas them And if you went out to survey people, 0:16:42.920 --> 0:16:46.160 which many people did, they pretty much categorically said, well, 0:16:46.160 --> 0:16:47.920 we're saving it. Of course we are because that's what 0:16:47.960 --> 0:16:50.840 we all intend to do. And and it's very hard 0:16:50.880 --> 0:16:53.400 to know what you're really doing with what amounts to 0:16:53.520 --> 0:16:55.920 five percent of total spend over the course of the year, 0:16:56.360 --> 0:16:58.120 even though for some people it's a lot and for 0:16:58.120 --> 0:17:01.960 other people's small amount. And our data lent itself very 0:17:02.080 --> 0:17:05.359 very powerfully to saying, well, at the household level, what 0:17:05.680 --> 0:17:09.119 decreases do we see in gas spending and what corresponding 0:17:09.400 --> 0:17:12.439 meaning causal that we can really map to that dropping 0:17:12.480 --> 0:17:15.080 gas spending can we link to different kinds of spending 0:17:15.440 --> 0:17:17.600 and we find out in fact people were spending most 0:17:17.600 --> 0:17:20.680 of that they were spending it on groceries, on restaurants. 0:17:20.720 --> 0:17:23.800 Now that matters a lot to central banks. At the time, 0:17:24.160 --> 0:17:28.880 the FED was and you know, it's constant deliberations as 0:17:28.920 --> 0:17:31.480 to whether it should start raising rates or not. And 0:17:31.520 --> 0:17:35.280 it was important to that conversation to know whether that 0:17:35.600 --> 0:17:40.520 gas savings was in effect still a buffer that could 0:17:40.960 --> 0:17:43.639 bring the economy forward or whether it was already built 0:17:43.680 --> 0:17:46.119 into the numbers as a way of understanding whether it 0:17:46.240 --> 0:17:48.720 was time to move or not. And um and I 0:17:48.760 --> 0:17:51.680 think this kind of microwork, of course, with many other 0:17:51.720 --> 0:17:55.080 things complementing it. To my earlier point was important to 0:17:55.520 --> 0:17:57.479 consider that maybe they should wait at least one more 0:17:57.560 --> 0:18:00.560 quarter before moving forward, since in fact that was already 0:18:00.560 --> 0:18:03.720 built into the GDP numbers and we couldn't reasonably expect 0:18:03.720 --> 0:18:05.639 that it would be a wind in the sales of 0:18:05.680 --> 0:18:09.080 the economy in the future. So, with the gasoline prices 0:18:09.200 --> 0:18:12.640 again declining in recent weeks, as the price of oils dropping, 0:18:13.080 --> 0:18:15.080 fuel prices at the pump are probably going to go 0:18:15.160 --> 0:18:20.280 down even further, has that earlier work inform the approach 0:18:20.320 --> 0:18:23.840 that policymakers should be taking now, given that they're already 0:18:23.920 --> 0:18:26.520 uh in this interest rate well into this interest rate 0:18:26.560 --> 0:18:29.800 hiking cycle. Well, it's a good question. And frankly, after 0:18:29.880 --> 0:18:32.240 we concluded that work, we did it once as a 0:18:32.400 --> 0:18:35.560 snapshot peak to trough, and then we did it again 0:18:35.960 --> 0:18:38.320 over the course of the whole year. And you know, 0:18:38.480 --> 0:18:42.879 people do habituate. You know, once they're used to a thing, 0:18:42.920 --> 0:18:46.040 they're more likely to keep doing it. And so we thought, okay, 0:18:46.119 --> 0:18:48.399 we think we have an understanding of how people change 0:18:48.440 --> 0:18:52.480 their behavior when gas prices go down significantly. Um, we're 0:18:52.520 --> 0:18:55.879 going to wait and see what happens when prices go up. 0:18:56.000 --> 0:18:58.040 Do you have a symmetrical effect the other way, Do 0:18:58.080 --> 0:19:01.080 people spend less because they now have to spend more 0:19:01.080 --> 0:19:03.520 on gas, you know, other