1 00:00:01,160 --> 00:00:05,240 Speaker 1: Hi, This is Daniel Moss from Bloomberg Opinion. Before this 2 00:00:05,320 --> 00:00:10,440 Speaker 1: week's episode of Benchmark gets under way, an announcement. Often 3 00:00:10,520 --> 00:00:13,320 Speaker 1: more than three years and a hundred and fifty episodes 4 00:00:13,720 --> 00:00:17,279 Speaker 1: will be taking a hiatus. We'd like to thank all 5 00:00:17,320 --> 00:00:24,200 Speaker 1: our listeners. Now enjoy the year's finale. Here's a secret 6 00:00:24,280 --> 00:00:27,040 Speaker 1: about the U. S economy. Most of the data you 7 00:00:27,120 --> 00:00:31,720 Speaker 1: hear about, such as jobs, GDP, consumer spending, and inflation, 8 00:00:32,080 --> 00:00:36,520 Speaker 1: is not actual data. Rather, these numbers are all extrapolated 9 00:00:36,560 --> 00:00:40,640 Speaker 1: from surveys of households and businesses. Now, these surveys tend 10 00:00:40,680 --> 00:00:44,080 Speaker 1: to be several times bigger than say, political polls, but 11 00:00:44,159 --> 00:00:47,879 Speaker 1: they're still samples of the broader population. But what if 12 00:00:47,920 --> 00:00:51,120 Speaker 1: we could measure consumer spending by looking at every single 13 00:00:51,200 --> 00:00:55,200 Speaker 1: purchase that Americans make, or look at every small business's 14 00:00:55,240 --> 00:00:59,120 Speaker 1: bank account to analyze cash flow. One think tank is 15 00:00:59,160 --> 00:01:02,520 Speaker 1: trying to do that and potentially reshaping how we look 16 00:01:02,520 --> 00:01:15,840 Speaker 1: at the economy. Welcome to Benchmark. I'm Start Landman, economics 17 00:01:15,920 --> 00:01:20,160 Speaker 1: editor with Bloomberg used Washington, and I'm Daniel Moss, columnist 18 00:01:20,240 --> 00:01:24,119 Speaker 1: at Bloomberg Opinion in New York. Don't get us wrong. 19 00:01:24,840 --> 00:01:28,800 Speaker 1: US economic data may be based on surveys, but it's 20 00:01:28,840 --> 00:01:32,920 Speaker 1: the gold standard for surveys. Yet they're still subject to 21 00:01:33,080 --> 00:01:37,040 Speaker 1: regular revisions and often face questions over whether they've properly 22 00:01:37,080 --> 00:01:42,040 Speaker 1: adjusted for seasonal events such as holidays, and they often 23 00:01:42,160 --> 00:01:46,160 Speaker 1: don't fully capture what's happening on a week to week, 24 00:01:46,640 --> 00:01:50,080 Speaker 1: let alone day to day basis. Now we have with 25 00:01:50,160 --> 00:01:52,680 Speaker 1: us in our DC studio a person who's using a 26 00:01:52,720 --> 00:01:56,120 Speaker 1: set of big data to explain trends in the economy 27 00:01:56,400 --> 00:01:59,520 Speaker 1: and also to find new ones. Diana Farrell is the 28 00:01:59,520 --> 00:02:03,080 Speaker 1: founding president and chief executive Officer of the JP Morgan 29 00:02:03,200 --> 00:02:08,079 Speaker 1: Chase Institute, a position she's held since Previously, she was 30 00:02:08,120 --> 00:02:10,880 Speaker 1: a senior partner at McKinsey and Company, where she was 31 00:02:10,919 --> 00:02:14,160 Speaker 1: the global head of the McKinsey Center for Government and 32 00:02:14,440 --> 00:02:18,320 Speaker 1: the McKinsey Global Institute. She served in the Obama administration 33 00:02:18,360 --> 00:02:22,000 Speaker 1: as Deputy Director of the National Economic Council and Deputy 34 00:02:22,040 --> 00:02:25,840 Speaker 1: Assistant to the President on Economic Policy. Dianna, thanks for 35 00:02:25,880 --> 00:02:28,120 Speaker 1: being with us on Benchmark. Thank you so much for 36 00:02:28,200 --> 00:02:30,840 Speaker 1: having me so. First of all, can you tell us 37 00:02:31,160 --> 00:02:36,160 Speaker 1: what exactly is the JP Morgan Chase Institute, why did 38 00:02:36,200 --> 00:02:39,800 Speaker 1: the company started, and what we're in are your goals. 39 00:02:40,760 --> 00:02:43,520 Speaker 1: I'd love to UM. The JP Morgan Chase Institute is 40 00:02:43,520 --> 00:02:47,280 Speaker 1: a relatively new initiative of the Bank UM and the 41 00:02:47,320 --> 00:02:49,480 Speaker 1: best way to think about it is it's a think 42 00:02:49,520 --> 00:02:53,720 Speaker 1: tank and it's trying to do original economics research, but 43 00:02:53,960 --> 00:02:57,160 Speaker 1: with a twist, which is much as you started to 44 00:02:57,200 --> 00:03:00,880 Speaker 1: introduce it. Instead of relying on the typical surveys that 45 00:03:00,919 --> 00:03:04,840 Speaker 1: are designed to answer specific questions on the economy and 46 00:03:04,880 --> 00:03:07,800 Speaker 1: then aggregate up to a view of the economy, we 47 00:03:07,880 --> 00:03:12,120 Speaker 1: start with the actual choices that people make as evidence 48 00:03:12,200 --> 00:03:18,799 Speaker 1: through the financial transactions. So think about the credit card, debit, card, loans, UM, 49 00:03:19,320 --> 00:03:21,840 Speaker 1: any number of finacial transactions that come to the bank 50 00:03:22,200 --> 00:03:24,600 Speaker 1: as the window that we have, and it's a pretty 51 00:03:24,680 --> 00:03:28,680 Speaker 1: extraordinary window. Consider that the JP Morgan chases two and 52 00:03:28,680 --> 00:03:32,080 Speaker 1: a half trillion dollars balance sheet worth of financial transactions. 