All right. Welcome back, everybody. We're glad to have our first paper session of the morning, afternoon, or evening, depending on your time zone. So my name is Ed Notek. I'm with the Federal Reserve Bank of Cleveland, and this is the Empirical Models Paper Session. We're going to have two great papers today. The first one is going to be Inflation at Risk presented by Francesca Luria from the Board of Governors of the Federal Reserve System. Our second presenter will be Paulo Bonamolo from the Netherlands Central Bank, presenting Long Run Philips Curve as a Curve, the Long Run Output Gap Inflation and Monetary Policy. So as we've done before, just as a reminder, or in case you weren't here yesterday, so please put all of your questions into the chat. We will accumulate them, and at the end of our two papers, we'll have some time for Q&A, or authors can potentially answer those questions at their leisure during the session, but we'll try and keep some of those for the end for Q&A to have a lively discussion. So with that, Francesca, the floor is yours. I can see you and I see your slides, so you are all set. Take it away, please. And you can also hear me, right? I can. You're good to go. So you have 20 to 22 minutes. Thank you. So let me start by saying that I'm particularly indebted to both organizing institutions of this conference. The ECB's Price and Cost Division hosted me for a PhD traineeship during my graduate studies, and Federal Reserve Bank of Cleveland invited me for a job market talk back when I still had no gray hair. And I really enjoyed the conference so far and really very much appreciate the opportunity to present my work among so many important contributions to the literature. Finally, thank you all for attending this session. So today I'm going to present some joint work with David Lopez-Salido, which studies inflation at risk and the usual disclaimer applies. Now, the first question that some of you might ask is why we should care at all about risks to the inflation outlook? Well, since the upheavals of the global financial crisis, the emergence of downside inflation risks have increasingly become a source of macroeconomic concern. And as already extensively discussed at this conference, uncertainty surrounding inflation has widened considerably across the globe since the start of the COVID-19 pandemic. And in fact, risks have shifted from persistently on the downside over the last decade or so to decidedly on the upside in the face of resurging demand and supply shortages as economies reopen. Now, in the presence of these tail risks and as President Draghi's quote on this slide reminds us of the conditional inflation mean may not necessarily portray a complete picture of the overall inflation outlook. So the starting point of our paper was precisely the desire to extend our understanding of these risks. After all, most of the analysis studied the muted response of the conditional mean of inflation, leaving fairly unexplored what happens to other parts of the inflation distribution. And with the literature pointing to quivering Phillips curve linkages, our question was whether there are some macroeconomic factors in the Phillips curve umbrella that are still at work perhaps in the tails of the inflation distribution. To be clear, by inflation distribution I mean the predictive distribution of inflation over the next year. So such a distribution can answer questions like what is the probability that inflation will be above or below say 2% over the next year. So to answer these questions in the paper we estimate a quantum Phillips curve, fairly well understood and microphone the time series framework that allows us to study the risk to the entire distribution of the inflation outlook, coming from both conventional inflation determinants as well as financial conditions. And what we find is that the recent muted response of the conditional mean of inflation to economic condition does not necessarily convey another quit representation of inflation dynamics. Indeed, we find ample variability in the tail risk to inflation, even when we focus on the post 2000 period of stable and law mean inflation. In particular, we find that financial conditions carry substantial and persistent downside risk to inflation. And our findings are consistent with evidence from a non linear DSG model survey data inflation options and the regime switching model of inflation. Some of these results I will present today. So we see our paper is ultimately offering a new empirical perspective to existing macro models and to policymakers by highlighting the presence of a significant relationship between credit conditions and risks to the financial, sorry, to the inflation outlook. Okay, our result that there have been sizable downside risk to the inflation outlook in the last 20 years, mainly accounted for by financial titanings is consistent with the idea that due to amplification mechanisms when financial conditions become tighter firms cut prices disproportionately more on average. And these concepts are related to recent research, some of which cited here, which shows that financial conditions matter also for inflation dynamics. Now, while these studies focus on the model outlook, we extend their insights to the study of the inflation tales. In fact, implications for the tales of the inflation distribution, and the role of financial conditions have been fairly unexplored by the literature in general, although there have been some efforts, and in particular the last two studies, some of which featuring ECB authors take our framework as a starting point and confirm our findings that financial conditions are indeed important determinants of inflation risks and especially of downside risks. Okay, so we now move to characterize these inflation risks. We model the conditional quantiles of average future inflation over the next four quarters as relating linearly to inflation determinants xt across quantiles tau. Notice that the determinant xt can exert nonlinear effects on inflation dynamics if it affects differently the median than the tales. Given data for the right hand side variables, inflation quantiles can be constructed for each point in time t, even beyond the estimation sample, as long as data for xt is available. To obtain the inflation densities, we then fit a flexible skewed t distribution by Azzolini and Capitano on the estimated quantiles, very much like in the paper by Adrian Boyacenco and Giannone. One last thing I would like to bring your attention to is the fact that running a regression of this type is akin to a direct forecast as opposed to an iterated forecast where the dependent variable would be pi t plus one, and where one would iterate the one step ahead prediction to obtain multi horizon forecasts. Now, as discussed in my paper with Dario Caldara, Dario Cascaldi Garcia and Pablo Cuba Barda on Markov switching models and growth at risk, in simulation direct and iterated forecast delivered the same results when they share the same VAR data generating process. In empirical data, both the direct and iterated model perform well in terms of predictive scores, and most importantly perhaps in the context of density forecasts in terms of coverage and correct calibration of the predictive density. All right, so to give you a glimpse into our augmented quantum Phillips curve model, I list here the variables conditional on which we are estimating the quantiles of average four quarter ahead for CPI inflation in the United States. The first set of conditioning variables directly relates to inflation. Okay, so these are average inflation over the previous four quarters and long term inflation expectations from consensus economics. To preserve the notion that inflation persistently deviates from inflation expectations we impose the homogeneity constraint and prices by constraining the two coefficients on these inflation determinants to some of two one. We also condition on measures of labor market slack through the unemployment gap of relative prices through import or all prices and no financial conditions through the Gilchrist-Zakrashek credit spread. So in the paper we look at how the importance of risk factors changed across time. And indeed, the two distinct subsamples emerge when characterizing the determinants of the inflation distribution in the United States. The first period running from 1973 to 1999 notably covers the OPEC shocks, the subsequent folk or disinflation in the early stages of the Great Moderation, whereas the second sub-sample running roughly from 2000 onwards is characterized by large movements and credit spreads progressively well anchored inflation expectations but subdued inflation pressures. Now in the figure presented here you can see the estimated quantile slopes for each inflation determinant for our two sub-sample. So each box stands for an inflation determinant and the blue, red and yellow bars are respectively for the 10th, 50th and 19th quantile regression coefficient. Now as you can appreciate in the middle right box over time, long run inflation expectations, which we here treat as exogenous, became the decisive inflation determinant. And this is consistent with the analysis, for instance, in Blanchard, Summers and Chirruti of a time varying parameter version of a Phillips curve model. Now I want to pause here for a second to recognize the fact that to an applied econometrician like me, it did not appear all that surprising that in the last sub-sample when regressing a relatively constant variable that of average future inflation on another relatively constant variable that of inflation expectations, one would find a strong relationship between the two. Well it turns out that if you decompose inflation into a cycle and the trend component, for instance say using the Elmar Martens 2016 restart model, inflation expectations became more important over time in terms of forecasting precisely for their predictive content of the more slow moving trend component of inflation. Now one important point that we also make in the paper is that it would however be misleading to dismiss the roles, the role of other factors. In fact credit conditions, the box at the bottom and to a lesser extent labor market outcomes, the upper left box are key drivers of the asymmetry in the inflation distribution in this more modern quantum Phillips curve for the last sub-sample. While inflation expectations only have a symmetric effect. Now focusing on the credit spread, you can see that there is a substantial sub-sample instability in its link to the inflation outlook. The first sub-period is characterized by relatively small variations in credit spreads in a period of high and volatile inflation, reason why the actual contribution of credit spreads to the inflation outlook in that period is smaller. And from 2000 onward, low and stable inflation has coexisted with substantial variation in credit spreads, especially as we know during the global financial crisis. Now focusing on this most recent sub-sample, a novel result is that the link