Hi Reddit! My name is Max Roser. I visualize global development data on OurWorldInData.org, a free online publication on how living conditions around the world are changing.
Now I am working with a great team and we want to cover global development as broadly as we can to show how our world is changing. Our World in Data now includes data and research on global health, violence, poverty, inequality, economic growth, environmental changes, food and agriculture, energy, technological change, education and more specific topics.
While much of the news is focussing on what happened yesterday or even what is currently “breaking news”, I think that many of the very important changes, which fundamentally reshaped the world that we are living in, happen very slowly and persistently over the course of decades or centuries. On ‘Our World in Data’ we don't report the 'breaking news' and instead zoom out to show the slow trends that dramatically change our world.
Other than that I am a researcher – mostly focussing on inequality and poverty – at the University of Oxford. (My personal site is here.)
It is easy to be cynical about the world and to maintain that nothing is ever getting better. I am working on this because I don’t want this cynical view dominate our understanding of the world we live. Because our hopes and efforts for building a better future are inextricably linked to our perception of the past it is important to understand and communicate the global development up to now. If you want to see some of the positive changes in the world you could have a look at my Short history of global living conditions and why it matters that we know it.
Globally we face so many very difficult challenges and I think we are making a mistake to not study good data and the empirical research that shows us what these challenges really are and how we were able to sometimes overcome those challenges in the past. If we see how far we have come, we can start to ask what made this progress possible so that we seek more of what works. I got into research on global development because I just couldn’t believe the very substantial progress the world has made and I hope that visualizing this data and making it accessible maybe motivates some others to work on these questions too. We have lots to do!
Our World In Data is entirely a public good: The publication is freely available online, all data is available for download, all visualizations are available under a permissive Creative Commons license, and all software that we developed is made available open source.
Here is proof that it is me via my twitter account.
I learned a lot from reddit over the last years, but did not contribute much to be honest. I hope I can change that a bit with this AMA. If possible I can do a few visualizations of trends, correlations, and maps – generally I am just really interested to hear which aspects of global development you are interested in. Ask me anything!
I will wait until 5pm (London time) and then get back to your questions.
Looking forward to hear from you!
EDIT at 11:00: Thank you for your great questions! These are much more than I expected, I have been asking questions for the last several hours. I am working my way though them..
EDIT at 1:45am: It is now pretty late and I'm still in the office. I need to get some sleep. Until now I got back to the most highly upvoted questions and spent the last 9 hours or so writing my answers. Many, many thanks for the great interest in this AMA ! It is great hearing from you what you are interested in and I hope some of the answers are useful for you! Also really fun that Bill Gates showed up here!
If you want to see what we are up to you can
like Our World in Data on Facebook here
follow Our World in Data on Twitter here
or follow me on Twitter here
EDIT 10:55am next morning: I added answers to all questions that have at least one upvote.
You always do such a good job visualizing the progress we’ve made in a time when headlines lead many people to believe things are getting worse. If you could only use one chart to convince someone that the world is getting better, what would you choose?
Hello Mr Gates!
It is great to hear that you are finding our work useful!!
I was thinking about this question just recently when I wrote this Short History of Living Conditions. I think what is particularly important about the progress that we achieved is that it is not restricted to one domain only. We need to show the progress – and challenges – in many different important aspects to see where we currently are. Because of that I made this visualization which shows the development for some of the most important aspects.
And that is something that I believe more generally. We should not focus on presenting and discussing the evidence in one domain only. We should take all the aspects of living conditions that are important for our lives into account. That is why on Our World in Data we are going through the research aspect by aspect and make this perspective on global development available in one place. Now we have more than 80 topics discussed – some in detail, some we just started to work on. And we have a long list of aspects that we need to work on in the future.. We hope we can continue doing this work for a pretty long time..
I think you are right to say that the headlines and news give many of us this idea that everything is just getting worse. Part of that is because the media focusses on single events and not on slow changes. And it is sadly a fact that terrible things can happen in an instant while global development always takes time, often a lot of time. "The industrial revolution is happening" was therefore never a headline, but every coward who shoots innocent people is dominating the news for days.
A second aspect here is that often the the most important progress is achieved when bad things do not happen anymore. When famines do not happen, wars do not erupt, children do not die – these are the most important changes. Progress is often the absence of stories. And stories that are not happening won't be headlines. The only way to even see these changes is to have good empirical data on famines, war, and child mortality and track these change over time.
The news are not really to blame for not covering these slow changes; it is just that we have to realize that it is not possible to understand how the world is changing by following the daily news. We need to see the slow changes which transformed our world completely and never made it into the news. That is why I am working on Our World in Data.
