 Okay, we should probably start. So, welcome everyone to session 4b, session which is dedicated to subjective well-being. We are going to have two papers on this topic. But before I present the speakers, I would like maybe to say that it's a really, probably a peculiar session, maybe a bit sad because the person who submitted the first abstract that is going to be presented actually passed away after a few weeks or a few days even after submitting the abstract. So, maybe the presenter could say, I will say a few more words about it. So, maybe just asking you to share a few thoughts or to have a few thoughts for that person who unfortunately left us far too early. So, are you tuned back in one day? Having said this, so let's go back to the proper presentation of the speakers. So, we are going to have two speakers. As I said, the first one and the first presentation is about the investigation of the influence of COVID-19 pandemic on the well-being of British population. So, this paper is presented by Gosha Voytich. I hope I pronounce your name properly. Gosha is a lecturer in statistics at the University of Plymouth since 2013. She's also a Southwest branch secretary of the Royal Statistical Society. And she is therefore being the PhD supervisor of Ayotunde. She will be presenting her paper. So, as we've done with the other presentation, maybe you can try to stick to 15-inch presentation plus a five-inch question. Thank you. And if you want to share your screen. Yes, thank you. Yeah, perfect. Thank you. So, yes, thank you very much for having me. And if I start to break in, then please let me know. I will then stop my camera. So, as mentioned before by Pierre, I would like to present the results of the study done by Ayotunde. So, I really talk for her work. So, just to present who was supposed to be the proper speaker in place of me. Ayotunde has submitted the abstract. So, this was actually her part of the master's data science that she did. And, yeah, I just want to say that she was a wonderful, wonderful person and very devoted to her work and very positive person. And I would like to see this event today as actually a celebration of her achievements, not to be sad, but rather celebrate her life. So, yeah. So, the aim of the study was to see how the subjective well-being of the British population changed during the COVID pandemic while taking into account other factors that can affect the well-being. So, we looked at two data sets from the annual population survey. These are the specific data sets that were selected. So, the first data set that covered the period just before the pandemic from October 2018 until September 2019. And since the pandemic was declared in March 2020, then the second data set we wanted it to be entirely during the pandemic time. So, it covered October 2020 until September 2021. So, these data sets, they include the labor force survey variables. There's a lot of them in particular, including education, employment, health and ethnicity. And we use the second edition of the data sets that have the well-being variables added to them. So, the four subjective well-being measurements that are included in the data are the happiness, how a person ranks their own level of happiness, the level of satisfaction with life, and the extent the person feels that things they do in their life are worthwhile. And all three of them are on a scale from 0 to 10. They are whole numbers, with 0 being the lowest and 10 the highest. And the fourth variable is the level of anxiety that person feels. And this one is also a whole number between 0 and 10, but it's sort of the scale is flipped because very low anxiety is obviously a positive thing. And number 10 high is a negative indication. So, the data sets are large. So, they had around 500 variables and well over 200,000 observations in each data set. But it was found that for the four well-being measures around 48% of the values were missing. So, those observations were completely removed from the data, which is something to keep in mind when looking at the results of our analysis. We did not do any modeling of that messiness. So, the first look at the data is just to look at the overall distributions of the four well-being variables before and during the pandemic. So, these are the histograms, the bar plots for happiness. The red or the pink color, it refers to pre-pandemic period. So, when we have the boxes with this pink color on top, it means that there were more responses in that category before pandemic. And the blue color shows that there were more responses for that particular range during the pandemic. So, we can see that there are differences for the high levels of happiness, which has significantly dropped during the pandemic. And those values went into the more medium levels. Whereas there isn't much change at the lowest end of the distribution. Obviously, the distribution is skewed, so there is very few respondents in these areas by percentage already. The mediums did not change, but the average values have significantly dropped. So, the confidence intervals do not overlap for them. For the satisfaction, we see more of a similar trend. So again, the levels, values for 9 and 10, which is the highest satisfaction, have dropped during the pandemic significantly and the values between 4 and 7, so in the middle have increased. For worth, we see also very similar trend. Whereas for the anxiety, the distribution is a little bit different and we see slightly different tendency. So, there is a big drop for the value of zero. So, no anxiety. The percentage of people has decreased. And during the pandemic, we see that the levels of anxiety have increased for all the values between 2 and 8, in particular, for the categories 6, between 5 and 7, there are more people during the pandemic with this medium to high anxiety levels. So, these are the aggregate results without taking into account any of the covariates. I also would like to mention that the four well-being measures, they are correlated, of course, with each other. There are some quite high correlations, for example, between satisfaction and worth around 0.66 and negative