between the inflation outlook and financial conditions is asymmetric. In that an increase in credit spreads is associated with a larger increase of downside risk than in a reduction of upside risks. And this is reminiscent of the results for the GDP growth outlook in Adrian Boyarchenko and Giannone and confirmed by other papers that they previously cited. Notice that all coefficients are statistically significant and that ANOVA tests on the equality of the slope coefficients across quantas reject the null hypothesis of equality between the slopes on the 10th and the 50th quanta. I now want to provide some theoretical grounding for our results. So in general, our findings can be rationalized by non-linear models featuring amplification mechanisms such that the relationship between credit spreads and inflation is more pronounced during bad times. Our findings can, for instance, be replicated by applying our quanta regression framework to simulated data generated from the Gertler, Kiyotaki and Priscipino model. Their model, just as a reminder, is a fully micro-founded non-linear DSGE model which features the possibility of a severe financial crisis through a bank run. And there are two equilibria in their model, one with and one without a financial panic. So when shocks are small, the economy fluctuates around the standard equilibrium. In contrast, a big negative shock pushes the economy into a bank run equilibrium. Now, combined with a sunspot shock, it triggers a financial panic and bank net worth collapses. Banks are forced to sell assets, which ultimately disrupts firms borrowing. Consequently, economic activity drops substantially more than in the equilibrium without a bank run. So we simulate the model using the original calibration of the D parameters and of the capital quality shock process. And in order to generate a rare financial crisis, we calibrate the process for the sunspot shock such that the bank equilibrium arises after a big negative shocks of above two standard deviations. We simulate this model a thousand times and store the inflation rate, the credit spread and the capital quality shock. We then run a quanta regression of current inflation on the credit spread. Okay. And the chart here shows the quanta regression slopes estimated on simulated data from that model as I just described. So the negative slope coefficient for the 10th quanta echoes our empirical findings that higher credit spreads are associated with an increase in downside risk to inflation. Now, in terms of identification, what delivers this result is that during bad times, those featuring a bank run, the conventional channel whereby lower demand results in subdued price pressures is strongest. But the reason why the 90th quanta indicates a positive relationship is that in good times, without the bank run, a capital quality shock reduces capital and thus results in an increase in the rental rate of capital and thus in marginal costs. Now, the median quanta captures the tension between these two effects. And indeed, it is around zero as a normal times these two effects almost offset each other. Okay, so to test whether this relationship also holds in financial markets, we run an OLS regression of options implied inflation probabilities of one year ahead CPI inflation on the credit spread. And in the left panel of the figure, we present the estimated coefficients of that regression. On the right, we report the estimated quanta regression slopes for us core CPI over the last sub sample that you just saw previously. Now the slopes on the left are rescaled so as to facilitate the comparison with those coming from our quantum Phillips curve model. And you can see that despite the vast disparities in the construction of the tails of the inflation distribution, the estimated slopes are very similar to each other. Most importantly, as the inflation probability cutoffs increase their relationship with credit spreads weekends. And this again is reminiscent of our key result from the estimated quantum Phillips curve model over the last 20 years of data that we have here on the right. Okay, so we also analyze how a no linear models such as a regime switching regression compares to our quanta regression estimates. We move to monthly frequency to allow for more observations and better identification of the regimes. This exercise is meant to show that the relationships we established in our main analysis are not an artifact of the quanta regression, but the genuine feature of the data that also alternative nonlinear models would identify. So when comparing realized average inflation over the next year in black against the estimated regime probabilities. I hope it becomes clear to you that the estimated regimes broadly correspond to states where inflation is low, moderate or high. And here you can now see the regime specific fitted values, along with the estimated contests from our quantum regression model, which as you can see are remarkably similar. In particular, the low inflation regime corresponds to the 10th quantum, the moderate inflation regime to the median and the high inflation regime to the 90th quantum. And for those of you who are interested, this result that the mark of switching regression and the quantum regression when put on equal footing deliver the same predictive densities is carefully studied and described in my recent work with Dario Danilo and Pablo that I mentioned before so you can read more about this there. So in the paper we also compare the United States experience with that of the Euro area. And there are some notable differences across the Euro area and the United States and how the determinants considered in our model affect the inflation outlook. For instance, in the Euro area inflation inertia, the unemployment gap and to a small degree also relative prices still play a role in shaping the inflation outlook. And when it comes to the credit spread it still creates downside risk to the inflation outlook but it does so in a symmetric way. And this can be seen in this figure, where we show the densities of one year ahead inflation in the Euro area using core HICP inflation, and in the United States for the periods at the onset of the great recession that is 2007 Q4 and 2008 Q4. This would be the blue densities in the left and right columns respectively. The black densities captured the partial effect of an experiment in which the credit spread is set to zero. And as you can see, while in the Euro area the distribution experiences a symmetric location shift to the right in the counterfactual experiment. The US distribution is not only pushed to a higher inflation value, but also exhibits way smaller tail risks. Right, so risk to the inflation outlook have been front and center at the peak and in the recovery from the COVID-19 crisis and still are nowadays. So we show how our model augmented by credit spreads allows to identify important changes in the United States inflation distribution during this historical episode. And in doing this we consider a monthly version of our model to allow for a more real time assessment of inflation risks. The model is estimated from January 2000 to April 2020 using as dependent variable one year ahead inflation up to April 2021. The chart shows results for average inflation over the next 12 months with core PC inflation on the left and core CPI inflation on the right. Specifically it shows the distribution since January 2020 with markers given at the median and at the 90th quantum. Now both core PC and core CPI inflation distributions, as you can see have shifted to the right since the beginning of this year. And indeed the selected months show the quick buildup of downside risk to inflation during the harsh initial months of the global pandemic. January to May 2020 in the top panel, which were then followed by the increase in upside risk to inflation in most recent months in the bottom panels. Alright, as a final exercise, we now ask what would have been the predictive distributions over the next 12 months had the financial conditions deterioration of March 2020 persisted in May 2021. The chart presents distributions again of average inflation over the next 12 months but now is of May 2021 for our baseline in the blue solid lines. And in this quote unquote counterfactual scenario in which credit conditions are those prevailing as of March 2020 in the black dashed lines. Now the easing of financial conditions since the onset of the pandemic has moved the distributions of both core PC and core CPI inflation rates to the right, reducing downside rates of low inflation. And indeed, had the financial conditions not eased during the period from their March 2020 state, the probability of inflation running at or above 2.5% would be about 7 percentage points lower for both core PC and core CPI inflation rates as of May 2021. To summarize, in this paper, we show that one needs to look beyond the conditional mean to fully understand inflation dynamics. And indeed, we find ample viability and the tail risk to inflation, even when focusing on the post 2000 period of stable and low mean inflation. Most importantly, we show that financial conditions carry substantial and persistent downside risk to inflation. Finally, our results, we hope, provide empirical guidance and suggest more efforts in modeling the linkages between the entire inflation distribution and financial markets in the context of nonlinear models. Thank you very much for your attention. All right, we will now turn to Paolo. So please feel free to submit questions in the Q&A and then we'll get to those in the chat and we'll get to those at the end for Q&A. Moving on to Paolo. Looks like I can see your slides and I can see you and I can hear you. So you're all set. Please take it away. You have 20 to 22 minutes. Thank you. Thank you, Ed, and thanks to all the organizers for including this paper in the photo. This is joint work with Guido Scari and Cassi Bacche. So the question of the paper is whether the long run philip school is vertical or not. This issue was highly debated in the past in the 60s. And back then, Phelps and Friedman proposed the natural rate hypothesis, saying that in the long run the philip school is vertical at the natural level of output or at the natural rate of unemployment. So this idea played a cornerstone role in macroeconomics both in theory and in practice. I think we can fairly say that this is one of the working assumptions that central bank use when they implement monetary policy. Now, given the importance of this concept, if you look at the literature, it is surprising to note that empirically there's not so much work devoted to understanding empirical validity of this idea. And from a theoretical point of view, if you consider model macroeconomic sticky price models, in general, they do not imply the absence of a long run relation between inflation and output. In particular, if you take the new case of model and you assume that the steady state of inflation is not zero as in the original simple version, but it is a positive