WEBVTT - Isabella Weber On a New Way to Think About Inflation 0:00:10.400 --> 0:00:13.720 Hello, and welcome to another episode of the Odd Lots Podcast. 0:00:13.760 --> 0:00:16.880 I'm Tracy Alloway and I'm Joe. Wisn't thal Joe. I 0:00:17.320 --> 0:00:20.319 feel like I'm gonna jinx things by saying this, But 0:00:21.239 --> 0:00:25.000 it feels like inflation is maybe starting to come down 0:00:25.040 --> 0:00:27.880 a little bit at least it's not accelerating. Let's put 0:00:27.920 --> 0:00:30.800 it that way. Well, here's how Here's what I've been 0:00:30.840 --> 0:00:33.120 thinking about, which is that for the last year, the 0:00:33.200 --> 0:00:36.880 last year and a half, we've done all of these episodes. 0:00:37.000 --> 0:00:40.960 I'm like supply chains and disruptions for this so that reason, 0:00:41.080 --> 0:00:45.239 and all of the various times we've used the term 0:00:45.280 --> 0:00:48.720 perfect storm to describe certain things in certain industries, perfect 0:00:48.760 --> 0:00:51.839 storm of perfect storms. My guess right now, you know, 0:00:51.880 --> 0:00:56.680 in January, is that episodes will be a little less 0:00:56.680 --> 0:00:59.400 dominated by these topics. That would be my guest. I'm 0:00:59.440 --> 0:01:01.760 guessing that this year we do a few fewer perfect 0:01:01.760 --> 0:01:04.720 storm episode. I think that's right. But I think, you know, 0:01:04.800 --> 0:01:07.040 we we spoke a lot about what it was that 0:01:07.080 --> 0:01:09.960 people didn't see coming when it comes to inflation. Why 0:01:10.000 --> 0:01:12.280 did a lot of economists get it wrong? Why was 0:01:12.360 --> 0:01:14.600 the inflation that was supposed to be transitory? You know, 0:01:14.680 --> 0:01:17.720 maybe it was transitory in the sense that it was narrow, 0:01:18.319 --> 0:01:21.680 you know, not a big sort of like macro unleashed inflation, 0:01:21.920 --> 0:01:24.160 but it definitely stuck around longer than a lot of 0:01:24.200 --> 0:01:27.960 people expected. And so every time we have these big 0:01:28.040 --> 0:01:32.640 questions like why aren't we better at forecasting inflation, it 0:01:32.720 --> 0:01:35.960 provides an opportunity to maybe learn something and start thinking 0:01:36.040 --> 0:01:39.000 about it in a slightly different way. Well, yeah, absolutely, 0:01:39.240 --> 0:01:41.840 And I would say there's really two things that I 0:01:41.880 --> 0:01:45.679 feel are unanswered by all of the conversations that we've 0:01:45.720 --> 0:01:49.200 had in the last year. So one is still like, 0:01:49.680 --> 0:01:52.639 is inflation like a macro or a micro thing didn't 0:01:52.680 --> 0:01:57.320 happen because a few categories really had some disruptions and 0:01:57.320 --> 0:02:00.920 then spilled elsewhere, And therefore it's not really about fiscal 0:02:01.000 --> 0:02:05.160 or monetary policy specifically. And be okay, we do have 0:02:05.560 --> 0:02:09.160 very high inflation right now, even if there's evidence coming down, 0:02:10.000 --> 0:02:12.640 what tools, like, if it is the case that a 0:02:12.680 --> 0:02:16.000 lot of it is related to disruptions and chip shortages 0:02:16.160 --> 0:02:19.880 and freezes in Texas, etcetera, what are the tools that 0:02:19.919 --> 0:02:22.240 are best to address that, because it is important to 0:02:22.280 --> 0:02:25.560 get inflation down. But on the other hand, there's a 0:02:25.560 --> 0:02:27.919 pretty good argument that if the issue is some sort 0:02:27.960 --> 0:02:30.960 of disruption at the ports or whatever, that sort of 0:02:30.960 --> 0:02:34.799 like strict blunt instruments like raising rates rates aren't necessarily 0:02:34.840 --> 0:02:37.440 the best approach to dealing with that kind of where 0:02:37.560 --> 0:02:40.440 raising rates won't grow more trees to turn into lumber 0:02:40.520 --> 0:02:43.079 and like that, or more births at the ports or 0:02:43.120 --> 0:02:45.200 anything like that. So I'm so glad you said that 0:02:45.280 --> 0:02:47.440 because today we are going to be speaking with one 0:02:47.480 --> 0:02:50.440 of our Odd Lots favorites, and she has just written 0:02:50.480 --> 0:02:53.800 a new paper which she