things go down or do 0:19:03.600 --> 0:19:07.120 they not? And so we were eagerly awaiting. But now 0:19:07.119 --> 0:19:10.000 what we have is the probability of a pretty significant 0:19:10.200 --> 0:19:14.119 continued decreases at least as as summer predicting it and um, 0:19:14.359 --> 0:19:17.840 so the question is do we have the same level 0:19:17.920 --> 0:19:22.160 impact in the increase in spending other things or are 0:19:22.200 --> 0:19:24.280 we at a different point in that curve? And you know, 0:19:24.440 --> 0:19:26.959 my guesses will turn back to that question when we 0:19:27.000 --> 0:19:29.679 have a few months of data to answer it. That 0:19:29.800 --> 0:19:32.040 that's an interesting thing. I think most of us know 0:19:32.160 --> 0:19:35.360 that that's not a linear curve, meaning that a ten 0:19:35.359 --> 0:19:39.560 percent increase in gas prices doesn't at at gas price 0:19:39.680 --> 0:19:42.560 two dollars is not the same as a ten percent 0:19:42.640 --> 0:19:45.560 increase at gas price four dollars um. And so we're 0:19:45.600 --> 0:19:48.200 interested to see what that curve looks like. The microwork 0:19:48.240 --> 0:19:50.159 we're doing, I think over time will help inform that 0:19:50.240 --> 0:19:51.960 both on the way up and on the way down 0:19:52.359 --> 0:19:54.960 as gas prices go up or down. Thank you for 0:19:55.000 --> 0:19:59.920 mentioning cities. The term urban rural divide is a popular 0:20:00.040 --> 0:20:04.480 one and it relates mainly to politics. Are you seeing 0:20:04.560 --> 0:20:09.280 a divide in your bottoms up economic data? I would 0:20:09.280 --> 0:20:13.120 say that getting a much better understanding of what's happening 0:20:13.200 --> 0:20:16.920 at the city level is important, even before we start 0:20:17.040 --> 0:20:20.920 informing the city to rural divide. I would argue, there's 0:20:20.920 --> 0:20:24.679 actually quite a variation in the experience of even what 0:20:24.680 --> 0:20:27.920 we would consider cities, and then that variation gets even 0:20:28.000 --> 0:20:31.120 wider when you include rural areas. The work that we've 0:20:31.160 --> 0:20:34.960 been doing on cities has been primarily focused on at 0:20:35.000 --> 0:20:38.239 this point fourteen larger cities than not. We have some 0:20:38.320 --> 0:20:40.520 that are larger, some of that are smaller, and that's 0:20:40.600 --> 0:20:45.720 the what we are mostly getting a window on. Partially, 0:20:45.920 --> 0:20:50.480 it's it's that we have enough representation, enough sort of observation, 0:20:50.600 --> 0:20:53.160 so to speak, to feel very confident that we're saying 0:20:53.160 --> 0:20:56.360 something important about those cities. And partly, as you can imagine, 0:20:56.520 --> 0:21:00.440 is that the bank's footprint is much stronger in cities 0:21:00.640 --> 0:21:03.000 than it is in the rural area. So I suspect 0:21:03.040 --> 0:21:06.680 that just as we're seeing significant variation across the city 0:21:06.720 --> 0:21:09.840 samples that we have, we would just increase that variation 0:21:09.880 --> 0:21:13.320 significantly if we had rural observations. But we have so 0:21:13.400 --> 0:21:17.080 far not actually done a rural urban divide. To inform 0:21:17.160 --> 0:21:22.399 that question, well, Diana, your former White House colleague Larry 0:21:22.440 --> 0:21:26.960 Summers has popularized the term from the nineteen thirties Alvin 0:21:27.000 --> 0:21:33.480 Hansen's secular stagnation. What does your data tell us about 0:21:33.560 --> 0:21:38.399 that term? Well, to try to mainstream the concept a 0:21:38.400 --> 0:21:40.520 little bit for those who may not be as familiar 0:21:40.600 --> 0:21:43.600 with it, I think the best way to phrase that 0:21:43.760 --> 0:21:46.239 kind of line of thinking is that we should be 0:21:46.280 --> 0:21:48.879 expecting a new normal, so to speak, that we're not 0:21:49.040 --> 0:21:52.240 likely to to see the kind of growth rates that 0:21:52.280 --> 0:21:54.840 we experienced it at peak levels. And that's not the 0:21:54.920 --> 0:21:57.120 only aspect of that theory. But but I think as 0:21:57.160 --> 0:22:00.680 we look into that one in particular, UM, there are 0:22:01.760 --> 0:22:05.639 some things that are incontrovertible that would correspond to that, 0:22:05.680 --> 0:22:09.600 which are, for example, that we are aging society, and therefore, 0:22:09.880 --> 0:22:14.760 as there's some growth that comes UM strictly from population 0:22:14.800 --> 0:22:17.480 growth that we have less of, and if we go 0:22:17.600 --> 0:22:22.080 down the path of UM limiting immigration more, that's one 0:22:22.160 --> 0:22:25.159 other source of demographic sort of weight down. And so 0:22:25.240 --> 0:22:27.920 in that sense, I think most people would say that 0:22:28.000 --> 0:22:30.439 stands to reason. I think in the other sense, we 0:22:30.560 --> 0:22:35.159 have seen even as you know, imperfectly measured as it is, 0:22:35.640 --> 0:22:39.720 GDP growth um pick up quite significantly in the last while, 0:22:39.800 --> 0:22:42.439 which suggests that there are some things that can be 0:22:42.480 --> 0:22:46.200 done to move in that direction, certainly some of the 0:22:46.240 --> 0:22:49.399 fiscal stimulus that was put in place or otherwise, although 0:22:49.400 --> 0:22:51.440 what's yet to be seen is how long lived any 0:22:51.480 --> 0:22:53.840 of that is. Our data are not the best to 0:22:53.880 --> 0:22:57.720 inform that question, because really that is a macro economic 0:22:57.800 --> 0:23:00.400 question and the strength of our data are that we're 0:23:00.400 --> 0:23:03.720 taking a micro view and then taking it up to 0:23:03.920 --> 0:23:07.240 a macro view. So I would argue that that is 0:23:07.280 --> 0:23:10.119 probably not the best question we can inform. All Right, 0:23:10.640 --> 0:23:14.560 last question, Dianna, what can we expect from JP Morgan 0:23:14.680 --> 0:23:21.000 Institute in the rest in terms of big insights projects 0:23:21.080 --> 0:23:23.679 or anything else exciting that you're planning. Well, thank you 0:23:23.720 --> 0:23:26.480 for asking, because this is my opportunity to say please 0:23:26.480 --> 0:23:30.159 follow us. And all our research is out in the open, 0:23:30.520 --> 0:23:33.439 available to all at our website. So um things that 0:23:33.480 --> 0:23:35.840 are coming up are will continue on some of these 0:23:35.920 --> 0:23:39.160 main themes, but with new twists. So I mentioned the 0:23:39.200 --> 0:23:42.520 financial economic well being of household. We want to keep 0:23:42.600 --> 0:23:47.680 understanding this um notion of what makes households resilient, and 0:23:47.960 --> 0:23:51.520 you know, besides having a saving buffer, what do we 0:23:51.560 --> 0:23:55.159 know about the behavior of those households that thrive versus 0:23:55.200 --> 0:23:57.760 those that not That might be good lessons for folks 0:23:57.800 --> 0:24:01.080 in the future. We are have been doing, but will 0:24:01.119 --> 0:24:04.400 continue to do, significant work on mortgages to understand how 0:24:04.440 --> 0:24:08.320 well does kind of that that portion of household debt 0:24:08.920 --> 0:24:13.480 interact with other financial outcomes, and that becomes important as 0:24:13.640 --> 0:24:18.480 we think about very significant changes in mortgage rate deductions 0:24:18.480 --> 0:24:21.879 and otherwise interest rate deductions on mortgages. We're starting and 0:24:22.000 --> 0:24:24.840