 I'm delighted to introduce you to two great speakers, both of them also good friends and long-standing collaborators. It's a real pleasure to see them both here. The first is Bruno Martorano. Bruno is an assistant professor at the University of Maastricht, the Graduate School of Governance and UNU Merit. Before joining Maastricht, Bruno worked at ETH in Zurich and at the Institute of Development Studies in Brighton, where we started our long-standing collaboration and also before at UNICEF in the Sente Center in Florence. Lina is a professor of political geography and conflict at the University of Sussex, where we met. She worked previously before joining Sussex at Trinity College Dublin and at PREO, the Peace Research Institute in Oslo. Lina is the director of the Armed Conflict Location and Event Data Project, the ACLAID project, which is familiar to anyone working on conflict and political violence, and it's been a tremendous public good for everyone in this field and we're very grateful for that, and it's also a large part of the presentation that Bruno will do in just a minute. Welcome to both of you. As usual, the way we've been doing this webinar is Bruno will speak for about 20 minutes, Lina will respond for about 10 minutes, and after that we have time for Q&A. So please keep posting your questions on Q&A, I am monitoring, and I will compile and address them to the speakers and if we have a little bit of spare time at the end, I'll try to unmute some of you maybe and you can ask your questions directly, so let's see how that goes with time. But without further ado, welcome to you both and over to Bruno. Thanks, thanks Patricia, thanks for your introduction and thanks to you and you and your wider for organizing this seminar. Today, I would like to talk about our recent work on basically on the cost of pandemics in terms of social and political stability, in particular in our recent paper, we investigate about the consequences of policy responses to COVID-19, inequality and inequality on protest in the context of USA. But let me to introduce our work. We know that COVID-19 is a global disaster that has taken the world by surprise and many governments and most of the governments were absolutely unprepared to this shock, of course. As reported in the official statistics, the first cases of COVID-19 were recorded in Guang in China in December of 2019, but soon the virus spread to the rest of the world. As the number of cases were increasing across the world, we also could observe that the social discontent started to increase in the population. In particular, we can observe that the number of protests increased over the last year. In particular, the increase of about 30% between January 2020 and January 2021. In the USA, in particular, were recorded many, several protests. In particular, they started in COVID-related protests, started since mid-April 2020 in Michigan, but then they started also to happen in other states such as Utah and many different states, including the less conservative ones such as California and Colorado. So, but why did protests rise when the pandemic was so rampant? So we think that it's not enough just to link the stringency of policies implemented to limit the spread of the virus with the increasing number of protests. But we think our hypothesis in this work is that COVID-19 is espousing existing inequalities. And in particular, the health shock and the policies implemented to reduce, to deal with the virus basically may generate some important economic costs. And most importantly, these economic costs are higher in the most unequal areas. In particular, in these areas, it's possible that the feeling of injustice, feeling of unfairness, basically are increasing and increasing. And what is explained in theory basically is that when these feelings achieve a certain threshold, basically we can observe that individuals and groups start to react, engaging in different forms of political engagement, including civil protest and demonstrations. So against this background, the aim of the objective of our work is to empirically demonstrate the role of existing, pre-existing inequality in explaining the relationship between policy restrictions against COVID-19 and the incidence of protests in Utah. To do that, we build a dataset including the different informations for more than 3,000 USA counties from 50 states and the District of Columbia. And in particular for each county, we had the time and variant information on weekly aggregate data on COVID-related protest events, weekly information on COVID-related policies, and information on the level of Gini index in 2019. Again, the period of analysis is 2020. So but let me do explain a little bit more about our data. All the protest events are recorded by ACLID and the Bridging Divide Initiative under the new project USA Crisis Monitor. In particular, this dataset is very important for us because it reports information about states, actors, locations of protests in the USA and give us the possibility of distinguished COVID-related protests to other types of protests happening in the USA. So basically in this work, we are able in this way to clearly understand the impact of these policy restrictions to protest related to COVID-19. The second important source of information that we are using in our study is related to the COVID-related policies. In particular, these information are from the Oxford COVID-19 government response tracker that reports systematic information on lockdown style measures, and in particular it scores the stringency, the restrictiveness