As you've been building OurWorldInData over the years, what are the most surprising/counterintuitive lessons you've learned?
– The two things that surprised me the most is who the readers and the authors of Our World in Data turn out to be – both are a very positive surprise:
On the reader's side I did not expect that there are so many other people that would find visualized data on global development interesting. I certainly did not expect that and thought that this would be only interesting for a very small niche of historians and development researchers.
And on the author's side I did not expect that I will work on Our World in Data together with a team. It is for sure the most positive change in this work that after years of working on it by myself we have now researchers and web developers in the team that bring great skills together. Esteban Ortiz-Ospina is an economist who has been working on poverty, the role of the public sector, and also on education. Hannah Ritchie is focussing on environmental changes, agriculture and food, and energy. With Joe Hasell I was working on famines this year. Sandra Tzvetkova has recently worked on women's labor force participation. With Ruby Mittal we worked on women's political participation. With Sophie Ochman I am currently working on polio. And with Diana Beltekian we are working on technological change and the adoption of technologies. (Of all the researchers only Esteban is working full time.)
And then we have the two web developers – Jaiden Mispy and Aibek Aldabergenov – whose work I have already discussed elsewhere here.
From the names alone it is probably obvious how very international the team is and from the list of topics it is obvious how diverse the interests and skills are that they bring to the project. If you would have told me five years ago that I'd be working with them in 2017 on OWID I would have been very, very surprised.
– The most negatively surprising thing is how very hard it is to find financial support to make Our World in Data possible.
How do you ensure that you prevent any biases from leaking into the data you gather?
We have the aim to not publish data or research that is biased in one way or another. I can tell you about what we are doing to achieve this aim:
An online publication like ours has several disadvantages for an author to bias the presented research. In a book or journal article it is always possible to select a couple of countries that fit a particular narrative. In our publication the reader always has access to all the data that is available and so we cannot hide evidence in the way it is possible on paper. To give an example: we could show a chart like this one and write about how across Africa people have become poorer. But that same chart would allow the reader to also see all these changes and it would become obvious that a sweeping assertion of declining economies across the African continent would be wrong.
We also have these guidelines on how we select the data that we present. We always show data over the longest time-frame for which that data is available (and never show data from a particular time that would fit a biased narrative). We also always show data for all countries in the world. And we put a lot of effort in finding the most reliable data.
The discussion of the sources and the quality of the data is really important for us. In some entries – for example here for extreme poverty – we have detailed explanations of where the data comes from, how it might possibly be mismeasured, and in which ways readers have to be careful to not draw false conclusions. But if you look at some other entries you will see that there we sometimes haven't written these data quality sections as careful as they should be.
One way that limits bias further is that we are working in a team and often are in close exchange with other researchers on a particular topic. I guess everyone knows that feeling that you are extra careful before you share it with someone important like a coworker or colleague.
And another factor is that we know we will get angry emails, twitter replies, reddit comments if we are not fair in describing how the world changed. I think that is ultimately the biggest factor. I understand that once people have a bad feeling when they need to rely on us then we are done. The fact that we are scared too screw up hopefully is pretty powerful in keeping us rigorous.
Question from a friend: Can you remember a time where the use of statistics dramatically changed your opinion on something? A scenario where the stats disproved many of your preconceived notions about a topic?
That happened quite often at the beginning. For example I remember very well how surprised I was when I first learned that the share of people in poverty is falling.
In my undergrad studies I did Philosophy and Geoscience. One subject is more gloomy than the other – geoscience for some good reasons and in continental philosophy there is this shared belief that it is all downhill since Aristotle.. When I then first went to see a lecture on economic history and the lecturer tried to convince us the global economy is a positive sum game and as productivity increased in more and more places around the world, more and more people were lifted out of poverty I did not believe it. It was so against everything else that I embarrassingly thought he must be wrong. I wish I knew now how I rationalized that I am right and he is wrong at that time as it might help me to do my work better today. But I definitely remember that lecture and thinking 'this is not true'. Reading more social and economic history then changed my mind.
But actually I think it is because I was so surprised myself about these changes – and angry actually that no one had told me that clearly before – that I am doing the work that I am doing today.
What would you consider to be the best example of a good data visualization? What about the worst?
In general I think data visualizations should present the empirical evidence as clearly as possible. Bad visualizations fail to do that and so I think the one question one should ask when working on a visualization is 'what does this show?' and then always just 'Does it show that as clearly as possible?'.
I think the main trap or vice in data visualization is when you go for something unusual not because it presents the data more clearly, but because it is unusual. Circular bar charts mostly fall into that category. Going for cheap tricks to make it unusual and at the cost of clarity is just a a sign of poor character and annoying.