53 00:03:32,639 --> 00:03:36,920 Speaker 1: That's you know, over half of US household UH, two 54 00:03:36,960 --> 00:03:40,800 Speaker 1: and a half small businesses UM and a very large 55 00:03:40,880 --> 00:03:43,520 Speaker 1: number of institutional investors. If you think about that, is 56 00:03:43,560 --> 00:03:46,760 Speaker 1: the window on economic activity not based on what people 57 00:03:46,880 --> 00:03:49,760 Speaker 1: say they are doing or are going to do, but 58 00:03:49,920 --> 00:03:52,560 Speaker 1: on what they are actually doing. That is the basis 59 00:03:52,600 --> 00:03:55,640 Speaker 1: for us to try to explain some of the things 60 00:03:55,680 --> 00:03:58,920 Speaker 1: that are transpiring in the economy, especially as the economy 61 00:03:58,960 --> 00:04:02,320 Speaker 1: evolves UH compared to the way some of the original 62 00:04:02,320 --> 00:04:05,640 Speaker 1: statistics were developed, so that we can inform what is 63 00:04:05,680 --> 00:04:09,280 Speaker 1: the economic and financial well being of households of small businesses? 64 00:04:09,600 --> 00:04:12,280 Speaker 1: What new developments do we see in labor markets, in 65 00:04:12,440 --> 00:04:15,600 Speaker 1: out of pocket healthcare spend at the city level? Do 66 00:04:15,640 --> 00:04:18,840 Speaker 1: we see, you know, with our very high frequency and 67 00:04:19,000 --> 00:04:23,560 Speaker 1: granular data, patterns of economic vibrancy that cannot be seen 68 00:04:23,600 --> 00:04:26,640 Speaker 1: through some of the other data. UM. Small businesses themselves 69 00:04:26,720 --> 00:04:30,120 Speaker 1: as a sector are really important, and increasingly we're turning 70 00:04:30,160 --> 00:04:33,400 Speaker 1: not just to the Chase platform to understand the economy, 71 00:04:33,480 --> 00:04:37,280 Speaker 1: but the JP Morgan platform, which are more the finacial 72 00:04:37,320 --> 00:04:42,120 Speaker 1: transactions of institutional investors UM. So short view is to say, 73 00:04:42,200 --> 00:04:45,039 Speaker 1: can we take this extraordinary window, which are the financial 74 00:04:45,120 --> 00:04:49,520 Speaker 1: transactions of JP Morgan Chase to inform important discussions that 75 00:04:49,560 --> 00:04:51,680 Speaker 1: the traditional data are not doing a good job of it. 76 00:04:52,000 --> 00:04:55,720 Speaker 1: And I think three years in the answers, Yes, Diana, 77 00:04:55,800 --> 00:04:58,480 Speaker 1: I lived in the Washington area for ten years, and 78 00:04:58,560 --> 00:05:04,480 Speaker 1: there's no shortage of think tanks working modeling the US economy. 79 00:05:04,640 --> 00:05:08,120 Speaker 1: It sounds like your approach is for one of a 80 00:05:08,160 --> 00:05:14,000 Speaker 1: better term disrupting big think tank. Well, that's an interesting question, 81 00:05:14,160 --> 00:05:17,839 Speaker 1: and I would say that it is disrupting certain areas 82 00:05:17,920 --> 00:05:22,320 Speaker 1: of economic research. I would venture to say that to 83 00:05:22,480 --> 00:05:26,440 Speaker 1: have a full understanding of the economy, we're gonna need 84 00:05:26,560 --> 00:05:28,960 Speaker 1: multiple lenses, and no one lens is going to give 85 00:05:28,960 --> 00:05:31,000 Speaker 1: that to us. So even though I think in some 86 00:05:31,040 --> 00:05:35,400 Speaker 1: ways we are informing important economic questions better than say 87 00:05:35,520 --> 00:05:38,920 Speaker 1: the government statistics are. Take for example, the work that 88 00:05:38,960 --> 00:05:42,360 Speaker 1: we've done on the so called gig economy, the online 89 00:05:42,360 --> 00:05:45,680 Speaker 1: platform economy. I think we have better data as as 90 00:05:45,720 --> 00:05:49,159 Speaker 1: even these statisticians at BLS at the Bureau of Labor 91 00:05:49,720 --> 00:05:54,360 Speaker 1: Statistics would would acknowledge. But where it's not a substitute 92 00:05:54,400 --> 00:05:56,760 Speaker 1: for that, it's a compliment to many of the other 93 00:05:56,839 --> 00:06:00,160 Speaker 1: data series that have a long shelf life that been 94 00:06:00,200 --> 00:06:01,680 Speaker 1: around for a while. And I would say the same 95 00:06:01,720 --> 00:06:04,560 Speaker 1: thing about the think tanks, that we are disrupting the 96 00:06:04,600 --> 00:06:07,159 Speaker 1: way economic research is being done. I think we're doing 97 00:06:07,200 --> 00:06:10,960 Speaker 1: that hand in hand with academia. But there's plenty of 98 00:06:11,040 --> 00:06:14,120 Speaker 1: room for multiple windows and we get a much richer 99 00:06:14,680 --> 00:06:18,000 Speaker 1: um tapestry of what is in fact a very rich economy. 100 00:06:18,200 --> 00:06:21,560 Speaker 1: With those various lenses. Well, well, that was actually one 101 00:06:21,600 --> 00:06:23,800 Speaker 1: of the areas I wanted to ask you about Theanna 102 00:06:23,960 --> 00:06:27,919 Speaker 1: the research on the online platform economy. It really is 103 00:06:27,960 --> 00:06:31,440 Speaker 1: a buzzy issue that you've been working on taking a 104 00:06:31,440 --> 00:06:34,080 Speaker 1: look at how people are using and generating income through 105 00:06:34,440 --> 00:06:38,279 Speaker 1: platforms like Uber selling things leasing. Can you tell us 106 00:06:38,320 --> 00:06:41,520 Speaker 1: a little bit about what you discovered in this research 107 00:06:41,920 --> 00:06:45,880 Speaker 1: how profitable it is for Americans to work this way? 108 00:06:46,080 --> 00:06:48,320 Speaker 1: I would love to um, As you say, it is 109 00:06:48,360 --> 00:06:49,919 Speaker 1: an area that we've done a lot of work in 110 00:06:50,560 --> 00:06:54,120 Speaker 1: and um what I would say about it that is 111 00:06:54,160 --> 00:06:57,680 Speaker 1: important is that we got into a view of what 112 00:06:57,800 --> 00:07:02,200 Speaker 1: was happening in the online platform economy by understanding that 113 00:07:02,480 --> 00:07:06,760 Speaker 1: if you observe households on a high frequency basis, you know, monthly, weekly, 114 00:07:07,040 --> 00:07:09,720 Speaker 1: what we observe is that most hustles are facing high 115 00:07:09,760 --> 