of these measures. In particular, there are 20 indicators and some of these indicators are aggregated in a stringency index that ranges between 1 and 100, with 100 that represent the situation of complete lockdown. So if we start to pull together this information, we can observe some initially interesting results. In particular, the level of the stringency of policy was much higher in the states in the coast, but also we can observe that this area has also observed a higher number of protests. But this is only a small piece to explain the protests recorded in the USA in the recent year, and to understand better about this complex relationship, we need also to understand the role of inequality. In particular, we know that inequality is high in the USA and is much higher than in other developed countries. But if we look at the level of inequality at subnational level, is it possible to observe that inequality ranges between 0.30 and 0.71? So there are huge differences across USA countries. But once again, looking at the map of USA, we can observe that the most unequal areas are in the coast, and in particular in this case, the most unequal counties are in the southeast. So let's move to our empirical strategy. In particular, we discussed it so far about we just pulled the different information, but we want to understand empirically the relationship between the stringency of anti-COVID-19 policies and the protest incidents in areas characterized by different levels of inequality in the USA. In particular, we formalized this model through this equation in which the dependent variable is a dummy variable, assuming the value of 1 if a protest related to COVID-19 happened at the county level in a certain week. And the two variable, independent variable of interest are the stringency index and the interaction of the stringency index with the level of inequality in each USA county. In particular, we are interested policy may translate into variation in protest activities in areas characterized by different levels of inequality. But it's important to highlight that the various equations implies that the effect of the stringency of policy on protest changes in a linear manner with the level of inequality, where we want to ensure the necessary flexibility to our data to our model and to capture this non-linearity. So basically, we replaced the Gini index with the quintiles of inequality. And we interact the levels of policy stringency with quintiles of the Gini index as formalized through this equation. So if we move to the results comparing the two different models, we can observe first of all that we can conclude that the effect of stringency increases, basically increases with the level of inequality. So the level of protest, the probability of protest is higher. But we observe thanks to the results related to the equation number two that the effect of stringency is not significant on areas characterized by low level of inequality, but it started to be significant and positive only in areas in the most unequal areas. So in particular, according to our results, we can show that one, the probability increase, one standard deviation of changing the stringency index lead to an increase of the probability of protest of about six percent. So we know, we are aware that there could be several problems in terms of indigeneity, of course with our analysis. And to reduce this concern, we tried to implement a different strategy. For example, we reduce the concerns in terms of omitted variable bias using counting state fixed effects. We also, to reduce the issue in terms of reverse casualty, we highlight in our work that we are considering the role of pre-existing inequality, which basically refers to the gene index recorded in 2019. The trends in the probability of protest are parallel in areas with different levels of stringency as well as different levels of inequality. So our results is not explained by previous trends in the outcome variable. And also, we finally instrument the stringency index, and in particular using the average number of new COVID cases recorded in neighboring states, or the average level of policies stringency in all the states. And in particular, of course, for reasons of time, I cannot explain the idea behind, but I want to show the results. Let's basically explain once again that our results are robust, and in particular, the coefficient of interest. I'm reporting here the interaction between stringency index and the highest, the areas, the quintile number five. So we cannot observe that the coefficient is almost the same. We've changed it only a little bit, and it's always statistically significant. But we tried also to understand the underlying mechanisms. And in particular, we tried to understand why the stringency of policy against COVID-19 tend to affect the number of protests in the most unequal areas, investigating in particular the role of economic factors. We know that the policy measures, the crisis and the policy measure to contain the COVID-19 have generated the important economic consequences. In particular, in February 2020, unemployment reached its peak since the end of the Second World War, with an average level of about 15%. And importantly, it's necessary to highlight that the most hit groups were the most vulnerable ones, such as the temporary workers and the member of minorities. Also, if we compare small business activities against the big companies, there are some information, some data showing that the most affected ones are, once again, the smaller activities. And in particular, as reported by the