number. Then you'll find that the highest is numbered lower will be the GDP in equilibrium. And these are now called generalized new kinds of models, we do and just put on paper on the journal of economic leader to in which they give a very comprehensive treatment of this topic. The question of this paper isn't difficult. We ask what is the long run relation between inflation and output. And the paper is divided into two parts. In the first part, we answer the question using a time series model. We find that in the long run, the school is not vertical, but is negatively slow, meaning that higher inflation is related to lower output in the long run. And what is key to get this result is to model the long run through school as non linear. And here we also have a methodological contribution we propose that's a convenient way to model non linear. So what we find in the in this first part, I would say is a mere statistical relation between the two unobserved values that are potential output and trend inflation. But we cannot really say anything about cars. So in the second part of the paper, we interpret our findings to the lens of a structural model and we choose exactly the generalized decays and model that I was mentioning before. In this model, the causality goes from inflation to GDP. So it's higher than inflation that causes lower GDP in the long run. And the reason why we choose this model is that it has the two key features that we find in the statistical analysis that are non linear and a negatively slow long run Philip school. So we estimate the model and we show that model is also able to capture the quantitative features of the time series analysis when it comes to measuring the cost related to trend inflation. Okay, so this is the overview of the paper with the results. Let me now describe the time series approach to be used. So I like to call it a time value it could be because it is a generalization of the steady state we are by my TSP land 2009. So consider here this equation x t is the vector with the observed variables at time t, and we call x bar t the vector with the long run values of x t. Now the deviation of x bar from the long run is so these deviations are modeled together with the VR with stochastic bulletin. Now we are not the first to use this framework. The neighbor, Janone, Janone, and Dambalotti used it. Recently, Johannes and Martens and there are also other papers around that are using the same framework. This is a trend cycle decomposition in which the vector of observable x t is decomposing into a long run component x bar and the short run component x. So the short run component is described by this VR and this VR is stable and has unconditional expectation equal to zero so that the gap component here is expected to converge to zero. And this is why we interpret bar component as the long run. And for the bar component we, we use the hypothesis that the x bar are a function of a vector data team. This is a latent vector. And this is a mark of process with stochastic dynamics. Now these two functions h and f are in general that can be no linear. There will be no linear in our case. So in particular what we do, we have a model for three observables GDP per capita inflation and the nominal interest rate. For the short run component, the VR has four legs. And for the long run, so we have three elements in our x bar vector output. So we have potential output in x bar inflation we have trained inflation x bar and the long run nominal interest rate. So for potential output we assume that this is the sum of two components, a trend and a function of trend inflation. Let's first concentrate on the trend. So this trend is quite standard. And many people are recognizing this because it has been used quite extensively since long time. So this trend has two shocks. We have a shock, a YT that changes the level of the trend and a shock to the growth rate of the trend that is around the work. This function delta is what we add on top of what is standard say. And in particular the assumption here is that when trend inflation is equal to zero, also this cost, this function is equal to zero. In this way, we can interpret this trend, y star T, as the counterfactual level of potential output under zero trend inflation. Then we assume that trend inflation is around the mall and for the nominal interest rate, we assume that the long run feature equation holds. So this is equal to trend inflation plus a measure of the real interest rates in the long run. Here with Bobo from Labrack and Williams, we assume that this is a function of the growth rate of potential output plus Z that is a random work that should capture all the slow moving trends that are not directly moded in this form. Now, the first equation here is our long run field score. What is delta? Our choice of delta, the function delta is a piecewise linear function. So we assume that delta is going to be equal to a slope K1 times trend inflation. When trend inflation is below a certain threshold tau, but we allow for that slope to change and add a constant when trend inflation is above that threshold tau. Why this choice? First of all, and the most important aspect is that this model is very simple to do. And here we have our methodological contribution that I will explain in the next slide. In general, we think that this model can approximate the kind of non-linearity we have in mind, and it's quite easy to interpret. Let me explain the methodological contribution. The model that I just described can be written in this state space form. Yt here is a vector of observed variables and theta t is our latent process. D, F, M, G and P are matrices, but these matrices are functions of the vector of latent process theta t. In particular, they are function of one element of theta t, which is trend inflation. We assume, in fact, that these matrices might belong to two groups, group one and group two, depending if trend inflation is below the threshold or above the threshold. Our methodological contribution is that we can find the likelihood function and the posterior distribution of theta t analytically. Now, we think that in general this is nice because this model represents a good compromise between efficiency and mis-specification. As an economist, I always find it desirable to estimate, to specify the model as non-linear, because if I think that there are important non-linearities, I would like to reduce the mis-specification of the model. However, for non-linear models, in general, the likelihood function is not a bit analytically. So I need to approximate it, and the question is how good is the approximation? If the approximation is not very good, the efficiency of the filter in a statistical sense is very low. On the other side of the spectrum, there are linear models that are very efficient because the likelihood function is available analytically. However, the risk is that we are missing important non-linearities, so the model is mis-specified. This was linear model, it's a compromise because we are capturing some of the non-linearity, but at the same time, since we have the analytical form of the likelihood, in terms of efficiency, this model, the estimator that we have is very comparable to the one we would get with the linear model. So we think in our case, this is a very good choice. This is not always the best choice. There is a cost with respect to the linear model, and you can see it from Equation 4. The piecewise linear specification requires the estimation of a higher number of parameters, including the threshold down. So going a bit more into the specific, our sample is, so we use US data for 1960 Q1 to 2008 Q2, we use a Bayesian approach. So remember, there are two sources of non-linearity. We have stochastic volatility, and we have a piecewise linear long-run Phillips group. So we find it convenient to estimate it to a particle filter. There are two aspects that I want to stress. The first is that, thanks to the analytical results on the piecewise linear model, we can improve the efficiency of the filter through the so-called Raoul black liquidization. So we use the Raoul black liquid theorem. And the second aspect is that we use the particle filter also to approximate the posterior distribution of the parameters. In particular, we use, we combine two techniques. The particle learning by Carvalho-Ionis, Lopez and Paulson, and the mixture of normal distributions, as in U.N. West, 2001. Okay, let me show you the results. The first thing that we do, we estimate the model under the assumption that the long-run Phillips group is linear. So we only have one slope at this cap. Here you see the posterior and the prior distribution. When we specify a linear long-run Phillips group, we estimate the slope equal to zero, as you can see. When we, instead, we estimate the non-linear version, the piecewise linear version, so we have two slopes. Again, here I'm comparing the prior in blue and posterior distribution of the two parameters. When trend inflation is below the threshold tau, then the slope is zero. But when inflation is above the threshold, the estimated slope is negative. It is important to stress that the linear model is a particular case of the piecewise linear. But it's rejected by the data, because the data, when you give the opportunity to the data to the model to have a non-linearity, then the model wants the non-linearity. And estimates the second slope in the negative table. So this is, instead, the estimated threshold tau. So the slope changes when trend inflation becomes above 4%, basically a bit more than 4%. When we combine this information together, then we get our estimated long-run Phillips group. Here you see on the x-axis values for trend inflation and on the y-axis the difference between the potential output and the counterfactual potential output under zero trend inflation. So these are the costs from trend inflation. You can see that the estimated costs are basically zero until we get to the threshold of 4%. After that threshold, this relation becomes non-linear. So this is an estimate of trend inflation plotted together with inflation. And if you combine the information in this chart with the long-run Phillips group of this chart, then we can get what we call the long-run output gap. This is the cost of trend inflation estimated over time. So here on the x-axis you have time, and here you have the distribution of, again, the difference between potential output and the counterfactual potential output under zero trend inflation over time. So before the 70s and during the great moderation when trend inflation was below the threshold, then this cost estimated to be equal to zero. While during the great inflation period, the median cost is roughly is around 3%. So potential output during the great inflation period is estimated to be 3% lower with respect to the case of zero trend inflation. So the point here is that the costs from trend inflation are quite substantial. Now we want to understand if we can interpret the findings in this model through