00:07:12,800 Speaker 1: levels of income and spending volatility. That's not something you 116 00:07:12,800 --> 00:07:17,760 Speaker 1: would capture with annual surveys, for example. And the conventional 117 00:07:17,760 --> 00:07:20,760 Speaker 1: wisdom of three years ago or so, as this economy 118 00:07:20,800 --> 00:07:24,200 Speaker 1: was getting started these gig jobs and otherwise is that's 119 00:07:24,200 --> 00:07:26,840 Speaker 1: why we have so much income volatility because people are 120 00:07:26,880 --> 00:07:30,640 Speaker 1: doing multiple gigs and and that explains it all, and 121 00:07:30,760 --> 00:07:33,040 Speaker 1: we thought, well, what kind of data exists on that, 122 00:07:33,200 --> 00:07:36,200 Speaker 1: And you know, there's the Contingent Worker Survey, but that's 123 00:07:36,240 --> 00:07:39,320 Speaker 1: only done every three years. There's the pieces of this, 124 00:07:39,440 --> 00:07:41,480 Speaker 1: some of those were discontinued as a matter of fact, 125 00:07:41,520 --> 00:07:43,600 Speaker 1: and we said, we can answer that question because we 126 00:07:43,640 --> 00:07:49,160 Speaker 1: can go and understand who is participating on these platforms 127 00:07:49,200 --> 00:07:52,440 Speaker 1: by observing who's receiving income from them and then linking 128 00:07:52,480 --> 00:07:56,600 Speaker 1: those to their overall economic and financial kind of outcomes. 129 00:07:56,680 --> 00:07:59,200 Speaker 1: And so what we learned is that counter to that 130 00:07:59,280 --> 00:08:02,720 Speaker 1: conventional whiz them, this is not really the future of 131 00:08:02,840 --> 00:08:07,520 Speaker 1: work as as cast glibly, because in fact, even now 132 00:08:07,720 --> 00:08:10,800 Speaker 1: that we've updated these numbers, at you know, at best, 133 00:08:10,840 --> 00:08:13,840 Speaker 1: it's one point six percent of the population is participating 134 00:08:14,080 --> 00:08:17,000 Speaker 1: in these platforms, and we can document that with very 135 00:08:17,080 --> 00:08:20,080 Speaker 1: large samples, and most of them are not working on 136 00:08:20,120 --> 00:08:23,640 Speaker 1: more than one platform, although that is increasingly a phenomenon. 137 00:08:24,120 --> 00:08:26,880 Speaker 1: The only link we had therefore to the income volatilities 138 00:08:26,960 --> 00:08:29,720 Speaker 1: that we noticed that for some of the participants, say 139 00:08:29,720 --> 00:08:33,679 Speaker 1: those that were participating in what we call the labor platforms, 140 00:08:34,120 --> 00:08:38,400 Speaker 1: think of the transportation services in their full kind, and uh, 141 00:08:38,440 --> 00:08:41,240 Speaker 1: there are now many many of those, but also all 142 00:08:41,240 --> 00:08:44,800 Speaker 1: the other services that walka dogs and the shop for 143 00:08:44,880 --> 00:08:48,000 Speaker 1: people and all that. There's a labeled platforms as distinct 144 00:08:48,000 --> 00:08:51,440 Speaker 1: from capital platforms like the airbnb s or other places 145 00:08:51,480 --> 00:08:53,960 Speaker 1: you can rent an asset or buy a good. That 146 00:08:54,080 --> 00:08:58,240 Speaker 1: for those participating in labor platforms, this was a way 147 00:08:58,280 --> 00:09:01,520 Speaker 1: of offsetting the income ball is tility from their traditional job. 148 00:09:02,040 --> 00:09:04,680 Speaker 1: But most of them actually had traditional jobs, so we 149 00:09:04,720 --> 00:09:08,360 Speaker 1: could observe that for most people this is supplemental income 150 00:09:08,400 --> 00:09:13,040 Speaker 1: to the tune of um, not the whole reality. In 151 00:09:13,080 --> 00:09:17,080 Speaker 1: any given month where they're participating, say driving or or 152 00:09:17,760 --> 00:09:22,400 Speaker 1: renting an Airbnb room or otherwise, it can be significant 153 00:09:22,480 --> 00:09:24,440 Speaker 1: for that month, but over the course of the year 154 00:09:24,600 --> 00:09:29,280 Speaker 1: it's really just supplemental income, often designed to with offset 155 00:09:29,320 --> 00:09:33,319 Speaker 1: in effect, the volatility from the traditional source of income. Diana. 156 00:09:33,440 --> 00:09:38,000 Speaker 1: The official unemployment rate in the US is three point 157 00:09:38,080 --> 00:09:42,760 Speaker 1: seven percent, and it's been below four for some months now, 158 00:09:43,200 --> 00:09:47,520 Speaker 1: and yet it isn't generating a surge in inflation. C 159 00:09:47,720 --> 00:09:50,720 Speaker 1: p I is more or less around the Federal reserves 160 00:09:50,760 --> 00:09:56,319 Speaker 1: target of two percent. What do those figures miss? Well? 161 00:09:56,440 --> 00:09:59,600 Speaker 1: I think there are many aspects of the economy that 162 00:10:00,040 --> 00:10:05,200 Speaker 1: are new and interact with inflation in wages in ways 163 00:10:05,240 --> 00:10:08,360 Speaker 1: that we are only beginning to understand. So if we 164 00:10:08,440 --> 00:10:14,640 Speaker 1: come back to these online platform economy businesses, particularly those 165 00:10:14,720 --> 00:10:18,560 Speaker 1: that are involving assets, whether it's driving a car or 166 00:10:18,600 --> 00:10:21,880 Speaker 1: renting a house or an apartment or a room, one 167 00:10:21,880 --> 00:10:24,520 Speaker 1: of the effects of that, if you think about it, 168 00:10:24,640 --> 00:10:27,800 Speaker 1: is that you are delivering a service or a good 169 00:10:28,240 --> 00:10:32,560 Speaker 1: without requiring more capital in the system. So before the 170 00:10:32,640 --> 00:10:35,400 Speaker 1: car would sit in a parking lot, now it's being 171 00:10:35,520 --> 00:10:37,520 Speaker 1: used at a much higher rate by those who