And how ‘bout them Dodgers, hunh? Actually, how about each division’s top team? That’s a lot of winning!
— A partial response to Marx’ claim that managers are expropriating the value produced by the workers while providing nothing themselves: “The study showed that managers didn’t just influence the results their teams achieved, they explained a full 70% of the variance. In other words, if it’s a superior team you’re after, hiring the right manager is nearly three-fourths of the battle.”
— Boudreaux wonders what supposedly-enormous transaction cost prevents firms from offering workers a choice of pay packages – buying more parental time for a lower wage, for instance. One commenter notes their firm does just that, letting workers buy back vacation time. This is also, of course, standard practice in much of academia, where faculty are allowed to reduce their teaching load in exchange for a salary cut – usually funded by a research grant.
— Sumner on how labor market reforms (including cutting unemployment benefits) helped Germany and Israel to lower average unemployment rates and increase economic growth.
— But there appears to be a great deal that only deregulation will not be able to change. A new paper by Berger and Engzell finds correlation between the European-country-of-origin of people in modern US and the level of inequality and intergenerational mobility. Institutions persist for a very, very long time … again. (Homework: How does this apply to the reparations debate?)
— Another new paper by Fone, Sabia, and Cesur finds that higher minimum wages increase property crime arrests – contra expectations – so that “a $15 Federal minimum wage could generate criminal externality costs of nearly $2.4 billion.”
— A history of civil asset forfeiture tells how the British Crown’s attempt to encourage the Royal Navy to enforce trade restrictions and tariffs became so widely used in modern America.
— Summers and Sarin show that wealth taxes will take in much less than their proponents hope.
For some years now, Phil Magness and myself have been working on improving the existing income inequality for the United States prior to World War II. One of the most important point we make concerns why we, as economists, ought to take data assumptions seriously. One of the most tenacious stylized facts (that we do not exactly dispute) is that income inequality in the United States has followed a U-curve trajectory over the 20th century. Income inequality was high in the early 1920s and descended gradually until the 1960s and then started to pick up again. That stylized fact comes from the work of Thomas Piketty and Emmanuel Saez with their data work (first image illustrated below). However, from the work of Auten and Splinter and Mechling et al. , we know that the increase post-1960 as measured by Piketty is somewhat overstated (see second image illustrated below). While the criticism suggest a milder post-1960 increase, me and Phil Magness believe that the real action is on the left side of the U-curve (pre-1960).
Why? Here is our case made simple: the IRS data used to measure inequality up to at least 1943 are deeply flawed. In another paper recently submitted, I made the argument that some of the assumptions made by Piketty and Saez had flaws. This did not question the validity of the data itself. We decided to use state-level income tax data from the IRS to compute the state-level inequality and compare them with state-income tax data (e.g. the IRS in Wisconsin versus Wisconsin’s own personal income tax data). What we found is that the IRS data overstates the level of inequality by appreciable proportions.
Why is that? There are two reasons. The first is that the federal tax system had wide fluctuations in tax rates between 1917 and 1943 which means wide fluctuations in tax compliance. Previous scholars such as Gene Smiley pointed out that when tax rates fell, compliance went up so that measured inequality went up. But measured inequality is not true inequality because “off-the-books” income (which was unmeasured) divorced true inequality from measured inequality. This is bound to generate false fluctuations in measurement as long as tax compliance was voluntary (which is true until 1943). State income taxes do not face that problem as their tax systems tended to be more stable throughout the period. The same is true with personal exemptions.
The second reason speaks to the manner the federal data is presented. The IRS created wide categories with the numbers of taxpayers according to net taxable income (rather than gross income) in each categories. For example, the categories go from 0$ to 1,000$ per filler and then increase by slice of 1,000$ until 10,000$ and then by slices of 5,000$ etc. This makes it hard to pinpoint where to start each the calculations for each of the fractiles of top earners. This is not true of all state income tax systems. For example, Delaware sliced the data by categories of 100$ and 500$ instead. Thus, we can more easily pinpoint the two. More importantly, most state-income tax systems reported the breakdown both for net taxable and gross income. This is crucial because Piketty and Saez need to adjust the pre-1943 IRS data – which are in net income – to that they can tie properly with the post-1943 IRS data – which are in adjusted gross income. Absent this correction, they would get an artificial increase in inequality in 1943. The problem is that the data for this adjustment is scant and their proposed solution has not been subjected to validation.