says is inspired by some 0:02:53.840 --> 0:02:56.280 of the conversations that we've had on Odd Lots, but 0:02:56.360 --> 0:02:59.359 it's also just really interesting because it presents a new 0:02:59.520 --> 0:03:05.080 sort of third way potentially of thinking about inflation, not transitory, 0:03:05.280 --> 0:03:09.360 not persistent, a new more interesting third option, and that 0:03:09.560 --> 0:03:13.600 maybe could help us think about ways of accepting, Yes, 0:03:13.760 --> 0:03:17.040 inflation is real, it is a problem, but that some 0:03:17.080 --> 0:03:19.960 of these blunt instruments that just treat inflation as a 0:03:20.000 --> 0:03:22.280 function of there's too much money in the economy, we 0:03:22.360 --> 0:03:25.160 need there to be less. Maybe there are better approaches 0:03:25.400 --> 0:03:28.640 than just this sort of like blunt monetary approaches to 0:03:28.720 --> 0:03:32.040 addressing them. Absolutely, So without further ado, we are going 0:03:32.120 --> 0:03:35.000 to be speaking today to Isabella Vabor. She has, of 0:03:35.040 --> 0:03:39.480 course a economics professor over at the University of Massachusetts Amherst, 0:03:39.520 --> 0:03:42.720 and you might remember her from from some previous episodes. 0:03:43.000 --> 0:03:45.040 So Isabella, thank you so much for coming back on 0:03:45.040 --> 0:03:48.440 odd Lots. Thank you so much for having me. It's 0:03:48.440 --> 0:03:52.400 a pleasure. Thank you. So the paper is called Inflation 0:03:52.560 --> 0:03:57.160 in Times of overlapping emergencies systemically significant prices from an 0:03:57.200 --> 0:04:00.480 input output perspective. But I just want to get you 0:04:00.520 --> 0:04:02.920 to say on camera that this is inspired by all 0:04:05.920 --> 0:04:08.160 it is. I mean, I have been listening to your 0:04:08.160 --> 0:04:10.800 podcast or through the pandemic um and as you were 0:04:10.880 --> 0:04:14.520 just saying, obviously you have been tracing all these price 0:04:14.560 --> 0:04:17.760 shocks that have been rippling through the economy. So um, 0:04:17.800 --> 0:04:21.039 this paper is trying to come up with the framework 0:04:21.120 --> 0:04:25.800 to trace these shocks and ripple effects in a somewhat 0:04:25.800 --> 0:04:29.839 more aggregate and possibly less fine print, but maybe a 0:04:29.880 --> 0:04:34.440 little bit more like formal kind of fashion. I love 0:04:34.480 --> 0:04:38.159 that the most self serving first question we've ever asked 0:04:38.200 --> 0:04:41.520 on an interview, What is you know, what what is 0:04:42.000 --> 0:04:45.840 an input output approach mean? Because my understanding is that 0:04:45.880 --> 0:04:50.000 this is actually like a very old idea in economics. 0:04:50.040 --> 0:04:52.440 But that is some kind of has actually been forgotten, 0:04:52.760 --> 0:04:56.039 is my understanding, And that the sort of like various 0:04:56.160 --> 0:04:59.800 versions of monitorist thinking which sort of treat if prices 0:04:59.839 --> 0:05:02.240 are hive, we want to understand prices, well, just look 0:05:02.279 --> 0:05:04.040 at how much money or how much credit is in 0:05:04.080 --> 0:05:07.360 the economy. Rein that in and you've seen this paper 0:05:07.400 --> 0:05:09.360 seems to be like going back to like an older 0:05:09.400 --> 0:05:11.920 tradition in economics. Can you talk a little bit about 0:05:11.960 --> 0:05:14.240 what this is. Yeah, So, as we said, we tend 0:05:14.279 --> 0:05:16.920 to think about inflation as a micro phenomenon, right, where 0:05:16.920 --> 0:05:21.680 it's basically just about the movement of aggregate measures. Whereas 0:05:21.720 --> 0:05:24.080 what we're trying to do here is to think of 0:05:24.520 --> 0:05:30.200 prices as kind of an interconnected network where since one 0:05:30.279 --> 0:05:34.479 sector's output is another sector's input um, and therefore one 0:05:34.520 --> 0:05:38.680 sector's output prices at the cost of another sector UM, 0:05:38.720 --> 0:05:42.320 you can't kind of trace um the price movements across 0:05:42.400 --> 0:05:46.840 the whole production network, which input out put tables allow 0:05:46.920 --> 0:05:50.719 you to do. So. These