correlations between anxiety and the three other indicators. What I would like to mention is that the structure of correlation did not change much before and during the pandemic. The numbers are very, very similar. The only difference is that this number, 0.66 for satisfaction and worth, has dropped a little bit to 0.62. That was the biggest change, but otherwise the numbers are almost identical. So, now we have chosen 12 covariates as predictors for well-being indicators. So, they include age, whether a person claims benefits, country, disability status, education, ethnicity, employment, housing status, marital status, long-lasting health conditions above one year, religion and sex. So, these were the 12 that were selected. Obviously, it was a lot of work for IOTUNDA to actually decide which variables to include because there is an abundance of them in the data. But all the results would depend on these variables only. For four of them, we have noticed there were some missing values. So, for benefits, claim, disability, long-term health condition and education, there were these between 12 and 25% of missing values. We had upon inspection, it was clear that almost all of those individuals, they were above 65 of age. So, instead of either imputing the values or removing them, we just created a separate category for those individuals. We described this as a labor pensioner, which was made up by us. So, to solve the missingness problem. So, yes, for example, for benefits claim, we had three categories that was yes or no, or a pensioner to describe those people who did not answer, and they were also above the age of 65. And the same we have for disability status. So, now for the model. So, we applied multiple linear regression for both variables and for each wellbeing index. Even though the distribution of the outcomes are all skewed, but because of the large dataset, the estimates would still meet approximate assumptions, approximately of the normal distribution. So, that's why we felt it was correct to apply the multiple linear regression. We did not include interactions due to very large computational requirements from the model because the data were quite big. But that's another thing to keep in mind while looking at results. So, now I want to just show a selection of results from the regression models. They're not all the results because there would be a lot of them, and I would have to take probably half an hour. But just to highlight some of the more interesting things that we found. So, for the happiness, when we look at the sex and employment status, these are the estimates, regression coefficients with 95% confidence intervals indicated in the graph. And again, the pink color refers to the pre-pandemic dataset, and the blue one is for the pandemic data. So, we can see very interesting phenomenon here that the coefficients, it was positive before the pandemic and negative during. So, that means before pandemic, females were having, on average, higher levels of happiness than males. Whereas during the pandemic, that coefficient became negative and significantly negative. So, they were less happy than males. We do not see significant changes in how the employment or lack of it affected happiness during or after the pandemic or before the pandemic, sorry. For marital status, a lot of coefficients for different categories stayed the same. We observed this one significant difference in the status of the person being without. So, before pandemic, individuals were significantly less happy than single or never married people. Whereas during the pandemic, that difference was not significantly, did not exist. So, not statistically significant. So, there were no difference between being without or single. Which doesn't mean that without people became more happy, just single people became less happy actually. But there is this change. And also for housing status, a couple of differences where the person had the house both with mortgage, so their happiness was still less than those who owned it, but the difference is smaller. And similarly for those who rented houses. So, there is all the coefficients as we can see in here, they became closer to zero, which means there is less differences in happiness on average. For the disability status, we have observed smaller effects of being disabled in terms of those who are not disabled, they are happier than those who are disabled. But the difference is not as big as it used to be before pandemic. And there is a number of variables that we didn't see any significant change overall for the two datasets. So ethnicity, religion, country, long-term health condition and benefits claim, they didn't change how they affect happiness. So that doesn't mean that they don't affect happiness, it's just that the nature of that relationship seems to stay the same more or less. Now I want to show a couple of results for satisfaction. Sorry for interrupting you, but we probably have only two or three minutes left. Yeah, I'll be fine because we have much fewer results now. Thank you. So for satisfaction, we see the same effect. So again, the relationship for the females have flipped, they became less satisfied than males, even though before they were more satisfied. And also for education status, we observed that people with no qualification or who don't know they became more satisfied than those who have degree. And last thing for satisfaction, the disability status, again, we see that coefficients are closer to zero. So people are more satisfied if they don't have a disability, but that difference is smaller. For warfri, again, the picture is very similar. So even though the relationship did not flip here for females, so I'm going to skip maybe one or two. Just to go to anxiety, that's the last indicator. So again, we see anxiety in females was greater before pandemic but even increased during the pandemic. And the last thing I wanted to highlight for the ethnicity, what we noticed is that, for example, the Chinese people, they were slightly less anxious before the pandemic, but they became more anxious than white people during, which I think is quite interesting. Even