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In this episode, Alteryx associate Peter Abrahamsen sat down with Matthew Ackman, a researcher under the editors of the UN World Happiness Report. Peter and Matthew chat about the methodologies used to analyze the data behind the World Happiness Report, and which findings in the report came as a surprise to Matthew during his analysis.
Special thanks to @andyuttley for the theme music track for this episode.
Hello. My name is Matthew Ackman. I'm an assistant researcher at the University of Alberta in Canada. I've been doing research in the fields of well-being and also financial economics for several years now. I've been working in research under the editor and the associate editor of the World Happiness Report. This report is produced by the United Nations Sustainable Development Solutions Network. This is a group affiliated with the UN and we use the UN Headquarters to launch this report and they do sponsor the report, although this body is not actually an official body of the United Nations. This largely frees us up to report on the finding of the report more freely. And what I'm really getting at here is that any statements or opinions that I express today is not to be taken as official opinion of the United Nations.
This Alter Everything, a podcast about data science and analytics culture. I'm Maddie Johannsen, your producer for this episode. Today my colleague Peter Abrahamson is our guest host and he'll be interviewing a buddy he knew in college, Matthew Ackman, whose voice you just heard. As Matthew said, he's an associate researcher for the 2019 and 2020 UN World Happiness Reports and he'll get into details about what it takes to analyze something like happiness. Now at the time that we recorded this episode, the world was in a different place than it is today. So please keep in mind that the discussion you're about to hear doesn't address the pandemic. Let's get started.
Yeah. Thanks, Matt. I mean, I definitely was excited to dig into this topic with you. But I guess a lot of people don't necessarily know what goes into the report or what the report is. So do you mind just telling me, what is the World Happiness Report?
Absolutely. So the World Happiness Report is an annual report released every year by the United Nations Sustainable Development Solutions Network which is a group affiliated with the UN. And what the report does, fundamentally, is not just to assess or create an assessment of the perceived well-being or happiness in every country, but to attempt to use data to decompose and to explain the different differentials and disparities we'll see between countries or in a country over time.
That's awesome. So what kind of data would go into it? Because a lot of people might say, "Happiness, it's kind of a subjective measure." So is it like Human Development Index-type measurement or what kind of data points would be utilized in the report?
The HDI, Human Development Index, isn't necessarily a component of the report. This report is actually largely complementary to the contribution to literature that the HDI has made. But most of the data that's used in this report is actually complimentary of our partners at Gallup World Poll. And it consists effectively of extremely large data sets of very consistent and very well done survey and poll results conducted in over 160 countries for just around 10 years now. So this has given us really excellent insight into how citizens of different countries around the world perceive their well-being, perceive their situation in their country, how they feel about their government, their police, their military, and whatnot. And further, we can also use at the aggregate or the macro national level data-- this is where we'll use everything as simple as GDP per capita to healthcare spending per country, military engagements, military spending, really any aspect or parameter about a country that might feed into how citizens enjoy their well-being and enjoy their standard of living. Any data we can get our hands in, we try to feed into the analysis and if it shows something significant, that makes a big contribution to the report.
So Matthew just said, any data they can get their hands on. What kind of data are they looking for?
Yeah. So my first thought was economic data. But here's what Matthew had to say.
Well first off, with respect to the HDI, they do use economic data, but I would parse that more as they use more hard data to try to explain the less economic side of standard of living. What I mean by that is they'll use hard data on the percentage of students at each age enrolled in school to try to assess the educational opportunities of students and of children around the world. That's what we call harder data because it's actually data that's collected, not just from the response of surveys.
And surveys don't necessarily count as hard data because it's self-reported, subjective, and not everyone participates. Correct?
What the World Happiness Report does is kind of a contribution to the HDI in that they try to assess roughly the same thing by allowing citizens of different countries to speak for themselves to get their actual opinion on, "What are your opportunities?" "How are the made available to you?"
That's really interesting. Happiness is kind of a-- whenever you talk about it with people they'll often kind of react with, "How could you possibly measure it?" And it's interesting that you kind of gather data on the ground with those Gallup Polls and then you integrate all the other hard and other types of data points into the analysis.