tables basically like Rchester the 0:05:50.760 --> 0:05:55.920 relationships of input and outputs across the whole economy. Historically, 0:05:56.360 --> 0:06:00.320 UM input output tables really had a breakthrough during the 0:06:00.400 --> 0:06:04.560 War time UM. Where the question was, how can we 0:06:04.800 --> 0:06:10.000 hit the enemy's UM economy in ways that we kind 0:06:10.040 --> 0:06:13.799 of like, with the minimum number of bombs UM, create 0:06:13.880 --> 0:06:19.120 the maximal damage to really UM, I mean undermine UM 0:06:19.440 --> 0:06:22.520 the enemy economy's ability to even fight a war. I 0:06:22.520 --> 0:06:25.159 mean concretely. Of course, this is mainly about the German 0:06:25.160 --> 0:06:29.880 economy UM, and so therefore it really is a method 0:06:30.080 --> 0:06:36.160 of identifying points UM that are of particular systemic significance 0:06:36.200 --> 0:06:38.679 for the economy as a whole. Back then, the idea 0:06:38.800 --> 0:06:42.800 was to identify these points of vulnerability to UM, I mean, 0:06:42.839 --> 0:06:46.119 as I said, create destruction UM. The idea of our 0:06:46.120 --> 0:06:51.320 papers to say, if we can identify these points of vulnerabilities, 0:06:51.680 --> 0:06:55.080 then we can actually UM kind of UM know what 0:06:55.080 --> 0:06:59.440 what the potential sources of UM of these report effects 0:06:59.440 --> 0:07:03.000 that can create UM macro outcomes UM could be so 0:07:03.080 --> 0:07:05.400 if some prices matter more than others, we want to 0:07:05.440 --> 0:07:08.560 know what these prices are, and input output is one 0:07:08.600 --> 0:07:13.720 method of trying to identify these systemically significant sectors. So 0:07:13.880 --> 0:07:16.080 Tracy might take away from that is that in a 0:07:16.160 --> 0:07:21.120 war it makes more sense to say bomb and oil refinery, 0:07:21.120 --> 0:07:23.560 even a candy factory like it, right, Like if you're 0:07:23.600 --> 0:07:25.840 thinking about, well, what are these sectors that will have 0:07:25.920 --> 0:07:29.040 the biggest ripple effects across the economy, Then that would 0:07:29.080 --> 0:07:31.240 be the implication, Well, maybe we should talk about morale 0:07:31.360 --> 0:07:35.120 in that context. But no, okay, there's another there's another 0:07:35.160 --> 0:07:37.760 analogy um that you use in the paper, which is, 0:07:37.880 --> 0:07:42.080 you know, if you're trying to identify systemically important sources 0:07:42.120 --> 0:07:47.120 of inflation and maybe address them before they start actually 0:07:47.160 --> 0:07:51.280 contributing to price increases, it's kind of like trying to 0:07:51.440 --> 0:07:55.720 identify systemically important banks and then making sure that they, 0:07:55.840 --> 0:07:59.520 you know, hold more regulatory capital or maybe go under 0:07:59.600 --> 0:08:02.240 preempt of stress tests or things like that. Can you 0:08:02.280 --> 0:08:05.720 talk about, maybe, you know, before we get into policy solutions, 0:08:05.720 --> 0:08:09.440 can you talk about how that approach maybe differs to 0:08:09.800 --> 0:08:13.760 traditional ways of thinking about inflation, because you know, in 0:08:13.800 --> 0:08:16.280 my mind, there's really there's the money terrorist view it's 0:08:16.280 --> 0:08:18.320 all about the money supply, and then there's a sort 0:08:18.360 --> 0:08:20.600 of new Canesian view where it's more about you know, 0:08:21.120 --> 0:08:23.600 supply side and capacity and demand and things like that. 0:08:23.840 --> 0:08:26.760 Can you place this new approach in the context of 0:08:27.000 --> 0:08:30.680 those two older um ways of thinking about it so 0:08:30.880 --> 0:08:34.960 as different as like kind of monetarism and new caneskiness 0:08:35.800 --> 0:08:40.160 can be. They share the understanding that inflation is always 0:08:40.240 --> 0:08:44.040 driven by macroeconomic factors. Right now, what we are doing here, 0:08:44.240 --> 0:08:48.680 I mean bond case, it's the distance from from aggregate 0:08:48.720 --> 0:08:51.800 capacity utilization. In the other case, it's more that like 0:08:51.920 --> 0:08:55.320 classic story of too much money chasing too few goods. 