 Welcome back to the second part of our session over lunch today. So we had before lunch a paper about what happens to the macroeconomy when policy rules change, monetary policy rules, for example, and how we can think about it in terms of counterfactuals and how the economy evolves. And now we will address another aspect of change and uncertainty, which is the question of how consumers react to increases in uncertain environment. As you can imagine, in the context in which we are at present here in Europe, but not only in Europe, this is an absolutely central question, not only for science but also for policy with the events that we all observe around us. The only issue is that so shortly after lunch, I believe it's not our primary problem, but okay, let's look beyond lunch. So I'm joined on the podium by Dimitri Georgarakas from the ECB and Jean-Ran Koumore from Sciences Po in Paris. And I would, without much further ado, give the floor to you, Dimitri. Thank you very much, Phil. And many thanks to the organizers for including the paper. I'm going to talk about the effects of macroeconomic uncertainty on household spending. Like this joint work with Oli Koibion, Yuri Goronichenko, my ECB colleague, Jeff Kenney, and Michael Weber. And of course, the usual disclaimer applies. So the idea that high uncertainty induces households to spend less and firms to reduce their investment and employment is a quite intuitive one that is generally present in policy discussions, especially during crisis times. As for example, you can see this quote here from Christina Romer that dates back to the great recession in the U.S. where she recognized, of course, the very high prevailing volatility at the time. And as she stressed, the resulting uncertainty has almost surely contributed to a decline in household spending. In his review of the literature, Nick Bloom has emphasized that the empirical evidence on economic agents' behavior is at best suggestive and, as Nick highlights, more empirical work on the effects of uncertainty can be valuable, particularly work which can identify clear causal relationships. So in case you wonder why it is so challenging to establish a clear causal link that runs from uncertainty to economic agents' behavior, for these there are at least three factors. First, they are confounding aggregate factors like pandemics, revolutions, natural disasters that are typically present during periods of elevated uncertainty. This makes it hard to identify and isolate the effect of interest. If you look at a more micro level, there are correlations with time varying households and observables you could think, for example, like time varying optimism or agents' outlook about the economic prospects, which makes, again, very hard to identify credibly the causal effect of interest, even if you use panel fixed effects models. And in addition, separately identifying the effects of expectations about first and second moments is, again, tricky because, generally, large uncertainty events are also associated with significant deteriorations in the expected economic outlook. So against this background, the present paper designs and implements a randomized control trial using a new Euro-area household survey. So via this experimental approach, we induce exogenous variation to household expectations, thus their first moment and uncertainty, their second moment, about future economic growth in the Euro-area. And utilizing this exogenous variation, we can estimate the causal effect of uncertainty, that is the second moment, net of first moments expectations on households spending on both as you will see non-durables and durable goods, and this is the main focus of interest for the paper, but also we take a look on how uncertainty can affect household's propensity to assume higher financial risk. Given that we use micro level data, we are able to also estimate the so-called heterogeneous treatment effects, that means we can estimate the effects of uncertainty into subgroups of households that are of interest. Let me give you a quick preview of our findings. Basically, we find that uncertainty reduces net of first moment expectations, the spending of households on non-durable and some larger ticket items, that higher macro uncertainty also reduces household's propensity to assume financial risk, especially by choosing to be less exposed to mutual funds. And in addition, we have to say something about the possible channels at work. So, one obvious channel via which uncertainty about the macroeconomy can transmit it on household's behavior is via uncertainty about only income expectations. We saw that this is one channel at work, but it is not the only one. So, there are also other channels at work that we cannot tell apart each of them, but we consider them so together. So, for example, expectations about taxes, real and financial asset prices, or even more generally, households use about the government quality. And as regards heterogeneity, we also identify some heterogeneous treatment effects. We see that consumption tends to be more responsive in response to macroeconomic uncertainty, especially among households working in riskier sectors and also households that have included in their portfolios risky financial assets, as they are more exposed to stock market risk. So, the data we use come from the new ECB's consumer expectation survey. This is an internet panel that started in its pilot phase in January 2020, run initially across the six largest euro area countries. Since January 21, the survey has been expanded to five more euro area countries, covering basically every month a very large sample of 19,000 households. The sample is a mixed probabilistic sample where responses are recruited via random dialing and non-probabilistic segment that basically