the lens of the generalized Newtonian model. So we consider generalized Newtonian model with three equations. An inter-temporal Euler equation with external evidence assumption. We have a generalized Newtonian Phillips group and the Taylor type monetary policy rule. Importantly, we allow for time variation in trend inflation. And this gives us, through the mechanism of the model, a non-linear and negative result long-run Phillips group. Another important assumption is that when taking decisions, the agent considers trend inflation as a constant parameter. So we are under what is called the anticipated utility model by Krebs. And here we're following the problem's border in 2008. Finally, we put stochastic volatility into the four shots we have. The discount factor of technology, monetary policy and trend inflation. Now, before showing you the results, I just want to stress what is the mechanism behind the model. So why do we have a long-run Phillips group, which is negative? In the Newtonian model, the friction is price taken. So the assumption, we have calval pricing. So the assumption here is that firms have a positive probability of not changing the price or changing the price at time t. So this creates price inspection, which leads to an inefficiency in the quantity problem. Now, the idea is the following, intuition. If there is high trend inflation, the high is a trend inflation. The high will be the price inspection because firms, when they get the opportunity to change the price, they will take into account that the cost may be quite high. So they have the incentive to increase the price even more when they get the opportunity to change it. And this increases price dispersion even more, increasing the inefficiency in output. Formally, you can see that considering the aggregate employment and the definition of aggregate employment is just the integral of the employment of the firm over the firm's eye. If you substitute in first the production function of the firm and then the demand for the good eye that the firms face, then you get this equation. And this integral here is what we call price dispersion because this gives you a measure of how prices differ from the general level of prices. So you can write all of these, making the output wide explicit, and then you get something that looks very familiar. So you say that output depends on technology and on employment, the usual fashion, but now you see that it also depends negatively on price dispersion. Importantly, price dispersion in the long run is a positive function, non-linear function of trends. For the reasons I tried to explain before. So we estimate the generalized organism model and then we can compare what are the costs that this model implies and compare it with what we found in the statistical analysis. In this graph, you see the same picture that I showed you before. So it's the long run Phillips curve from the time series model in blue compared with the long run Phillips curve implied by the estimated generalized organism model in black. As you can see, the cost implied by this model are very much in line with what we find from the statistical analysis. So I can conclude saying that, okay, the question of the papers, what is the long run relation between inflation and output? So we use a time series model and the time series model suggests that the long run Phillips curve is non-linear and negatively slowed. We then interpret these findings to the lens of a generalized organism model and we find that this model is able to measure the cost implied by the long run Phillips curve in a very consistent way with the time series model. Thank you for staying on time. All right, so at this point we have some time for questions. So I see we have one from Christian in the chat or Francesca. So maybe we can answer that one first. So Francesca, this one's to you. The credit channel can explain downside risk to inflation. What about upside risk? What kinds of variables would you use to proxy supply bottlenecks or other types of supply side shocks? Of course, this is extremely relevant for our present day circumstances and I think on many people's minds so we'd be happy to hear your thoughts on it. Yeah, as you anticipated, Christian hit the nail on its head with this question. So as you can see in the results that I've presented you, the model in its current setting already has the ability to capture some of those upside risk that we have seen in recent months. However, if I had to make this model fully operational for policy purposes, I would indeed have to think about supply bottlenecks and also pent up demand. Thinking about pent up demand, I would probably need to think about some measures of disposable income that would better correlate with inflation than say the unemployment gap in current times. As to supply bottlenecks, any variables relating to shipping costs or the cost of used goods on top of the measure of import and oil prices that is already considered in the model would more appropriately describe the inflation process as well. But at the end of the day, I think that including these variables probably won't be enough to make the model fully operational for this current times. And probably what is needed is also to look at this model in a panel dimension that also considers some sectoral measures of inflation rates, and also, you know, different inflation components in separation of each other. Since, as we know, the pandemic has had very asymmetric effects across, you know, different sectors of the economy, notably services versus manufacturing, and also across inflation components, some of which, as we know, now more prominently feature in their past through to realize the inflation. Alright, thank you. Maybe we'll move to Ali's next. Is there any empirical, this is for Paolo. Is there any empirical reason to think causation runs from inflation to output and principle output can worsen budget balances and increase the incentives of policymakers. I've lost the question to raise inflation to finance those deficits leading to a negative steady state correlation with opposite causation. How can we distinguish between these two. Please Paolo. Yeah, so it's perfectly possible, I think, and so our starting point is the time series model doesn't give it doesn't give any answer in that so what we do we interpret this finding with the with the generalizing case and model with that causation but we can't really say if the story that that all years in mind is perfectly, perfectly fine. So it's important to stress that the point of the second part of the paper is just to say that the workers model and this and the cover pricing way. It does a good job in, in measuring the cost from time to inflation, but we can't really say, you know, written install that causation is that what we just saying that is a good model when it comes to to measuring those costs, but I think he has a point. So the answer is no we can't say for sure that the causation is that. Thanks Paolo so Andrea Tomilotti had a question for each of you so Andrea I will send a request to unmute you and you can ask your questions at the same time if you wouldn't mind. Awesome. Thanks so much. So Francesca I was curious to know if you guys looked at the connection between financial conditions and inflation say three years on the road, as opposed to sort of more contemporaneously the way the way you're doing it and and you know that's the same flip there I'm thinking of the work that David has with the Stein and the ego and I think about again sort of compress the spreads sort of leading to problems may say three years down the road. And then the question for for Paolo is very interesting paper what is it possible that what you guys are picking up, which I mean again is probably there in the data is essentially the fact that over those 15 or so years in the 70s when inflation was high, output was low. And this goes a little bit to all this question, you know, maybe it's a little bit hard to say what the channels through which that correlation, you know, does it go from up to inflation or the other way around this is probably a little bit hard to tell. I mean, I think it's a reasonable way of characterizing the data but is there more that your model gives you then just as people for the long run correlation. Thank you so much. All right, I'm going to start. Good. Thank you. Go ahead. Thank you for your question there. So actually, no, we have not looked at that. I can see where that question is coming from. I was already a bit skeptical or yet skeptical in using our model to explain such, you know, long horizon movements in inflation. Because, you know, we have both a cyclical and the trend component in there and I always thought that the three years ahead with asking too much from the data in terms of the forecasting ability of credit spreads. The reason why when I wanted to dig further into the role of inflation expectations. Also, at the horizons of three or five years in some separate work that we have done, I reverted to that model that I mentioned in my presentation of Elmar Mertens, where he he looks at such long horizons and rather at that more slow moving trend component of inflation. This being said, I will just take the suggestion and perhaps also look at measures of leverage that I know have good ability in forecasting GDP growth. You know, it's such long horizons and see whether there's something more than I can say there I can see where the question comes from and obviously nowadays that's a really very relevant question. Okay, so thank you Andrea for the question and so it is to so what we are capturing is exactly what you were described so the great inflation period is a period in which inflation is high GDP is low. And we give the opportunity in the time series model to interpret this or as a cyclical temporary phenomenon, right, because we have the VR that describes the short term dynamics, you know, as rich dynamics, four laks and stochastic volatility, you know, or as something that pertains to slow moving trend. And the model says, well, it's something that is better described by the slow moving part, the trend part because it's persistent phenomenon. And just to clarify, you know, in the statistical model that this is exactly a correlation. So when you look at the Phillips there, you know, the slow parameters appear in the correlation matrix. So we can't really say that we can deduce where the causality goes, you know, and so we are very clear on that. So the causality can go both ways. So in some sense, what we find is very much in line with the traditional concept of Phillips, right, is a statistical relation, what we find between two unobserved variables that are trend inflation and potential. So, if you have, if you have in mind models that can interpret this in another way, so there's a possibility to see as we did for the new kinds of model, the quantitative implications are similar or not. I don't know if I answered the question. Thanks, Paulo. It looks like the next two are for you. All