0:08:55.320 --> 0:08:58.479 But still it's like trying to locate the the origins 0:08:58.520 --> 0:09:01.840 of inflation on the aggregate level. What we're trying to 0:09:01.880 --> 0:09:04.480 do here is to say, well, if there are micro 0:09:04.640 --> 0:09:09.960 origins of inflation, if shocks to specific sectors can matter 0:09:10.120 --> 0:09:14.559 in ways that they can unleash processes that actually unsettled 0:09:14.920 --> 0:09:19.720 the stability of prices overall, that we want to understand 0:09:20.040 --> 0:09:23.079 what these sectors are. We want to know where these 0:09:23.120 --> 0:09:27.800 points of vulnerability are so that we can react to 0:09:28.000 --> 0:09:32.440 these shocks before they kind of ripple throughout the whole system. 0:09:32.760 --> 0:09:35.880 And as you said, um interest rates are already being 0:09:36.000 --> 0:09:40.839 recognized as a systemically significant kind of price, right That's 0:09:40.840 --> 0:09:43.720 why we have central banks, which of course historically at 0:09:43.720 --> 0:09:45.360 some point was also not the case. So it was 0:09:45.400 --> 0:09:50.360 a historical evolution to recognize the systemic significance of interest rates. 0:09:50.440 --> 0:09:52.719 In some sense, what we are arguing here is to 0:09:53.160 --> 0:09:56.320 um is to say that there are more prices than 0:09:56.360 --> 0:10:01.199 the price of boring money that can acquire systemic significance 0:10:01.240 --> 0:10:04.959 in bays, that can have a very large implications for 0:10:05.080 --> 0:10:08.080 monitor stability. Just a shout, I'll be drawn here on 0:10:08.120 --> 0:10:10.560 the workous of salad Amarova, who has been working on 0:10:10.559 --> 0:10:14.440 systemically significant price what someone else We definitely have to 0:10:14.480 --> 0:10:17.960 have on the podcast at some point. It's so funny 0:10:18.000 --> 0:10:20.240 because you know, of course, and we talked about this 0:10:20.320 --> 0:10:23.520 the last time you're on late last year. You took 0:10:23.559 --> 0:10:26.000 a lot of heat for saying, well, maybe there's a 0:10:26.040 --> 0:10:29.320 time for having some discussion about price controls, and everyone 0:10:29.440 --> 0:10:32.560 freaked out about that. And yet they're like, Okay, now 0:10:32.640 --> 0:10:35.160 let's control the price of money, as if that isn't 0:10:35.240 --> 0:10:38.160 a form of price control. And yet of course central 0:10:38.160 --> 0:10:41.960 banking ultimate price control, the ultimate price control. But you know, 0:10:42.160 --> 0:10:45.400 so I joked, but I guess it's not really joked that, Like, 0:10:45.440 --> 0:10:47.840 there are some areas where it's kind of obvious that 0:10:48.280 --> 0:10:51.120 some sort of some functions in the economy are more 0:10:51.160 --> 0:10:54.199 crucial to other industries than others. So an oil refinery 0:10:54.320 --> 0:10:57.400 is going to be more crucial to other industries than 0:10:57.440 --> 0:11:00.719 a candy factory. But that's obvious. How do you go 0:11:00.800 --> 0:11:07.280 about systematically identifying beyond the sort of really crude examples. 0:11:07.600 --> 0:11:10.120 What is this? This sort of like a rigorous or 0:11:10.120 --> 0:11:14.000 empirical approach to actually identifying what parts of the economy 0:11:14.160 --> 0:11:18.160 are in fact the most likely to have ripple effects elsewhere. So, 0:11:18.280 --> 0:11:20.160 but what we have done in this paper is that 0:11:20.200 --> 0:11:23.880 we have simulated shocks to every industry in the input 0:11:23.880 --> 0:11:26.600 out the table, and just for orientation, seventy one industry. 0:11:26.640 --> 0:11:29.480 So it's not super disaggregated, I mean also not super 0:11:29.520 --> 0:11:34.199 aggregate compared to microeconomic variables, but it's the fairly broad right. 