recruitment takes place by existing online panels. And sample weight serves there to make the samples representative of the underlying national populations. As the name suggests of the survey, we interview households about various expectations, not only inflation, but also their expectations and perceptions about other macro and idiosycratic variables. And importantly, we also ask households about their behavior. Every quarter we collect data on their consumption on a number of non-durable items that they bought over the past one month. And the survey features a mixed frequency modular approach where we ask questions at different frequencies to the households. Of course, a nice feature is its panel dimension where you can link respondents across time. Back in September 2020, we filled it a 10-minute special purpose survey following the regular monthly wave. And as you will see also, we utilized data from other waves close to September 2020. But in this special purpose survey, we are able to field our randomized control trial. And in subsequent waves, we can measure also household behaviors. And as I said, we can link these via the panel structure of the survey. In case you are interested in finding more, perhaps you can take a look at this reference. Also recently, we have developed a nice webpage where we provide a lot of information. We update every month the information about the survey. So let me walk you through the steps we take in order to field this randomized control trial. First, we take the entire sample and we ask respondents questions in order to elicit their first and second expectations about GDP growth in the euro area. Subsequently, we randomly split the sample into sub-samples. One set of groups, the so-called information treatments, they receive information that I will show you in a second about actual numbers referring to the GDP growth. While there is also a control group that you can think of this is akin to these placebo groups in medical trials that receives no information. After this information provision stage, we get back again to households and we elicit once more their first and second moment expectations as regards euro area GDP growth. And we see whether the information treatments we provided them with make a difference so move their posteriors basically. And via the panel structure of the survey, subsequent waves, we can also track and assess whether there are deviations between the treatment and control group in the actual consumption behavior. So now let me go across these steps one by one. So initially we ask a relatively simple question to households in order to elicit their first and second moment expectations. So we ask them to give their best guess about the lowest growth rate that is your prediction most pessimistic scenario for the euro area growth rate over the next 12 months and the highest growth rate that people expect that is their most optimistic prediction. Just based on these two numbers they report, you can assume a symmetric triangular distribution that basically attaches progressively lower weight to this reported extreme but importantly allow to deduce first and second moments with respect to these expectations for each household. In addition we ask another question that allow to fit a more kind of realistic if you like split triangular distribution about the probabilities for each of these two of these scenarios. So basically if you see these statistics from using the road date out of these answers as I said what is important is that for each single household we are able to estimate both their first and second moments as regards euro area GDP growth. On the left hand side you see the distribution of answers by countries and from the overall sample with the black line that are symmetric around 3-4%. And on the right hand side you can see their uncertainty that is more skewn. So you see there are people that some people that perceive high levels of uncertainty as regards euro area GDP growth. As I said an advantage of this design is that you have for each single household both moments so you can graph the one versus the other from the road data. So this is scatterplot graphing this association that shows this usage that basically suggests that among those who either expect very high growth or they are very pessimistic about growth prospects these two groups also hold higher levels of uncertainty. So following this pre-treatment stage where we listed household's priors we randomly split the sample and we go to each treatment group and we provide the following piece of info. So the first treatment group receives this information so that the average prediction among professional forecasters is that the euro area economy will grow at a rate of 5.6% in 2021 and the qualitative statement in support of this that says that by historical standards this is a strong growth. Instead the second treatment group receives the following issue. The following information is profession forecasters are uncertain about economic growth in the euro area in 2021 with the difference between the most optimistic and the most pessimistic predictions being 4.8% as points. Again that by historical standards this is a big difference. So clearly these aims to move more the second than the first expectation moments in expectations and a third treatment group receives information on both. We have experimented with the fourth treatment group receiving information about this agreement profession forecasters about foreign countries growth but this didn't prove to be very influential so we don't use it in the analysis. So following this information provision stage we need to go back to households and ask them again at least it again first and second moment expectations. But generally when you design surveys it is prescribed