Because the clarity of a data visualization is higher for a type of visualization that many people are familiar with, I think it is true that it makes sense to be conservative and stick to visualizations that have that advantage in displaying data clearly. Therefore I think if you stay away from cheap tricks and stick to good line charts, bar charts, map tools and scatter plots you can visualize most of the data in a very good way.
But there are of course exceptional people that are not only clear but also innovative. People in that category achieve to further advance the ways in which we look at data in useful ways. Just to name three people whose work I like a lot – with links to their work:
- Moritz Stefaner – including his Remixing Rosling
- Nadieh Bremer – I like her presentations a lot and she did an AMA here two weeks ago
- And Neil Halloran who basically single-handedly invented the genre of the data visualization documentary. He has a pretty well-known project called Fallen (a history of war which just shows statistics and is emotionally gripping – that is really not easy to do) and he has a more recent project called Shadow Peace and part 1 focusses on the nuclear threat.
- Do you already have a successor to the current grant by the Bill and Melinda Gates Foundation which ends on Nov. 15th?
- What was the tipping point which made this project so popular?
Thank you for all the work - it helped me a lot to change my personal point-of-view on a lot of topics and was also very well received by a lot of people I recommended it to.
It is great to hear that our work changed your perspective! Thanks for sharing that!
To your questions:
Do you already have a successor to the current grant by the Bill and Melinda Gates Foundation which ends on Nov. 15th?
No we do not. We are in touch with two large foundations and I very much hope that it works out that we find the support that we need to keep on doing our work. If possible I'd like to do this work for many decades and not just the next four weeks..
In addition to the support we have from the Bill and Melinda Gates Foundation we are currently able to do our work because many readers of Our World in Data donated to us. Without that support our project would long be dead and I am very grateful for all of the people who donated. We list them on our About page.
What was the tipping point which made this project so popular?
There wasn't a tipping point. For sure there were times when I was pretty excited to see that people actually find this work useful, but it never 'tipped' and instead rather accumulated over time.
What software di you use to produce your data visualization?
The interactive visualizations are done with a visualization tool that we have developed in our team! Jaiden Mispy has done most of the work, I think it is pretty incredible what he has been able to do. As everything else that we do it is free for everyone to use and completely open source.
We call this tool the 'Our World in Data-Grapher' and on this page we show some of the different types of visualizations it can do.
Over the last months Aibek Aldabergenov joined our team and he built a number of scrapers that are directly pulling the most important global development data (FAO, World Bank, UNESCO, etc.) into our Grapher.
When I do static visualizations I start either with our Grapher or Tableau and finish it in Illustrator.
A nice thing about the Grapher, that I wish more people know, is that whenever you change something in a chart – select different countries on a line chart, change the year for a map, highlight a continent on a scatter plot etc. etc. – the URL changes and you can directly link to that view. Here is an example:
This URL gets you the world map of child mortality in 1970: https://ourworldindata.org/grapher/child-mortality?year=1970
And this one gets you the world map of child mortality in 2015: https://ourworldindata.org/grapher/child-mortality?year=2015
And in the same chart you can switch to the Graph tab and see the change for a single country: https://ourworldindata.org/grapher/child-mortality?tab=chart&year=1971&country=SWE
Or compare the decline of Sweden with the catch-up of South Korea: https://ourworldindata.org/grapher/child-mortality?tab=chart&year=1971&country=SWE+KOR
What led to the development of your own graphing tool instead of using an existing data visualization product?
We built the Grapher because there is no similar external tool available. Datawrapper, Tableau, Plotly, various libraries based on d3 are out there but nothing is similar to what the Grapher does for our project.
The owid-grapher solves this problem by using a single visualization codebase and crucially a single database into which all of our data is placed. Once the data has been imported, the process of creating a visualization is reduced to simply choosing what kind of visualization is needed and then selecting the relevant variables in the Grapher user interface. The result may then be customized, and is published to the web with the press of a button.
Using our own system has very important advantages:
• Integration with our global development database: Our database of global development metrics is integrated into our visualization tool so that when we add and update empirical data the visualizations are all updated. (In contrast to this, a pre-existing tool would make the exploration of a database impossible and would require the preparation of each dataset separately for each visualisation.)
• Flexibility: We can use automation to change our entire system all at once. For example, if we decide we want to use a different source referencing style, we could easily update this across hundreds of charts. This makes it possible to scale our publication and to sustainably improve our work without starting from scratch at each round.