though the confidence intervals are quite wide in this case. So yes, this is just to summarize that the interesting thing that we found is that the direction of the relationship have flipped in several cases, which I think is important. And I think I should end. That's basically most of what I wanted to say. Thank you for your attention. And this is my email address. Our second presenter, Simona Tenalia, I hope I'm pronouncing your name properly, is going to present a paper on subjective well-being by occupation in the UK looking at trans growth time between 2012 and 2022. So Simona is a senior analyst at the World Works Center for well-being. Maybe you've heard about that center if you're specialized in well-being. And she's a doctor in economic theory with more than 10 years experience in labour market research. So Simona, the floor is yours. What I'm going to present is a work about well-being by occupation in the UK for 2022. This analysis has been to look at well-being across occupation and how well-being has changed over time by occupation and how it has been affected by pandemic using the very granular data of the animal population survey. This analysis was a part of this analysis was already done in 2016 by even McKinnon at the World Works Center. He was looking at the relationship between the gross annual salary in 2013 and the mean life satisfaction for the period 2011-2013 and he found a positive relationship looking at 90 groups of occupation. So what we wanted to do was to update this analysis and deepen this analysis because our idea is that especially for people who work full-time work occupies a major part of the day in their life and so it has a lot of consequences on our well-being. And also because in our workplace to work there are also several activities like commuting and when we have lots of information available on jobs like pay, hours of work for example during the pandemic if we have to be in person or we can work by home we have few information about well-being for different occupation and it is very important for young people who are choosing their careers but also for employers and for policy makers because having information about well-being for different occupation you can shape more targeted intervention aimed at promoting well-being and while there are lots of work not a lot that look at well-being for occupation but at a very general level there are not a lot of studies that use more granular data and this is why we did this analysis and what I'm going to present is exactly this the data we used and the groups of occupation we used we will look at well-being across occupation over time and we look also at proportion of high and low well-being during the pandemic and then the last two steps we will look at relation between life satisfaction and trip job quality aspect and finally the determinants of well-being within occupation using another regression so we use the from 2012 to 2021 and the annual survey of hours and earnings for 2020 we used the classification of the occupation which is the standard occupational classification where jobs are classified according to the concept of skill level which means we know that there are four skill levels from the lowest one which correspond to a complete compulsory education so a general level of education to the fourth one which corresponds to a degree or equivalent period of relevant work or work experience but we know that for each group there is heterogeneity across occupation of course but in fact for each group they have the same skill level and as in the presentation before we will use the ones for questions on well-being for life satisfaction, happiness, worthwhile and anxiety so the first thing we look at is of course the distribution of the well-being variable the four well-being variable here I'm presenting only two we can see that looking at different occupation they more or less have the same shape even if running a test we saw there are slight differences then we look at the weighted mean for the four well-being variable by occupation and for the mean level of life satisfaction managers and directors and professional occupation the two green circle at the top they show the highest level of life satisfaction while carrying leisure and other services occupation show the highest level of worthwhile but also the highest level of anxiety while a skill trades occupation and process plant and machine operative show the lowest level of anxiety then what we did is to look at the trend over time of the mean level of the four well-being variables and what we can see is that we had the positive trend of course was interrupted during the pandemic so we had an increasing trend till 2019 then a drop in the year of the pandemic 2020 and then again an increase in 2020-2021 but what we can see from this graph is that managers and directors and professional occupation are those that show the highest mean level of life satisfaction while if we look at anxiety always for all the nine major group of occupation we have that the opposite trend decreasing we have decreasing value till 2019 then a peak in 2020-2021 and then a decrease in 2021-2022 in this case we can see that managers and directors are those that show the lowest level of anxiety while a carry leisure and other services occupation during the pandemic show the highest level of anxiety then we look at the proportion of people that answered that gave the highest answers to life satisfaction which means that they answered 9 or 10 in a scale from 0 to 10 for the three years just before during and after the pandemic we can see that for all types of occupation the proportion of people that answered to have high life satisfaction dropped during the 2020-2021 with professional occupation and associated professional technical occupation that show the highest degrees then we look at the answer for high anxiety which means we look at the proportion of people that answered to have an anxiety level comprised between 6 and 10 and we can see that for all types of occupation there was an increase of people answering to have an higher level of anxiety during 2020-2021 