to avoid using exactly the same wording when you ask a specific concept to respondents because otherwise you typically annoy them a lot. So what we did is again we elicit first and second moments but here using a different question that has been used in the literature where people have to assign probabilities this over three scenarios for the prospects of euro area GDP growth. So basically the differences in design between the pre-treatment and this question will be absorbed by the control group. So do people update their first and second moment expectations after receiving information? The answer first is yes. On the left panel you see how they update their average expectations after receiving the treatment so basically comparing priors with posteriors. You see that particularly those groups that receive information about the point forecast they update towards the signal they receive. So those who hold very high expectations priors initially they reduce their priors about growth while those with very low ones they increase them. As regards uncertainty because there was high initial uncertainty in the sample our treatments for most households reduced the perceived uncertainty but what is really important here is that our different treatments induce different relative changes in the first and second moments. This gives us enough power in order to identify the effect of interest in the same regression so to identify separately the effects of second moments net of the first moments. And as I said in follow-up months we are able to measure behavior so for example in October people report with respect to these bundles of goods how much weather and how much they bought over the previous month and this nicely aligns with the time they basically received information just in the previous month. And also we have information back then only on the extensive margin of whether people purchased some bigger ticket items, more durable items like car, durable holiday and luxury goods. So this is the main equation we estimate so basically if you think of non-durables these are reduced form consumption regression where we regress low spending on the two endogenous variables of interest so the first and the second moments about your area GDP growth plus some controls to reduce the noise. Of course these two variables are endogenous and we use our instruments to instrument for each of them. So via IV we can credibly estimate the effect of both of each of them on consumption. So these are the results from the baseline regression. Basically you see at the first row and the first column refers to consumption adjustments just one month following our formation treatment so what people report in October and in the second column four months after fielding the information experiment which what people report in January 2021. And you see what really matters is uncertainty and the underlying effects are relatively large so this tells you that one percentage point increase in the measure of uncertainty that is roughly about one standard deviation of the sectional distribution post treatment reduces by 3.43% the spending on non-durable just in one month later on and the effect seems to be pretty persistent although a bit less precisely estimated four months later. And you can see from the F statistics at the bottom both for the first and second states that the instruments were successful in giving us enough power for micro data to identify separately the effect of interest. We have a number of robustness checks in a recently revised version of the paper you can see here some panels with additional set of results for example if you use pre-treatment this more realistic distribution to measure the two moments the effects are if anything even stronger. If we use as a first stage log of uncertainty instead of levels the effects again are similar also quantitatively similar and another consideration is to control for skewness because by eliciting this individual specific distribution in principle you can also have a third moment so you can calculate a measure of skewness we do so post treatment so we control for this measure of skewness by construction this is endogenous we don't have enough instruments to have three endogenous variables estimated simultaneously in the same regression but still our IV approach goes through and clearly suggests that if you include skewness the results are broadly the same. Basically another thing we are doing for consumption is we look at budget shares so whether people in response to this exogenous change in their perceived uncertainty they adjust more certain goods than the others generally the adjustment seems to be broad based across most categories we find there are some small difference for categories that are more discretionary nature like say recreational activities but these broad based results suggest that the primary reason for this adjustment seems to be precautionary saving. Now would like also to say something more about the underlying channel so how macro uncertainty by which channels transmits to households affects their perceptions and then in turn affects their behavior so one obvious channel via which this can operate is about households own income uncertainty unfortunately in the survey every month we measure both first and second moments about households perceptions about their future income but other channels could be like expectors about future interest rates or taxes or even more broadly government quality or expectations about real and financial asset prices we cannot tell apart each of them what we can do is we can quantify the importance of personal income growth we do some robustness there that I don't have the time to explain in detail but the main finding is that the effects do not operate solely by expectations