• Risk mitigation: We hope(!) that Our World in Data is a long-term project and we want the visualizations we produce to continue to be useful and available years from now. An external web service may be shut down or change for reasons we cannot control. We have had this experience in the past and learned our lesson from it.
• Keeping everything up-to-date: Because we want to be a useful resource for some time we make sure that we have a technology in place that allows us to keep all of our work up-to-date without starting from scratch each time. We have our global development database directly integrated in the Grapher and as soon as new data becomes available (for example from a UN agency) we can run a script that pulls in that data and updates all the visualizations that present that data.
Hi Max, thanks for doing this! What could we do on a societal level to increase the importance of data in the decisions we make? Especially in politics, I find that decision making is very often driven by emotional arguments and ideology rather than hard facts.
I think you are right that this needs to change from the societal level and that other domains will then change. We need to make the discussion and analysis of quantitative information a much more important part of our education. Data on global development could be used in mathematics classes in school to teach statistics – and instead of just discussing lotteries the students would learn a bit about development at the same time. And it could be integrated in history teaching. I had a fantastic history teacher in school, but we never ever saw a chart on the decline of child mortality or even the rise of incomes in history. If we start there then I think we can expect people to later rely more often on quantitative information to understand the world.
Is this project inspired by or related to Hans Roslings work at gapminder foundation as it looks somehow similar?
Yes, very much so!
I am sure that I wouldn't be doing what I do today if Hans Rosling did not do what he did. His passion for global development and how he told the story of global change like no one else made a huge impression on me. In 2015 he did a documentary on the progress against extreme poverty and while we were in contact before it was then that we collaborated properly. As a person he was just the same enthusiastic, funny, passionate man that he was 'on stage' and what impressed me a lot was how thorough and very hard working he was. He took the train from London sat down here at the same desk where I am now at 9am and did not get up until late in the evening. For lunch we had sandwiches while we kept on discussing which data to chose, which researchers to involve, how to present the data, and how to explain everything on stage. This documentary on poverty is freely available online – maybe a nice thing to watch on today's 'International day for the eradication of poverty'.
Hans' son Ola and his wife Anna founded Gapminder and they are doing very good work. A project that Anna did and which I think was extremely well done is 'Dollar Street'. The idea there is to show what it actually means for people's material living conditions to have an income/consumption level of $2, $5, $10 etc. These consumption levels can be very abstract and it is really hard to imagine what it means to live on a particular income. They travelled around the world and portrait families in many countries and documented which bed they have, how they brush their teeth, what toys their children play with, how they wash their clothes. What is really striking are two things for me. First, to see the difference that higher incomes make. As Hans Rosling emphasized again and again, income really matters. And second, how surprisingly similar families in very different cultures and countries live when they have the same income. So many aspects that we think of as the traditional way of life in a particular place – Germany, Kenya, Indonesia, – really is determined to a surprising amount by the prosperity of the society you live in. The link between income and 'culture' is really visible there.
And what is also awesome is that Hans, Ola, and Anna have been working on a book for some years now – and from what they say they are close to finishing it. It is even on Amazon already.
As a development nerd, I almost let out a tiny shriek when I saw your AMA. I'm actually not familiar with this project and I'm really excited to explore your website! A few questions:
- How did you get in to this line of work, and more importantly, how can I get into this line of work? (Currently holding a bachelor's in political science from Canada and am considering, among other things, an MA/MSc in Econ)
Have any there been any positive/negative correlations that have really surprised you?
Not having explored your project yet, is there any way to account for the effectiveness of aid, intervention, development projects, etc.? If not, do you have any insight as to some effective ways to measure this? I feel like global development practice would be so much better if we had more rigorous ways of measuring effectiveness.
As someone who approaches development from a statistical/economic angle, what are your thoughts on the work done by Abhijit Banerjee, Esther Duflo, and the Poverty Action Lab at MIT? I've always found their work to be really interesting but I've heard mixed opinions from global development academics.
Thanks so much :)
Very good to hear that you are interested in Our World in Data – and great that you found out through this!
A correlation I knew of but was still surprised to see so very strong and consistent across countries, within countries, over time was that between people's income and their self reported life-satisfaction. Esteban and I wrote an entry earlier this about that topic and explored that in some detail: https://ourworldindata.org/happiness-and-life-satisfaction/
We have not done much work on aid yet, but plan to do that. I think their work is hugely valuable, surely some of the most important social science research that there is. But I also think that it is true that critics have a point that the focus on these aspects shouldn't come at the cost of forgetting that really large transformations are needed to improve health, prosperity, education very substantially – this was a very good episode on Russ Roberts podcast about that: http://www.econtalk.org/archives/2017/05/lant_pritchett_1.html
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scholarly communication doesn’t just happen in journals
Could you let the universities know about that?