What do our data say? We compared them to the work of Mark Frank et al. who used the same methodology and Piketty Saez but at the state-level using the same sources. The image below pretty much sums it up! If the points are above the red line, the IRS data overestimates inequality. If below, the IRS underestimates. Overall, the bias tends towards overestimation. In fact, when we investigated all of the points separately, we found that those below the red line result merely from the way that Delaware’s (DE) was adjusted to convert net income into gross income. When we compared only net income-based measures of inequality, none are below the red line except Delaware from 1929 to 1931 (and by much smaller margins than shown in the figure below).
In our paper, we highlight how the state-level data is conceptually superior to the federal-level data. The problem that we face is that we cannot convert those measures into adjustments for the national level of inequality. All that our data do is suggest which way the bias cuts. While we find this unfortunate, we highlight that this would unavoidably alter the left side of the curve in the first graph of this blog post. The initial level of inequality would be less than it is now. Thus, combining this with the criticisms made for the post-1960 era, we may be in presence of a U-curve that looks more like a shallow tea saucer than the pronounced U-curve generally highlighted. The U-curve form is not invalidated (i.e. is it a quadratic-looking function of time or not), but the shape of the curve’s tails is dramatically changed.
Doing the economist’s job well, Nobel Laureate Paul Romer once quipped, “means disagreeing openly when someone makes an assertion that seems wrong.”
Following this inspiration guideline of mine in the constrained, hostile, and fairly anti-intellectual environment that is Twitter sometimes goes astray. That the modern intellectual left is vicious we all know, even if it’s only through observing them from afar. Accidentally engaging with them over the last twenty-four hours provided some hands-on experience for which I’m not sure I’m grateful. Admittedly, most interactions on twitter loses all nuance and (un)intentionally inflammatory tweets spin off even more anger from the opposite tribe. However, this episode was still pretty interesting.
It started with Noah Smith’s shout-out for economic history. Instead of taking the win for our often neglected and ignored field, some twitterstorians objected to the small number of women scholars highlighted in Noah’s piece. Fair enough, Noah did neglect a number of top economic historians (many of them women) which any brief and uncomprehensive overview of a field would do.
His omission raised a question I’ve been hooked on for a while: why are the authors of the most important publications in my subfields (financial history, banking history, central banking) almost exclusively male?
Maybe, I offered tongue-in-cheek in the exaggerated language of Twitter, because the contribution of women aren’t good enough…?
Being the twenty-first century – and Twitter – this obviously meant “women are inferior – he’s a heretic! GET HIM!”. And so it began: diversity is important in its own right; there are scholarly entry gates guarded by men; your judgment of what’s important is subjective, duped, and oppressive; what I care about “is socially conditioned” and so cannot be trusted; indeed, there is no objectivity and all scholarly contribution are equally valuable.
Now, most of this is just standard postmodern relativism stuff that I couldn’t care less about (though, I am curious as to how it is that the acolytes of this religion came to their supreme knowledge of the world, given that all information and judgments are socially conditioned – the attentive reader recognises the revival of Historical Materialism here). But the “unequal” outcome is worthy of attention, and principally the issue of where to place the blame and to suggest remedies that might prove effective.
On a first-pass analysis we would ask about the sample. Is it really a reflection of gender oppression and sexist bias when the (top) outcome in a field does not conform to 50:50 gender ratios? Of course not. There are countless, perfectly reasonable explanations, from hangover from decades past (when that indeed was the case), the Greater Male Variability hypothesis, or that women – for whatever reason – have been disproportionately interested in some fields rather than others, leaving those others to be annoyingly male.
- If we believe that revolutionising and top academic contributions have a long production line – meaning that today’s composition of academics is determined by the composition of bright students, say, 30-40 years ago – we should not be surprised that the top-5% (or 10% or whatever) of current academic output is predominantly male. Indeed, there have been many more of them, for longer periods of time: chances are they would have managed to produce the best work.