are 172 00:10:37,679 --> 00:10:42,160 Speaker 1: driving folks around or delivering things for them. So to 173 00:10:42,640 --> 00:10:44,960 Speaker 1: that house that used to sit empty is now being 174 00:10:45,080 --> 00:10:47,320 Speaker 1: used without having to build a new house, and so 175 00:10:47,400 --> 00:10:50,280 Speaker 1: that in effect it has a deflationary impact on the 176 00:10:50,280 --> 00:10:53,440 Speaker 1: economy that we're not entirely measuring it because the way 177 00:10:53,480 --> 00:10:58,439 Speaker 1: we think about GDP growth or even unemployment UM is 178 00:10:58,600 --> 00:11:02,720 Speaker 1: through the category worries of employment and GDP that we 179 00:11:02,880 --> 00:11:07,600 Speaker 1: used to think we're the norm. Uh, new forms are 180 00:11:07,600 --> 00:11:10,360 Speaker 1: being introduced that are sort of challenging that. So I 181 00:11:10,360 --> 00:11:14,080 Speaker 1: think that that's one of the reasons we're not seeing UM. 182 00:11:14,160 --> 00:11:18,920 Speaker 1: The pressure on inflation or pressure on wages is that 183 00:11:19,120 --> 00:11:21,800 Speaker 1: assets are being utilized better and that keeps costs down 184 00:11:21,840 --> 00:11:24,920 Speaker 1: in other ways, and your data tells us about that 185 00:11:25,080 --> 00:11:28,120 Speaker 1: car and the garage and that house that's being used 186 00:11:28,360 --> 00:11:32,240 Speaker 1: very clevely, very clearly, and we really are note seeing 187 00:11:32,280 --> 00:11:36,120 Speaker 1: that it's it's assets sort of being utilized that would 188 00:11:36,120 --> 00:11:39,120 Speaker 1: otherwise be laying fallow, and so you're not creating new 189 00:11:39,160 --> 00:11:42,840 Speaker 1: production to make those available and therefore not driving the 190 00:11:42,880 --> 00:11:46,559 Speaker 1: inflation that would that might go with that. That's really interesting, Diana. 191 00:11:46,720 --> 00:11:50,120 Speaker 1: Let's talk a little bit more about the ways that 192 00:11:50,160 --> 00:11:53,640 Speaker 1: you measure cash flow in Americans bank accounts and their 193 00:11:54,000 --> 00:11:57,439 Speaker 1: day to day spending. One of the most interesting things 194 00:11:57,520 --> 00:12:00,640 Speaker 1: that I thought you put out was looking at out 195 00:12:00,679 --> 00:12:04,880 Speaker 1: of pockets spending on healthcare and and the financial burden, 196 00:12:05,400 --> 00:12:08,320 Speaker 1: uh it places on Americans where we have this kind 197 00:12:08,320 --> 00:12:12,200 Speaker 1: of crazy health care system in this country that people 198 00:12:12,240 --> 00:12:15,320 Speaker 1: have to kind of really manage their expenses and be 199 00:12:15,440 --> 00:12:21,520 Speaker 1: careful about. What have you found about about Americans healthcare 200 00:12:21,880 --> 00:12:25,920 Speaker 1: spending and in something you know, really interesting things that 201 00:12:25,960 --> 00:12:29,520 Speaker 1: stand out about that. Um. Yes, So let me um 202 00:12:29,800 --> 00:12:32,080 Speaker 1: zoom out a little bit to understand the out of 203 00:12:32,080 --> 00:12:35,760 Speaker 1: pocket healthcare send because it's important. I mentioned earlier that 204 00:12:35,880 --> 00:12:40,160 Speaker 1: we observe through this high frequency lens at the individual 205 00:12:40,160 --> 00:12:44,080 Speaker 1: household level, very high levels of income and spending volatility 206 00:12:44,600 --> 00:12:47,600 Speaker 1: and um when you double click on what is driving 207 00:12:47,720 --> 00:12:52,320 Speaker 1: that volatility on the spending side, not surprisingly, one of 208 00:12:52,360 --> 00:12:56,520 Speaker 1: the big buckets is healthcare. So the three are healthcare, 209 00:12:56,559 --> 00:13:00,959 Speaker 1: auto repairs, and tax payments. Those three events will be 210 00:13:01,040 --> 00:13:05,160 Speaker 1: an extraordinary expense for nearly one out of four Americans 211 00:13:05,200 --> 00:13:09,360 Speaker 1: every year. But healthcare is important because even for those 212 00:13:09,400 --> 00:13:12,800 Speaker 1: who have insurance, if they have a high deductible plan, 213 00:13:13,120 --> 00:13:16,400 Speaker 1: which most of us have, UH, that will require a 214 00:13:16,480 --> 00:13:19,760 Speaker 1: cash outflow. Now, some of that cash outflow may eventually 215 00:13:19,800 --> 00:13:22,560 Speaker 1: be reimbursed, and by insurance, some of it will not 216 00:13:22,600 --> 00:13:25,959 Speaker 1: be reimbursed, but it creates a real cash flow event um. 217 00:13:26,040 --> 00:13:28,800 Speaker 1: And we know from our data, but also other data 218 00:13:29,400 --> 00:13:32,320 Speaker 1: the Federal Reserve and others have put out that most 219 00:13:32,320 --> 00:13:35,720 Speaker 1: Americans don't have a financial buffer to sort of withstand 220 00:13:35,800 --> 00:13:39,520 Speaker 1: that extraordinary expense or withstand a drop and income of 221 00:13:39,600 --> 00:13:42,800 Speaker 1: some sort, and so the result is large levels of 222 00:13:43,120 --> 00:13:46,840 Speaker 1: out of pocket healthcare spend that can either translate into 223 00:13:46,880 --> 00:13:49,720 Speaker 1: deferred care, people not seeking the care they need until 224 00:13:49,760 --> 00:13:51,920 Speaker 1: they have that cash flow, say a tax refund or 225 00:13:52,000 --> 00:13:55,080 Speaker 1: something to do it, or just a real impact on 226 00:13:55,120 --> 00:13:59,040 Speaker 1: their financial outcomes, meaning they go into debt through credit 227 00:13:59,080 --> 00:14:03,560 Speaker 1: card or a need to harness other resources. UM. And 228 00:14:03,640 --> 00:14:05,520 Speaker 1: we do find that to the tune of over a 229 00:14:05,559 --> 00:14:09,040 Speaker 1: thousand dollars. You know, families are going to face that 230 00:14:09,120 --> 00:14:11,840 Speaker 1: kind of out of pocket hit, which is extraordinary given 231 00:14:11,840 --> 00:14:39,720 Speaker 1: that they don't have the financial buffer to withstand that. Deanna, 232 00:14:39,960 --> 00:14:42,760 Speaker 1: you've also sat on the other side of the table. 