And that's certainly some of the most prominent feedback we'll hear, mostly from casual observers, perhaps on the news or even just colleagues that we'll speak with. When you say that you'll produce a report, and it's a fairly influential report, that assesses and ranks the happiness of individuals around the world, you'll even get some scoffs. "How could you possibly quantify something like that?" And the actual quantifying of happiness, and thus collecting the measure and ranking the countries, isn't necessarily the novel contribution of this report. What I mean by that is that the actual level of happiness that's aggregated per country is effectively done with one of the questions from the Gallup World Poll.The actual background research behind the validity of using this question to assess individuals' perception of their own well-being is relatively well-founded and we take that as being effectively exogenous to the report.
Yeah. Umm. So that is a confusing response. So an exogenous variable in an economic model is a variable which has a value that's determined outside of the model that you're looking at. So it's like if you had a model for your household's income, an exogenous factor might be the state of the economy. So you make your budget and you might be doing a study on your household budget to optimize it or to figure out a savings rate, but there's going to be exogenous factors that happen outside of the value of them, the value of which are determined outside of your model. So that could be your income, for example. But you can determine your spending and your savings but you can't determine your income, or you can't determine the unemployment rate.
Okay. Got it. So they're looking at a comprehensive view of world happiness, but then they're taking the Gallup World Poll question into account and because that is from Gallup and collected in a different way, that is an exogenous factor to the report.
The main contributions of the World Happiness Report to the literature, rather than just the rankings and the assessment of well-being, is trying to decompose between countries and find out, okay, well, we know how happy this country is and how happy this country is. Let's take all the data we can about those countries and also the micro-level data about the respondents and try to assess exactly what it is that makes different countries happier than other countries and what makes some countries change over time.
So I guess before we kind of get into some of the details of your findings, we had talked about kind of the data points that you gather, but what goes into a large scale report like this? What does the analysis look like behind the scenes?
Like I mentioned earlier, the data mostly comes from our partners at Gallup World Poll. But the actual analysis of this report is-- when I say analysis, I mean the teams that had the research in the different chapters.
So the report is organized into different chapters. The new 2020 report, focused on environments for happiness, has seven chapters including Social Environments for World Happiness, Cities and Happiness, Natural and Built Environments, and Urban/Rural Happiness Differentials Across the World.
By analysis, I mean the teams that had the research in the different chapters. It is spread out quite well around the world. And this has been an initiative that we've been trying to do over the past several years, is to increase not just the geographical scope of where our research has come from, but also the fields they're from. Now what do I mean by that? Well, this report was largely founded by economists. And the reason for that is because, and like I said, it is largely a contribution or a complementary report to the Human Development Index. I would say that this report is part of a larger movement within the field of economics to look into how are we - and by we, I mean policy makers and academics - how are we assessing well-being between countries? Because historically, it's all simply been hard, real GDP per capita, nominal GDP per capita, it's purely a standard of material well-being. And this has faced a lot of contention between fields and whatnot. And so the Human Development Index has tried to reach out and say, okay, we can assess people's standard of living between countries and thus evaluate the success or failure of policy makers in those countries by the opportunities they have available to them. And the Happiness Report kind of builds onto that by saying, okay, maybe the success of countries is really just determined by how happy the citizens are. Because in the opinions of a lot of people that is ultimately the objective of policy makers.
And that kind of leads into another good question which has to do with was there anything that you found in the research relating to money factors and kind of the traditional way that they were measuring happiness and what the report produces? Or anything interesting that you found in that realm?
Well, certainly a lot of the results that we've published have been relatively impactful in that sense and for myself as well. I've been quite surprised at a lot of the results. And I wouldn't say we see contention about this, but does seem somewhat ironic that the objective of this report is largely to look at more nuanced measures of well-being between countries. And one of the largest inputs you see in our actual regression analyses is GDP per capita. But like I said before, we're mainly using that to assess what constitutes the happiness in every country. Within each country, if you break it down, how much of the well-being is associated with it, whether it be GDP per capita or social support between friends and family or confidence in your government or whatnot.
You mentioned kind of your social support system. How do you go about quantifying that when you ultimately feed it into analysis? What kind of analysis are you doing? And then how do you take those sociological factors and then distill them down into a number that you can ultimately plug into a model?