right, maybe even the next three. So, the 4% from the statistical exercise of possible indication we may consider a higher target. Are there other possible costs not considered a higher volatility of output, for example. Also, just as the result of your vertical Phillips curve from 0 to 4%, an assumption or result from your method. And then one from Todd that another check is it possible to estimate the model for a couple of other economies that had less like Germany or more of a run up in inflation during the great inflation to see if the estimated output costs change as expected, given the model. Okay, yeah. So let me start then. Okay, so this is me right is 4%. So, okay, the, the, the point here so when we interpret is the going back to the causality issue. And if the causality goes from inflation to GDP, then we say 4% is when there is a probability distribution so 4% is already in a, in a zone where it's kind of risky to stay right you already might have substantial losses that are statistically right so, but we don't you know we don't want to go into these, these are more policy issues that we need to policy makers, we just want to document this, this variation. So the, the, the, the, the, this, this first result so we're applying to Augustino. So, from zero to four, this is estimated. Okay, so the model wants so when we saw the model estimates the first look. Equal to zero case or is not is a result from the estimation we're not imposing it we were thinking about maybe a model which we can even impose that in the first part but we haven't done it so what you see is the result of an estimation. And while you're planning to, to talk about this is a very good, very good suggestion. I think we should probably try to do that. Yes. Yes, and I think it would be also interesting to see how the costs are different because the cost front and inflation might really depend also on the structure of the economy so it's a very good suggestion. Thank you. So Francesca I had a question for you if we can shift gears for a moment. So, and this relates to your direct approach in essence the question that I had was, to what extent are these financial conditions affecting inflation directly. And can you use your framework to assess that they're affecting inflation directly, or to what extent are the financial conditions affecting inflation indirectly potentially through affecting the forecast for the real economy. Inflation expectations, commodity prices. So in other words, you know, can you can you leverage some of the growth at risk literature because you're kind of building on that so can you use your framework and basically separate those two things. Yes, so a very cheap answer to your question would be that well you know we have a model where we are also controlling for a measure of inflation expectations and of the unemployment gap on top of these financial conditions. But the I perhaps I think more appropriate answer to your question would be that in an ideal world one would precisely use that methodology that I developed with Dario Danilo and Pablo and use Markov switching because they are featuring you know multiple variables that can interact with each other to be able to answer to that question and in an ideal world also you know identify a very exogenous structural measure of financial shocks and see how financial shocks affect the entire inflation distribution. So this is just work for us down the road and you know, so your suggestion is a spot on I think. I'll follow one more question for you in the chat there. So models, featuring downward now I'm going to wait for Judy going back to Akerlof Dickens and Perry also imply that the long run Phillips curve is nonlinear in that lower inflation implies an increase in longer run unemployment would it be feasible to assess the relative importance of that mechanism from that of relative price distortion. So Manu is suggesting I think another paper is very interesting question I think. I think in general it is possible so if you estimate model, a model like that, you can check you know the empirical performance of this model and and but that's another, this is another paper I don't think we're going into this direction in this paper. Thanks for the thanks for for suggesting. Right, looks like we still have a few more minutes so we'll just keep on coming so okay next one for you Paolo. So linking back to the question asked that Kira asked Hassan yesterday about high inflation and attention. If high inflation would put inflation back on the radar. If you have an effort and change prices more often, then why would you have higher price dispersion thinking also mixed evidence on higher price dispersion higher inflation. So how central is that to your model, and you know I would voice the same question you're using a calval framework to think about changes to the inflation steady state you know I think that that always raises some concerns there so any thoughts that you had along those lines. Yeah, so now it's clear that if you have a model like that in mind, you know the cost from price dispersion will be lower. If you have a state dependent model for example right so the cost will be lower yes that that's perfectly true. So when we when we choose that model the question that we have in mind is really about if the calval pricing you know is a is a good way to not not in general you know but to the specific dimension which is capturing the cost from an inflation is really exposed it does. Then, if it does because maybe you know it's capturing something that is not in the model that I don't know. In general one might think about when you when I think about the cost from an inflation I think about for example labor market is not present in our, in our simple model you know, for example that this is another one that we should consider as well right so there are many aspects that we should consider, but the point of the exercise is just to say okay this class of models in general they do imply costs from an inflation that tie in line with what we find in the statistical this is the key message then if you want to enhance the model one direction or the other that's a good these are good points but you know this is not the purpose of our exercise. So I think you know we missed a question from Mikhail. I can answer very, so Mikhail is asking if it could be that inflation and real activity transfer common factor. I think it can definitely be the case and this is something for example we can check so we have the model and we can put this common factor in the model and check so we can actually check for this perfectly possible thank you. Thank you for catching that Paulo sorry about that McKellie. Alright so with that word 11 o'clock Eastern time, which means that we will conclude this session. So big thank you to our presenters. You know, thank you for this work and thank you for presenting it at our conference we really appreciate and thanks to our audience for all of the great questions. So we have a 15 minute break for now, we will resume at 1115 Eastern time, which I assume is 515 Frankfurt time with structural Phillips curves so we'll take a short break. See you in a little bit.


When she's available, can everyone see this PowerPoint presentation that's on the screen? Gina, I'm just going to start the recording. Yes, Jack Perry, ready? Can you hear me? You're good to go. It is a whole way to make good time to change people's lives. I always have to start like that, Jack Perry. Jack Perry, that's correct. Welcome from Montmont to another episode of the Canada Subcommittee. So today's October the 25th, 2021. We'll start by taking attendance from the subcommittee. Robert Hachin. President. Ashley Reynolds. President. Leo Thompson. Erin. Okay, and then in the NAACP, Gina Cranley. President. And then for the room, can you tell us who's there? Yeah, it's just me from the board, Julie Holbert, and one member of the public. So for the next topic, we have received unfortunately no public comments for this installment. So for anybody in the Vermont citizenry who's following along, please feel free to offer your perspectives and thoughts at this rate that's posted here on the slide. And then we will also move to, we need to approve the minutes for the last meeting. So, man, please have a motion to approve the minutes. Motion. All right, thank you. Any second? Excellent. Okay. Previous meetings minutes have been approved. I'll turn it over to Gina. Thank you. Hi everyone, happy diversity, equity, inclusion program later on today. I'm hoping to wrap this up at the end of the week and do our start of our finishing our phase one and then on to phase two, which is where we come to a town near you and get public comments and how social equity candidates would like to see these programs run. And we'll be able to hear your story and be part of the cold design of this. And then phase three, everything goes to Vermont cannabis control board to finalize what they do would be the best for the social equity programs. So I, it was a little hard to find some information about who should be a disproportionately impacted community. I know what the basis that we made was based on who are social equity candidates. So, you know, high market areas and large areas of incarceration due to the war on drugs. Some of the information that I found is sort of like health rankings and, you know, traffic stops. So here's some information about traffic stops. This is a number of traffic stops. So, you know, in towns that are going to have more people in, of course, those numbers will sort of be increased. This was for 2020. So I'm not really sure if that's really a good example of information or data that we want to use. And then we also had from 2019, sort of the county health rankings. So, you know, the green box is indicating the health outcomes and the blue box are some different health factors. Now, the lower numbers indicate better performances. So, you know, Bennington has a 12 and, you know, health outcomes and health factors, which would kind of put it on a really high level of, you know, health risk. And people not necessarily, you know, having more health issues in those counties where Edison is, you know, two and two. So they're doing pretty well. This is just some information of some data that I was able to pull off. I know we have also sent this around to some different departments in Vermont to try to get these issues. But I know we've spoken about this last week, and it really sounded like from everyone's perspective on the subcommittee that these certainly were counties to include in disproportionately impacted communities. So I'm going to start with, what are your thoughts about this? So, I mean, broadly speaking, my thoughts remain the same. I think I brought it up last time, you know, when I look at this list and, you know, when I was thinking of towns that might have been disproportionately impacted. You know, my first thought was the war on drugs and communities that would be overpoliced. And, you know, the first community that