Fortune magazine, by September 2020, about 100,000 commercial establishments shut down permanently. So basically, there are some evidence, some data showing the importance of the economic channel. And to understand the role of economic factors in explaining our results, we just replaced it in our main estimation of the dependent variable with a number of economic outcomes, such as the number of small business activities that are happening in the recent days, the revenue of these business activities, consumer expenditure, as well as the level of employment. And when we are replacing these different outcomes, when we are using these different variables, we can observe once again that the impact of the crisis, and in particular the impact of policy responses to COVID-19, is stronger in the most unequal areas. In particular, there are negative consequences in the number of new small business activities in their revenue, in the level of consumption, as well as in the level of unemployment. And in our understanding, this is the reason behind this increasing level of dissatisfaction that is leading this to this increasing level of social and political instability in these areas. So we also tried to include to analyze other social and political factors. In particular, our results showed that protests are more likely to take place in the most unequal areas, where trust in the president and satisfaction with democracy are lower than in other areas, as well as in areas where the levels of social trust and civic engagement is higher. And these results are not surprising because they are aligned with the expectation of the theory. So let me conclude. In particular, our results show that the implementation of policy restrictions to contain the spread of the virus lead to increases in the incidents and in the number of protests in USA counties, but only in areas with the highest levels, regarding the highest levels of inequality. And these results, of course, if we want to link these results into the theory, they validate a longstanding theory of civil unrest that emphasizes the role of inequality motivating deprived groups and individuals to take the street. Also, in this work, we emphasize that these changes are managed by changes in economic conditions in countries with higher disparities. We think also that our work has some interesting policy implications. In particular, once again, this work highlight the issue of inequality, the fact that we must act, we must fight inequality, especially in good time, to avoid that in the time of crisis, such as our time, inequality amplifies the negative consequences of the shock. Moreover, we are at the role of economic channels to explain the rising level of protest. And this would suggest, let's say, the fact that implementing restrictions to the mobility of people, we also should implement new social protection measures to buffer the consequences, the negative consequences of the crisis, and ensuring promoting better living conditions of people affected by, most more affected by the crisis. Finally, I would like to also refer to an important point. In particular, we are observing here the consequences of the crisis and policies in the short term. But we know from a previous crisis that these types of shock could generate long-term consequences, especially on political outcomes, leading to more social and political instability, conflicts between different groups, polarization arising of populism. So, that's all for my presentation. And thanks a lot. Thank you very much, Bruno. Perfect timing. And Kleena, over to you. Great. Thank you. Oh, sorry. One second. Oh, great. I'm not unmuted. I'm just going to share my screen. Firstly, thank you very much. I hope everybody can see that. I would like to definitely just at the moment thank, of course, Patricia for inviting me to this very interesting session and the opportunity to read the paper, which was excellent. I'm going to talk a little bit about Aclad's work on the coronavirus pandemic and also, of course, our new work featured in your paper about the U.S. crisis monitor, which has now become effectively Aclad's U.S. coding as it continues. They both, I want to be specifically clear, they're both being led by Dr. Rudabay Kishi, who's in the audience. So, any specific questions about either of those things should be directed towards Rudabay, but I will hopefully represent her well in these discussions. They're both excellent projects. We're very proud of both of them. They came during a very busy year while we were expanding globally and then, of course, taking a much deeper investigation into the U.S. And in fact, that came at a fortuitous time. It wasn't planned to coincide with a time when the U.S. had more demonstrations than the top other two countries combined, which I believe is India and Pakistan. It was really quite shocking, the amount of social movement and, of course, how it's morphing even now into new subject matter as the people who've organized locally now want to keep it and keep it going because it has had such important implications. So, I will go through some of these reports that have recently come out. This is a national emergency, how COVID-19 is fueling unrest in the U.S. It seems appropriate. Give them the subject matter today. That came out in March. There's also this work here, excuse me. There's also this work here, which is our annual report, which is able to note exactly the ways in which conflict had shifted from 2019 through 2020. Less definite as people might think in part because coronavirus, while incredibly important