Certainly. So at least, certainly the head editors, - and this is becoming less and less true - but a very large portion of the researchers working on this report are economists. What's useful about economists is that they're very good working with large sets of data. And then naturally the kinds of analysis that we use tend to lean towards kind of your standard econometric methods. So we're talking multivariate regressions and explanatory factor analysis. This is effectively how we'll take the one, the objective measure, the explanatory variable, which is people's perceived well-being. And we'll try to decompose that down into, okay, well how well does this actually correlate on the aggregate level with GDP per capita or these other soft data indicators? So what do I mean by soft data? Well, this is what you were asking earlier about how do we feed in the social support networks into this report? Because this is something that's very hard to get hard data on and, realistically, it's not something that we really have the option of doing. So the soft data, which would be like social support, these do come from our Gallup World Poll data sets. And so this would be every individual respondent's assessment of, if I recall correctly, the most important or the most significant question in the survey that we saw was, "If you were in a time of need or if you needed help, do you have family and friends you can count on?" And this is actually, if I recall correctly, the only soft data set that actually had a higher correlation than GDP.
So if you're actually looking at the rankings graphic you can see the constituent bars that add up to the aggregate bar to assess how happy each country is. The perceived social support survey questions of those results would be one of the largest explanatory factors.
This all kind of reminds me of the Cantril Ladder Scale.
Yeah. I actually asked Matthew about that and here's how he responded.
What I suspect you're referring to is what we refer to, at least, as GWP16. That's Gallup World Poll 16 and that is the fundamental building block that we use to assess happiness between countries. So what does the GWP16 measure? It's the question posed to the respondents that goes effectively, and I'm paraphrasing here, "If you were on a ladder-- imagine yourself on a ladder where the bottom rung is the worst possible life you can have and the top rung is the best possible life you can have. If there are 10 rungs, which rung would you place yourself on?" And that's effectively the main question that we use to assess how well do people thing they're doing in every country. And this can come as a bit of a surprise to some people, because other questions that we ask, and ones that we would think would be very impactful in this report, or one that at least I thought would be very impactful, was, "Did you smile a lot yesterday?" or "Did you feel stress or did you feel anger yesterday?" And what we found really on the aggregate level was that these questions exhibit very small differences between countries. For example, you usually see the Nordic countries at the top of the ranking. They're quite consistently the happiest countries in the world. They're generally not the ones who smile the most. They're not the ones who feel the least anger. But they're very consistently the ones to place themselves highest on that ladder with 10 rungs.
That's interesting. What's your interpretation of that? If you had to guess either based on the data or based on your knowledge of economics, what's causing the Nordic countries to always be ranked so highly?
Yeah, yeah, no, that's a question we get quite often. And it's not just the World Happiness Report. In many indices around the world, whether it be Global Peace Index or HDI or anything like that, the Nordic countries very consistently rank extremely well relative to the rest of the world. And there is quite a large field of literature trying to explain exactly why it is the Nordic countries are so good at what they're doing. It can be relatively challenging to take a minimum difference analysis, and that is to find a country that is very similar except one difference, and try to account for what difference does that, say, policy or that natural resource have on the country. Because there aren't that many countries in the same situation as the Nordic countries and the only ones that are similar are other Nordic countries that are also very well-off and wealthy and whatnot. So with regards to what makes them so happy, all I could really do is defer that to the actual decomposition of the ranking. But really, if you look at the decomposition of happiness in those countries, we see very large coefficients assigned to, like I said, the social support networks, which is evidently very impactful in the Nordic countries. But we also see very large residuals and so fundamentally we can assess that they're the happiest countries and we can do the best that we can to determine why they're happier than others. But fundamentally there's still a lot to be answered by those kind of analysis.
Okay. And by residuals you mean kind of unaccounted for differences or--?
Exactly. Yeah. And on the graphic in our analysis, we do stack it as in that-- I'm not sure how to explain this. So if you're looking at the actual graphic, and for anyone with access to the 2019 report it's on page 24. If you just google the World Happiness Report and hit google images, every image is just this graphic. It's all that makes the news. One thing that's important to remember in interpreting it is that the aggregate length of the bar is not determined by the constituents inside. it's the other way around. And that's why the residual exists. And so we'll have the aggregate level of happiness per country and we'll see how much of that, using these regression analyses, can we attribute to GDP per capita, how much to social support networks. And everything left over we call dystopia plus residual. Now, what does dystopia mean? We get a lot of questions on that. Well, it's effectively our way of quantifying anything that can't necessarily be answered by these regressions and these surveys. Because we don't necessarily expect to answer every single thing. Because of the very nuanced nature of happiness it's hard to break it down quantitatively like that. And so what we've defined dystopia to be is comparing the country that the respondent is in, subconsciously, to the hypothetical worst possible country. Now what is this worst possible country? Well, that's what we call the dystopia. But it's a hypothetical country that ranks the worst in absolutely every measure. Of course, it doesn't exist and so the dystopia is kind of the unaccounted residual for what people can't explain about their own happiness, not only what we can't explain. And then plus the residual is obviously just the residual in the OLS regressions that we're running.