233 00:14:43,320 --> 00:14:49,400 Speaker 1: You are a policymaker in an Obama administration, governments and 234 00:14:49,600 --> 00:14:54,040 Speaker 1: central banks inching closer to your model. What are you 235 00:14:54,080 --> 00:14:57,040 Speaker 1: hearing from them? Well, let me I'm glad you mentioned that, 236 00:14:57,200 --> 00:15:01,480 Speaker 1: because one of the inspirations for the institute was this 237 00:15:01,640 --> 00:15:05,880 Speaker 1: recognition that at times of crisis in particular, but maybe 238 00:15:06,000 --> 00:15:10,680 Speaker 1: at other times too, policymakers aren't always equipped with the 239 00:15:10,720 --> 00:15:15,240 Speaker 1: real time, granular, high frequency information that they need to 240 00:15:15,280 --> 00:15:17,880 Speaker 1: make the best possible decisions. And so part of what 241 00:15:18,600 --> 00:15:21,080 Speaker 1: I think that the bank excited certainly got me excited, 242 00:15:21,160 --> 00:15:24,880 Speaker 1: was imagine if we could begin to bring that into 243 00:15:25,440 --> 00:15:30,600 Speaker 1: the decision making that policymakers have, that that other people 244 00:15:30,600 --> 00:15:33,280 Speaker 1: who are making decisions have, and and so that is 245 00:15:33,480 --> 00:15:36,000 Speaker 1: a bit of the inspiration behind this UM. I think 246 00:15:36,480 --> 00:15:40,000 Speaker 1: many are moving close to that direction, and one as 247 00:15:40,040 --> 00:15:42,760 Speaker 1: an example, one of the data series that we have 248 00:15:42,880 --> 00:15:44,840 Speaker 1: now put up for two and a half years is 249 00:15:44,880 --> 00:15:48,120 Speaker 1: what we call local consumer Commerce, and this is a 250 00:15:48,240 --> 00:15:52,160 Speaker 1: view of sales if you like, that transpire in a 251 00:15:52,240 --> 00:15:55,120 Speaker 1: given city as a measure of the vibrancy of that city. 252 00:15:55,880 --> 00:15:58,960 Speaker 1: The inspiration behind that was a set of conversations we'd 253 00:15:58,960 --> 00:16:02,240 Speaker 1: had with the statistical agencies that said, we don't have 254 00:16:02,280 --> 00:16:05,960 Speaker 1: good enough data at the city level. If we take 255 00:16:06,000 --> 00:16:08,320 Speaker 1: our national data and then bring it down to the 256 00:16:08,360 --> 00:16:10,200 Speaker 1: city level, it would be very helpful to have a 257 00:16:10,200 --> 00:16:12,560 Speaker 1: corroborating view of what's happening at the city level that 258 00:16:12,600 --> 00:16:14,560 Speaker 1: will improve what they do. So I think that's a 259 00:16:14,560 --> 00:16:19,840 Speaker 1: good example. Another example that that we are very keen 260 00:16:19,960 --> 00:16:22,760 Speaker 1: on is you recall and we may be entering a 261 00:16:22,800 --> 00:16:26,440 Speaker 1: period like this, this very steep drop in gas prices 262 00:16:26,520 --> 00:16:31,760 Speaker 1: that transpired from it's a very dramatic drop in gas 263 00:16:31,760 --> 00:16:36,800 Speaker 1: prices and very vaccine question for traditional surveys and others 264 00:16:36,920 --> 00:16:39,120 Speaker 1: is what are people doing with the savings at the 265 00:16:39,160 --> 00:16:42,880 Speaker 1: gas them And if you went out to survey people, 266 00:16:42,920 --> 00:16:46,160 Speaker 1: which many people did, they pretty much categorically said, well, 267 00:16:46,160 --> 00:16:47,920 Speaker 1: we're saving it. Of course we are because that's what 268 00:16:47,960 --> 00:16:50,840 Speaker 1: we all intend to do. And and it's very hard 269 00:16:50,880 --> 00:16:53,400 Speaker 1: to know what you're really doing with what amounts to 270 00:16:53,520 --> 00:16:55,920 Speaker 1: five percent of total spend over the course of the year, 271 00:16:56,360 --> 00:16:58,120 Speaker 1: even though for some people it's a lot and for 272 00:16:58,120 --> 00:17:01,960 Speaker 1: other people's small amount. And our data lent itself very 273 00:17:02,080 --> 00:17:05,359 Speaker 1: very powerfully to saying, well, at the household level, what 274 00:17:05,680 --> 00:17:09,119 Speaker 1: decreases do we see in gas spending and what corresponding 275 00:17:09,400 --> 00:17:12,439 Speaker 1: meaning causal that we can really map to that dropping 276 00:17:12,480 --> 00:17:15,080 Speaker 1: gas spending can we link to different kinds of spending 277 00:17:15,440 --> 00:17:17,600 Speaker 1: and we find out in fact people were spending most 278 00:17:17,600 --> 00:17:20,680 Speaker 1: of that they were spending it on groceries, on restaurants. 