I am a labor economist who does only the most boring data visualizations (regression tables, maybe a scatter plot or two). Do you have suggestions for good ways to dip my toe into more interesting/informative data visualizations, either for presentations or for papers? I love your stuff BTW, and use it when teaching undergrads about some broad global trends.
Great to hear that you use our work in teaching!!
One little advice that I think might sometimes be useful for researchers in particular is to spend less time on teaching yourself the very difficult skills of making good visualizations with a statistical package (R, Python, Stata, etc.) and instead learning how to use a vector-graphic-application such as Illustrator or the free and open-source Inkscape.
What I see sometimes is that people write pages of code in a statistical software package and spend a lot of time to then only produce an okayish graphic. What often is much easier and leads to better results is to use the statistical software package mostly for the actual statistical work and then only produce a very basic graphic and to then take that graphic into Inkscape and finish it there.
What is the best way to find reliable sources with which to construct data visualizations?
I can only speak a bit about global development data.
The best is to search whether you can find an assessment of researchers in that field that did an evaluation of the available data. A paper like this one on political regimes for example. Or that one by Atkinson and Brandolini on inequality measures. Or books that more broadly criticize data, like for example this one by Morten Jerven.
Another option is to rely on researchers in the field and either ask them or assume that they have carefully considered which data to use and follow them.
We also try to help with this on Our World in Data:
On OWID we end each entry with a list of data sources that refer you to the most important data repositories on that topic. https://ourworldindata.org/income-inequality/#data-sources
And in each interactive visualization on Our World in Data it is possible to download the data directly from the chart. If you take any chart – for example this one – and you switch to the Data tab you can download a clean csv with that data.
1) What data most surprised you over the course of this project? (Wow this is higher/lower than I thought, etc.)
2) What effect, if any, has the anti-vaccination movement had on viral disease in developed countries? Developing countries?
3) In the same vein, what statistical comparisons can be made between developed countries and developing countries that shows the benefits of vaccination and/or the risks of not vaccinating?
Thank you for doing this AMA! I hope to see your reply!
The first one I have answered elsewhere. Many, many things. Just like everyone else on the planet I don't have an intuition for statistics and am wrong all the time if I rely on my tiny one-person perspective.
On 2 and 3 we are actually currently working on! We are right now doing several projects on vaccinations and will have them online in the next months. We are collaborating with Samantha Vanderslott from the University of Oxford on that and she has previously published on related questions – and used OWID for that as well. For example this one is relevant Despite scepticism, Europe has high vaccination rates – but it shouldn’t be complacent.
What is your biggest area of missing data? How would you want to collect that data?
In general I am mostly surprised what detailed data is available and how useful much of this data is. But you are right that there are also big aspects where we would love to have data that is not available.
We are never out in the field to collect our own data. Instead we often bring together data that is available across many different publications and build a new dataset based on that. This year we have done that for the history of famines – here is our entry. Joe Hasell, who was doing most of this work, read through the literature and searched for estimates of the mortality caused by famines over the last 150 years. In a similar project that we want to start soon we want to build a good global dataset on wars over the long run. We hope to cover this properly next year.
Elsewhere in this AMA I mentioned that data that would be very valuable for the world would cover the quality of education around the world so that we can better understand how to turn schooling into teaching.
What are the five books that you would recommend to a history undergrad which could prepare her for the disruptive world that is to come and also provide her the understanding of the way our world has been shaped over millennia?
Some books about development that I can recommend are:
Amartya Sen's Development as Freedom. It is not up-to-date anymore of course, but it is a fantastic book about development.
Angus Deaton's The Great Escape is very good about the global history of poverty, inequality, and health.
Steven Pinker's The Better Angels of Our Nature on the history of violence.
Federico's Feeding the World is a good (somewhat technical) history of agriculture.
Ruth DeFries' The Big Ratchet is a good (less technical) history of agriculture.
About economic history in particular:
The books by Joel Mokyr if you have time.
And if you don't have much time for economic history then maybe Robert Allen's A Very Short Introduction: Global Economic History
How does a project like Our World In Data start? What do you feel is the biggest difference from what the project was supposed to be compared to what is has become today?
Right now I am trying to get back to the initial idea of writing a book.
Pineapple on pizza or no?
If there is pizza I won't complain.
Dr. Roser, with the Gates Foundation's grant ending next month, can you please give a sense of OWID's future path in the near term? Are other sources of funding secured, and if not is there some risk to the current team, and how much funding would you need?
Frankly, having followed the project for some time, I am astounded that more people have not realized the value of your work.