That's really interesting. And that kind of leads into my next question which is, what was the most striking find for you as a researcher?
The most striking find for me, and there's two come to mind. One that had been developed before I actually began working on these reports, and it's a bit older. But it was largely a product of this kind of research into well-being between countries. And it's known as the Easterlin paradox. And this actually answers a bit more of your question earlier about the nominal material, GDP per capita, well-being between people and between countries. And the Easterlin paradox states that - and it reiterates a finding we have relatively consistently in these reports - GDP per capita on the intra-country level, so international or between countries, has a much smaller effect on median happiness than we expect. What does that mean? Well, let's take a relatively high-income country, let's say Canada. The median income is about $50,000 in Canada. And compare it to, let's say Nepal or Bhutan where the median income is much lower. The median Canadian has a very similar level of perceived well-being to the median Nepalese or Bhutanese individual. Does this indicate that material well-being has no impact on happiness? Well, no. If you look further into the analysis-- and actually this is something that's only available because of the density of the data that's made available to us from our partners at Gallup. We're able to look at each individual respondent's annual income and whatnot and use this data to find out, okay, within a country, let's take the top 20% Canadian and the bottom 20% Canadian individuals. Is there disparity in happiness there? Yes, and it's very large, much larger than we expected. And so what we find is that the contributions of GDP to happiness is much, much more pronounced on the intra-country level, so within a country, the bottom and top say 20th percentile than, it is between countries. And this has sort of opened the door into a slightly newer field of research in that does material well-being, how much does it actually contribute to your well-being versus your perception of your well-being derived from your relative perceptions of those around you? If you're not well-off, but everyone around you isn't well-off either, you're not going to think of yourself as poor necessarily, no matter how rich the countries around you might be, because it's not something you're seeing every day.
Absolutely. And how do you think it's kind of changed over time? Do you think we've gotten better at measuring happiness?
Well, I certainly think we make improvements every year. With regards to actually measuring happiness, most of it, like I said, derives from GWP16. But the contributions we have been able to make is being more and more effective in analyzing what causes countries to be happier than others, breaking down, looking deeper into what data we have available, and being able to run these regressions to try to figure out exactly what's contributing to these disparities we see between countries. And that feeds into another point I wanted to bring up. And also you asked for what surprised me and I promised you two and I only gave you one. So I'll give you the other one now. The other one was a report that we actually found on last year's report in the second chapter. The main topic of discussion in that chapter was migration and happiness. And the motivation, really, for pursuing this topic is that in the micro data that we have available from Gallup-- and when I say micro data I mean every observation is available for every single respondent in every single country. It's an absolutely massive data set. I actually need to defer a lot of the statistical computations to my supervisor because my computer can't handle a data set that large. But it does give us every single individual's income per capita and lots of other information including if they were born in the country that they're being surveyed in. And if they weren't, where were they born? Now this gives us a really interesting opportunity to effectively create a dummy variable and filter out any first-generation migrants in the survey population and try to assess how is their happiness behaving really, relative to those around them in the host country and to those around them in the destination country. Now what was the motivating factor behind this side of the report? Well, there is a lot of discussion over whether perceived well-being is a function of individual factors, being potentially the culture you were raised in, values you were raised with, and these would be products of your country of origin. Or whether it's determined by environmental factors and that is the country around you, the government, the people you interact with every day. And this really offers us an excellent opportunity to try to decompose those two effects in that every single migrant-- we'll have data on their country of origin and we'll have data on their country of destination. And we'll have them as kind of the intermediary between the two to see how has their happiness changed since they've had the opportunity to migrate to another country. And really what we found is that the correlation is extremely high between the migrants and their host country. Now what does that mean? It means that at least the hypothesis that we can go with right now is that the factors that influence perceptions of individual well-being are mostly a product of the environment that people live in rather than the country that their were raised in, perhaps the cultural values they were raised with.
