 to our final deep dive in which Professor Helliwell will take us through chapter two entitled World Happiness, Trust and Deaths Under COVID-19. Thank you. Thank you. It's a pleasure to be back with some details. You see there the whole team that have produced this chapter and corresponding the year before under much greater time and content pressures this time. Let's avoid the time pressures by proceeding to the first slide. So this is what is the official ranking based on three-year averages and you'll see it's familiar. Those of you who follow the World Happiness Report regularly will see that we have the overall rankings on the right. What's different between this and last year is very little. There is a 0.99 correlation between the rankings from 2018 to 2020 and from 2017 to 2019. One thing that's happened is for almost the first time we have the first country statistically ahead of the other top countries. So Finland is not only again at the top but it's by a greater margin. We'll get back later on and see that this happiness was both supportive of and correlated with high success in handling COVID as well. Let me just go through the six factors because we'll come back to them later. This shows the amount of for each country that's explained by G... Let's go back to the first slide please. By each country, by GDP per capita, healthy life expectancy, generosity, social support for you to make life choices and perceptions of corruption and the rest of it is the value we call the value of happiness in dystopia which is a mythical country that has the world's lowest values of each of those explanatory variables and it also includes each country's residual because of course we explain these differences across countries but we use people's own evaluations of their own lives to give the scores. Our analysis on the six factors doesn't determine those scores at all. It's the other way around. Second slide please. So right at the very beginning we wanted to see how things were going under COVID compared to how they had gone before so this takes the... for the 95 countries that we so far have data on, we then rank them in terms of their subjective well-being and then we take the same countries, the same 95 countries and order them by their 2017 to 2019 score. So you see in this case Luxembourg is missing from both because it didn't have a 2020 score but you'll see even with this comparison and they look at those figures in brackets which are the confidence intervals and you see that with the exception of Finland at the top the differences among nearby countries are tiny and for example Sweden and Norway which is an interesting pair because Norway is always ranked higher than Sweden and here in COVID times where the surveys in both countries were taken in April when perhaps this Swedish easy touch was slightly easier to live under than the Norway clearer one and it'd be interesting to see how those two nations compare next year because as we'll see they've had very different success in handling COVID and their economies since that time. Next slide please. There's supposed to be two maps there. There they are the second one. So here's just a map of happiness across the world using the 27 2019 data on one map and the 2020 on the other. This is another way of saying what I've now said four or five times that the rankings are very similar and part of it is the countries that were at the very top turned out to handle COVID pretty well too and the differences across countries didn't change very much in any event. Two countries it's worth casting your eye on however that did change color as you move from 2019 to 2020. Our two countries that together include almost half the world's population India and China both of whose well-being scores rose significantly in India's case it was a reversal of a long-term decline that had been going on on a very welcome reversal it was and the other was in China that in fact has found itself with very few negative consequences of anything during this year towards the end of the year. Anyway next slide please. Now this is another way of saying we're going back and now we're looking at individual level data for all the people who computed contributed surveys in 2020 and we're asking at the individual level how did these variables rank and you can see how important counting on friends is there's a way in which that lines up with what Yan was saying about workplace belonging it's one way of measuring belonging and a sense of freedom to make your key life decisions very important institutional trust this is not one of our regular ones but we've added it in used it in WHR 2020 very important for people to have confidence in their governments for their general happiness but we'll see later on it's absolutely crucial in supporting their abilities to find successful covid strategies donation is one one measure we use of generosity which will also find out is very important in in fueling a successful covid strategy and household income is there as well and as others have mentioned you can see that it's a relative importance to the social factors is is much smaller we and perceptions of corruption are also important it's an alternative measure and you can see both confidence in government which the institutional trust measure captures and corruption which is a measure of the absence of something bad they both come through independently for individuals and having a health problem which is the equivalent of the micro level to the healthy life expectancy we use at the macro level you can see is a very important predictor these are things that don't appear in our regular graph because they're individual level variables and we see below 30 and above 60 where the baseline is the people between 30 and 60 and you can see there is this traditional U shape for this sample that older end of the U shape is is not as large as it is in some studies but it's true in in the 95 countries that those under 30 are systematically happier and females here are happier by the amount on the life satisfaction scale that's pretty usual in these studies between point one and point two now we show elsewhere in the chapter but I hardly need to have a separate slide for us here but these effects remained essentially the same in 2020 with some exceptions that are worth reporting that those age 60 gained about to 10th of a point relative to those in the other two age groups during covid the gender difference did not alter that to link back to something we heard earlier about from the social connections team about people's women clearly spent more time doing child care than men during this period and if there's no change in happiness what's that's telling you and there's some studies showing this that time spent in child care in fact is not regarded as a cost to life satisfaction because of course the child care in question here especially may be helpful because it's care of your own children that families that used to have to fight for quality time together now had it forced on them and many of them found on average they found that it was an advantage and not a disadvantage so that helps to explain what otherwise might seem inconsistent but the male and female life satisfactions were equally well maintained the bottom coefficient covid 19 we then said all right here's an equation that applies to all four years of data if covid came in as a really big