around the world, has had very different effects. And in some cases, like here in Ethiopia where I am, it has not been a feature in any of the conflicts that have increased in the last six months to a year, despite it being now a growing threat in some of the more urban areas. So, I think we sometimes risk overstating the impact of coronavirus on existing social movements and existing conflicts where they are occurring because they are, in fact, having such a huge effect on our day-to-day lives in often Western places. And then this report, which might be especially important if people are interested in the effects of coronavirus, it's called a year of COVID. Sorry, everybody. This happens here. Let me make sure that I'm still sharing my screen and that everybody can still hear me when I was getting back on there. Can everybody still hear sorry about that? That really does happen all the time. That will happen again. I'm just going to say it now. I'm going to apologize, blanket apology for all the times it's going to happen between now and 10 minutes from now. But this came out this month from April 2020 and it focuses on the impact of the global conflict and demonstration trends. And I just want to mention a few of those things now because I think they're relevant to what we're talking about. So, despite the pandemic, of course, demonstrations increased worldwide last year, which is in some ways a surprising conclusion for many people. There was an initial drop at the start of the health crisis with food discussed and overall demonstration activity rose 7% last year. And that is very much in keeping with the same sourcing guidelines. And all countries covered by Aklad, there has been effectively 93% of all demonstrations have been peaceful with 7% met by some form of intervention. And that's been an especially important indication in the U.S. In fact, as we found 93% of the social justice protests were peaceful. And that came at a time when demonstration was incredibly politicized within the states. And I think it's very important to think about the ways in which, as Bruno has mentioned, kind of grievance, geography, and new crises have coalesced in the U.S. in a very unique way that has led us to be able to look very clearly at the economic ramifications of a new crisis hitting an already, I would say, disturbed political environment and how they're building on each other. But demonstrations were less deadly overall in 2020. So whereby they are certainly being, let's say, encouraged by some of the motivations of the pandemic, we're not seeing a particularly solid trend in one way outside of the U.S. which you demonstrated here. There's been a 38% decline in the number of fatalities associated with demonstrations in particularly in the Middle East. And that's driven by a decrease in the lethality in Iran and Iraq. Because Aklad's coverage of the U.S. does not extend to 2019, although, of course, as noted in the paper's graphs, I think that we have no reason to think that 2019 was anywhere near as active as 2020. The United States, as I mentioned, and this is quite important, it did register the highest number of demonstrations in the world in 2020. And nearly as many as demonstrations as I mentioned, India and Pakistan combined, which is very important in part because India has often been heralded as a really good crucible of social movement studies and especially rioting studies because of the sheer number of protests and riots that occur therein. And for that to switch to the U.S., a very different context where we really should shift our mindset about what exactly is fueling social movements that are incredibly localized is, I think, an excellent opportunity for researchers. So I'm very excited to see this work and work like it coming out. But I did want to mention very clearly that the demonstrations that we've seen increase and perpetuate throughout the crisis have really engaged with governance rather than just been a reaction to governance. And in particular, you know, governments as they implemented lockdowns and movement restrictions, protests resurged very much in reaction to that. And initially, this resurgence took the form of direct responses to government mismanagement, which was a huge issue, and continues to be an issue in places like Brazil. And then they evolved into a continuation of the social movements that had occurred pre-pandemic. So in some ways, what we might be seeing in the U.S. is there had been almost missing protests around some of the issues that were affecting people locally. And once they became organized over issues surrounding coronavirus, we saw that they in fact became much more active in other ways. And I would say that, and this is something, again, that would have been mentioned recently that where pandemic protests were very active, of course, throughout the year, but social justice much more so than pro and anti-Trump election protests simultaneously, we're now seeing the stop the steal protests really kind of continue into gun control protests, where likely to continue. And then of course, reopening protests. So and those things are moving around more than we would think. In fact, one of the things I would like to just demonstrate here is a map of the protests in the U.S., which really are quite widespread. And this is, of course, just a short indication of February 8th, 2020, 10th and 19th of February. But one of the things that I wanted to mention is this particular study that I'm showing you now, notes that trends in pandemic-related demonstrations are closely correlated with