of own income growth so income growth and uncertainty about own income growth is indeed a channel via which macro uncertainty can transmit to households spending but it's not the only channel so then we conjecture that a number of other possible channels that I list above are also likely at work another thing we are doing again in examining the effects of uncertainty on household consumption is to look at the extensive margins so whether households bought or not major items, larger ticket items that are of more durable nature and every month in the survey we ask indeed whether people bought over the previous month a house, durables, cars, holiday packages or luxury goods we also know from the data their plans of households to buy goods over the next 12 months so conditioning for this the effect we estimate is more like a surprise effect of the exogenously moved macro uncertainty on spending and we find especially with regards to the last two categories so holiday packages and luxury goods some economically sizable statistically significant negative effects of higher perceived uncertainty these effects were just estimated next month so the effects tend to fade four months after the treatment so for the durable goods this might be more consistent with some models that have this way emphasize this wait and see channel the second margin although it's not the main focus of the paper that we look at regards post treatment behavior in financial investing as we know households are typically quite inert so they very sluggish and generally they don't resupply their portfolios so they tend to stay with the same mixture of assets over very long periods in time so it would have been challenging to identify this through the data even if who had to use many panel follow-up waves so what we did instead was to ask houses after receiving the formation treatments to think that they have received a windfall of 10k euro in this case how they would invest in several financial asset categories so we ask them to allocate across this 10k across the these categories that you see on the slide and there we can see whether they indeed are willing to assume more financial risk after receiving this information treatment and these are the budget shares that would allocate out of this 10k windfall on different assets conditioning on their actual share of investments that we had already asked one month before fielding our reformation treatment that was basically in August 2020 and you see that we find sizeable effects especially as regards the effect of uncertainty or reducing exposure in mutual funds which is a kind of standard way where via which households hold stocks and in direct way holding stocks and the last piece of evidence regards the heterogeneity that we like to look at more closely across various groups of interest so one first split we try is we split house across those working in high risk sectors so sectors that were affected by the pandemic low risk sectors that were less affected and retired that are typically worry less about their future streams of income and when we do this you see results in the first three columns we see that the effect of uncertainty mainly operates among households that work in risky in high risk sectors another split we are doing is to split households between those that hold only safe assets in their portfolios and those that include at least some risky financial assets so they are more exposed to stock market risk and when we do this it's the last two columns you see in column 4 those households that do hold they do have some exposure to stock market risk the effect of uncertainty on reducing spending operates mainly through this group of households so let me conclude as you saw we used a randomized control trial an approach that becomes increasingly popular in microeconomic research using recent advancements in household and firm service to address empirical challenges in identifying the causal effect of macro uncertainty on household behavior we find that elevated macro uncertainty strongly reduces consumer spending both on non-durables but also on selected durable goods and the effect seems also in some specifications to persist over time at the same time it looks like it reduces household's capacity to invest in risky financial assets and as I said an advantage of using micro data is that you can really look closer into certain groups of interest so we find out some plausible heterogeneous effects by sector of employment and portfolios riskiness and this is my last slide in fighting with repercussions of the great depression President Roosevelt had famously said that the only thing we have to fear is fear itself recessions are characterized by increased macro uncertainty and thus an economic recovery may require as we partly argue in the paper management of expectations, assurances by policy makers pretty much like the assurances that President Roosevelt gave at the time a provision of stronger safety nets and ultimately this is targeting the more vulnerable groups like groups in sectors that have been greatly affected during the pandemic as we showed. Thank you very much and I'm looking forward to the discussion. Indeed Jean-Como is a discussant and needless to say as you realize Dimitri is a very very prolific researcher in this field with randomized controlled trials the exploitation of these surveys for highly significant policy questions for central banks are really a powerhouse with his co-authors of research in this field but we will hear more from Jean. Yes, so thanks a lot for the organizers to ask me to discuss this paper. It's a great paper. So the main question, it's a very straightforward paper with one main question that throughout the papers they go after the main question is how does the uncertainty about