279 00:17:20,720 --> 00:17:23,800 Speaker 1: Now that matters a lot to central banks. At the time, 280 00:17:24,160 --> 00:17:28,880 Speaker 1: the FED was and you know, it's constant deliberations as 281 00:17:28,920 --> 00:17:31,480 Speaker 1: to whether it should start raising rates or not. And 282 00:17:31,520 --> 00:17:35,280 Speaker 1: it was important to that conversation to know whether that 283 00:17:35,600 --> 00:17:40,520 Speaker 1: gas savings was in effect still a buffer that could 284 00:17:40,960 --> 00:17:43,639 Speaker 1: bring the economy forward or whether it was already built 285 00:17:43,680 --> 00:17:46,119 Speaker 1: into the numbers as a way of understanding whether it 286 00:17:46,240 --> 00:17:48,720 Speaker 1: was time to move or not. And um and I 287 00:17:48,760 --> 00:17:51,680 Speaker 1: think this kind of microwork, of course, with many other 288 00:17:51,720 --> 00:17:55,080 Speaker 1: things complementing it. To my earlier point was important to 289 00:17:55,520 --> 00:17:57,479 Speaker 1: consider that maybe they should wait at least one more 290 00:17:57,560 --> 00:18:00,560 Speaker 1: quarter before moving forward, since in fact that was already 291 00:18:00,560 --> 00:18:03,720 Speaker 1: built into the GDP numbers and we couldn't reasonably expect 292 00:18:03,720 --> 00:18:05,639 Speaker 1: that it would be a wind in the sales of 293 00:18:05,680 --> 00:18:09,080 Speaker 1: the economy in the future. So, with the gasoline prices 294 00:18:09,200 --> 00:18:12,640 Speaker 1: again declining in recent weeks, as the price of oils dropping, 295 00:18:13,080 --> 00:18:15,080 Speaker 1: fuel prices at the pump are probably going to go 296 00:18:15,160 --> 00:18:20,280 Speaker 1: down even further, has that earlier work inform the approach 297 00:18:20,320 --> 00:18:23,840 Speaker 1: that policymakers should be taking now, given that they're already 298 00:18:23,920 --> 00:18:26,520 Speaker 1: uh in this interest rate well into this interest rate 299 00:18:26,560 --> 00:18:29,800 Speaker 1: hiking cycle. Well, it's a good question. And frankly, after 300 00:18:29,880 --> 00:18:32,240 Speaker 1: we concluded that work, we did it once as a 301 00:18:32,400 --> 00:18:35,560 Speaker 1: snapshot peak to trough, and then we did it again 302 00:18:35,960 --> 00:18:38,320 Speaker 1: over the course of the whole year. And you know, 303 00:18:38,480 --> 00:18:42,879 Speaker 1: people do habituate. You know, once they're used to a thing, 304 00:18:42,920 --> 00:18:46,040 Speaker 1: they're more likely to keep doing it. And so we thought, okay, 305 00:18:46,119 --> 00:18:48,399 Speaker 1: we think we have an understanding of how people change 306 00:18:48,440 --> 00:18:52,480 Speaker 1: their behavior when gas prices go down significantly. Um, we're 307 00:18:52,520 --> 00:18:55,879 Speaker 1: going to wait and see what happens when prices go up. 308 00:18:56,000 --> 00:18:58,040 Speaker 1: Do you have a symmetrical effect the other way, Do 309 00:18:58,080 --> 00:19:01,080 Speaker 1: people spend less because they now have to spend more 310 00:19:01,080 --> 00:19:03,520 Speaker 1: on gas, you know, other things go down or do 311 00:19:03,600 --> 00:19:07,120 Speaker 1: they not? And so we were eagerly awaiting. But now 312 00:19:07,119 --> 00:19:10,000 Speaker 1: what we have is the probability of a pretty significant 313 00:19:10,200 --> 00:19:14,119 Speaker 1: continued decreases at least as as summer predicting it and um, 314 00:19:14,359 --> 00:19:17,840 Speaker 1: so the question is do we have the same level 315 00:19:17,920 --> 00:19:22,160 Speaker 1: impact in the increase in spending other things or are 316 00:19:22,200 --> 00:19:24,280 Speaker 1: we at a different point in that curve? And you know, 317 00:19:24,440 --> 00:19:26,959 Speaker 1: my guesses will turn back to that question when we 318 00:19:27,000 --> 00:19:29,679 Speaker 1: have a few months of data to answer it. That 319 00:19:29,800 --> 00:19:32,040 Speaker 1: that's an interesting thing. I think most of us know 320 00:19:32,160 --> 00:19:35,360 Speaker 1: that that's not a linear curve, meaning that a ten 321 00:19:35,359 --> 00:19:39,560 Speaker 1: percent increase in gas prices doesn't at at gas price 322 00:19:39,680 --> 00:19:42,560 Speaker 1: two dollars is not the same as a ten percent 323 00:19:42,640 --> 00:19:45,560 Speaker 1: increase at gas price four dollars um. And so we're 324 00:19:45,600 --> 00:19:48,200 Speaker 1: interested to see what that curve looks like. The microwork 325 00:19:48,240 --> 00:19:50,159 Speaker 1: we're doing, I think over time will help inform that 326 00:19:50,240 --> 00:19:51,960 Speaker 1: both on the way up and on the way down 327 00:19:52,359 --> 00:19:54,960 Speaker 1: as gas prices go up or down. Thank you for 328 00:19:55,000 --> 00:19:59,920 Speaker 1: mentioning cities. The term urban rural divide is a popular 329 00:20:00,040 --> 00:20:04,480 Speaker 1: one and it relates mainly to politics. Are you seeing 330 00:20:04,560 --> 00:20:09,280 Speaker 1: a divide in your bottoms up economic data? I would 331 00:20:09,280 --> 00:20:13,120 Speaker 1: say that getting a much better understanding of what's happening 332 00:20:13,200 --> 00:20:16,920 Speaker 1: at the city level is important, even before we start 333 00:20:17,040 --> 00:20:20,920 Speaker 1: informing the city to rural divide. I would argue, there's 334 00:20:20,920 --> 00:20:24,679 Speaker 1: actually quite a variation in the experience of even what 335 00:20:24,680 --> 00:20:27,920 Speaker 1: we would consider cities, and then that variation gets even 336 00:20:28,000 --> 00:20:31,120 Speaker 1: wider when you include rural areas. The work that we've 337 00:20:31,160 --> 00:20:34,960 Speaker 1: been doing on cities has been primarily focused on at 338 00:20:35,000 --> 00:20:38,239 Speaker 1: this point fourteen larger cities than not. We have some 339 00:20:38,320 --> 00:20:40,520 Speaker 1: that are larger, some of that are smaller, and that's 340 00:20:40,600 --> 00:20:45,720 Speaker 1: the what we are mostly getting a window on. Partially, 341 00:20:45,920 --> 00:20:50,480 Speaker 1: it's it's that we have enough representation, enough sort of observation, 342 00:20:50,600 --> 00:20:53,160 Speaker 1: so to speak, to feel very confident that we're saying 343 00:20:53,160 --> 00:20:56,360 Speaker 1: something important about those cities. And partly, as you can imagine, 344 00:20:56,520 --> 00:21:00,440 Speaker 1: is that the bank's footprint is much stronger in cities 345 00:21:00,640 --> 00:21:03,000 Speaker 1: than it is in the rural area. So I suspect 346 00:21:03,040 --> 00:21:06,680 Speaker 1: that just as we're seeing significant variation across the city 347 00:21:06,720 --> 00:21:09,840 Speaker 1: samples that we have, we would just increase that variation 348 00:21:09,880 --> 00:21:13,320 Speaker 1: significantly if we had rural observations. But we have so 349 00:21:13,400 --> 00:21:17,080 Speaker 1: far not actually done a rural urban divide. To inform 350 00:21:17,160 --> 00:21:22,399 Speaker 1: that question, well, Diana, your former White House colleague Larry 351 00:21:22,440 --> 00:21:26,960 Speaker 1: Summers has popularized the term from the nineteen thirties Alvin 352 00:21:27,000 --> 00:21:33,480 Speaker 1: Hansen's secular stagnation. What does your data tell us about 353 00:21:33,560 --> 00:21:38,399 Speaker 1: that term? Well, to try to mainstream the concept a 354 00:21:38,400 --> 00:21:40,520 Speaker 1: little bit for those who may not be as familiar 355 00:21:40,600 --> 00:21:43,600 Speaker 1: with it, I think the best way to phrase that 356 00:21:43,760 --> 00:21:46,239 Speaker 1: kind of line of thinking is that we should be 357 00:21:46,280 --> 00:21:48,879 Speaker 1: expecting a new normal, so to speak, that we're not 358 00:21:49,040 --> 00:21:52,240 Speaker 1: likely to to see the kind of growth rates that 359 00:21:52,280 --> 00:21:54,840 Speaker 1: we experienced it at peak levels. And that's not the 360 00:21:54,920 --> 00:21:57,120 Speaker 1: only aspect of that theory. But but I think as 361 00:21:57,160 --> 00:22:00,680 Speaker 1: we look into that one in particular, UM, there are 362 00:22:01,760 --> 00:22:05,639 Speaker 1: some things that are incontrovertible that would correspond to that, 363 00:22:05,680 --> 00:22:09,600 Speaker 1: which are, for example, that we are aging society, and therefore, 364 00:22:09,880 --> 00:22:14,760 Speaker 1: as there's some growth that comes UM strictly from population 365 00:22:14,800 --> 00:22:17,480 Speaker 1: growth that we have less of, and if we go 366 00:22:17,600 --> 00:22:22,080 Speaker 1: down the path of UM limiting immigration more, that's one 367 00:22:22,160 --> 00:22:25,159 Speaker 1: other source of demographic sort of weight down. And so 368 00:22:25,240 --> 00:22:27,920 Speaker 1: in that sense, I think most people would say that 369 00:22:28,000 --> 00:22:30,439 Speaker 1: stands to reason. I think in the other sense, we 370 00:22:30,560 --> 00:22:35,159 Speaker 1: have seen even as you know, imperfectly measured as it is, 371 00:22:35,640 --> 00:22:39,720 Speaker 1: GDP growth um pick up quite significantly in the last while, 372 00:22:39,800 --> 00:22:42,439 Speaker 1: which suggests that there are some things that can be 373 00:22:42,480 --> 00:22:46,200 Speaker 1: done to move in that direction, certainly some of the 374 00:22:46,240 --> 00:22:49,399 Speaker 1: fiscal stimulus that was put in place or otherwise, although 375 00:22:49,400 --> 00:22:51,440 Speaker 1: what's yet to be seen is how long lived any 376 00:22:51,480 --> 00:22:53,840 Speaker 1: of that is. Our data are not the best to 377 00:22:53,880 --> 00:22:57,720 Speaker 1: inform that question, because really that is a macro economic 378 00:22:57,800 --> 00:23:00,400 Speaker 1: question and the strength of our data are that we're 379 00:23:00,400 --> 00:23:03,720 Speaker 1: taking a micro view and then taking it up to 380 00:23:03,920 --> 00:23:07,240 Speaker 1: a macro view. So I would argue that that is 381 00:23:07,280 --> 00:23:10,119 Speaker 1: probably not the best question we can inform. All Right, 382 00:23:10,640 --> 00:23:14,560 Speaker 1: last question, Dianna, what can we expect from JP Morgan 383 00:23:14,680 --> 00:23:21,000 Speaker 1: Institute in the rest in terms of big insights projects 384 00:23:21,080 --> 00:23:23,679 Speaker 1: or anything else exciting that you're planning. Well, thank you 385 00:23:23,720 --> 00:23:26,480 Speaker 1: for asking, because this is my opportunity to say please 386 00:23:26,480 --> 00:23:30,159 Speaker 1: follow us. And all our research is out in the open, 387 00:23:30,520 --> 00:23:33,439 Speaker 1: available to all at our website. So um things that 388 00:23:33,480 --> 00:23:35,840 Speaker 1: are coming up are will continue on some of these 389 00:23:35,920 --> 00:23:39,160 Speaker 1: main themes, but with new twists. So I mentioned the 390 00:23:39,200 --> 00:23:42,520 Speaker 1: financial economic well being of household. We want to keep 391 00:23:42,600 --> 00:23:47,680 Speaker 1: understanding this um notion of what makes households resilient, and 392 00:23:47,960 --> 00:23:51,520 Speaker 1: you know, besides having a saving buffer, what do we 393 00:23:51,560 --> 00:23:55,159 Speaker 1: know about the behavior of those households that thrive versus 394 00:23:55,200 --> 00:23:57,760 Speaker 1: those that not That might be good lessons for folks 395 00:23:57,800 --> 00:24:01,080 Speaker 1: in the future. We are have been doing, but will 396 00:24:01,119 --> 00:24:04,400 Speaker 1: continue to do, significant work on mortgages to understand how 397 00:24:04,440 --> 00:24:08,320 Speaker 1: well does kind of that that portion of household debt 398 00:24:08,920 --> 00:24:13,480 Speaker 1: interact with other financial outcomes, and that becomes important as 399 00:24:13,640 --> 00:24:18,480 Speaker 1: we think about very significant changes in mortgage rate deductions 400 00:24:18,480 --> 00:24:21,879 Speaker 1: and otherwise interest rate deductions on mortgages. We're starting and 401 00:24:22,000 --> 00:24:24,840 