Innumeracy and Generosity – Don’t be deceived by big numbers

Just a quick note here.  Lots of people today, especially the media, are making a big deal out of Jeff Bezos and his wife’s donation of $33 million for a scholarship fund for DACA Dreamers. For example there’s this CNN article.  Lots of tweets. It’s a nice gesture. It’s definitely a worthy cause – although worthy causes are legion.

My problem is with the intimation that this is somehow a noble sacrifice. The problem here is common in economics data. We get lost in big numbers and get fooled.  $33 million sounds like a lot. To over 99.9% of Americans, it’s a number we can’t really fathom. It sounds like so much money.  Let’s take a closer look. Bezos household net worth – the value of his personally owned assets minus their debt – is estimated at $105 billon (Bloomberg) or $104 billion (Forbes) (source: Google on Jan 13, 2018 ).  That’s billion with a B. Bezos is 54 years old.

The median household net worth for Americans in his age bracket was $100,404 according to the most recent data for 2013/2014 from Census Survey of Income.  The median means there are as many households with more assets as there are with less assets. It’s the middle observation. It’s typical.

So Bezos has pretty close to a million-times larger net worth than the typical household for somebody of his age. He and his wife sacrificed $33 million of their assets to make this donation. On a strict linear scale, that’s the equivalent of the typical household for his age bracket donating $33.  Yep, that’s all. $33.

Bezos’ sacrifice is the equivalent of an ordinary, typical 54-year old giving $33. Actually, it’s less of a sacrifice. Economics teaches us about diminishing marginal utility of income or money. Basically, when you’re rich each additional dollar of income or asset is much less valuable to you than if you’re poor. To a poor person, the $33 means eating or healthcare. When you’re really rich, it’s just another digit you’ll round-off on your financial statement.

I laud the Bezos family for making a donation. It’s a good thing to do. But let’s not make it out to be more noble than it is.  The bottom 20% of households in that age bracket have zero or negative net worth. The single mother with no assets that stuffs a twenty in the Salvation Army bucket at Christmas makes a lot bigger personal sacrifice.

Critical Analytics: It’s Stories All the Way Down

I’ve been hearing much lately about stories, narratives, analytics, data, and “big data”.  I have no need to call out exactly who or which pieces of writing. You know who you are. My aim here is not to criticize, oppose, or take sides. It’s to take a brief critical look at what’s being discussed.

Much of the discussion strikes me as one tribe (I’ll call them non-quants) pleading that stories and narratives are important too!  All of which is an understandable reaction to how the other tribe (I’ll call them quants) have seemingly gained a favored position and perceived superiority at divining the “truth” because they are evidence based!  Because data! I’m actually a member of both tribes and find the posturing of stories and narratives as alternative to quantitative analysis disheartening.

The most encouraging blog piece I’ve read recently comes from Michael Feldstein.  In his lengthy (and excellent) post called Analytics Literacy is a Major Limiter of Edtech Growth.  Please do read it.   He argues for the dissolving this false juxtaposition between “stories” and “data”.

…some of these arguments position analytics in opposition to narratives. That part is not right. Analytics are narratives. They are stories that we tell, or that machines tell, in order to make meaning out of data points. The problem is that most of us aren’t especially literate in this kind of narrative and don’t know how to critique it well.

I wholeheartedly agree.  Feldstein is (correctly) arguing that data points are nothing without stories.  The meaning we take from the data is itself nothing but a story we weave using the data points as we might use punctuation or particular words.  In essence, quantitative analysis is itself a story.

This really isn’t news or at least it shouldn’t be.  I remember how powerful McCloskey’s Rhetoric of Economics was for me when I read it decades ago.  McCloskey powerfully made the point that no matter how much we wrapped an idea in data, mathematical formalism, or econometric analysis, everything we said in economics was just a metaphor or a story we imposed on the data. Alan Grossman long ago pointed out that even that high temple of data-driven evidence, Science(tm), it’s still just rhetoric and it’s still just stories.

Yes, the meaning we attach to a set of data is itself a story.  So stories are not alternatives to data. Data is a story.  But it’s not just the obvious story we tell with the data. There’s a story unstated underneath the data the we use. Our choice of particular data variables constitutes a story itself. We (or at least the data collector) have in mind a story and narrative of what’s important before they collect the data.  They don’t collect data about the context that they don’t see as important or relevant (or easy enough to collect), so they assume a story about that uncollected contextual data holds no meaning.  There’s a story underneath the story we told with the data.

But it keeps getting deeper. Much like the philosophical turtles, it’s stories all the way down. That measure of the data you’re using. The one you think is just basic stats or math, something like the average (properly called arithmetic mean), or the variance, or correlation, or whatever.  It has a story too.  Let’s take that arithmetic mean (average) and each observation’s difference from the average. We think of that average as “the norm” – but that’s just a story invented by a couple of different statisticians in the 19th century.

I can’t really do justice here to the story of how that story of what the average or norm is.  I strongly urge you to read The End of Average by Todd Rose.  It’s fully accessible to members of both tribes, quants and non-quants.  You’ll never use your quantitative data the same way again. Todd Quinn writing in the Elearning magazine of the ACM had the same kind of dramatic reaction as I had.

I’ve finished reading Todd Rose’s The End of Average, and I have to say it was transformative in ways that few books are. I read a fair bit, and sometimes what I read adds some nuance to my thinking, and other times I think the books could stand to extend their own nuances. Few books fundamentally make me “think different,” but The End of Average was one that did, and I believe it has important implications for learning and business.

