Shanghai's skyline at night. According to the GDP (PPP) map, China is the world's largest economy. But that oft-cited statistic says more about the problems of PPP as a yardstick than about the economic prominence of China per se.Credit: Adi Constantin, CC0 1.0
If you want to rank the regions and countries of the world, area and population are but crude predictors of their importance. A better yardstick is GDP, or gross domestic product, defined as the economic value produced in a given region or country over a year.
Who's hot and who's not
And these two maps are possibly the best instruments to show who's hot and who's not, economically speaking. They are in fact cartograms, meaning they abandon geographic accuracy in order to represent the values of another dataset, in this case GDP: the larger a region or country is shown relative to its actual size, the greater its GDP, and vice versa.
So far, so familiar. What's unique about these maps is how this is done. Both are composed of hexagons, exactly 1,000 each. And each of those hexagons represents 0.1 percent of global GDP. That makes it fascinatingly easy to assess and compare the economic weight of various regions and countries throughout the world.
Did we say easy? Scratch that. GDP comes in two main flavors: nominal and PPP-adjusted, with each map showing one.
Nominal GDP does not take into account differences in standard of living. It simply converts local GDP values into U.S. dollars based on foreign exchange rates. GDP adjusted for purchasing power parity (PPP) takes into account living standards. $100 buys more stuff in poor countries than it does in rich countries. If you get more bang for your buck in country A, its PPP-adjusted GDP will be relatively higher than in country B.
Nominal GDP is a good way of comparing the crude economic size of various countries and regions, while GDP (PPP) is an attempt to measure the relative living standards between countries and regions. But this is also just an approximation, since it does not measure the distribution of personal income. For that, we have the Gini index, which measures the relative (in)equality of income distribution.
In other words, PPP factors in the high cost of living in mature markets as an economic disadvantage, while giving slightly more room to low-cost economies elsewhere. Think of it as the Peters projection of GDP models.
Who's number one: the U.S. or China?
The economy of the world, divided into a thousand hexagons.Credit: BerryBlue_BlueBerry, reproduced with kind permission
The difference is important, though, since the versions produce significantly different outcomes. The most salient one: on the nominal GDP map, the United States remains the world's largest economy. But on the PPP-adjusted GDP map, China takes the top spot. However, it is wrong to assume on this basis that China is the world's biggest economy.
As this article explains in some detail, PPP-adjusted GDP is not a good yardstick for comparing the size of economies – nominal GPD is the obvious measure for that. GDP (PPP) is an attempt to compare living standards; but even in that respect, it has its limitations. For example, $100 might buy you more in country B, but you might not be able to buy the stuff you can get in country A.
Both maps, shown below, are based on data from the IMF published in the first quarter of 2021. For the sake of brevity, we will have a closer look at the nominal GDP map and leave comparisons with the PPP map to you.
For the nominal map, global GDP is just over U.S. $93.86 trillion. That means each of the hexagons represents about U.S. $93.86 billion.
The worldwide overview clearly shows which three regions are the world's economic powerhouses. Despite the rise of East Asia (265 hexagons), North America (282) is still number one, with Europe (250) placing a close third. Added up, that's just three hexagons shy of 80 percent of the world's GDP. The remaining one-fifth of the world's economy is spread — rather thinly, by necessity — across Southeast Asia & Oceania (56), South Asia (41), the Middle East (38), South America (32), Africa (27), and North & Central Asia (9).
California über alles
California's economy is bigger than that of all of South America or Africa.Credit: BerryBlue_BlueBerry, reproduced with kind permission
Thanks to the hexagons, the maps get more interesting the closer you zoom in on them.
In North America, the United States (242) overshadows Canada (20) and Mexico (13); and within the U.S., California (37) outperforms not just all other states, but also most other countries — and a few continents — worldwide. To be fair, Texas (21), New York (20), Florida (13), and Illinois (10) also do better than many individual nations.
Interestingly, states that look the same on a "regular" map are way out of each others' leagues on this one. Missouri is four hexagons but Nebraska only one. Alabama has three but Mississippi only one.
The granularity of the map goes beyond the state level, showing (in red) the economic heft of certain Metropolitan Statistical Areas (MSAs), within or across state lines. The New York City-Newark-Jersey City one is 20 hexagons, that is, 2 percent of the world's GDP. The Greater Toronto Area is five hexagons, a quarter of all of Canada. And Greater Mexico City is three hexagons. That's the same as the entire state of Oregon.