0:11:34.520 --> 0:11:37.040 So we run a shock on each of these sectors, 0:11:37.280 --> 0:11:40.680 and then we simulate how this shock runs through the 0:11:40.679 --> 0:11:47.080 whole economy, reciting in an indirect impact on on on 0:11:47.160 --> 0:11:49.680 the CPI, right, Because if the price of oil goes up, 0:11:49.720 --> 0:11:52.160 the price of plastic goes up, the price of plastic 0:11:52.200 --> 0:11:55.080 toys goes up. So therefore in the CPI you do 0:11:55.160 --> 0:11:58.400 not only have the direct effect of people of people 0:11:58.440 --> 0:12:02.280 consuming fewer or gas, but you also have indirect effects 0:12:02.320 --> 0:12:05.880 of plastic and plastic toys. And then in all sorts 0:12:05.880 --> 0:12:08.079 of packaging and so on. Right, So we are tracing 0:12:08.120 --> 0:12:12.160 this direct and indirect effect that results from a price 0:12:12.160 --> 0:12:16.040 shock in any one individual sector, and we run this 0:12:16.280 --> 0:12:20.640 UM this simulation UM for for every separate sector, so 0:12:20.720 --> 0:12:24.800 that we then get distinct magnitudes that show us whether 0:12:24.880 --> 0:12:28.560 as shocked to UM to to one sector matters more 0:12:28.679 --> 0:12:31.319 in comparison to another sector. In other words, we can 0:12:31.360 --> 0:12:34.400 create a ranking of what we called the total inflation 0:12:34.440 --> 0:12:39.280 impact from a shock in these sectors. Now, UM, with 0:12:39.320 --> 0:12:43.880 the simulation, we basically have three determinants that can render 0:12:43.920 --> 0:12:48.120 a sector systemically significant. The first determinant is the bait 0:12:48.240 --> 0:12:52.320 in the CPI UM, and housing is a great example here. 0:12:52.320 --> 0:12:55.160 Housing is not something that is very upstream and that 0:12:55.280 --> 0:12:58.680 creates a lot of propert effects in other industries, but 0:12:58.760 --> 0:13:01.320 it has a very large bay in the CPI right. 0:13:01.360 --> 0:13:04.400 So therefore UM, if there is a price change in housing, 0:13:04.480 --> 0:13:09.160 it has a pretty large impact on on the CPI UM. 0:13:09.320 --> 0:13:14.760 Something like UM like oil and gas is actually pretty upstream, 0:13:14.840 --> 0:13:18.440 but not as upstream as something like wholesale trade because 0:13:18.440 --> 0:13:21.200 of the ways in which the upstreamness measures are constructed. 0:13:21.600 --> 0:13:24.760 But for oil and gas UM you have very large 0:13:24.920 --> 0:13:27.559 price movements. So the magnitude of the shocks that we 0:13:27.760 --> 0:13:32.360 use UM are either using average volatilities UM in the 0:13:32.600 --> 0:13:35.920 in the two decades before the pandemic, or using the 0:13:35.960 --> 0:13:38.880 actual price change in the pandemic and in the context 0:13:38.920 --> 0:13:41.640 of the Ukraine War. So in oil and gas we 0:13:41.679 --> 0:13:45.480 actually had very large price movements already before the pandemic 0:13:45.679 --> 0:13:48.880 and then again during the pandemic UM and in the 0:13:48.880 --> 0:13:51.679 context of the war. So here the drivers would be 0:13:52.000 --> 0:13:55.720 kind of all three components the importance UM in terms 0:13:55.720 --> 0:14:00.120 of indirect effects, creating a relatively large total the eight 0:14:00.200 --> 0:14:05.000 in the CPI UM the large price movements and relatively 0:14:05.120 --> 0:14:08.400 upstream even though not as upstream as hole sate trade, 0:14:08.440 --> 0:14:13.560 where it's for holes trade, it's really basically because of 0:14:13.600 --> 0:14:16.880 the upstream nous of that sector. And then in the pandemic, 0:14:16.880 --> 0:14:20.040 of course we also had fairly large price movements there. 0:14:20.560 --> 0:14:22.960 But before the pandemic the price movements in hole say 0:14:23.040 --> 0:14:26.040 trade would have been much smaller than in in something 0:14:26.080 --> 0:14:28.640 like oil and gas extraction. Right, So it's it's these 0:14:28.680 --> 0:14:32.080 three dimensions that we are capturing in in UM in 0:14:32.080 --> 0:14:35.240 creating this ranking. So just on this point, can I 0:14:35.280 --> 0:14:38.880 just press you when it comes to identifying the systemically 0:14:38.960 --> 0:14:43.640 important industries or I think you call them ubiquitous industries. Like, 0:14:43.840 --> 0:14:47.760 how do you just just aggregate their weight in the 0:14:47.800 --> 0:14:52.920 inflation indusseries versus the extent to which they matter for 0:14:53.040 --> 0:14:56.120 other