And one thing as well that I should mention. In this report we haven't been able to control for I suppose what you could say as cultural practices. So we can't control for the degree to which culturally migrants assimilate to the host country versus preserving and maintaining their cultures from the destination country. However, the fact that we haven't been able to control for this is even a more valuable contribution to this finding is that regardless of which culture or cultural practices you're following, your level of happiness and your perception of well-being is still relatively exogenous to that.
Yeah. It's almost a more meaningful measurement because it kind of shows that you might have different practices than the country that you're moving to but overall it doesn't really affect happiness.
That's really cool. So I guess, moving back to the perception of the report, are there any misconceptions about the report that you'd like to clear up or--?
There certainly are, actually--
I'm sure that's a loaded question.
--and this is something I really should touch up on. And so like I probably mentioned before, whenever this report is released naturally there's always some push back and there's some contention with individuals who generally misperceive this report and how it's done. The most common misperception with this report is that we, the researchers who are generally concentrated largely in one or two fields and even in just two or three countries, are imposing our standards of what it means for individuals to be happy on different countries based on a number of factors in those countries and then ranking those factors. That's not true at all. As I mentioned previously, the actual assessment of the well-being of individuals in any country is done and collected by Gallup Poll who is on the ground in these countries, speaking to individuals and getting their perceived well-being from them, themselves. There's no imposing on the standards of what it means to be happy by any of the researchers. We largely, like I mentioned, take the actual level of happiness as exogenous to the report. And another misconception that's kind of along the same lines, but if individuals will see the ranking graphic which is, like I said, the most commonly distributed graphic through the media. And they'll see the sub-bars that aggregate up to the larger bar. And they'll assume that we're taking data on the countries such as the GDP or whatnot. And we're really just stacking these measures and saying, "Well, this country is this happy because they have this GDP and this and that." Again, and like I mentioned with the interpretation of the residual in that graphic, that's not what we're doing at all. The assessment of happiness is done before the decomposition. The decomposition there is just in effect to try to assess what is constituting each bar. And sometimes, evidently, we're clearly more successful with some countries than others. And perhaps that opens another door into research in that perhaps there's a relationship or a pattern between the countries that we're more successful with doing in the sense that we would be regressing effectively the dystopia variable on other indicators in the country. And saying, "Well, what is it about or between countries that makes it harder for us and our analysis to determine why certain countries are happier than others?" One of the main issues this report was trying to address in that traditional measures of well-being between individuals really just look at GDP per capita and Singapore would rank extremely high. It's an extremely high-income country. Whereas El Salvadore is a much much lower income than Singapore. However, evidently as the report here points out with regards to actual perceived well-being or how happy the citizens of the two countries perceive themselves to be, they're neck and neck.
That's interesting. And this is a great example in the data science community they would say it's the "what" and then you want to dig into the "why." So I guess when you're doing analysis in the World Happiness Report, it might be interesting for a lot of audience members out here, I know we've go a very enthusiastic data science community that's going to be listening. So what kind of techniques-- I know you mentioned multivariate regression. But what programs are you using, what techniques do you use to analyze? You mentioned it's huge sets of data, yeah, maybe just dig into a little bit of that.
So all of my analysis is done with Python because naturally that's the language that I learned how to code in.
That's interesting. And when you use Python, I mean what packages do you use, what models did you use?
So working with data on the Happiness Report, the arithmetic is not extremely rigorous. You're not doing intense matrix algebra. So the more quantitative packages like NumPy and SciPy aren't that useful. Mostly pandas has been my favorite package for using this data. The pandas package is actually named after panel data, so it's actually perfect for this kind of a report. It's very, very good at handling very, very large data sets of categorical and time series data. Because, of course, in trying to assess how countries change over time, we need time series data. Generally this is aggregated at the yearly level and, especially, it can be very challenging to fill in missing data, I would suppose, because data availability is probably the biggest constraint we face here, working at the WHR. There are these very large organizations. Gallup really only started collecting what they call the core questions, and these are the consistent questions in the surveys they ask every year. They only started in, I believe, 2005 or 2006. But we're certainly not constrained to only using the Gallup measures. We also try to use a lot of measures made available from the World Bank, the World Peace Index as well, from a think tank down in Australia, they've been extremely helpful with us in helping us out with data availability issues. And quite frankly, we need to use a lot of statistical methods, I would say, to allow these analyses to be done with a certain percentage of the variables or the observation's just missing. Especially in countries that are more likely to incur stuff like domestic conflict, it's unlikely that a census will be carried out in a country that's going through a civil war. However, those countries tend to be some of the biggest stars of this analysis because their perceived well-being does fluctuate so significantly. And it's going to allow us to extrapolate how does this certain crisis or an election or something happen, how will that effect how a country perceives its well-being? But fundamentally what I'm getting at here is that in a lot of countries where something will occur-- and that's exactly the time frame that we'll be looking at the most specifically, trying to figure out exactly how did perceived well-being change before and after this event. Those tend to be the instances where data availability is the most impaired simply due to the fact that either that government won't be releasing data about their own country or they won't be permitting perhaps other third-party data collectors into the country to collect the data for themselves.
