How does one draw the dynamics of a changing curve? This article will be dedicated to experiments of drawing the evolution of the whole yield curve over time.
I saw that Equatorial Guinea, the African country that increased its GDP per capita the most during 1980-2018 had done this due to newly found oil. I saw that Gabon, the African country that decreased its GDP per capita the most during the same period, had done this due to diminishing its oil reserves. So I thought, the same applies to the most of rapidly growing (or contracting) African economies, but that is not true (except for Libya).
The sources of growth might not be very sustainable just like oil, but finding them requires a deeper analysis of every separate country.
What: GDP per capita divided into oil rents, rents from other natural resources and GDP from other sources. Grey bars indicate full GDP value with the unknown division. When: From 1980 till 2018. Not all countries had a full range of data. No country had its GDP divided for 2018. Where: Countries of the African continent with the biggest GDP per capita growth during 1980-2018 (top 8) and biggest fall (bottom 4, except South Sudan, which had very short data range) Source: WB
To see nominal numbers of debt increasing won’t tell us much, so it is better to look at debt expressed as percentage of GDP. Also we need to compare current growth to something, so I am comparing growth in 10 years until the most recent data (2008-2018) with growth in 10 years until the Great Recession (1998-2008).
One thing is seen at once – the governments in most countries are getting more debt than before.
Corporations and household are increasing their debt in more countries than decreasing, but the rate of increase is slower now and more countries are decreasing than before. We’re less crazy than in those crazy times.
What: Debt made of loans and debt securities expressed in % of GDP. When: 1998 – 2018 Where: 103 countries of the world. Iceland was removed from the chart and they know why. (Because of extreme numbers, debt levels reaching over 700%). Also, there might be some bias in the data, because not all countries have data for all periods and all debt receivers. Source: BIS
We spend more and more money on movies even in times of television and the internet. So, not a big surprise, that more and more movies are being made.
Median rating of all movies in the IMDB database is declining. Is it a sign of movies getting worse? No, its a sign of more movies being made, when this art is accessible to not only exceptional talents but mediocre talents as well. Anyway, who watches 2000 movies a year? Let’s become picky.
Let’s pick the most popular 200 movies each year and calculate their median. Now there is a stable trend – median popular movie usually has a rating of just around 6.4. (Movie popularity is measured in the number of ratings on IMDB). That’s good.
Now let’s take 200 top-rated movies each year with at least 25 000 ratings. A sharp decline becomes visible! And before the 80s not many such movies existed at all. So, if you like only top-rated popular movies, you might experience deterioration in their quality. Sequels of Transformers are getting worse and worse.
However there are great lesser-known movies, especially those made in non-English speaking countries, so they get less attention and therefore fewer ratings. I’d subjectively say that a good lesser-known movie has at least 4000 ratings. If we pick 200 best such movies, we get a different view – often a median movie will have a rating higher than 7, and no permanent downward trend is present. Great!
What: Movie earnings in USD billion adjusted for inflation. The number of movies – with more than 100, 4000 and 25 000 ratings. IMDB movie ratings indicate the median rating calculated for every year, and a trend line fitted using a generalized additive model. All movies – those which have at least 100 ratings Most popular movies – 200 most rated but having at least 100 ratings (because there were times when less than 200 movies were made). Top-rated popular movies – 200 top-rated movies from those having at least 25 000 ratings. Top-rated movies for cinephiles – 200 top-rated movies from those having at least 4000 ratings. If less than 40 movies left remaining after filtering – no rating calculated for that year. When: 1968 – 2019 Where: Movie earnings represent earnings around the globe. Source: IMDB for ratings and number of movies the-numbers.com for movie earnings. WB and IMF for inflation used to adjust earnings.
The good news is that we are really reducing poverty. Fewer and fewer people live for 1,90 dollars a day, fewer and fewer experience hunger, fewer and fewer experience struggle to get water.
The sad news is the realization that behind nice world trends there are still countries where one-third of the total population are poor, experience hunger and struggle to get water. And even worse – while over 40% of people in low-income countries don’t have access to BASIC drinking water service, 73% don’t have access to SAFE drinking water service.
But we’re reducing. There is still much to achieve and let’s hope for the best.
What: Very poor people = Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population), Hungry people = Prevalence of undernourishment (% of population), People not having water = People not using at least basic drinking water services (% of population) When: Improvements by income class are shown between 2000 and 2017 with possible deviations a year or two for different category combinations. Where: Total world + the world divided into 4 income groups by the World Bank. Source: WB
That seems not to be the case. There are many wars in Africa, but not so much money spent. Also, China and countries in Western Europe spend a lot, however, they’re not actively fighting (some missions to some hot spots do not count).
In the Middle East – the boiling point of wars – the biggest spenders are Saudi Arabia, which fights satellite war in Yemen, and Israel which fights against Palestine (as I understand it). But many more casualties come from Iraq, Syria, and Afghanistan, all of which seem to be a mixture of civil wars and satellite wars.
The special case is the USA which will have its separate chart.
This topic is so geopolitical, that I refrain from diving into deeper conclusions, but it’s tempting to say, that more weapons do not make more deaths. More stupidity does.
What: > Military expenditure in constant USD – some data for USSR and UAE were interpolated using very rough methods – just to fill the gaps and avoid fake jumps. This data have lots of gaps. > Deaths due to Conflict and Terrorism. When: 1980-2018 (Data for deaths until 2017) Where: Probably the whole world Source: Institute for Health Metrics and Evaluation for deaths + Stockholm International Peace Research Institute for expenditure