came to mind was Rutland followed by Bennington. I know Brattleboro is up there as well. And then you have Burlington, which has the highest concentration of BIPOC people in Vermont. So when I was thinking of disproportionately impacted, my mind was focused on war on drugs. I know there are other factors that we're going to come up, such as health and economic factors as well. What was that last county that you mentioned? I think it was Burlington, I mentioned, that has the highest concentration of BIPOC. Which all of those counties we have are there? Are there any counties that we have here that you might want us to take off to make us add? Nothing was coming to mind at the moment. I think it looks good. I don't know a lot about it, but, yeah. Thank you. Cool, Leo. Yeah. Thank you. Any ones that you think we should add or do you think this list consists of the major ones that we want to focus on? As far as I know, these would be the main ones. Thank you. And, Ashley, your thoughts? I agree that these are the ones that I would expect and I think these are the ones that we should concentrate on. Thank you so much. So one of the things that I did want to state was that these impacted communities are the ones that we always concentrate on. That they do not change annually because these are the ones that were impacted on the war on drugs. And these are the ones who sustain the harm. And we don't want to constantly be changing that just because an area has more of a BIPOC community. We know they will shift and change, but this is about time that should be addressed in another way. But we have seen in the last few years as the test in handling the war on drugs. We're not just capping this and saying that these were not shifting or changing anything annually about these communities. That these are communities where the harm was done and we're not fluctuating and changing anything every year. Quite sure how I feel about that. I mean, if we're talking about decades of police work and how long the war on drugs has lasted, then I can see how that makes sense. I mean, there's always new data and information coming out of different areas that might be over-policed. For example, I think it was just last year that the report came out that Brattleboro had the highest number of bias-related incidents in terms of traffic stops. So I'm not sure how I feel about making it set in concrete that this list will never change. So my thoughts on this is that this is past harm that has occurred, not future harm. I mean, with the legislation that's put into control where this is now legalized, all of the harm occurred when it was not legalized. And that's what we're trying to focus on, which is why I'm trying to say that don't revisit or change these counties every year. Think these communities stay because that's where the harm was previously done before cannabis was legalized. Yeah, I do think that makes sense, but I do also think that there will be legislators who want to know what sort of metrics we use to pick these counties. Because I know everyone, it seems like everyone generally agrees that these are the areas, but I think there will be people who want to know how we came about choosing these areas beyond just anecdotes that places like Brattleboro were over-policed. No, very good. And I definitely work with Susanna's office, Davis's office. I wish she was on the call now. She also recommended very similar to this to us. So, Ashley, what are your thoughts? I'm currently here, where Natter's coming from, and I do think that's from what I've seen in the existing industry out west and otherwise where things are illegal. It doesn't mean that criminal activity is just completely eradicated as a result of the legalization, so I do really sympathize to Natter's point. However, I don't know what we have to reevaluate this, let's say, every two years, every three years, every five years. Hopefully we see some of these counties are off the board, but there could be some that are added as a result. So, I don't know that... I feel like maybe we are kind of over-complicating this a little bit because we want to do the most good in the places where there's the most harm. So, I think for the purpose of getting this program off the ground, I think it's focusing on past harm. Thank you, Ashley. And Julio? Up on the screen are consistent with kind of longer term issues in a criminal justice system, not just something, you know, like it's an issue just one year. I think there are these are areas that also have, as you pointed out, other risks that are associated with them that are reflected in health outcomes and so forth. So, I don't... there's nothing here that you have here that strikes me as like inappropriate or something that haven't had long term issues to deal with particularly, you know, the high tide of war on drugs in Vermont and in Nashville. And are you okay with keeping this as fixed communities and not changing them every year? No, I think if you're talking about forward, I think the question will be, you know, it sort of depends how forward we are because when we look at outcomes that we want to see in these, you know, these communities, we don't want these to be viewed in the same light as they were in the past. And so I think that it's a good idea going forward to have, you know, an evaluation or evaluation. But I think of your, you know, if the time period that you're looking at is like the pre-legalization period or the pre... maybe decriminalization might be a better period to start looking at. I think their past is going to change. But I think they're, you know, hopefully, you know, their president and their future are going to change. So that was... I think that's sort of what I heard, not our saying, and I would agree with that. Thank you. Yeah. So I don't mind if it wants to be a revisit where in the future maybe counties come off. I just don't want to add any more on if there was no harm that we have found in between from these counties that may be determined by the Vermont Cannabis Control Board. Well, if I could just follow up, then if you're talking about adding other communities, I mean, in the last meeting we had, we mentioned two communities that are on this screen. I mean, I risked a question as to whether Berry City might have outcomes where there was heavily, you know, disproportionate impact. And I think Suzana raised Paterson County where there is a... It's known to have a large migrant farmer or migrant farm worker in an English might have its own challenges historically with law enforcement. So those two, you know, we're not, have not been headed to the list. Well, are those counties that you want us to look into, would you like us to add those counties? You know, I think that, yes, I think we ought to look into them. I think Suzana made a good point. I think when we are looking at negative interactions with law enforcement, you have to look at communities that come up against that system for a variety of reasons. And so that could be poverty, that could be a history or a reputation, or new Vermonters who come in and, you know, over, you know, historically might have had, you know, not as positive interactions with law enforcement might not be as welcoming in some communities, at least historically. I think recent history is different, but if you're looking at the longer term, the longer backwards look, I think, than Addison and Addison County, I would feel more confident about because I know more about that, the migrant community there. The berry would be something that I would just want folks to take a look at. It's not, it's nothing disparaging about Berry City, but it is just one that has had a lot of policing in the past, and it might be that they would fall within the criteria you want to look at. Yeah, we were not looking at low income, as that was not one of the social equity criteria. But when we were saying it was largely policed, and I know that you work in that sector for quite a long time, what are your thoughts on that? Let's just knock on my door a second ago, so I have to step away, can you please repeat the question? Julio indicated that Berry was highly policed over the past few decades, thoughts on that, and adding them to the disproportionately impacted community. Yeah, so I'm not super familiar with Berry. If there's a belief that Berry was overpoliced, then I support adding it to this. Susanna, hi. So there are two people who joined us just a moment ago, so it was Lindsay and Susanna. Please add that to the minutes. Also, Susanna, I know that you've helped us a lot with these disproportionately impacted communities, and your office deals with that quite often. What are your thoughts about Berry and Addison being added as disproportionately impacted communities? One of the things that we wanted to do with disproportionately impacted communities are that these were the ones that were hogged by drugs, whether they be BIPOC or high arrest areas due to cannabis, and that we were not going to change these communities going forward because these communities based on past harm, not on future harm. So Susanna, what are your thoughts on Berry and Addison? Yeah, I would agree with adding them to the totality. First of all, sorry for being late. Also, I think that being underrepresented in all of the Berry's factors that may be underrepresented in business and in other factors, then you're going to see Berry popping up anyway. There's a lot of economic issues. There's a lot of health disparities. There's a lot of housing issues. So Berry is an interesting example of a place that we care about in this program. The data show in terms of war on drugs specifically for Berry added in. Addison living in that area are often sort of under the radar because there is a struggle with immigration. And so I think that that would be too great. Also, on the question of past harm versus future harm, I do think that it should probably be a combination. I mean, I know that this program is designed to rectify historical injustice. And so I think that our focus on past harm is important and yet the system continues to cause harm for people and it tends to be the same groups. So as that, I think that I would like to see a program that stays in full and that recognizes, hey, you know what? We're seeing a lot of inequities. A couple of years passed and we're seeing more patterns that are showing that there's inequities we may want to reconsider. Or like one of these disproportionate impact communities might come off the list because I don't know. I'm stopped talking, but those are two of my thoughts. Thank you. Thanks, Susan. I appreciate those thoughts. And I agree, you know, maybe we have a community that's no longer disproportionately impacted. But if there's new harm that's occurring, I think, you know, we need to really focus on, you know, especially now what the past harm has occurred before legalization. So, you know, from this, you know, we're basically saying we kind of got it wrong that this joke should not have been barred. And then people