prices, Because I'm sure there will be some people 0:14:56.160 --> 0:14:59.960 who listen to this and say, like, well, obviously, energy 0:15:00.040 --> 0:15:02.880 and you know, maybe some consumer goods and things like 0:15:02.920 --> 0:15:04.680 that have a higher weight in the c p I. 0:15:04.800 --> 0:15:07.360 And so that's why you're getting these results. If you 0:15:07.960 --> 0:15:10.440 look at the paper, which I'm not expecting anyone to do, 0:15:11.600 --> 0:15:15.760 we can we can distinguish between a direct and an 0:15:15.760 --> 0:15:20.520 indirect effect. Right, So what our direct inflation impact is 0:15:20.680 --> 0:15:22.800 is just the weight in the CPI, Right, this is 0:15:22.840 --> 0:15:26.240 just giving you this is what the CPI shows us 0:15:26.440 --> 0:15:30.280 UM is the weight of UM of the change in 0:15:30.440 --> 0:15:34.200 save petroleum in core products UM for the change in 0:15:34.240 --> 0:15:37.840 the CPI UM. But then there's this addition is share, 0:15:38.280 --> 0:15:42.560 which we call the indirect effect, which comes from tracing 0:15:43.000 --> 0:15:48.480 the indirect price UM movements that reside from this initiative 0:15:48.480 --> 0:15:51.200 shock in say petroleum in core products. Now, of course 0:15:51.280 --> 0:15:55.840 we have to make assumptions on how industries UM hand 0:15:56.040 --> 0:16:00.520 over um UH cost increases, right, and be in the paper, 0:16:00.600 --> 0:16:03.880 we make two distinct assumptions. One is that it's just 0:16:03.960 --> 0:16:08.280 a pastor of one, so firms just have a cost 0:16:08.320 --> 0:16:11.480 increase and just passed us on to their customers. The 0:16:11.520 --> 0:16:15.000 second assumption that we make is to say, um, what 0:16:15.040 --> 0:16:18.560 if firms actually don't just pass on the cost, but 0:16:18.640 --> 0:16:21.840 they actually want to protect their profit margins. Now, if 0:16:21.840 --> 0:16:24.520 their cost goes up, go up, and they were to 0:16:24.640 --> 0:16:28.280 increase prices by just the amount of the increasing costs, 0:16:28.320 --> 0:16:30.720 their profit margin would go down, right, So what if 0:16:31.280 --> 0:16:34.280 what if they actually protect their profit margins are therefore 0:16:34.560 --> 0:16:39.960 increase prices by more than the increase in costs um. 0:16:40.000 --> 0:16:43.800 So this then gives us different magnitudes of the total effect, 0:16:44.200 --> 0:16:48.160 but we find that the rankings are relatively stable independent 0:16:48.240 --> 0:16:51.880 of these different assumptions that we are making. And the 0:16:51.920 --> 0:16:54.800 CPI that we are using here is a synthactic CPI, 0:16:55.040 --> 0:16:56.960 because of course we have to break it down to 0:16:57.520 --> 0:17:01.080 UM these seventy one industries that we half UM. So 0:17:01.120 --> 0:17:04.159 it's it's it's it's not the CPI that you're downard 0:17:04.200 --> 0:17:06.760 from the b A UM if you just look for CPI, 0:17:06.880 --> 0:17:08.560 but it's a CPI that you get from the b 0:17:08.680 --> 0:17:11.000 A if you look into it put out for Tavist. 0:17:27.840 --> 0:17:30.479 You know, something I'm interested in, and I don't know 0:17:30.520 --> 0:17:33.720 if it's something you've specifically looked at, but it sort 0:17:33.720 --> 0:17:35.600 of reminds me of this, Like, you know, we talked 0:17:35.640 --> 0:17:38.199 a lot about or economists have talked a lot in 0:17:38.200 --> 0:17:42.120 the last year about goods versus services. Inflation is if 0:17:42.200 --> 0:17:45.440 these are like two distinct categories of types of things 0:17:45.440 --> 0:17:47.400 that people buy, that you can draw a bright line 0:17:47.440 --> 0:17:49.399 and say, Okay, goods have gone down, but service is 0:17:49.400 --> 0:17:52.200 still up. But it seems to me that any good 0:17:52.280 --> 0:17:56.040 that we buy is also implicitly a bundle of services 0:17:56.080 --> 0:17:58.240 that need to go into the You know, if I 0:17:58.280 --> 0:18:01.399 buy a refrigerator, well, there's some sort of service person 0:18:01.440 --> 0:18:05.400 who helped delivery deliver the furniture. Um, and there are 0:18:05.400 --> 0:18:08.400 services