COVID-19 cases, as we noted, with spikes in unrest matching infection ways reported throughout 2020. And the data that we collected show that the majority of these demonstrations have been organized around key drivers and the risks being felt or experienced by health workers, the safety of prisoners and ICE detainees, anti-restriction mobilization, which we've discussed here, the eviction crisis and school closures. And I would say that two of those, more so than all five, are associated with concerns about economic inequality. And further, there was a report today that found that women believe that their income is likely to have been severely affected as a whole because of the crisis in the U.S. I think they believe that there's been a potential 25% in decredition protest data where women as a group would coalesce to protest that. And I think that there's some limitations in thinking about how groups were doing advocacy protests rather than just grievance-related protests at the same time, whereas, as I mentioned before, several crises co-occurred within the U.S., especially around the election and social movements, which will complicate how we understand even a coronavirus protest, which of course, as Bruno mentioned, is tied to such in the data. So like I said, overwhelmingly, I'm incredibly supportive of work that takes the U.S. as a hot bed of political demonstrations and violence much more seriously than it has been in the past and then tries to apply what we know about unrest to a much more developed context to see if these results hold up. And I think what we find is that as we see in developing states, or in states like Ethiopia here, there's quite a mix of motivations and, in fact, often down to the extremely localized level. And I will stop there. Thank you very much for the opportunity to participate. Like I said, these reports are available on the Aklad website under both the U.S. Crisis Monitor and the Coronavirus Conflict Tracker. Thanks. Thank you very much, Kleena. Pleasure, as usual. I have quite a few questions on Q&A, and because we are on time, you both have been perfect. I'm going to allow people to talk and also mention that, like Kleena mentioned, Rudebe Kishi, who was leading on the U.S. protest study, is on the audience. So, Rudebe, if you want to add anything, just drop me a note, and I'll unmute you. Let me start. So, we have now, we have four questions that still start us off, and I will unmute all of you. So, we have Tillman Honing, Romar Geha, Liable Cells, and Adi Day. And Tillman, I will please ask you a question. Yeah, perfect. Thanks. Can you hear me? Yeah. Yeah, thank you. Thanks for this interesting talk. So, I was wondering in one, in basically, I have a question regarding your main specification. So, you have state weak fixed effects, if I understand this correctly, and then you are identifying the basically the relative effects of increasing inequality on the relationship between stringency and protest probability. And I'm wondering how you can calculate absolute effects here. So, essentially, I think this parameter is called V to 1. I'm wondering how you can identify this. How can you identify the relationship between just stringency and probability of protest if you have all the state weak effects here? Isn't that varying at the stringency? Is that varying at the state weak level as well? I will not really answer that, but I think it is. Yes. Romar Geha, I will please ask a question as well. I'll connect for and then pass on to Bruno and Bruno. Romar? Hello, can you hear me? Yeah. Well, thank you, Professor Clonat, Professor Bruno. I'm curious about the, indeed, the variables that you were using in your research. And my question is very simple. Can we consider that the political rebellion acts during 2020 were due to the stress generated by the pandemic, or instead they were due to the displeasure of the U.S. citizens who are the political leaders by some policy facts that affect them, the, I don't know, the Black Lives Matter movement, the struggles between these different social groups, and, well, is there a way to completely control for the effects of one detainment or the other? Can we identify which acts, because we are indeed counting acts of rebellion, can we identify which one is related to which cause? Thank you very much. And we have a question also from Laya Balcells. Laya, I think I, yeah. Can you hear me? Yeah. No, my question is very simple. I was wondering about the revasins of the results with you using other measures for maybe poverty, or like, because, I mean, I just wonder if like inequality at the county level is really capturing this kind of like deprivation that you're trying to get at, right? Because like there you could have like a place where there's just not a lot of inequality at the county level, because it's a very poor country, or it's a very rich country, right? So I was wondering about that. Thanks, Laya. And Aditi? Hi. Yeah, I think my question's already been resated by Romar. I was mostly wondering about like, how do you record data for different types of protests like or protests that are directly a relation from like COVID lockdown and regulation versus protests about social justice movement, which I've been building up for years and years. So even within the US, how do you distinguish between them where you're putting them in the model? Thank you. Thank you very much. Yeah, that's an important question we both asked. I'll pass on to Bruno and then to Kleena to add. And please keep adding questions to the Q&A button, not the chat button, the Q&A, and