future income in their case future macroeconomic uncertainty affect people's consumption. So they mainly like frame it in terms of literature about the effect of macro uncertainty and so evolution of maybe saving over time along the business cycle I think the result are, so this is such an important question that the authors result could also speak to other literatures and just trying to measure uncertainty at the macro level but also household finance literature is a precautionary saving some paper looking about the phenomenon saving on the rainy day so when it's the world is very bad you actually keep saving why and one suggestion is exactly this effect of uncertainty because the correlation between the first moment so average GDP is really low but also lots of uncertainties so people do save even though it is a rainy day and so the results would validate at least qualitatively this challenge which again we lack causal evidence of that in the household finance literature as well. So overall I think it's a very important question the paper is really straightforward and the point that you're trying to make is very clear and they've got this methodology that is really we are looking for causal relationship so let's do an RCT and let's try to establish this causal relationship so what they do more precisely is they build this RCT in which they expose some respondents to different pieces of information about professional forecast of growth in the Euro area over the next 12 months and they do that in September 2020 the information they give is a mean of different professional forecast and the maximum differences between the forecasters from this in the survey they also elicit people's distribution of growth in the Euro area and they show that this treatment so giving people information actually do affect their own perception of the first and second order effect on the distribution, individual level distribution of growth in the Euro area so the treatment does work in the sense that it does change people's prediction of growth in the Euro area and so they've got an exogenous variation that they can use to estimate the effect of the first and second order moment of the distribution of expected growth in the Euro area on people's spending what do they find that a one point decrease in uncertainty about growth in the Euro area over the next 12 months raises monthly non-durable spending by 3% in some other specification they find up to 5% it's more so among people in the sectors that were more exposed to COVID the uncertainty also affects the composition of spending with some goods being more prominent in the consumption basket and a decrease in uncertainty raises spending on durables and investment in mutual funds and crypto pretty consistent stories and that's why I find the paper great now I'm gonna make a few comments but these are more just things I would like to see maybe discussed a bit more in the paper but I find the paper very clear and very important so the first thing is what is uncertainty you've got this number going from 0 to 15 but so uncertainty is one standard deviation in this individual level distribution of expected growth in the Euro area in the next 12 months and I computed that for instance to have an uncertainty of 1 so these distributions are based on the people's reporting the lowest and highest forecast of possible growth in the Euro area and if people have sort of symmetric distribution an uncertainty of 1 would mean a 5% difference between the lowest and the highest and uncertainty of 4 would mean a 10% difference between the lowest the worst case scenario and the highest possible growth that you expect so I think maybe giving a bit more content to this number could be interesting and then my point is that on this channel you see that the implied mean actually you see a very large number here so it goes from minus 20, 20, minus 15, minus 10 and above 15, 20, 10 and so one question that I want to raise and you see that uncertainty is really high above 2.5 but for those people with more extreme predictions about the growth rate in the Euro area so would that be possible that people with very large uncertainty actually have no real idea what the growth rate has been what would be a normal number to give for the growth rate and so before the prior uncertainty is really large because they have a bit no idea what it could be and then you treat them so you give them some sort of anchoring or reference point and then their uncertainty changes because they didn't know before what could be growth in the Euro what would be a normal number for that this could explain possibly why you know there's this treatment you find that when you form people so the others find that when they form people even about the average growth which would be I think 5.6% this changes people's prediction about the uncertainty and this is based from lowest, highest, etc so probably even by giving them just a number so an average nothing about the uncertainty the disagreement between forecasters is still going to change people's uncertainty their own forecast so this could be the case if the mechanism is that you reassure you sort of give people a reference point it could also explain why you find this strange but small effect that a decrease in the mean expected growth actually comes with an increasing consumption holding uncertainty constant and this would be the case if again the main treatment is to sort of reassure people like things are not going to be crazy it's probably not going to be minus 20% it's probably not going to be 30% so by giving this sort of reassuring message people at the same time raise their consumption and also get towards more normal expectations of growth rate or closer to what the professional forecasters have the second