We have some support from readers who donated to our work and that could keep us going for some time. Buy as I was mentioning elsewhere here, we have no longer term funding for the time after mid-November, but we are talking to two possible funders. But if these things shouldn't work out it would be a disaster for us yes.
Out of all of the fields of global development you have studied, which one(s) do you believe are in the most need of statistical insight to solve their respective problems?
I like that question!
First I agree with your assumption that measurement matters for making progress on a particular problem. Over the last decades we had really a revolution in development data. With the development of the GDP concept and its measurement we started to focus on growth and while that is very important it is hopefully clear by now that increasing output is a means rather than an end in itself. We also got better data on such ends of development – crucially we got very good data on global health and really made very substantial progress in figuring out what works in improving health and then following through and implementing it. The IHME runs a massive global health project in which they aim to quantify the entire burden of disease (mortality & morbidity) of all people in the world – for data people it should be interesting to check that out. (Much of it is really important, good data – not all of it is beautiful. You would do useful work, if you'd help to change that :-) )
To your main question: A field that I would hope will benefit from better data becoming available is education. In the last decades the world focussed on measuring schooling and we have pretty good data on that – measuring in some form or another how much time children spend in educational institutions. And then we did achieve a lot of progress with respect to these metrics and achieved to actually get children into school, at least primary schools. But of course the aim should not be to get children into school, the aim should be to give children a good education. And unfortunately we have much less data on what children actually learn. And the little data that we have is sometimes not very encouraging – in some places, children with several years of education only achieve small gains in actual learning. Lant Pritchett has a great book about this if you are interested.
What we know from the little data we have is that there are very large differences between schools and educational systems and so I hope that once we understand better where these differences come from we can achieve progress in a very important aspect and give children today a good education. This is an aspect that will be so very important in a world in which technological change will mean we will have to do very different jobs in just some years.
How do you factor in and account for places that may not keep good health records nationally? (e.g South Sudan, Somalia, India)
True, in many places health and vital statistics are far from perfect. This map gives an idea by showing how even on a very basic measure the coverage can be very low in some places.
What we don't do on OWID is to estimate data ourselves. We always rely on the work of others and see our job as bringing the available data together and not as creating new estimates – that would be way too much for our small team. So when there is just no data then we simply don't show any data. But often enough researchers find ways to estimate the health data from other sources. These can be surveys like the DHS or MICS or similar ones. And in other cases the estimates can be modeled – this is for example done for maternal mortality (here is the documentation) when the records are not complete.
Elsewhere I have already mentioned the massive efforts at the IHME where researchers are aiming to estimate health statistics in very large detail for the entire world population. You can see this data here. The documentation of how they arrive at these estimates are on their site and many of these studies are published in The Lancet, the last one is here – it is all open-access!
Someone currently pointed out (on your project's Facebook site IIRC) that the decrease of relative poverty in the world could be read as an argument for capitalism. What's your stance there?
I agree that markets with private owners aiming to increase profits have been important in achieving economic growth which led to the very substantial reduction in global extreme poverty.
What I would not agree with is that free markets are the one reason why poverty declined. What is fundamentally important for growth is innovation that boosts productivity and many properly important innovations (including the one that we are right now using, the Internet) have been supported by research and development activities funded by governments rather than capitalists.
A second related point is that social policies have been very important in helping the worst off in societies and often these policies are most important when innovations have disrupting effects on labor markets. These disruptions – caused by technological innovations or international trade – can be very beneficial for every one over the long run, but they can be painful over the short run and a social safety net can then be extremely important. My colleague Esteban Ortiz-Ospina has just recently written this short text on OWID in which argues that "Both economic theory and the empirical evidence from the fight against extreme poverty suggest that this is a mistake: globalization and social policy should be treated as complements rather than substitutes."
The way information is presented is bound by what the currently available visualization tools provide. There has been continuous progress in this area as well. What types of visualizations, interactions and behaviors would you have in mind if you imagined OurWorldInData so much in the future that you could wish for anything?
We are not really limited by that because the 'visualization tool' that we use is developed by us – elsewhere on this page I discussed the 'Grapher' already.
But your question is of course still relevant because we are always planning which visualizations we want to make available next.
A set of features that we want to work on soon should focus on making comparisons over time more easily possible for the reader in line charts and maps.