for you know, the truck driver whatever it is. 0:18:09.000 --> 0:18:13.280 Does your approach the sort of input output approach sort 0:18:13.320 --> 0:18:16.679 of Um, I'm trying to think exactly the way to 0:18:16.680 --> 0:18:18.960 phrase it, But in your view, does it offer a 0:18:19.000 --> 0:18:22.600 more useful way of thinking about categories of goods beyond 0:18:22.640 --> 0:18:25.080 just or sort of would seem to me like these 0:18:25.320 --> 0:18:28.400 arbitrary distinctions between between the types of things that get 0:18:28.400 --> 0:18:32.560 bought in the economy. Good question, Yeah, great question. UM. 0:18:33.640 --> 0:18:37.520 I mean I would of course say yes, thank you, 0:18:37.920 --> 0:18:41.879 thank you for saying that. A great ka. Sorry, keep going, UM. 0:18:41.880 --> 0:18:45.080 To be sure, services are part of the input out 0:18:45.080 --> 0:18:47.520 put tables, and we actually find that they are pretty 0:18:47.640 --> 0:18:50.600 upstream because of the fact that you just described, right, 0:18:50.720 --> 0:18:54.480 because it's like some form of even like small administrative 0:18:54.520 --> 0:18:57.720 service UM involved in pretty much everything. So if we 0:18:57.760 --> 0:19:01.680 talk about it administ sorry, if we talk about upstream sectors, 0:19:01.680 --> 0:19:05.119 we tend to think about physical stuff like oil and 0:19:05.119 --> 0:19:08.919 gas or matters or candicurstans on, right. But what we 0:19:09.160 --> 0:19:11.560 actually see when we do the analysis that is that 0:19:11.760 --> 0:19:16.479 some that some services are very upstream. Now, the price 0:19:16.560 --> 0:19:21.960 movements in services are relatively small on average over time 0:19:22.000 --> 0:19:25.480 because they are very much UM tied to wages, right, 0:19:25.480 --> 0:19:28.240 and wages tend to move much less than let's say, 0:19:28.280 --> 0:19:32.480 commodity prices. Right. So therefore, when we run these shocks, 0:19:32.840 --> 0:19:37.119 the service sectoris even though um they are pretty upstream, 0:19:37.600 --> 0:19:41.240 end up not being very important for the general movement 0:19:41.720 --> 0:19:47.160 of prices in in this model because just the initial 0:19:47.200 --> 0:19:50.359 shock um is so small. If we model the shock 0:19:50.600 --> 0:19:55.160 um based on magnitudes of past price movements and price 0:19:55.200 --> 0:20:00.520 movements in the in the in the the COVID nineteen inflation. Now, 0:20:00.920 --> 0:20:03.119 I do think that this kind of dust give us 0:20:03.160 --> 0:20:08.320 a different way of distinguishing um categories of of of 0:20:08.320 --> 0:20:11.400 of sectors if you want so, because the idea here 0:20:11.400 --> 0:20:14.840 really is to say, Okay, we don't care if it's services, 0:20:14.960 --> 0:20:18.240 or if it's commodities, or if it's processed goods, or 0:20:18.240 --> 0:20:21.120 if it's manufacturing of whatever it might be. But all 0:20:21.160 --> 0:20:24.640 that we care about is um the importance of this sector. 0:20:24.800 --> 0:20:27.399 It's if you want so, it's centrality in relation to 0:20:28.200 --> 0:20:32.800 all other sectors, and in relation to people, it's um 0:20:33.240 --> 0:20:37.320 um uh consumption patterns. Right. So what we find then 0:20:37.520 --> 0:20:42.600 is that the sectors that we identify systemically significant basically 0:20:42.640 --> 0:20:46.400 in three groups. So it's like basic necessities stuff like housing, 0:20:46.560 --> 0:20:49.960 food forms that of course produce a lot of food 0:20:50.400 --> 0:20:54.000 utilities um, and of course also energy. And then basic 0:20:54.080 --> 0:20:58.760 production inputs stuff like the fosil fields that we haven't 0:20:58.760 --> 0:21:01.440 really talked about but also can a good products um. 0:21:01.480 --> 0:21:05.480 And then kind of like basic circulation infrastructure, so things 0:21:05.520 --> 0:21:09.560 like always say trade right, which is critical for commerce. 0:21:09.560 --> 0:21:13.439 It's a kind of a basic commercial infrastructure. So I 0:21:13.480 --> 0:21:16.040 do think that this does give us a different way 0:21:16.040 --> 0:21:20.280 of kind of UM distinguishing the nature of different sectors. 