I'll, and hopefully we'll still have time for another round. Bruno? Okay, first of all, I'm sorry, I had the connection problems. And I lost some questions. So I apologize if I'm not replying to everything, but I will try to reply. So the first question on my understanding is, which was the model specification? We are using county fixed effects and state per week fixed effects. So this basically will ensure the possibility that we are taking in consideration all the structural conditions of each county to reduce the issue of omitted variable bias. Then I lost the second part of the question, sorry. So maybe if you want to ask, yes, Patricia. Still many if you're there, can you add or I can? Yeah, so basically I understand that you can identify all the relative effects. So you can basically say that, you know, with inequality increasing the effect of stringency on the probability of protest gets larger. And I think that's the kind of main statement of the paper. But you're also, if I remember correctly, you made some kind of absolute statement, he said, if you increase stringency by, I think a standard deviation, then the probability of protest increases by what I don't remember the exact number. And I'm wondering how you can identify these absolute effects. Because if I understand your specification correctly, you have country state week, sorry, state by week fixed effects in there and stringency only varies at the state week level, is that right? Perhaps I'm messing up one of the variations. So all of this will be washed out by the fixed effect. No, you're correct, because we are taking this number from the interaction of the stringency with the inequality defined at county level. So this is the results coming from our analysis that show that once that deviation of change in the stringency index in the most unequal areas would lead to an increase in the probability of protest. Right. Yeah, that makes sense. I think then I'm missing this from the next segment. Thanks. Thank you very much. I think the second question was also related to the last one. So I will try to reply to the last questions. We basically with our work try to specifically identify the COVID related protest events. And this is possible thanks to the data because I reported the actors motivations behind the protests and so on. So in our paper, we clearly identified the protests related to COVID and we use three or four different words for identifying them such as virus, pandemic restrictions, lockdown, and also distinguish these types of protests from other types of protests such as those related to Black Lives Matter movements or protests against Trump, protests against referred to the presidential elections. And the following up also on the point of Cleo, I think we tried to do a nice exercise in our paper because we replied basically the main dependent variables. So the protest related to COVID with the other types of protest and the results are completely different. So we basically showing the paper that the stringency tended to reduce the probability of these different types of protests. So our main conclusion in this exercise is that the stringency of policy related to COVID tend to increase the probability of protests related to COVID in the most unequal area. And then again, I think the point of Lila, if we tried to look about different economic factors to replace inequality with maybe poverty. No, we didn't do that so far. I think this could be a nice exercise. The main point about inequality is that I think it's especially in developing the economies inequality matters because basically also the way which poverty is measured is more in terms in relative terms in terms of changes in comparison to median income and so on. So we focused on inequality because it's one of the big issues in our society and in the USA society. But we will, for sure, take in consideration this very nice suggestion we could use on support attacks. Yeah, thanks very much. Cleo, would you like to add or Rudabe? Thanks very much. I'd be happy to. And Rudabe, in fact, would be the expert on this. But there was a very rigorous system set up to distinguish the tenor and the characteristics of each of the protests. And in fact, as Bruno has alluded to, in the notes category of every single event of which there are several, several thousand, we note whether or not the protest was directly related to one of these main characteristics that defined U.S. protests in the 2020 period. Social justice being an entirely separate category to the coronavirus related work or coronavirus related episodes, the stop the steel episodes, the pro and anti-Trump episodes, the precursor to the election episodes. It's not too difficult to distinguish what a protest is about when you have an incredibly information-rich environment to support it. It gets much more difficult when you're using one source and it's about something that happened in, you know, a remote area of southern Iran. It becomes much more easier when it's happening in Tallahassee and it's reported about 50 times and you can confirm all sorts of details. But again, Rudabe would be the perfect person to address this. And there are several different methodology, exhaustive methodology documents about the U.S. project that I would encourage anybody to read if they have any questions about how that was done. Right. Rudabe, we're probably putting you on the spot, but if you'd like to come in, I can use it. No, it's okay. Thanks for including me and thanks for, I think you did justice to all the work we've been doing, Kleena. That was a great