By comparison, South America (32) and Africa (27) are small fry on the GDP world map. But each little pond has its own big fish. In the former, it's Brazil (16), in particular, the state of São Paulo (5), which on its own is bigger than any other country in South America. In Africa, there is one regional leader each in the north, center, and south: Egypt (4), Nigeria (5), and South Africa (3), respectively.
Economically, Italy is bigger than Russia
Europe's "Big Five" represent three-fifths of the continent's GDP. The Asian part of the former Soviet Union is an economic afterthought.Credit: BerryBlue_BlueBerry, reproduced with kind permission
Europe is bewilderingly diverse, so it helps to focus on the "Big Five" economies: Germany (46), UK (33), France (31), Italy (22), and Spain (16). They comprise three-fifths of Europe's GDP.
Each of these five has one or more regional economic engines. In Germany, it's the state of North Rhine-Westphalia, and in France, it's Île de France (both 10). In the UK, it's obviously London (8), in Italy Lombardy (5), and in Spain, it's a photo-finish between Madrid and Catalonia (both 3).
Interesting about Europe's economies are the small countries that punch well above their geographic and/or demographic weight, such as the Netherlands (11) and Switzerland (9).
Slide across to Eastern Europe and things get pretty mono-hexagonal. Poland (7) stands out positively and Russia (18) negatively. The former superpower, spread out over two continents, has an economy smaller than Italy's. Three individual German states have a GDP larger than that of the Moscow Metropolitan Area (5), the seat and bulk of Russia's economic power.
China, the biggest fish in a big pond
Australia and South Korea's GDPs are about equal, and each is about a third of Japan's. But even put together, these three add up to barely half of China's economic weight.Credit: BerryBlue_BlueBerry, reproduced with kind permission
In the 1980s, the United States was wary of Japan's rise to global prominence. But as this map shows, that fear was misguided — or rather, slightly misdirected. It's China (177) that now dominates the region economically, putting even the land of the Rising Sun (57) in the shade. South Korea (19) and Taiwan (8) look a lot larger than on a "regular" map, but it's clear who rules the roost here.
Interestingly, China's hubs are mainly but not exclusively coastal. Yes, there's Guangdong (19), Jiangsu (18), and Shandong (13), plus a few other provinces with access to the sea. But the inland provinces of Henan (10), Sichuan (9), and Hubei (8) are economically as important as any mid-sized European country. Tibet (1) and Xinjiang (2), huge on the "regular" map, are almost invisible here.
In the ASEAN countries (36), Thailand (6), Singapore (4), and the Indonesian island of Java (7) stand out. Economically, Oceania is virtually synonymous with Australia (17) — sorry, New Zealand (3).
As for South Asia and the Middle East, India (32) is clearly the dominant player, outperforming near neighbors Bangladesh (4) and Pakistan (3), as well as more distant ones like Saudi Arabia (9), Turkey (8), and Iran (7). But that's cold comfort for a country that sees itself as a challenger to China's dominance.
The PPP-adjusted GDP world map looks slightly different from the nominal GDP one. China is the #1 country and East Asia the #1 region.Credit: BerryBlue_BlueBerry, reproduced with kind permission
Maps created by Reddit user BerryBlue_BlueBerry, reproduced with kind permission. For a closer look and for detailed rankings of the regions, check out both maps here at the MapPorn subreddit.
Strange Maps #1089
Got a strange map? Let me know at strangemaps@gmail.com.
What's one way to get a quick boost of confidence? If you watched the widely shared 2012 TED Talk "Your body language may shape who you are," you might think the answer is to strike a power pose.
The idea, detailed in a 2010 paper published in Psychological Science, is that striking a triumphant posture for a couple minutes causes neuroendocrine and behavioral changes in people, helping them to feel more powerful and perform better at various tasks.
U.K. political candidatesCredit: Kieron Bryan (@kieronjbryan) / Twitter
In addition to looking ridiculous, the benefits of the "power pose" probably aren't real. Since 2015, more than a dozen studies have tried and failed to replicate the effects reported in that 2010 paper. It's far from the first failed replication.