question is about the comment it's about the choice of the instrument so I think the usefulness of the instrument is to remove any correlation between consumption and uncertainty that would be coming from characteristics affecting both consumption and uncertainty so if I write it in this very very simple way C here stands for consumption U for uncertainty C would be a linear function of uncertainty plus some demographics then and uncertainty would be like some random variable here plus the effect of demographics and you see that if you just regress uncertainty of a consumption they get a bias because you're going to get the effect and just a correlation between people's characteristics when you've got an exogenous treatment the really nice thing is that now you get an exogenous variation treat here and so if you look at the effect of this treatment on consumption if the treatment does affect uncertainty you're going to correctly capture the alpha without the bias now what I was surprised when I read your paper but maybe there's a very good way to do that is that your instrument is not just a treatment but the treatment interacted with the prior and my belief is that maybe the prior capture a bit some demographic characteristics people with a very high prior may have a different level of education different jobs than other people and if that's the case because you're doing this type of instrumentation maybe you know it's coming back by the window this effect of demographics and maybe you're capturing in part a covariance between the reason why people spend more in September 2022 which is due to the demographics and the fact that they respond a lot to the treatment which is due to the demographics so you do control when you do this regression it's not just directly the effect of uncertainty on consumption with the treatment you also control linearly for Z so this would partly disappear unless Z affects consumption non-linearly in which case I think the bias with the comeback like it could be there and so trying to think about the situation in which this might be a problem is that if people who have high prior uncertainty also have a high spending in September because a high prior uncertainty in what the artist find associates with also a high effect of the treatment like those with a really high prior uncertainty are those who reduced their uncertainty following the treatment and I was thinking maybe a test with that would be to do a placebo look at the effect of if we see exactly the same procedure look at spending in August 2020 and if you don't find any effect it's good and you're sure that your treatment with what creates the response on that so it's a bit of a related comment you find that the positive effect of prior uncertainty on spending so this is really great I think because what the artist find is that the exogenous part of uncertainty the one that is actually coming from the treatment does affect spending negatively while the prior uncertainty affects spending positively so this validates the choice of an exogenous variation we do need an exogenous variation because otherwise you're going to get some the two go opposite way so you do need your instruments so I think you can emphasize a bit that maybe more but also like this is a bit consistent with this comment that people with a high prior uncertainty also save more so last comment is that so that's the timing of the take up so I try to educate myself a bit about this survey and what I understood is that the treatment is added to the survey and apparently but maybe I'm not a juicy expert here there's a window that is opened for them to take the survey from the first Thursday of the month until some point later on and then they can take up the survey at any point during this window and so it might be that in what apparently 70% of the responses are usually completed within the first 10 days of the data collection period in September 2020 the window started on September on the 3rd of September this means that 70% of the people were treated between the 13th and the 13th but also that 30% of the people were treated quite late so they're looking at the effect of the treatment on the whole spending of September while half of the 30% of the people were only actually could have changed the spending because of the treatment only after half of the month so maybe you could use this sort of margin of when people were treated exactly during the month of September to check whether the effect is stronger for those who took the survey on their own which would be the case because they could adjust the spending even more to their reduced uncertainty so that's these are my comments thanks a lot for having me discuss the paper it's a great paper Thank you, John take following usual practice you have any answers to or you want to answer a little bit? Thanks a lot John indeed very thoughtful discussion paying attention in many details we need also to take probably again a look this is a longer project I have tried several things several robustness so some of the things you have may seem to have tried but probably forgot to include in this version as robustness one thing I would like to be absolutely clear is how do we measure uncertainty so it is prescribed also by Chuck Manskey to when you measure in expectation not just to ask about only 0.4 because people have different underlying distributions so just reporting that I expect inflation to be 2% and you report the same this can be quite different due to underlying distributions and we need to measure this and these questions we use are particularly designed for this so when we measure uncertainty we need to apply formula where for each of these type of questions we calculate an individual specific measure and then we assume not to understand the