0:21:20.359 --> 0:21:25.399 So once you've identified these systemically important industries, you know, 0:21:25.600 --> 0:21:31.760 these ubiquitous industries for inflation, things like basic necessities housing, farms, 0:21:31.800 --> 0:21:36.960 food and utilities, and energy, how does that inform the 0:21:37.480 --> 0:21:42.840 policy response. So the idea here is that because these 0:21:42.960 --> 0:21:47.240 UM centers are so important that if there are large 0:21:47.720 --> 0:21:51.200 price movements in these sectors that have this has implications 0:21:51.400 --> 0:21:56.240 way beyond these specific sectors. We should be paying more 0:21:56.280 --> 0:21:59.119 attention to what is happening in these sectors. So the 0:21:59.160 --> 0:22:03.840 first occasion is to say we need more monitoring capacity. 0:22:03.840 --> 0:22:06.920 Why do we need more monitoring capacity? Because we are 0:22:06.960 --> 0:22:10.600 living in some sort of a age of overlapping emergencies 0:22:10.720 --> 0:22:13.520 right where UM we have, of course a pandemic that 0:22:13.600 --> 0:22:16.520 is not over looking for example, at what's happening in 0:22:16.600 --> 0:22:20.080 China and how this UM impact global production networks, but 0:22:20.160 --> 0:22:23.840 also looking at climate change and you're a great episode 0:22:23.840 --> 0:22:26.520 on the Mississippi River and how this is a kind 0:22:26.560 --> 0:22:31.560 of just making a whole UM sector. In this case, 0:22:31.600 --> 0:22:35.080 of course grain UM and other commodities m gryan to 0:22:35.119 --> 0:22:38.840 a hold UM. But we also have these massive geopolitical 0:22:39.000 --> 0:22:42.480 tensions that can have huge implications for the ways in 0:22:42.520 --> 0:22:46.199 which production is organized globally. So in other words, it 0:22:46.280 --> 0:22:50.680 seems very likely from my perspective that more shocks maybe 0:22:50.760 --> 0:22:53.360 in the pipeline. Of course, no one wants these shocks, 0:22:53.400 --> 0:22:57.200 and everybody is hoping that things will be calm and stable. UM. 0:22:57.240 --> 0:23:01.680 But um. But like from the perspective of the dynamics 0:23:01.680 --> 0:23:05.840 of overlapping emergencies, even if inflation is now easing and 0:23:06.040 --> 0:23:07.960 it seems like in the next couple of years these 0:23:08.040 --> 0:23:11.719 kind of shocks UM are likely to keep coming. So 0:23:11.760 --> 0:23:14.640 if that is the case, you kin't want to have 0:23:14.800 --> 0:23:17.399 capacity on the side of the state to be able 0:23:17.440 --> 0:23:21.480 to monitor these sectors that are so important in UM 0:23:21.560 --> 0:23:24.840 in in ways that allow you to react to these 0:23:24.880 --> 0:23:30.919 shocks before they kind of create these huge cascading effects 0:23:31.200 --> 0:23:34.080 throughout the whole economy, and you then actually get some 0:23:34.119 --> 0:23:39.960 sort of potentially more generalized kind of inflation UM. Beyond 0:23:40.080 --> 0:23:42.800 monitoring capacity, of course, not enough to watch. You want 0:23:42.840 --> 0:23:45.640 to be able to kind of step in and stabilize, 0:23:45.760 --> 0:23:50.000 right and UM here then UM, I think the big 0:23:50.040 --> 0:23:54.320 shift in in policy think that emergence from this paper 0:23:54.640 --> 0:23:58.240 is that once we go on the sector level, we 0:23:58.320 --> 0:24:02.400 kind of leave the world of more or less homogeneous 0:24:02.400 --> 0:24:05.320 aggregates where we can talk about interest rates up at 0:24:05.359 --> 0:24:07.800 one percent or down by one percent or point five 0:24:07.880 --> 0:24:11.040 or point seven five or whatever. But it's like pretty 0:24:11.520 --> 0:24:14.720 one dimension, right, and pretty clear that there's a one 0:24:14.760 --> 0:24:18.240 dimension that we can measure in very clear ways quantitatively 0:24:18.400 --> 0:24:22.280 in percentage points, very straightforward. If we now think about 0:24:23.200 --> 0:24:27.639 the prices of chemicals or um the stability of the 0:24:27.720 --> 0:24:31.600 flow of goods and holes trade and therefore the prices 0:24:31.880 --> 0:24:35.320