Yeah. How does Gallup get data from countries like that? Do they ever have issues getting in or--?
That's an excellent question. I know that they're extremely good at what they do because they have very consistent data for a very wide array of countries over a very long time frame. They seem to have access, getting into a lot of countries that I'm surprised they would have access getting into. They have an excellent reputation around the world. But fundamentally, that's an excellent question I would love to hear the answer to.
Good for Gallup. I noticed there was a chapter in the World Happiness Report on cities. Do you mind kind of-- what were your findings or what kind of interesting information did you find when you were analyzing cities? And what are you looking for when you're analyzing cities? What are you looking at?
So this was a really interesting topic or chapter that the WHR decided to touch on this year. Unfortunately, it wasn't the topic I was most engaged with. I had other obligations within the report that unfortunately I'm not able to discuss. However, I did have some conversations with the editors about some issues they were having with the topic of cities and with regards to the report that I found relatively interesting. And the main issue is, really, that fundamentally this report always has been and will continue to be a report on the national level. It tries to explain national-level policies that differ between countries that lead to different perceptions in well-being. Well, cities and policy within cities especially, is generally a much more tangible policy that citizens engage with. And what this means is that a policy change in a city, we hypothesize, could have a much more significant effect on people's well-being than on the country level. Well, an issue is that policies between cities and even just governments between cities within one country can change quite significantly. I mean, I'm here in Canada, in Edmonton right now. And I can say that the municipal government in Edmonton will change much differently from that in Toronto. And so if you were to take a national average of Canada for the cities, it might not explain that much and therefore, at least the way the issue was parsed to me, is that they might need to kind of unveil the national ranking for a period of time and instead focus more on cities between countries or perhaps looking at cities that behave like sister cities in the world. So cities within two different countries that have relatively similar layouts and try to use these comparisons to explain what city policies is it, whether it be something as simple as trash collection or, I think public transportation was what they expected to be a very significant one, or public sporting facilities and whatnot, to use these measures to explain the differences between countries.
That's really cool. I guess, I mean, yeah, that would make sense, because there could be huge differences in local policy between cities and it may not necessarily reflect the-- I mean, the average of a country is essentially, it could be a collection of a lot of different ways of living. I mean, you mentioned Edmonton. You could compare Dallas to New York or Los Angeles where, in Dallas there's no public transportation.
Yeah, I'm sure the spread between cities might be larger in the United States and Canada, that's just a hypothesis on my end.
And for any Dallas folks out there, sorry, I didn't mean no public transportation. They have some, it's just not nearly at the level of New York.
Yeah. I actually lived in Dallas for a year. I quite liked it. But one contribution that-- and again, had I been more involved with the cities chapter I wish I could've done was, and kind of feeding on what we're discussing now, the difference between cities in a country. I wonder if, between countries, there would be disparity in, I supposed you could almost say, the standard deviation of happiness between cities. What I mean by that is, let's look at a country like France which has a unitary government, is very top-down from the center. City policy in France between cities, even between Nice and Paris, might be much more consistent than it would be in say, the United States, where it's much more bottom-up. And this could even feed into an argument between the unitary governments or more federal governments to facilitate the conditions to improve the standard of well-being for individuals in those countries.
Yeah. Absolutely. Well, I mean, I guess the good news for economists and researchers is it seems like there is a lot more research that could be done on the topic of happiness. I know it's a central issue in economics, the concept of utility. So it seems like there's going to be plenty of work cut out for the researchers in the years to come.
It's a topic that's becoming more and more discussed, certainly. And luckily for us, more and more data is becoming available every year. And like I think I mentioned previously, data - really, the availability of data - is the only thing that makes this report possible. So we really have huge thanks to all of our partners who make this data available for us.
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This episode of Alter Everything was produced by Maddie Johannsen (@MaddieJ).