We have done this already for scatter plots:
– You can simply look at the correlation between two variables – like child mortality and the number of children per woman: https://ourworldindata.org/grapher/fertility-vs-child-mortality?stackMode=absolute
– To see the change over time you can then change it to a connected scatter plot by clicking on the time slider below (or pressing play) – you will then get this view: https://ourworldindata.org/grapher/fertility-vs-child-mortality?stackMode=absolute&time=1950..2015
– And if you would then be interested in the correlation of the change in both measures you can click the tick-box below the chart and you get this view: https://ourworldindata.org/grapher/fertility-vs-child-mortality?stackMode=relative&time=1950..2015
For line charts and maps we want to make it possible that you can explore change in similar ways. So that on maps you cannot only see the child mortality rate in 1980 or in 2015, but also the relative change in each country over this period. Or in line chart you will be able to index the measures to a particular year and see the change since then.
For completely new visualizations I would really like to see this type of a Marimekko chart in an interactive visualization. It is not the most intuitive chart, but once you are familiar with it, it can be very helpful for showing global changes.
What was the most surprising/unexpected result that you got from any of your Data?
There were very many, especially at the beginning. I have written about it some hours ago here: https://www.reddit.com/r/dataisbeautiful/comments/76yknx/hi_reddit_i_am_max_roser_founder_of_the_online/dohrcwp/
Hi! Do you have any data or insight into how institutions and policies affect development? Particularly I’m interested in public institutions such as government, and also social policies. Thanks in advance.
That is an extremely wide question, what aspect would you be interested in?
There has been a lot of research on this wide field and maybe a useful starting point to read this literature would be the work of Douglass North. More recently Daron Acemoglu did important work in this field and he also wrote a popular book that you might find interesting: Why Nations Fail.
Is every part of the world better off than it was 100 years ago? How about 50 years?
That will depend very much on what aspects you value most. For many places in the world I think it is fortunately hard to argue that people there are not better off than a century or half a century ago. In places where political freedom increase, life expectancies doubled, incomes of the entire population increased several-times, self-reported life satisfaction increased etc. it is hard to argue that things are not better.
But there are unfortunately some (few) places in the world where these changes have not been as positive. In some of the poorest places of the world incomes have been stagnating – this chart shows this for the last 5 decades. It is not that people in these places are clearly much worse off than before in terms of income, but that is because things could not become much worse.
In other aspects however even the worst-off places made substantial progress. This chart shows that in all countries of the world child mortality has declined hugely. While in 1950 there were many countries in which more than 15% of all children died before they were 5 years old, there is no country in the world where this is the case today.
Hi Max! Thanks for taking the time to answer some of our questions!
What do consider the most immediate problem we ought to focus on wherein the problem is understood to be workable if allocated the resources? Also - are you ever in doubt at humanity's capacity to sustain itself in light of the data you must be cognizant of? Are we doomed? Are you hopeful?
Regarding the last part of your question. Yes, I am worried that there are some problems that are so difficult to solve and pose such a high risk that they can endanger humanity's survival. I certainly don't think we are doomed since it is far from certain that any of these risks will indeed threaten our survival, but I do think we should prepare for the possibility of a pandemic and take the risk of a nuclear war very seriously. It is not that the probability of these or other bad things occurring is very, but if one of these things happen it might be very bad.
Regarding the future of global living conditions: What would definitely be wrong is to assume that because things got better in the past they necessarily will continue to get better in the future. Things got better because we did research, pushed for innovations, fought for political freedom, and we need to continue to do that if we want to achieve further progress. So the question is whether one is optimistic that the majority of this planet's people will continue this fight. And about that I am optimistic, yes. The world population has never been better educated, never been healthier, never been richer, and these are pretty good conditions to make difficult things possible.
About the first part of your question – where should we focus our efforts to achieve progress – I can recommend the very good work that the various 'effective altruist' communities have put together. Effective altruists are asking exactly the kind of question you are asking. What should we do if we want to do most good? And much of their writing is very thoughtful and importantly also easily accessible on the web. The community 80,000 hours helps people to think about which career they could chose to do good work and on a personal level their career guide might be helpful and if you want to have a big picture answer to your question have a look at their List of the most urgent global issues.
Hi, Max, why do peripheric countries not have as much data as developed ones? Is it harder to get it?
Collecting good data is expensive and a fundamental issue in development research is that the available data and the quality of that data correlates with the prosperity of the country.
That is true for the poorer countries in the world today, and it is true over time – where we don't have much information about the distant past when our ancestors were much poorer than we today.
One way to deal with that is to look for proxy measures, for the past researchers for example rely on human height as a proxy for food provision and health. (Here is our entry on height.)
Max, great stuff.
I think there is a correlation between air conditioning and obesity and climate. ie warmer places who are too poor for AC have much less obesity than rich places in the same climate region. Might be an idea for a info graph?
Keep up the good work.