- If we believe the Greater Male Variability hypothesis we can model even a perfectly unbiased and equal opportunity setting between men and women and still end up with the top contribution belonging to men. If higher-value research requires smarter people working harder, and both of those characteristics are distributed unequally between sexes (as the Greater Male Variability hypothesis suggests), then it follows naturally that most top contributions would be men.
- In an extension of the insight above, it may be the case that women – for entirely non-malevolent reasons – have interests that diverge from men’s (establishing precise reasons would be a task for psychology and evolutionary biology, for which I’m highly unqualified to assess). Indeed, this is the entire foundation on which the value of diversity is argued: women (or other identity groups) have different enriching experiences, approach problems differently and can thus uncover research nobody thought to look at. If this is true, then why would we expect that superpower to be applied equally across all fields simultaneously? No, indeed, we’d expect to see some fields or some regions or some parts of society dominated by women before others, leaving other fields to be overwhelmingly male. Indeed, any society that values individual choice will unavoidably see differences in participation rates, academic outcomes and performance for precisely such individual-choice reasons.
Note that none of this excludes the possibility of spiteful sexist oppression, but it means judging academic participation on the basis of surveys responses or that only 2 out of 11 economic historians cited in an op-ed were women, may be premature judgments indeed.
Timely, both in our post-truth world and for my current thinking, Bobby Duffy of the British polling company IPSOS Mori recently released The Perils of Perception, stealing the subtitle I have (humbly enough) planned for years: Why We’re Wrong About Nearly Everything. Duffy and IPSOS’s Perils of Perception surveys are hardly unknown for an informed audience, but the book’s collection and succint summary of the psychological literature behind our astonishingly uninformed opinions, nevertheless provide much food for thought.
Producing reactions of chuckles, indignation, anger, and unseeming self-indulgent pride, Duffy takes me on a journey of the sometimes unbelievably large divergence between the state of the world and our polled beliefs about the world. And we’re not primarily talking about unobservable things like “values” here; we’re almost always talking about objective, uncontroversial measures of things we keep pretty good track of: wealth inequality, share of immigrants in society, medically defined obesity, number of Facebook accounts, murder and unemployment rates. On subject after subject, people guess the most outlandish things: almost 80% of Britons believed that the number of deaths from terrorist attacks between 2002 and 2016 were more or about the same as 1985-2000, when the actual number was a reduction of 81% (p. 131); Argentinians and Brazilians seem to believe that roughly a third and a quarter of their population, respectivelly, are foreign-born, when the actual numbers are low single-digits (p. 97); American and British men believe that American and British women aged 18-29 have had sex as many as 23 times in the last month, when the real (admittedly self-reported) number is something like 5 times (p. 57).
We can keep adding astonishing misperceptions all day: Americans believe that more than every third person aged 25-34 live with their parents (reality: 12%), but Britons are even worse, guessing almost half (43%) of this age bracket, when reality is something like 14%; Australians on average believe that 32% of their population has diabetes (reality more like 5%) and Germans (31% vs 7%), Italians (35% vs 5%), Indians (47% vs 9%) and Britons (27% vs 5%) are similarly mistaken.
The most fascinating cognitive misconception is Britain’s infected relationship with inequality. Admittedly a confusing topic, where even top-economists get their statistical analyses wrong, inequality makes more than just the British public go bananas. When asked how large a share of British household wealth is owned by the top-1% (p. 90), Britons on average answered 59% when the reality is 23% (with French and Australian respondents similarly deluded: 56% against 23% for France and 54% against 21% for Australia). The follow-up question is even more remarkable: asked what the distribution should be, the average response is in the low-20s, which, for most European countries, is where it actually is. In France, ironically enough given its current tax riots, the respondents’ reported ideal household wealth proportion owned by the top-1% is higher than it already is (27% vs 23%). Rather than favoring upward redistribution, Duffy draws the correct conclusion:
“we need to know what people think the current situation is before we ask them what they think it should be […] not knowing how wrong we are about realities can lead us to very wrong conclusions about what we should do.” (p. 93)
Another one of my favorite results is the guesses for how prevalent teen pregnancies are in various countries. All of the 37 listed countries (p. 60) report numbers around less than 3% (except South Africa and noticeable Latin American and South-East Asian outliers at 4-6%), but respondents on average quote absolutely insane numbers: Brazil (48%), South Africa (44%) Japan (27%), US (24%), UK (19%).
