A cartogram makes it easy to compare regional and national GDPs at a glance.
- On these maps, each hexagon represents one-thousandth of the world's economy.
- That makes it easy to compare the GDP of regions and nations across the globe.
- There are versions for nominal GDP and GDP adjusted for purchasing power.
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
Strange Maps #1089
Got a strange map? Let me know at firstname.lastname@example.org.
Information economics suggests that "no news" means somebody is hiding something. But people are bad at noticing that.
- An experiment in information sharing shows that no news often means people have something to hide.
- Other people seem to be blissfully unaware of this.
- The results suggest that market forces are insufficient to "close the information gap" between buyers and sellers.
A proverb that never seems to die is the oft-heard, "No news is good news." However, a new study published in the American Economic Journal: Microeconomics suggests that this not only goes against logic but also reduces returns in an information sharing game.
A marketplace for information
According to the principles of information economics, most firms have an incentive to release information on their products if the cost of doing so is low. The belief is that customers will treat firms that don't release information as being the same, so the one that does provide information — say, about the quality of the manufacturing — looks better in comparison for doing so. Over time, this should lead to more information being disclosed voluntarily as people try to cash in on the effect. An "unraveling" effect also occurs, with customers assuming the worst about those keeping secrets.
However, like a lot of ideas in economics, this one rests on the assumption that consumers will behave rationally or according to theory. Sometimes, people just don't catch on to the fact that some businesses might have an incentive only to reveal good news or to brush unflattering details under the rug.
To help shed light on how people actually behave and why they do so, the researchers set up an experiment based on an information sharing game where participants could win cash prizes for using information economics to their advantage.
An experiment in information economics
The main set of experimental sessions involved two randomly matched players, one as an information sender and one as an information receiver. The sender would be given a secret number between one and five by a computer. They then had a choice to reveal this number to the receiver or not. Lying was not allowed.
The receiver, who either saw the number or a blank space on their screen, then reported what they thought the secret number was. While the senders always saw and submitted whole numbers, receivers could guess any half unit between 1 and 5.
Sender players earned rewards as receiver guesses went higher, no matter what the secret number actually was. Receivers earned more for accuracy, with perfect determination of the secret number getting the most money.
The experimental set-up matches the theory; namely, the best "moves" are for the sender to always show the number unless it is one, and the receiver should always guess the number is one if they don't see a number.
Theory meets reality
But, things get muddy when theory meets reality.
After 45 rounds of play, senders tended to be between 3 and 7 percent off from the highest possible payouts, while receivers were 9 to 13 percent off. Numbers that you would expect to have been shown by the senders remained secret. Receivers made strange guesses, sometimes not trusting the values they saw (despite being told lying was against the rules) or guessing higher than they should have when shown a blank screen.
The authors mention that the receivers often seemed "insufficiently skeptical of nondisclosure" and failed to realize that the sender was probably hiding something from them. This effect could be somewhat mitigated by providing them feedback, though it had to be offered repeatedly for their improvement to be sustained.
They also opined that some receivers might have been confused by the rules of the game and played poorly as a result.
For the first time in a study like this, the researchers also asked the senders what they thought about the receivers. Their choices to reveal the number or not were often driven by a belief that the receivers would respond to not getting any information by guessing just below the middle of the number range rather than the lowest value as they should have.
As it turns out, they were right, with the average blind guess being above two. While the senders were not following the theory by acting this way, neither were their opponents — and thus, they were playing the game optimally.
It seems you really should play your opponent and not your hand.
Market forces are insufficient to close the information gap
The authors summarize the possible real world application of these findings in their report:
"These findings suggest that unless buyers receive fast and precise feedback about mistakes after each transaction, market forces can be insufficient to close the information gap between sellers and buyers. For the products that naturally offer such feedback — say cereals that taste crunchy and t-shirts that hold color fast — voluntary disclosure may converge to the unraveling predictions after a buyer purchases the product many times. However, for product attributes with less immediate feedback — such as the fat content of salad dressing and the cleanliness of a restaurant kitchen — voluntary disclosure may not converge to the unraveling results. In these situations, mandatory disclosure may be necessary if the policy goal is complete disclosure."
Oh, before you go, I promise that I didn't leave out any information from the study. Nothing important anyway.
Say hello to your new colleague, the Workplace Environment Architect.
As some countries begin to pull out of pandemic-induced lockdown, and the corporate engines of "return to the office" begin to whir, an open question hangs: What kind of jobs will people return to following months of work-from-home exile in "Remotopia"?
Will the online "big-bang" of the 2020s (when everything that could go online did go online) accelerate digitally enabled jobs? And which jobs will top the post-pandemic jobs list, in the next, new future of work?
Over the past several years, the Cognizant Center for the Future of Work has published a series of reports on the Jobs of the Future that propose new roles which will emerge over the next decade and be central to businesses and employees everywhere. Because of the virus, time has compressed, resulting in a handful of these jobs of the future becoming 'jobs of the now'.