presentation by both panelists. So in terms of the point of protests or the drivers, I guess, as Kleena says, I mean, in some context where, you know, the media environment is so robust, like in the U.S., it can be somewhat simpler to deduce those things as opposed to, you know, like the example of rural Iran. I mean, it can sometimes all we have from a source, especially given, you know, relies so heavily on secondary sources, it can be difficult to know what drivers were. That said, it's an entire new can of worms to try to understand what exactly drivers or protests are, especially given that individuals can be motivated by a variety of factors to show up and take to the streets. In some of the work in those reports, I mean, some kind of, some types of protests can fall, of course, within different groupings, right? Someone that is against coronavirus restrictions might be against a mask mandate, but they also might be against school closures. And so the school closures one might also fall under the categorization around schools. And so we did try to go through and categorize these different protests, you know, they're not mutually exclusive, and they can be part of different ones to understand the trends that drive each of those separate drivers. I think part of this is why, you know, I still hasn't formally introduced a protest driver variable or category, given the difficulties in introducing those things. I mean, I think even grappling with something like, I don't know what BLM protests, is that about minority rights? Is that about state brutality? Is that about something else? I mean, we can kind of divvy up these things in a variety of ways. So those are actually some of the things we're currently grappling with at Accoled as we work on a pilot around protest motivation. So I think these are really important questions that I promise you, we are weighing up and down on sideways these days. As we take on that work. Thank you very much. And I couldn't agree more. I mean, the work on motivational protests is, there's quite a bit out there, but it's been very difficult to theorize and also spot patterns across different contexts. And clean, I mentioned before, about how a lot of us working on protests have been focusing on, you know, areas where there's been lots of them, like India, for instance, or even the early history in the US and in Latin America. But there's still a lot to be done here. I'll take advantage of my chair position to ask a cleaner question along those lines. You did mention before that the US is bringing out or what we are observing currently in the US is bringing out new empirical patterns that could be quite useful for theorizing further about protest motivation and the origins of social movements. And I was wondering if you could tell us a bit more about that. And I have also a question here from Ricardo, Ricardo Santos. I will unmute you now, Ricardo. Hello. Thank you. Thank you, Patricia. Thank you, Bruno and Quina for a very, very interesting presentation. Bruno, I was wondering about so if there would be a space to explore wondering about the nature of inequality and specifically if it would be possible to assess whether within group inequality and between group inequalities have different rules. I was kind of thinking instead of using Cheney, using a decomposable index that could allow us to do that and then check whether that would be interesting. Over. Thank you. And I'll pass on then to Bruno and then Quina. We just have a few minutes left. So, Bruno. Yeah, thanks Ricardo. It's a very interesting point. We didn't check about group inequalities and I think this is, we just focused on economic inequality, just on vertical inequality, but I think this could be another way of understanding the role of inequality and explaining the relationship between the implementation of these policies and the increasing number of protests. Thanks a lot for this suggestion. I'm happy. Great. So with regard to new work on the US, I think that there's a lot of space to discuss the effects of populism and the shape it takes within different contexts. So recently we wrote a paper talking about how much of the conflict in the Sahel is in many ways using a pastoralist populism to generate quite a lot of social support for the different jihadist groups there. And I think that populism is something that can be eminently flexible to different political contexts. And we're seeing of course that take a hold in the States. Although after the January 6th insurrection, I think that people are a little bit less likely to be as open about it as they had previously been about specifically militia behavior and how that's encouraging people to go from demonstrations towards more organized forms of engagement. But we are taking a close look at it. We've just released, I think yesterday, a discussion about the Proud Boys and their structure and organization and all the rest of it and how that might affect future violence within the US context. But Ruta Bay again is spearheading those US-based efforts so that she would like to yet again, in fact, probably a better person for this panel than me. I doubt better. But some of the US work that we're taking on now, we've been really interested in understanding how different brands or mantles can be used by a lot of these groups. I mean, we look at the right in quotes so often in the US context, but it's important to remember that they're not a monolith. And we've seen organization early last year, for example, around anti-coronavirus restrictions and mass mandates and the like. Then we saw