deviation of this but a unit change of this to see the effect of interest it is not the standard deviation we vary there so as regards possible outliers we know there is a lot of noise in this data often you need to filter out to have tried also other regressions that are lower weight to outliers so the negative effect we find in the first baseline regression about the first moment is despite being significant and we also comment on its quantitative importance it is really trivial so we think it is more noise yes we need to explain a bit more about the IV strategy because indeed we interact with priors but note that the priors are orthogonal to the rudimentization so to the experiment so this is not a priori an issue but what you can do in order also to be statistically say waterproof is to report and have done this and there are the evidences clear over IDs you can test for over ID and you see that we clearly fail to reject them now there because there you can use as instruments only the dummies of the treatments which are of course you trust but also great suggestion about running a kind of backward looking placebo actually we did it because in August we don't collect data on consumption we collect in July though because we didn't report but great suggestion now for late respondents yes we can look a bit more but generally late respondents are always late respondents also we do some other filtering some people are speeders so take the survey in a very fast manner so we also take this but thank you very much again for all these points are well taken okay now we open the floor so if you have any questions before I take the question I just want to ask I to my colleagues in the back I have a question on Slido but it is from 5 to 3 so I'm precious from the last session so I won't take it until you tell me that that is actually for this session it's from last session okay good so flaws open for questions if you want to structure your thoughts I can ask my first own first question if you need some time after heavy lunch and so I wanted to follow up where John actually started a little bit maybe for those of us who are not actively researching in this literature you could juxtapose your result about the size of the fact the economic significance of the fact with the macro other literatures that may use different approaches to measuring uncertainty and to identifying the effect of uncertainty on consumption maybe put your number of these 3-4 percentage points into perspective with that literature that will be very helpful for some of us that's a very good point thanks so generally for those working with micro data this is a very big effect but also if you kind of aggregate it is a kind of huge effect because it affects non durable spending you know just the one month afterwards of course this effect is conditional on what kind of change you assume in the underlying uncertainty so this is straightforward to read if you assume one percentage point assumed increase in uncertainty that corresponds to as I said roughly one standard deviation of the cross section distribution post treatment but if you go to the raw data and you see actually after receiving our information treatments how much people on average change their level of uncertainty and you this which is exactly what raw data tell you you find an adjustment in the following month of non durable spending of the order of about 0.7 to 0.8 percent which again because it's monthly so it's again a very sizable effect so there seems to be a very sizable effect on non-durable spending that it looks also that persists sometime until in December that is reported in January 2021 and also in some of the durable goods with extensive margin again by the standards of the literature these are very sizable effects in the in a recently revised version we have done also some they are back to the envelope calculation to contrast versus macro figures this comes with many assumptions we had to make but it is broadly consistent with what you get from macro literature thank you don't be shy, here we go Morten I have a hard time formulating this question in my head try my best I'm thinking at the point when you ask about actual consumption of course people are also hit by actual shocks in between so I'm wondering whether the impact of the mean that's because that matters less than the actual shock you are hit with because I'm not hit with an actual shock on certainties so there you may have a bigger impact thanks so indeed in between you can have many shocks that people face and of course the longer the horizon over which you look at what happens but what is really crucial here for the validity of the experiment is the randomization and allocation across control and treatment groups because this tells you this ensures you actually that the shocks that people will receive they have equal probability to receive them either being in any of the treatment groups or being in the control group or resort whatever systematic deviation you identify in their behavior comes from basically the information provision that you did in this random way so the nice feature about this randomized control trial is exactly this we know and we are aware that in this data there is a lot of also measurement error right but also by kind of similar arguments measurement error can be dealt with via this randomization because for example people who are more prone, this is self-reported spending right, people who are more prone to misreporting spending they are equally likely to be present in each of the treatment and the control group so you always estimate vis-à-vis the control group okay so I understand the answer you gave but if the actual shock if that was big enough I guess that could wash out the effect of the first moment of the expectation