And the top jobs are...
The following is a top-ten summary of professions emerging in the wake of the pandemic.
1. Work from Home Facilitator – Prior to 2020, it's estimated that less than 5% of companies had remote policies. Now, with the full post-pandemic expectation that remote work remains the norm, companies want to apply lessons learned to optimize the work-from-home experience. Far from being a futuristic job of tomorrow, WFH facilitators have become undeniable "jobs of the now."
2. Fitness Commitment Counsellor – We cringe at the extra kilos, pounds and stones packed-on during months of pandemic-induced lockdown. To remedy the situation, predictive and preventative approaches to counselling, paired with digital wearables like Apple Watches and FitBit dashboards couple human accountability to maintaining fitness. And per the Cognizant Jobs of the Future (CJoF) Index, it's a role that grew 28.7% in Q1 '21.
3. Smart Home Design Manager – A lasting lesson of the virus for many will be that "everyone's home is their castle." The rise of smart home design managers will boom as homes are built – or retrofitted – with dedicated home office spaces, replete with routers in the right place, soundproofing, separate voice-driven entrances, and even Gorilla Glass wall screens.
4. XR Immersion Counsellor – As Zoom-intensive "Remotopia" inexorably gives way to 3D realms of virtual space, XR immersion counselors will work with technical artists and software engineering, training and workforce collaboration leads to massively scale the rollout of best-in-class AR and VR for learn-by-doing workforce training and collaboration (using platforms like Strivr) or apprenticeships (such as Mursion, for example) to get employees productive – fast.
5. Workplace Environment Architect – Everything from health screenings to "elevator commutes" in post-pandemic office architecture is about to go through a major rethink. The importance of employee well-being, and how human-centered design of a company's real estate holdings can impact it, are now crucial to the future of work.
6. Algorithm Bias Auditor – "All online, all the time" lifestyles for work and leisure accelerated the competitive advantage derived from algorithms by digital firms everywhere. But from Brussels to Washington, given the increasing statutory scrutiny on data, it's a near certainty that when it comes to how they're built, verification through audits will help ensure the future workforce is also the fair workforce.
7. Data Detective – Openings for data scientists remain the fastest growing job in the tech-heavy "Algorithms, Automation and AI" family of the CJoF Index since its inception, and continued to see 42% growth in Q1 '21. Given this high demand, they're also scarce; that's where data detectives help bridge the gap to get companies to investigate the mysteries in big data.
8. Cyber Calamity Forecaster – Aside from COVID-19, it's arguable that the other, big catastrophe of 2020 was the continued onslaught of both massive state-sponsored cyberattacks like Solar Winds, down to individual bad actors promulgating ransomware exploits. The ability to forecast events like these is critical to forewarn of culture events. The CJoF Index bears this out: growth in openings for Cyber Calamity Forecasters grew 28% in Q1 '21.
9. Tidewater Architect – The global challenge of climate change and sea level rise will remain an omnipresent challenge. Tidewater architects will work with nature – not against it – in some of the biggest civil engineering projects of the 21st century. And per the CJoF Index, openings for these jobs grew 37% in Q1 '21.
10. Human-Machine Teaming Manager – Pandemic or no, the unceasing rise of robots in the workplace continues unabated. Human-Machine Teaming Managers will operate at the intersection of people and robots and create seamless collaborations. Already, openings for forerunner roles like robotics technicians grew 50% in the Q1 '21 CJoF Index.
While it is impossible to predict exactly how global labour markets will rebound in the wake of the virus, leaders can and should use the future of work as a prism for their own organizations to plan ahead. If there's one lesson the pandemic has taught us, it's to anticipate change.
Leaders need to see how the future of work will play out in real time through leading indicators that reveal how the jobs market is adapting in the face of technology-based innovation and disruption. The CJoF Index uses real data on US job openings to see the imagined possibilities of jobs of the future starting to emerge.
By combining strategic planning resources like "21 Jobs of the Future" and the CJoF Index, it's possible to get a look into the not-too-distant future to see which roles are the top contenders in the post-COVID future.
2021 will be a reset moment, a period where more examples of the theoretical become "jobs-made-real". Before they can be built, however, jobs of the future have to be dreamed - and this requires vision and some imagination.
Science journals may be lowering their standards to publish studies with eye-grabbing — but probably incorrect — results.
- Science is facing a replication crisis, namely, that many studies published in top journals fail to replicate.
- A new study examined the citation count of "failed" studies, finding that these nonreplicable studies accumulated 153 more citations than more reliable research, even after they are shown to be nonreplicable.
- The study suggests the replication crisis might be driven, in part, by incentives that encourage researchers to generate "interesting" results.
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.
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.
As bad as this sounds, a new essay suggests that we live in a surprisingly egalitarian age.
- A new essay depicts 700 years of economic inequality in Europe.
- The only stretch of time more egalitarian than today was the period between 1350 to approximately the year 1700.
- Data suggest that, without intervention, inequality does not decrease on its own.
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."