were still trying to do a consumer for whatever purpose it was. And if people are doing things legally with the drug after it is legalized, that are different issues to the past injustice of it. But I do understand wanting to help us as many people as possible. Lindsay, I hear that you added a comment and I would love your thoughts on, you know, disproportionately impacted communities. Then just to give you some background is that we're trying to focus on communities that have been harmed due to the war on cannabis. And we have determined a social equity candidate is a BIPOC community member and or an area for, excuse me for a minute, someone who was incarcerated or arrested due to cannabis. So we're trying to look into areas where there were like high rates of incarceration due to cannabis and or large population of BIPOC. I can't specifically speak to either of those, but earlier in the conversation, I'm not sure who spoke just a couple of courses on it, but talked a little bit about like the reputation and sort of people treating a community simply because of the reputation. And I think that very city definitely struggles with reputation of what's going on on the streets. And I think that sounded the alarm back in September saying, you know, there's a, there's a large, let me say it, large, a larger batch of illegal drugs here. So again, I don't I don't know specific drugs that he was talking about. I guess I just more was reacting to having grown up in central Vermont. I wouldn't look into. So I don't have. Okay, great. So maybe some of the recommendations in our discussion that we would include Barry and Addison to this chart. And obviously these are just recommendations for the cannabis control board and they make their final deliberations of exactly which communities. It seems like we should not make a determination if this is just pass on or future harm to communities and allow the Vermont Cannabis Control Board to determine if it is just going to make sure that these communities had to have harm before the war on cannabis or after if there are still some activities within communities that are still being harmed by cannabis. And we're going to look for future communities in a year or or revising from these communities are every year. So another with the addition of Addison and Barry, do you approve these recommendations of disproportionately impacted communities to the Vermont Cannabis Control Board? Yes. Thank you. Julia. Yes. And Ashley. Okay. Thank you. So please stay for the record that the two members of the social equity subcommittee were still on the call have voted yes to those additions of disproportionately impacted communities and the current ones that we have on the screen. So is before we get off the social equity program is there anything that anyone would like to add that may not have discussed or would like to discuss that should be included in social equity. Now over the past seven weeks we have discussed lots and lots and lots of stuff about it and I will send you over a report later this week for you just to sort of have a summary of all of the recommendations that we've made in case there is something before our last meeting that you guys would want to add but is there anything that you would like to add or discuss right now before we move on to diversity equity and inclusion programs. No, no. Okay, well please think about it in the next few days so that we can have our final recommendations forwarded to the Cannabis Control Board but celebrate we are done with social equity programs for the moment. That was an intense like seven and a half weeks right now. So congratulations and I think you're made some incredible recommendations and certainly will help to shape the lives of other people. So now let's move on to some other ones we're going to talk about our diversity equity and inclusion program. You know, as we stated, the social equity program was about people who sustained harm due to the prohibition of cannabis and their diversity equity inclusion is about making sure that historically underrepresented groups and society included in the new industries that Ramon creates. So that is what the purpose of this program is to encourage historically underrepresented groups and also to ensure a diverse and inclusive cannabis industry, which is really so, so very important. So we got on to our next question. What groups shouldn't be included? You know, what are some groups that have been underrepresented now we've spoken about women, you know, people with disabilities. I know LDP, TQ, Julio, what are your thoughts about some groups that should be included in this program? For me, the place that I would look at would be Vermont's laws that offer protections for groups that have been either historically persecuted or marginalized. So statutes like the Vermont Public Accommodations or Fair Housing Act, Vermont's Fair Plugment Practices Act, or the Hate Crimes Laws, because I think when you're looking at people who have a legacy of, let's put it mildly, challenges with pursuing their rights in the state. You're also talking about immigrant communities. You're talking about people who, for whom English is not their first or primary language. You're talking about religious groups and, you know, you're talking about people of color. So when you look at political representation and participation and government, you're talking about those same groups again. It's not just a phenomenon in one part of our life. It seems to be a problem in all of our parts of our life in Vermont. So what are some groups that you're thinking about? I think I'm just matching up and I'll match them again. So people who are part of the immigrant.  I share the same opinion as Julio. My first thought to add to this would be immigrants and refugees. So I would be in support of adding that group. So it's about the groups that are here. Women, people with disability, LGBTQ. I'm satisfied with the way the list looks right now. I'm trying to think of any other groups that right now the only ones that are coming to mind are immigrants and refugees. With anything else pops up, I'll make sure to add it. Thank you. Susanna? I'm comfortable with that list also. And I hear Julio perhaps looking at what are the different categories overall. Let's hear those. Lindsay, your thoughts? I'm mature. So Julio, I just want to make sure that I got the additions that you would like to see on the list as immigrants. We got refugees, more specifically, from Nader. And then anyone who has faced discrimination based on race, color, or religion. I think that many of these people on a color at least will be in the social equity program. So maybe I'll be just indicated as a religion. Or are there some other discriminations that you would like to include? Yeah, I just want to point out in this maybe something that at least for folks who aren't from Vermont. Vermont's was specifically prohibit discrimination on the basis of color. And it's not just race. So that's why. And so it could be that if you're looking at social equity aspect of it and you are including race, then you should have color associated with that because that's specifically protected in all the laws that I mentioned before. But if that is already if race and color is handled in the other category, then I would just say discriminated on the basis of religion. You know, from our at least the information we receive in terms of biases and ends or hate crimes or discrimination that occurs in the state. So after you get really the largest group is folks who are being mistreated on the basis of their race or color. Right after that is religion and national origin. So I think immigrants are more than just refugees as immigrants would include refugees certainly, but it's not limited to them. And I think that would probably also capture the subs that I mentioned with your people. English is not their primary language, but that's likely to be the class of immigrants as well. So English is not their primary language. So I have the three additional bullet points to glue in people with disabilities, LGBTQ, as immigrants or English or people whose English is not their primary language. So I would say maybe English is their second language. The concern is the people who may have lived in Vermont their entire lives, but their whole language was maybe something else than English. But they grew up speaking English in schools throughout their entire lives. So it might not be their primary language, but I just don't want to overlap and have people who maybe the program isn't for. Who are you? Do you understand what I mean by that? I apologize, I just stepped away for a moment to answer another call. I think you were talking about basically immigrant families here and their language proficiency here. Yeah, so we could have someone who has English as their second language, but has gone through all of their schooling and was born in Vermont. But the first language that they learned at home was the native tongue of their parents. So, you know, English would be their second language, but in hindsight, you know, English may be their most dominant language as well. So are you okay with just including immigrants so that we don't fall into another category trying to really figure out... Yes, I think that's what I was driving as I think immigrant would cover that, although I would say immigrant families. So you may be a Vermont-born child of someone who is a new American and growing up in a household where the primary language isn't English. The sad truth is that the very difficulty with speaking English or speaking even with a heavy non-American accent creates problems for people. Even if they are native from Monterres or born here in the U.S., they are often mistaken for people who aren't born here. Or, you know, where are you from? Well, you know, I'm from Monterres, that's where I was born. Nowhere are you really from. It's the sort of treatment that they can get starting out from primary education through employment and public life. So that's really, I think, if you're talking about part of that immigrant family, that first-generation household, that's what I would encompass. Or that I would address here. So I think immigrant household would encompass that. Because we see those cases and it's not just people, unfortunately, are not as welcoming when they perceive someone as being different. Thank you for that, Julio. Unfortunately, I don't think it stems just from someone who has first-generation.  No, I'm from New York. Where is your family from? From New York. So, you know, I think, unfortunately, it can last quite a long time, especially if you may look colored, even though I come from such a diverse city myself. So I definitely do understand that. Julio, what are your thoughts about choosing an immigrant or saying immigrant household? But I think we may have to link to maybe first-generation because, you know, some immigrant household may have multiple generational options. I'm going to take the broader approach and, you know, part of an immigrant household where you have, you know, you have a blood relative in the family who's living in your household.  