and among them there are carry leisure and other services occupation professional occupation process plant and machine operative and administrative and secretarial occupation these are the the occupation that had the highest increase then we wanted to look at three job quality aspects because we are interested in looking at three job quality aspects that are the average gross annual salary the type of job if they have a permanent job or non-permanent till some way we were interested in the place where the work is mainly carried out and this was interesting for us not only for updating the work run in 2016 by Eva McKinnon but also because well-being in the workplace has an effect in the overall life satisfaction and given that at the Workforce Centre we worked a lot on the 5K drivers of workplace well-being we can see that these three job quality aspects have an effect of workplace well-being and fall in three of these big five drivers which are the gross annual salary fall in the security driver in the financial security in particular and also to have a permanent job or not having a permanent job fall in this in this driver but to have a permanent or not having a permanent job or having a job not permanent in some way fall also in the purpose driver because to have a permanent job allows you to invest more in your job maybe to have more career opportunities to be more attached to your job and while the place where the work is mainly carried out fall in the environment driver because it is of course related to the physical environment it is related to commuting to the tools you have to work and all these aspects of course have an effect on the overall life satisfaction so the first thing we did we updated the graph that we saw at the beginning and we used the gross annual salary for 2020 and the midlife satisfaction from 2015 a midlife satisfaction for the period 2015-2020 and we can see that there is a positive relationship between these two variables and we can see that in the top right quadrant we have mainly managers and directors while in the bottom left quadrant we have a lot of elementary occupation then we looked at midlife satisfaction and mean anxiety for all types of occupation considering those having permanent job or not permanent job in some way what we found is that for all types of occupation those having a permanent job show highest mean level of life satisfaction and lowest mean level of anxiety and finally looking at place where the work is mainly carried out we can see of course that during the pandemic 2020-2021 the percentage of people working for home increased of course and people working somewhere quite separate from home decreased but most interesting that the midlife satisfaction for people working in a place that is the same ground or building as home they show the highest mean level of life satisfaction for the period followed by people who work from home the people that work in the same ground or building as home are those that work in a place maybe in the same house but not a place which is not for domestic use so maybe it can be a garage or garden office where we run a regression where the dependent variable are the four variable for well-being life satisfaction, happiness, worthwhile and anxiety and the independent variable are age, sex, ethnicity, marital status level of education economic activity and also types of occupation and we used also gross week pay but in a second regression where we selected only employees and what we found first of all is that all coefficients have explanatory variable explanatory value, sorry and then we found some results that confirm what is already known in the literature which means that the relation between age and well-being is U-shaped younger and older are more happy and more satisfied that women are happier and more satisfied and also fighting they are doing more worthwhile with respect to men but they are also more anxious and that people without qualification show lower well-being with respect to people with all other levels of education I'm sorry if I'm interrupting you but just to remind you that we have about two minutes left I'm almost finished and I'm going very quickly we found that unemployed show lowest well-being with respect to employees and then going to types of occupation we can see that all occupation have lower life satisfaction than managers and directors occupation and caring leisure and other services occupation show higher worthwhile with respect to managers and directors so what are the key takeaways of this work and first that to be employed is important for well-being and this is an acquired result in the literature but also the type of occupation matter there are some occupations that show higher level of life satisfaction and directors and others that show higher worthwhile like caring leisure and other services occupation that analysis on job quality aspects can give us a lot of information on workers well-being and this is an effect on overall life satisfaction and this can help in shaping more targeted intervention and that promote the measurement of well-being in private and public sector should be a priority and it is for sure this kind of information is an asset for also for employer it can be an instrument for policy makers and also for citizens thanks a lot for the conference that we would not go back to a planning session to conclude the conference so it is my duty here to conclude the conference for this session so thanks again for all the speakers who kindly submitted a paper and made this interesting presentation I hope that you found interesting comments and ideas in relation to your research thank you of course to all those of you who took part to the event I hope you found it interesting please as reminded by Emma fill in our evaluation survey if you don't see it so you will see it in an email you will receive after the event tomorrow if I'm correct that will really be precious for us when organizing the next conference and yeah anyway if you have any suggestions we will be more than happy to take that into account so thank you again and well enjoy the rest of your day