organization by many of these right-wing groups that were engaged in those types of things after having built new networks and coalitions, engaging in WLN protests in the fall. We saw this kind of mutate more towards the stop of steel movement and much more direct involvement by these types of groups and militias in demonstrations. And since then, I mean, like the insurrection that Clena mentioned, I mean, we did see a lot of these named groups laying low for a while, but they have started to come back out of the woodwork now. And we're seeing many of these groups engaging again around whether it's COVID restrictions, but also around other mantles, whether it's anti-vaccine movements, whether it's around the Second Amendment in the US. And we're seeing different kind of brands being taken by these groups and co-opted and used to help them in networking, used to help them in attracting new entities to join these types of and these broader kind of mantles allow for a variety of different types of entities. And so as a result, we've been seeing a really serious proliferation of these types of militia groups and non-state actors in the US. And so we expect to continue seeing this and we expect to see, especially as we're beginning to see some movement around Second Amendment kind of conversations in the US, especially as we're starting to unfortunately see more of these mass shootings, especially as COVID restrictions begin to be lifted, that will be much more part of the conversation. And it is very likely that we'll again see organization around those types of branding by these types of groups. So I think it's really important to understand these militia groups, which are quite proliferate, like a lot of other contexts. I mean, I think, Kalyna, the point she made about this American exceptionalism and how it's important to kind of look at the US alongside all the others is really a point we try to underline, which is why the end of the US crisis law under work, the US data collection is now part of Aklah's, you know, quote unquote, regular data coverage. And so now the US is yet another country that we cover at Aklah. And I just wanted to mention here that what we're looking for as being a particularly dangerous context is where the grievance of these populations, which are manifold, as we've just heard, become aligned with elite grievances about accessing power. That's when they are at their most difficult when they both have representation in formal roles, and they have a support group that they can come back on. And I think that January 6th is a very classic interpretation of how you have elite representation in the former president, and then you had groups believing that they were effectively there to do his bidding. And that became that became a much bigger issue and a much more violent context than it would normally have done. Yeah. Well, thank you very much. Sadly, we're coming to the end of our time here. But I think what just bringing all this together, what this is showing us is yet again, although we're probably living through, we're definitely living through a new pandemic, we don't have a lot of experience dealing with these issues, but it's yet again a massive shock which is magnifying problems that were happening already before we had the rise of inequalities, we had a variety of cultural backlash type discourses, we had a series of losers in witness from process of globalization and so forth. We have issues around gender and minority discrimination and those are being amplified. And what we're seeing here is they amplify to the extent they actually result resulting in some cases in quite forms of political violence. And also, I think one of the things that this shows especially from cleaners and really best like this intervention is the distinction between developed countries and developing countries and how these movements and these various groups form is becoming quite narrow. So we're talking about, I'm just talking about militia groups in the Sahel militia groups in the US and I presume the formation of these groups are not so different and the structural mechanisms explaining why these groups become salient at certain times and are used by political elites in a variety of ways is happening all over the world and not just in certain parts. So this was one of the motivations why we actually thought it was very interesting to take a leap forward and look at the US. Also really great to hear that Acclade will continue collecting data in the US, a bit disappointed when I thought that it was just a one-year project but that's really great to hear because obviously these things are going to be really important. Also like Bruno mentioned, we're only observing very immediate effects, right? So I think we need to come back in sort of three, four, five years down the road and see what implications of these different changes will have for political systems more broadly. I mean, in terms of this will have implications in terms of discussing things around the survival of democracy and whether some of these protests translate into higher levels of violence in different parts of the world and so forth. So I hope I'll be able to invite you all in five years time to reflect on this again. In the meantime, thank you very much, Klinna from Addis and Bruno from Maastricht and Rodebev for the last moment of intervention in Prompto. Thank you very much to all and thank you for everyone that for all your questions and for staying with us until now. Take care and see you and see you again. Thanks for your attention. Bye. Thanks. Bye-bye.