Because I just think that Vermont historically is very protective and takes a very hard stand in the law on people who are perceived as not being from Vermont. So immigrant household, I think, is kind of a category that you sometimes see in the American survey from the Census Bureau, which is a recognition that you have those households. So that's what I really meant. I think household is a way to deal with that because you're right. People do have extended families, especially when they're trying to get on their feet and find a place in the community and the economy. I would just add, parenthetically, about the comment about New York. Vermont is one of the few states, maybe it might even now be the second state that prohibits discrimination on place of birth. So it's illegal of Vermont to discriminate and employment against somebody because they're viewed as a New Yorker or they're from the South. And that is a product of, I think, a historical recognition that what some of my Vermont born relatives, I wasn't born in Vermont. Some of my Vermont born relatives would call flatlanders. I have people who are not viewed as part of the home-spot culture of my legislature. I think a long time ago, very wisely recognized that people who are new to the state might face hostilities or challenges as well. I think that's a little, as a practical matter, I think that's very hard to apply and practice in this context. But I just wanted to throw that out because it's a very fair point and it is something that we do encounter. And it's not just New York that is sometimes allied with suspicion. Yeah, I mean, I'm just showcasing New York because it is one of the most diversities in the world. And so, unfortunately, you know, this is an occurrence just as human nature, almost. But yes, I agree with you about immigrants. One of the things that we don't want to include everything that is necessarily protected, like, you know, oh, if you come from another state, you know, we want to try to make, promote and encourage people to be included in this new industry. Julio, what are your thoughts about saying immigrants or immigrant household? I would go with immigrant household. Thank you, Julio. Not are your thoughts. I think that sounds good. You know, I'm wondering if though if that might create any confusion, you know, somebody is a first generation American or first generation promoter, and the description is immigrant household, might they think that it's only referring to their parents or relatives, Julio, should we add a note that includes that if you're a first generation American, you're covered in this? How would you like to see it? Do you want it to say immigrant? Do you want it to say immigrant and first generation American or immigrant household? I think having immigrant household, including first generation or Americans, I think would provide the more clarity for some of them. Not for a potential bad kid is looking at it. Okay, so I will have it as immigrants or current member of an immigrant household or a first generation American, because I want us to be very careful of saying first generation household, because that could include their children, that may mean now second generation. Yeah. Okay, great. Suzanne, are there your thoughts? Just putting in a note that clarifies what we mean when we say household or first generation household. I'm sorry if I missed this earlier, if it was already said, but does the person have to be first generation just by the age of one parent? Of course. Good question. Well, I might consider that to be both. The framework that I agree to all of this is to think about groups that historically encounter unfair challenges and discrimination and parents and immigrant. They come here, they meet someone, they fall in love, they raise a family here. I mean, it is just the fact that their children can bear the brunt of discrimination just by their association with them. So I just, I wouldn't stench you with the way that you're drawing the line, because I think that experience shows that they can have a very, very different life here than maybe their classmates or their peers in the workplace. And Vermont has recognized that and in a variety of laws, legislatively, because there's been a history of problems. So I would say one parent's line. However, they wouldn't necessarily be considered first generation if one of their parents was considered to be in America for several generations. And may not face the same issues. But would they still be considered a member of an immigrant household if their mother or father was an immigrant? I think that's what we're trying to figure out right now. I'm going to let you guys think for a few minutes because we need to pause to have public comments and we will come back to this. There's no public comments today, Gina. Okay, thank you. So, Susanna, your thoughts on what should it be one family household to one member or both? Yeah, I think that one is enough. And also, whether you count as first generation, one of your parents is the previous generation of American. I think that makes you not first generation. However, the lineage that you have with one of your parents being an immigrant does create a potential for you to experience negative outcomes. So you may not qualify as first generation, but you would be part of an immigrant household. And gosh, I was going to say something. Oh, I was going to say, you know, I mean the federal government, for example, during the CARES Act, stimulus payments excluded an entire household of people from payment. And one person didn't have legal immigration status here. So, you know, that's one of the things where our very government was creating harm for people, even if they were born here. Just if they lived in someone who wasn't. I would feel strongly that we would take the same tack and say, you have one member of your household. That has historically been something that could jeopardize the whole household. So, you know. Can we say parental figure, maybe? Because if they have an aunt or uncle who lives in their household, it was an immigrant level. How do you feel about that? It's a family that's going to be put together. Yeah. I think that I understand that people can be raised by different family members or have a couple of generations in the household. So, I, you know, I'm not favoring the more inclusive result. And I think if there is, you know, a suspicion by the board that someone is trying to gain the system by saying, you know, saying, well, I have someone emigrated to Vermont from Cincinnati, you know, and then that's not really the spirit or the eligible. When you're keying it back at eligibility, that should be something that would be subject to review. People aren't going to have to make truthful representations. And so I'm not, I'm not to worry about people squeaking by or trying to take advantage of these benefits because it was really their older cousin who was only a few years older than them was really the person who was in the household. Well, the parents were trying to make a life for the family temporarily, you know, looking for work away from their household. So, yeah, I'm just not more comfortable. That's why I'm comfortable with the immigrant household where you're talking about the family, you know, that's the unit I would be using. How about immigrant or have a definition of a family number? So that would be a parent or upbringing or et cetera, instead of saying household because then there would mean obviously clarity of that. And then a first generation American would then or considers or some side of that is first generation would then include if their parent was an immigrant et cetera. So I think that's fair. Or if someone was a stepchild or grew up with someone who was an immigrant as well. So we already have that definition of family, not how are you with going with immigrant or current member of an immigrant family, or member of an immigrant family, which we already have that definition of an immigrant which is why I didn't. I think that's what. Okay. Let us vote on this. So what groups should be included a woman and people with disabilities, LGBTQ community. Immigrant or current member of an immigrant family, a refugee or someone who's face discrimination based on race, color or religion. I'm going to leave color or race in there just in case it is not covered for some reason, because, you know, they face some sort of discrimination that does not was not incorporated by social equity program. Yes. Thank you. And Leo. Yes. Thank you for the record. There are two guesses for the groups that should be included and we will be using the definition of our current family that we have on record for the number of other families for immigrants. So, the last thing that we have to go through are what some of the benefits that these groups should receive. Now, this is just to encourage them to be in the cannabis industry. Now, we are not focusing, we're not saying that any harm was done to, due to the prohibition of cannabis. Some of the benefits that we have down and you have spoken about our educational programs like certificate programs if they're created workshops that are created, you know, so our online or in person educational programs. Priority processing and then a suggestion about an application being waiver to try to include and suggested that they may want to be more encouraged to come into the industry. And I'm just going to leave it right there and pause because we only have one minute left of this call. So just things for you to consider. You know, what benefits do you like on here and what you may dislike? What would you like to add? And that is where we will start off with on Thursday. Is there anything else that you may want to discuss for diversity, equity and inclusion program that other than benefits? Is there anything that you'd like to talk about this committee to discuss? Nothing's jumping to mind right now, but if anything comes to mind until our next meeting, I'll bring it up at the next meeting. But yeah, I'll think at the moment. Thank you. And Julio? I'm the same. Nothing comes to mind right now. Thank you, Susanna. Same. Thank you. Lindsay? Same. Thank you. And Julie, anything that comes to mind? No, but thank you for asking. And Jeffrey, we will discuss this after this call, so I'm sure you might have some thoughts on your mind. So with that, we can close out this session. I do have a motion to close out this meeting. I'll make that motion. Second. Thank you so much. That was so quick this time, Julio. Just ready to go. The recommendations that were made today, I think it was very successful, I'm not really sure. But it seems that way. So maybe, and I will be sending you out the reports of recommendations that were made up into this point so that we can discuss a little bit about all of the recommendations and make sure that we're still in alignment. Okay. Thank you so much.