You might find this recent Nature paper interesting: http://www.nature.com/nature/journal/v547/n7663/full/nature23018.html
"Here we leverage the wide usage of smartphones with built-in accelerometry to measure physical activity at the global scale. We study a dataset consisting of 68 million days of physical activity for 717,527 people, giving us a window into activity in 111 countries across the globe. We find inequality in how activity is distributed within countries and that this inequality is a better predictor of obesity prevalence in the population than average activity volume."
And physical activity is surely correlated to temperature as you can see in the map already.
Here is a shorter write-up by Stanford: https://news.stanford.edu/2017/07/10/stanford-researchers-find-intriguing-clues-obesity-counting-steps-via-smartphones/
Absolutely love your work and will sink the next few evenings into OurWorldInData. Thank you for bringing such an unbiased perspective.
Question for a friend: Do you think data visualization is dead or thriving?
That is great to hear! I hope you will have a good time on OWID!
I think data visualization is only beginning. In the last years we saw new technologies that made it so much easier to do useful visualizations, we have an audience that is better educated, and we have crucially much more, and mostly much better data than in the past. There wasn't that much useful information to visualize in the past. Today there is.
Hey Max! I'm kind of a fan-boy of your project, and have been using it regurarly in debates and research over the last few years. From your work it's pretty evident that every welfare index regarding makind has been improving, but there's one outlier: the environment. Apart from deforestation, I find the most worrying finding that carbon emissions are quite strongly correlated with GDP, and global warming has already begun to lead to huge issues (e.g. water poisoning, the biggest coral reef is already mostly dead). I would like your opinion on the topic: will technology manage to lessen this correlation in the next few decades or will "progress" start to bring about our destruction?
Very nice to hear that you could use our work in discussions and research! That is great!
Yes the correlation between prosperity and CO2 emissions is shockingly strong. I don't think it is a question that this correlation become less steep in the future. The main question seems to be whether it will become so fast enough. And there I am less optimistic, but I am also definitely not an expert in forecasting the changes in technology that we will see.
Here is a useful paper on forecasting these changes: https://arxiv.org/abs/1502.05274
And about the broader point Hannah has just recently written: How long before we run out of fossil fuels?.
Do you think that data visualization is "only" a tool to give insights into the status quo or can it (the visualization itself, not the processes that follow) be an active tool for transformation?
I wouldn't want anyone working for transformations without making sure to get some insight before. Insight is necessary and the visualization of data can be pretty insightful.
There are also many, many examples where the visualization of data had that role. The Ghost Map is a famous example, also Florence Nightingale's data visualizations on deaths in the army, and we recently did another video with Kurz Gesagt that tells the history of how we started to understand why mothers died so very often: Measurement Matters: Saving Mothers' Lives – https://www.youtube.com/watch?v=6Ju8yP_ZHR0
Hi! I am considering changing to a plant vlbased diet. Have you found any evidence in the data you work with that suggests a olant based diet is better for the environment? Thank you for your time! The data visualizations are eye opening.
Yes, it is the argument that also compelled me to eat at least much less meat.
Hannah Ritchie recently wrote a post to answer the question "How much of the world’s land would we need in order to feed the global population with the average diet of a given country?"
And the answer to that question has a lot to do with how much meat (and which meat) people eat.
And there will be more research on this on our site in the next weeks.
The charts and info on https://ourworldindata.org are seriously brilliant because they condense massive amounts of data into immediately-comprehensible charts, graphs and maps.
That is great to hear! Thanks!
We have come along way as a #HumanRace however it seems we cant catch up it always seems eg to eradicate poverty. Yes we are halving problems but barely eradicating them. Is it the the exponential population boom that is hindering us?
zoniation has already answered part of your question, but I wanted to get back to your point about population growth.
Yes, over the last two centuries we saw very rapid population growth. From around 1 billion people in 1800 to over 7 billion people today – all data are shown in our entry on global population growth. And while it is true that rapid population growth makes some things harder, there are other aspects that get easier. More people working together gets you ahead faster. If you are interested in this have a look at this paper by Michael Kremer.
For the future we know that rapid population growth is coming to an end. One of the most important changes of all is that the global 'total fertility rate' has halved over the last 50 years. The total fertility rate measures how many children a woman on average has during her life. In the past women had 5, 6, 7, or even more children on average, but the population did not grow because the health of the population was so poor that many died before they could have children themselves. Rapid population growth then happened in the time when fertility rates were still high, but mortality was already low. Now after decades of declining fertility rates this period is coming to an end.
Together with the awesome team from Kurz Gesagt we made a video to tell this story: https://www.youtube.com/watch?v=QsBT5EQt348
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