Note that there are many ways to trick people in surveys and report statistics unfaithfully and if you don’t believe my or Duffy’s account of the IPSOS data, go figure it out for yourself. Regardless, is the take-away lesson from the imagine presented really that people are monumentally stupid? Ignorant in the literal sense of the world (“uninstructed, untututored, untaught”), or even worse than ignorant, having systematically and unidirectionally mistaken ideas about the world?
Let me confess to one very ironic reaction while reading the book, before arguing that it’s really not the correct conclusion.
Throughout reading Duffy’s entertaining work, learning about one extraordinarily silly response after another, the purring of my self-indulgent pride and anger at others’ stupidity gradually increased. Glad that, if nothing else, that I’m not as stupid as these people (and I’m not: I consistently do fairly well on most questions – at least for the countries I have some insight into: Sweden, UK, USA, Australia) all I wanna do is slap them in the face with the truth, in a reaction not unlike the fact-checking initiatives and fact-providing journalists, editorial pages, magazines, and pundits after the Trump and Brexit votes. As intuitively seems the case when people neither grasp nor have access to basic information – objective, undeniable facts, if you wish – a solution might be to bash them in the head or shower them with avalanches of data. Mixed metaphors aside, couldn’t we simply provide what seems to be rather statistically challenged and uninformed people with some extra data, force them to read, watch, and learn – hoping that in the process they will update their beliefs?
Frustratingly enough, the very same research that indicate’s peoples inability to understand reality also suggests that attempts of presenting them with contrary evidence run into what psychologists have aptly named ‘The Backfire Effect’. Like all force-feeding, forcing facts down the throats of factually resistent ignoramuses makes them double down on their convictions. My desire to cure them of their systematic ignorance is more likely to see them enshrine their erroneous beliefs further.
Then I realize my mistake: this is my field. Or at least a core interest of the field that is my professional career. It would be strange if I didn’t have a fairly informed idea about what I spend most waking hours studying. But the people polled by IPSOS are not economists, statisticians or data-savvy political scientists – a tenth of them can’t even do elementary percent (p. 74) – they’re regular blokes and gals whose interest, knowledge and brainpower is focused on quite different things. If IPSOS had polled me on Premier League results, NBA records, chords or tunes in well-known music, chemical components of a regular pen or even how to effectively iron my shirt, my responses would be equally dumbfunded.
Now, here’s the difference and why it matters: the respondents of the above data are routinely required to have an opinion on things they evidently know less-than-nothing about. I’m not. They’re asked to vote for a government, assess its policies, form a political opinion based on what they (mis)perceive the world to be, make decisions on their pension plans or daily purchases. And, quite a lot of them are poorly equipped to do that.
Conversely, I’m poorly equipped to repair literally anything, work a machine, run a home or apply my clumsy hands to any kind of creative or artful endeavour. Luckily for me, the world rarely requires me to. Division of Labor works.
What’s so hard with accepting absence of knowledge? I literally know nothing about God’s plans, how my screen is lit up, my car propels me forward or where to get food at 2 a.m. in Shanghai. What’s so wrong with extending the respectable position of “I don’t have a clue” to areas where you’re habitually expected to have a clue (politics, philosophy, virtues of immigration, economics)?
Note that this is not a value judgment that the knowledge and understanding of some fields are more important than others, but a charge against the societal institutions that (unnaturally) forces us to. Why do I need a position on immigration? Why am I required (or “entitled”, if you believe it’s a useful duty) to select a government, passing laws and dealing with questions I’m thoroughly unequipped to answer? Why ought I have a halfway reasonable idea about what team is likely to win next year’s Superbowl, Eurovision, or Miss USA?
Books like Duffy’s (Or Rosling’s, or Norberg‘s or Pinkers) are important, educational and entertaining to-a-t for someone like me. But we should remember that the implicit premium they place on certain kinds of knowledge (statistics and numerical memory, economics, history) are useful in very selected areas of life – and rightly so. I have no knowledge of art, literature, construction, sports, chemistry or aptness to repair or make a single thing. Why should I have?
Similarly, there ought to be no reason for the Average Joe to know the extent of diabetes, immigration or wealth inequality in his country.