The replication crisis
Over the past two decades, the repeated failure to reproduce findings in the research literature, especially in the social and biomedical sciences, has been dubbed the replication crisis. Why is it a "crisis"?
Replication is a key principle of the scientific method. Successful replication increases the probability, and therefore confidence, that a given claim or effect is true: After all, if one study finds X, other studies should also find X, assuming they follow or build upon the original study design.
Despite widespread controversies and concern about the replication crisis over the past two decades, there's little evidence that things are getting better. The problem isn't just that many studies are nonreplicable but also that findings from nonreplicable studies continue to be cited by subsequent studies. "Failed papers," as a 2020 analysis dubbed them, "circulate through the literature as quickly as replicating papers."
Bad science travels fast
A new study published in Science Advances suggests the problem may be even worse than we thought, finding that nonreplicable papers receive 16 more citations per year than replicable ones, on average. Over time, that translates to 153 more citations.
This imbalance generally held true even after replication attempts revealed "failed" papers to be nonreplicable. It also persisted after controlling for factors like number of authors, percentage of male authors, language, and location.
Why do journals publish nonreplicable studies? It may come down to hype. "When the results are more 'interesting,' they apply lower standards regarding their reproducibility," the new study suggests.
Stuart Richie made a similar argument in his 2020 book titled Science Fictions: How Fraud, Bias, Negligence, and Hype Undermine the Search for Truth. He suggested that because researchers face institutional pressures to publish papers and earn grants, they're less likely to conduct dry yet valuable "workhouse studies" and more likely to pursue "showy and ostentatious findings" that generate media attention.
In short, incentives may be pushing some researchers away from the pursuit of truth.
Comparing citations
The new research included data from studies featured in three major replication projects conducted between 2015 and 2018. According to the paper, each of the three projects:
"tried to systematically replicate the findings in top psychology, economics, and general science journals. In psychology, only 39% of the experiments yielded significant findings in the replication study, compared to 97% of the original experiments. In economics, 61% of 18 studies replicated, and among Nature/Science publications, 62% of 21 studies did."
The researchers then compared this replicability data with the number of citations those studies received, collected from Google Scholar from the date of publication until the end of 2019. The results showed that when replication projects published data revealing studies to be nonreplicable, there was no significant effect on how often those studies were cited in the future. In other words, the studies continued to be cited, even though they were shown to be incorrect.
The average yearly citation count per year for studies that were not replicated (according to P value of the replication) in each replication study [(A) for Nature/Science, (B) for Economics, and (C) for Psychology papers in replication markets] and for those that were replicated. Serra-Garcia et al.
But couldn't some citations of nonreplicable studies have come from studies that were critical of the past findings? The researchers acknowledged this possibility but noted that only twelve percent of subsequent papers acknowledged that the findings they cited had failed to replicate.
Predicting replicability isn't difficult
Ignorance or a lack of intuition likely doesn't explain why the reviewers of top academic journals accept nonreplicable papers or publish subsequent papers that cite those findings. After all, academics and laypeople alike are quite good at predicting which studies will replicate. A 2020 study found, for example, that laypeople were able to guess the replicability of social science studies with above-chance accuracy (59 percent).
Similarly, a 2018 analysis found that psychologists correctly predicted the replicability of psychology studies with an accuracy of 70 percent, while a 2021 paper found that experts could predict the replicability of behavioral and social science papers 73 percent of the time.
These findings seem to bolster the argument that hype-related incentives are contributing to the replication crisis. Still, in the spirit of replication, it's probably worth waiting until these findings themselves are replicated by future research.
Economic inequality is a constant topic. No matter the cycle — boom or bust — somebody is making a lot of money, and the question of fairness is never far behind.
A recently published essay in the Journal of Economic Literature by Professor Guido Alfani adds an intriguing perspective to the discussion by showing the evolution of income inequality in Europe over the last several hundred years. As it turns out, we currently live in a comparatively egalitarian epoch.
Seven centuries of economic history
Figure 8 from Guido Alfani, Journal of Economic Literature, 2021.
This graph shows the amount of wealth controlled by the top ten percent in certain parts of Europe over the last seven hundred years. Archival documentation similar to — and often of a similar quality as — modern economic data allows researchers to get a glimpse of what economic conditions were like centuries ago. Sources like property tax records and documents listing the rental value of homes can be used to determine how much a person's estate was worth. (While these methods leave out those without property, the data is not particularly distorted.)