Speaker 1: launching a new segment on student loans, which we think 402 00:24:24,920 --> 00:24:28,000 Speaker 1: is very important. Many of you will have seen how 403 00:24:28,240 --> 00:24:31,600 Speaker 1: much of an increase in student loan debt burdens there 404 00:24:31,600 --> 00:24:33,440 Speaker 1: has been in the last while, and we're going to 405 00:24:33,520 --> 00:24:35,840 Speaker 1: try to understand what does that mean in terms of 406 00:24:36,200 --> 00:24:39,720 Speaker 1: consumption patterns, other decisions that households with that debt are 407 00:24:39,800 --> 00:24:43,240 Speaker 1: taking on or not. Will continue our work on healthcare 408 00:24:43,359 --> 00:24:45,760 Speaker 1: and very proud of the work we just put out, 409 00:24:45,800 --> 00:24:48,520 Speaker 1: but we'll continue to update that shows the out of 410 00:24:48,560 --> 00:24:52,720 Speaker 1: pocket healthcare spending. We talked about um by county, by 411 00:24:52,720 --> 00:24:55,879 Speaker 1: demographic group. What are the levels of spend, what are 412 00:24:55,920 --> 00:25:00,200 Speaker 1: the burdens as a share of income, and how can 413 00:25:00,000 --> 00:25:03,760 Speaker 1: and how does that interface with the overall economic picture 414 00:25:04,200 --> 00:25:06,640 Speaker 1: in the city. Escape Since you mentioned it, the first 415 00:25:06,720 --> 00:25:10,479 Speaker 1: foray we had was on let's understand what's happening with 416 00:25:10,600 --> 00:25:15,280 Speaker 1: purchases at local merchants. We've now done a companion view 417 00:25:15,320 --> 00:25:18,840 Speaker 1: that will come out soon on what about local residents? 418 00:25:19,240 --> 00:25:21,840 Speaker 1: And the reason that's kind of an interesting view is 419 00:25:21,880 --> 00:25:24,480 Speaker 1: that we know increasingly people are spending not just at 420 00:25:24,560 --> 00:25:28,200 Speaker 1: local merchants, but online and at places other than their 421 00:25:28,240 --> 00:25:31,560 Speaker 1: own UM city, and we really want to start mapping 422 00:25:31,560 --> 00:25:34,359 Speaker 1: that out. There are some data series that commerce and 423 00:25:34,400 --> 00:25:37,160 Speaker 1: others do on that, but what we're learning is that 424 00:25:37,400 --> 00:25:40,520 Speaker 1: as the economy evolves in that way, we're not capturing 425 00:25:40,560 --> 00:25:42,960 Speaker 1: that as well as we think we can because we 426 00:25:43,040 --> 00:25:45,960 Speaker 1: really know where exactly that purchase took place and by whom. 427 00:25:46,600 --> 00:25:48,720 Speaker 1: So we'll do a lot of work on the online 428 00:25:48,800 --> 00:25:53,280 Speaker 1: economy and and the health of residents in key cities. 429 00:25:53,640 --> 00:25:56,280 Speaker 1: The small business area is one that we will continue 430 00:25:56,359 --> 00:26:00,000 Speaker 1: to further understand, and we really want to bring um 431 00:26:00,119 --> 00:26:03,960 Speaker 1: a demographic lens into that so that we understand better 432 00:26:04,040 --> 00:26:08,919 Speaker 1: the performance of women and minority small businesses. Will have 433 00:26:09,000 --> 00:26:11,720 Speaker 1: some of that view into households as well, which we're 434 00:26:11,720 --> 00:26:14,480 Speaker 1: excited about. And then I mentioned very briefly, we haven't 435 00:26:14,520 --> 00:26:16,480 Speaker 1: talked about it, but for those of you who might 436 00:26:16,520 --> 00:26:19,119 Speaker 1: be interested on the financial market side, we've done some 437 00:26:19,240 --> 00:26:23,520 Speaker 1: interesting work to understand institutional investor behavior, So what actually 438 00:26:23,520 --> 00:26:27,560 Speaker 1: happens minute by minute, hour by hour when big events 439 00:26:27,600 --> 00:26:31,240 Speaker 1: like Brexit, the US election, uh, the Swiss Bank moving 440 00:26:31,280 --> 00:26:34,159 Speaker 1: the floor on the Swiss franc Um And we're mapping 441 00:26:34,160 --> 00:26:36,000 Speaker 1: those kinds of events out and we plan to do 442 00:26:36,160 --> 00:26:38,560 Speaker 1: much more of that next year. All right, well, it 443 00:26:38,560 --> 00:26:41,600 Speaker 1: sounds like you have plenty to keep you busy for 444 00:26:41,760 --> 00:26:45,240 Speaker 1: quite a while. Diana Farrell, President of the JP Morgan 445 00:26:45,400 --> 00:26:48,359 Speaker 1: Chase Institute, Thank you so much for taking the time 446 00:26:48,400 --> 00:26:50,679 Speaker 1: with us on Benchmark, and thank you for having me. 447 00:26:54,720 --> 00:26:57,400 Speaker 1: Thanks for listening to Benchmark. You can find all our 448 00:26:57,440 --> 00:27:00,960 Speaker 1: past episodes on the Bloomberg terminal, bloom work dot com, 449 00:27:00,960 --> 00:27:04,200 Speaker 1: our Bloomberg app, as well as podcast destinations such as 450 00:27:04,240 --> 00:27:08,359 Speaker 1: Apple Podcasts, Spotify, or wherever you listen. We'd love it 451 00:27:08,400 --> 00:27:10,359 Speaker 1: if you took the time to rate and review the 452 00:27:10,359 --> 00:27:13,400 Speaker 1: show so more listeners can find us. And you can 453 00:27:13,440 --> 00:27:17,000 Speaker 1: find us on Twitter, follow me at scott Landman Dan, 454 00:27:17,119 --> 00:27:21,040 Speaker 1: you're at Moss Underscore Eco and our guest is at 455 00:27:21,160 --> 00:27:25,679 Speaker 1: Barrel Underscore. D I A n N Benchmark is produced 456 00:27:25,680 --> 00:27:29,960 Speaker 1: by Topor Foreheads. Francesca Levy is the head of Bloomberg Podcasts. 457 00:27:30,280 --> 00:27:32,720 Speaker 1: Thank you for listening for the past three years. We 458 00:27:32,760 --> 00:27:35,680 Speaker 1: wish everyone a happy holiday season and New Year.