no? if there was in between a very big shock might but you know back then there was no actually an advantage of the paper that and actually of this data set that we are able to fill the information experiment and then based on a different survey that is just one month after people report their spending and then finally aligns because they report their spending with respect to the past 30 days that is exactly they count these days started counting from the time they received the information so it's their actual behavior following this so as far as I recall back in October 2020 there was no kind of major you know shock of this kind other stuff there was a large literature that has a lot of trouble finding effects on you know expectations of any form and response to shocks obviously using a very different methodology and design to what you're using here but here you're finding effects not just on you know expectations you're finding these effects that filter through to consumption as well so you do is kind of your opinion on this that it's just due to a very different sort of methodology or do you think there's something kind of more of these existing studies in the literature right so there are indeed you know a lot of papers both on the micro and micro front on the micro front papers that I'm aware of they do find effects on say of consumption are sensitive to background income risk on spending and this is also a model through state of the art model in macro there are also papers yes with some conflicting results the thing is that's why we put so prominently there what Nick Bloom has you know summarized out of this literature that precisely by the nature of this question is extremely tricky and challenging to clearly identify and make causal inference both moments in principle are moving we were lucky that our instrument that our treatments not only moved posterior but also to a different extent so this gave us indeed the space we wanted we needed to identify then separately the two effects so it's a completely different approach for sure here but it's also I think also the power of this kind of methods that they become more popular and we see that by providing information treatments can change indeed people's behavior in many respects it's not only consumption or macro there are also recent studies that look at information that graduate students receive on their majors and these on the salaries of the possible sector they can work for in the future and this changes their choices over majors so you know you find in various fronts and this consistent also with limited prior information and knowledge that people have for many of these issues sorry Daniel for misspelling your name sorry for before other questions if not I can take another one from my primitive list of questions so but no I it's actually the flip side of the previous one so one issue of these random control trials is that they always take place in a very peculiar and very specific circumstance and a very specific time which has not the long time dimension so what makes you confident what you measure in times of the consumption reaction the different goods the composition is not a special COVID effect rather than a general consumption effect where we as economists and you know briefing people of policy makers can be confident that they are of a certain general nature how do you go about these issues thanks for this actually question we receive often in seminars we will give this this paper because apparently we fielded the experiment during COVID although it was not exactly you know at the time of a lockdown or say when the October's when the infections went up again January was deep into it but I mean there were no local restrictions at place etc so again my answer relates a bit to my earlier answer to more than that the beauty of this is via atomization you can make sure that people that are a priori sensitive or affected by COVID they are equally present in the control and treatment groups so this somehow ensures you know that you don't buy as your effects this way now exposed as we did actually you can split the sample and look at people working in sectors that have been affected more from COVID versus those that have been affected less from COVID but this is you know totally legitimate in this context is more kind of estimating heterogeneous treatment effects so your baseline effect you can trust it in terms of this causal estimate you identify and then you can split the sample in various ways to see these another piece of factors that have much time to go to talk about was these consumption budget shares where basically we saw that the effects of our information treatments were across the board so it was not the case that we so concentrated effect of increasing consumption uncertainty on certain goods that was more difficult say to access due to COVID there was some bigger effect on recreational activities or on goods that are subject to supply constraints so it was across the board so this suggests that it was more a kind of precautionary saving response so people wanted but not strictly say a COVID a COVID effect okay thank you we are at the end of our time obviously this new survey of the ECB would give tremendous scope for future research so if people are interested in what are the prospects for using this maybe you talk to Dimitris how this will be gone about and if you have become interested through this little appetizer that would lead me to close this session now and tell you that we break for 15 minutes and then I'm sure you will be all coming back for a fascinating discussion with Paul Krugman and Larry Summers about something they think less often about than US inflation which is Eurare inflation so I'm very curious what they come up with thank you very much