The first part of the line, shown in black, represents work by Prof. Alfani and represents the average inequality level of the Sabaudian State in Northern Italy, The Florentine State, The Kingdom of Naples, and the Republic of Venice. The latter part, in gray, is based on the work of French economist Thomas Piketty and represents an average of inequality in France, the United Kingdom, and Sweden during that time period.
Despite the shift in location, the level of inequality and rate of increase are very similar between the two data sets.
Apocalyptic events cause decreases in inequality
Note that there are two substantial declines in inequality. Both are tied to truly apocalyptic events. The first is the Black Death, the common name for the bubonic plague pandemic in the 14th century, which killed off anywhere between 30 and 50 percent of Europe. The second, at the dawn of the 20th century, was the result of World War I and the many major events in its aftermath.
The 20th century as a whole was a time of tremendous economic change, and the periods not featuring major wars are notable for having large experiments in distributive economic policies, particularly in the countries Piketty considers.
The slight stall in the rise of inequality during the 17th century is the result of the Thirty Years' War, a terrible religious conflict that ravaged Europe and left eight million people dead, and of major plagues that affected South Europe. However, the recurrent outbreaks of the plague after the Black Death no longer had much effect on inequality. This was due to a number of factors, not the least of which was the adaptation of European institutions to handle pandemics without causing such a shift in wealth.
In 2010, the last year covered by the essay, inequality levels were similar to those of 1340, with 66 percent of the wealth of society being held by the top ten percent. Also, inequality levels were continuing to rise, and the trends have not ended since. As Prof. Alfani explained in an email to BigThink:
"During the decade preceding the Covid pandemic, economic inequality has shown a slow tendency towards further inequality growth. The Great Recession that began in 2008 possibly contributed to slow down inequality growth, especially in Europe, but it did not stop it. However, the expectation is that Covid-19 will tend to increase inequality and poverty. This, because it tends to create a relatively greater economic damage to those having unstable occupations, or who need physical strength to work (think of the effects of the so-called "long-Covid," which can prove physically invalidating for a long time). Additionally, and thankfully, Covid is not lethal enough to force major leveling dynamics upon society."
Can only disasters change inequality?
That is the subject of some debate. While inequality can occur in any economy, even one that doesn't grow all that much, some things appear to make it more likely to rise or fall.
Thomas Piketty suggested that the cause of changes in inequality levels is the difference in the rate of return on capital and the overall growth rate of the economy. Since the return on capital is typically higher than the overall growth rate, this means that those who have capital to invest tend to get richer faster than everybody else.
While this does explain a great deal of the graph after 1800, his model fails to explain why inequality fell after the Black Death. Indeed, since the plague destroyed human capital and left material goods alone, we would expect the ratio of wealth over income to increase and for inequality to rise. His model can provide explanations for the decline in inequality in the decades after the pandemic, however- it is possible that the abundance of capital could have lowered returns over a longer time span.
The catastrophe theory put forth by Walter Scheidel suggests that the only force strong enough to wrest economic power from those who have it is a world-shattering event like the Black Death, the fall of the Roman Empire, or World War I. While each event changed the world in a different way, they all had a tremendous leveling effect on society.
But not even this explains everything in the above graph. Pandemics subsequent to the Black Death had little effect on inequality, and inequality continued to fall for decades after World War II ended. Prof. Alfani suggests that we remember the importance of human agency through institutional change. He attributes much of the post-WWII decline in inequality to "the redistributive policies and the development of the welfare states from the 1950s to the early 1970s."
What does this mean for us now?
As Professor Alfani put it in his email:
"[H]istory does not necessarily teach us whether we should consider the current trend toward growth in economic inequality as an undesirable outcome or a problem per se (although I personally believe that there is some ground to argue for that). Nor does it teach us that high inequality is destiny. What it does teach us, is that if we do not act, we have no reason whatsoever to expect that inequality will, one day, decline on its own. History also offers abundant evidence that past trends in inequality have been deeply influenced by our collective decisions, as they shaped the institutional framework across time. So, it is really up to us to decide whether we want to live in a more, or a less unequal society."