hit on life evaluations then you'd see the covid 19 would be a big negative as you can see it's not a negative at all next slide please now this is getting on to the linking section in the chapter because we're going to be one of the first things we did is said what what which of our main factors supporting happiness in fact were important for delivering success against covid and we discovered that the main one and i'll get to this in a moment was institutional and trust and social trust and here we present some new research just to show how important trust is but in particular we're using a slightly more novel and interesting measure of trust where it's more than trust it's about benevolence people who think that they're like their wallet would be very likely to be returned if discovered by the police or a neighbor or a stranger that's what the three alternatives offered in this version of the gallup world poll sponsored by Lloyd's register foundation you could see they're both of them are more important than a doubling of income and both of them separately together if you take both police neighbor and stranger as i mentioned in the first one and you enter them together it's worth more than a full point on the income distribution you can see the Lloyd's survey was set up with wallet return as a measure of negative risk the risk of something good happening exactly the same question was asked about some bad things that might happen about how likely harm is from mental health issues so people who think it's very likely to suffer mental health problems their well-being life satisfaction is lower by four tenths of a point harm from violent crime point two two current unemployment point four three these are all major hits on your well-being but you can see that none of them are as big uh as this idea the the life satisfaction you get from living in a society where people care about each other now this links we've discovered before to all crises because what crises do is give you the chance and in fact force you into a situation where you get to see how kind and benevolent and caring your neighbors are and we'll see that that's a very big part of the total story so let's let's go to the next slide i'm can't spend i don't need to spend very much time on this one because this is precisely the same picture that jeff sacks showed earlier uh and so it's telling you there's a huge range of death rates uh from covid across countries let's go to the next slide when we get into explaining it jeff talked earlier about a number of dogs that didn't bark what we've done is put together a model explaining for 163 countries or trying to explain for 163 countries what determined uh a success or failure in controlling direct covid 19 death rates and the median age which jeff mentioned is is pretty well the most important of all as he said being in a country that's an island is the help and part because they're this isn't just talking about australia and new zealand they're 23 of them in this in in this sample of 163 uh it makes it easier to control access and egress and so it if you've got a will uh the way is slightly easier uh exposure to infections in other countries that's also something that's less in australia and new zealand because of course they were physically removed uh and the countries nearest to them were countries that had the pandemic relatively well well controlled so this is infections as they were at the end of march in nearby countries so it's a gravity model based measure of how many sick people were near your borders and if it was a big number then that makes it harder for you to control it doesn't mean it's impossible so this isn't something that's given to you as something you can't deal with you can deal with it because it means it's more important to act on your own for example this exposure to infections which explains a good part of the high death rates in europe that exposure was just as high for the nordic countries as the other countries and you find the nordic countries with the exception of sweden all had very low uh death rates so they were able to apply not quite to the same level as australia and new zealand about a strategy that was much more effective in controlling uh infections and we'll get on to see what that's based on very shortly now jeff mentioned knowledge how did how was it he had some hypotheses about why would this what was known from earlier pandemics not have been more readily picked up by others and secondly why were people so resistant to understanding the importance of asymptomatic transmission when it became obvious and also the fact that the transmission was also by aerosols and hence the importance of masks and jeff wanted us particularly to look at the sars experience and so we have two variables that do that together if you just use either alone it's highly significant we sort of split it between the two here um because they're they both have something to play one was being in the who western pacific region which had some documents that laid out this strategy very clearly and they were discussed among the countries in that region the people in the regional office shake their heads a bit about why this didn't get back to head office and become more of the standard global line faster than it did and the other was the average distance from any between any country and the sars countries and the idea that you might learn something from being closer to the countries that had had that experience another is female heads of government it's been studied before in a number of other papers and it's not just a story about the female leaders you might know there are 22 of them in the sample and it's a material change these are all these are all in standardized betas by the way we have the actual coefficients in terms of death rates in that chapter as well this shows the relative importance then institutional trust here we are that i've emphasized before it's a it's a mixture of people's assessment to several answers about their confidence in their government actions we don't have for this full sample of 163 countries a good measure of social trust so what we did is putting in the genie coefficient of income inequality and that's important because it has been shown in a number of countries that income inequality is linked to lower social trust harder to maintain social trust in an unequal society and a more equal society in economic terms this is it is less likely to arise when social trust is higher so the causality goes both ways and it's a it's a measure of of inequality john clifton earlier in this show uh said that well-being inequality is more important than income inequality and we too have found that when it comes to explaining subjective well-being but we tested income inequality and well-being inequality and explaining death rates and here it was the income inequality that had the more important role to play and there are a number of studies that have shown that in a country with unequal income distribution there are more people put down in high risk living conditions uh and then it does make it more likely for them to to suffer death as a consequence so it's no surprise to us that income inequality is playing a double role here in giving you higher death rates for other reasons as well as giving you higher death rates because it's it's social trust is lower and we're convinced of course that social trust is very important next slide please now here we're getting to something that's actually going beyond the chapter uh because we found that since we did the major research we are now able to put together what are called excess death statistics so it's the extent to which total deaths in a country in 2020 exceeded their average in the three preceding years uh and in almost everywhere these were positive numbers uh there were a few countries where they were not big numbers and even negative and you'll remember some people have talked about there being a trade-off between policies tight enough uh to reduce transmission to zero and what they would then do to people who would then be inclined to mental illness or suicide or whole range or untreated cancers or a whole range of other threats to livelihood in other words were you purchasing uh your reduction in COVID deaths by threatening lives livelihoods and lives and livelihoods uh by an amount elsewhere and Richard Laird has already reminded you that in fact the countries that suppressed COVID the best actually had better economic outcomes so they weren't wasn't a trade-off with the economy what we're showing here is there wasn't a trade-off wasn't there just wasn't a trade-off with the economy there wasn't even a trade-off with other deaths uh because you see the countries in the Asia-Pacific and we've put Australia and New Zealand in the Asia-Pacific group here we put their COVID deaths there and that's the dark bar and then we put in the total excess mortality uh in those countries and this is death rates per hundred k that's the notion we've been using throughout here uh and you can see that the areas that were least good at handling COVID were actually had even greater excess deaths from other causes let me help you through that if it's not entirely clear because it wasn't in the chapter so in that sense it's new to you today uh the blue bars show total excess deaths this is the extent to which deaths in 2020 have been higher than the three preceding years and so if they've got to be traceable to COVID in some sense or another uh and you can see that those who handled COVID direct deaths best also kept uh the non-COVID part see the non-COVID part or the indirect COVID deaths is the right way to think about them is the difference between the height of the black bar and the height of the blue bar and so that difference in terms of deaths is tiny for the Asia-Pacific uh it's in the middle ground in Western Europe and it's much bigger in Central and Eastern Europe and Russia and especially in the Americas where it's that's driven of course the number of countries by Latin America so you could see that this these new data are telling you that the costs of COVID to the kind of well-being calculations that Richard Laird was showing you before are in fact even greater than the ones he presented which were based on the direct COVID deaths because these excess deaths in fact made it even more expensive not to control uh COVID and uh we've been done some preliminary attempts to say what governed the international differences in the in the uh rates of indirect COVID fatalities and there we've found so far that among the things we've looked at that having someone to count on in times of trouble uh is very important as is generosity so it's saying the societies that have higher levels of social capital were better at stopping COVID and they were also uh much better at providing other protecting populations against indirect COVID uh deaths and another way of looking at that is that's another way of saying why was it the Nordic countries did so much better than the rest of Europe uh the Sweden aside uh and the answer is they had these high levels of social capital and they kept both direct deaths down but they also had very low excess deaths in total so they were able to get their low COVID direct deaths without creating a lot of deaths of other kinds in the country now this was all done in analysis is here is only for the 65 countries that we can get excess death statistics from the best far but it's a very large chunk of world population it excludes Africa unfortunately but it's telling you where this is going and it's only 2000 and as you well know in a number of countries the deaths but from the direct deaths from COVID since January 1 are 50% as large as their uh deaths during 2020 so this pandemic is by no means over and this analysis obviously will need to be fleshed out and continued I think that's enough to give an overview of what's in the chapter if my timing is okay thank you very much John for that lovely presentation we are close to time so I think I'll pose one question and then I will follow up with all the questions I've saved and direct them to you by email and I can try to do that for the other speakers as well but I think one question that I noticed came up earlier that is best directed to you John is what how can we make sense of some of the data that that seems to be conflicting in this year's report perhaps about how we see nearly very little change in happiness despite the life evaluations despite the results on work and emotions well emotions play into life evaluations we've always seen that a lot of the workplace and so this one one way of thinking about it if life's getting really bad for some people and the average isn't changed there are other people who if anything are finding themselves better there's a nice paper came out from Leonardo Bachetti the other day showing that even in Italy one of the worst hit countries there are a number of people who are going up in their life evaluation scales and they're the people who are taking and rediscovering a reality that it's the immediate social context that's very important for life evaluation so these are people who aren't traveling the world they're now rediscovering their neighborhoods these are the people who didn't have time to spend time with their children and now finding the family is reconstructed and working for them so that a lot of these reworking of work life are in fact helping people to rediscover it's classic in in our work we thought it would have been a disaster because we know how important for well-being personal relationships are relative to facebook friends and now we find all we have are facebook friends in a sense and yet we're doing rather well with it and the answer is you know the social media are becoming more social in their effects and so that's all part of it and i think there's sort of essence of hope you see from several of the chapters that even the prospect of building back better is a sustaining feature because having a purpose is very important not in our equations because we don't have purpose data but it's very important a lot of people who are now brought into a situation where their life really other people really do rely on them and they have a purpose not just in their workplaces but in their families and communities and that's very supportive and they have discovered that their neighbors are much nicer people than they thought they were because that's what crises do would put you in a situation where you rediscover that the high levels of social capital where they exist and that makes you happy well thank you for that i that proactively actually addressed a couple of the other questions and speaks to many of the themes in the report so what an appropriate place to end