Rose’s point is pretty simple: All our efforts to try to categorize people on a dimension like GPA or SAT or IQ are, essentially, nonsensical.

But going another level down, as Rose explains in End of Average, there are assumptions beneath the calculation and use of ordinary stats like the average or the variance.  Let’s face it, “assumptions” is another way of staying “believed a story to be so true that it didn’t need to be stated”.  In the case of the average and the calculation of differences from “the norm”, that assumed story has to do with the ergodic properties of what’s being examined.  So what’s “ergodic  properties”? Well here’s Wikipedia’s attempt to explain ergodicity. It’s not very accessible to non-quants (or even most quants!).  Again, I would refer you  to Rose’s book for a beginning glimpse of what ergodicity means. I can’t explain it here, but the essence is that mathematically, statistically the vast majority of the stories being told with quantitative analytics are complete nonsense. Garbage. Invalid. Wishful alchemy.

It’s stories all the way down.  At first this might seem discouraging. But it’s not. I’m calling for not just analytics literacy but a critical analytics.  We need to investigate and become aware of not only the stories we tell using data, but also the assumed stories we slide under the table by choosing particular measures and statistical techniques without thinking about them. We wouldn’t let the semantics of narratives escape critical examination. Why should we let analytics?


Data and Visualization Resources for Incomes and Inequality

Posting links to two incredibly useful resources for students and people doing research on incomes, income distribution, and income inequality. These resources are useful for both historical data and visualizations as well as cross-country comparisons.

The first is the World Top Incomes Database from the Paris School of Economics. Many thanks to the Paris School and researchers Facundo Alvaredo, Tony Atkinson, Thomas Piketty and Emmanuel Saez. It’s a a tremendous resource.

The second is a tremendous resource also. It’s Our World In Data. It’s a work in progress project by Max Roser,   but it’s already jam packed with great data and visualizations on incomes, health, war and violence, poverty, and food and hunger. And best of all, it’s all CC-BY-SA licensed.  I love it when collaboration and the commons come together to support learning.

China, Growth, and the Weakness of Real GDP

Sara Hsu asks if All Growth is Good? The Case of China Of course, not all growth is good. It makes little difference, whether it’s economic or human tissue growth. Edward Abbey famously wrote that “growth for the sake of growth is the ideology of the cancer cell”. Obesity is another form of high-growth, yet it hardly improves well-being or health.

Unfortunately, we economists have not (yet?) developed measures that help us or policy-makers distinguish between healthy growth and malignant growth.  The only real comprehensive measure of growth we have is growth of real GDP. We do know better, as Sara notes:

Since the seventies, with the assertion by Gunnar Myrdal that economic development should prioritize equality, economists have increasingly come to believe that not all types of growth are wholly “good.” Growth that ignores human well-being and equality are viewed as problematic.  Certainly growth that results in severe environmental destruction, as in the case of China over the past twenty years, cannot be classified as good, either, despite the country’s much-lauded successes during this period. Real-world views of growth depicted in the mainstream media do not fall in line, however, with the economic development literature. The focus on China’s growth in the news has distracted from a more balanced view of the looming inequality problems or polluting production methods in the world’s most populous nation.  As China’s growth has slowed, headlines have read, “China’s Economic Growth at Stake,” “China’s Economic Growth Slows,” and “China’s Second Quarter Growth Slows.” –

Yes, China’s real GDP growth rate has been spectacular for several decades now. That growth has lifted literally hundreds of millions of people into better lives. Yet, in strange case of the metaphor becoming real, that economic growth has literally brought cancer with it. Specifically, many “cancer villages” along the Huaihe River.

China’s economy illustrates the problem of growth measured in numbers versus measured in real economic change. The surge in fixed asset investment carried out post-global crisis resulted in an inflation of growth figures, despite the creation of uninhabited apartment buildings, or even entire cities. This is socially unproductive growth, wasteful production, “bad” or false growth. Although the distinction between “good” and “bad” growth exists only in theory, it is essential to clarify the difference to the public in order to move along the path of long-term development.

Admittedly, it may be overambitious to request that a more comprehensive view of growth penetrate the media. However, it would benefit our understanding of China’s economic performance; reconceiving growth would increase competition to generate “good” growth and discourage the race to build businesses that produce “bad” growth.

Yes, I agree. It is indeed an ambitious project, the idea that we could create more comprehensive measures of growth that help us to separate healthy improvement in well-being from cancerous, destructive economic growth. But it seems to me no more an ambitious goal than the vision less than 100 years ago to create the national accounts systems and begin collecting the data (from whence we get GDP measures).

Not Performing Up To Potential

When I was kid there was a comment I dreaded but got too often on too many grade reports to my parents: Not performing up to potential. I hated that. I must say, though, that there times when it did motivate me to do better.

The same comment, not performing up to potential, can easily be applied to the U.S. economy for at least the last 6 years.  I really wish it would motivate our economic policymakers to do better, but alas, they seem to be indifferent to the challenges.

For the details of just how much we are under-performing, I give you the Center for Budget Policy Priorities summation of the latest Congressional Budget Agency report on the economy (below the fold): Continue reading

Busting the Medicare Myths – Presentation

I gave a presentation today to the Michigan Intergenerational Network at Madonna University on the economic prospects of Medicare (U.S.). Thanks to the Madonna Univ. Gerontology Department for support and assistance.

For a downloadable and viewable copy of the presentation, see:

Yes, Inflation/Deflation is Hard to Measure

One of the hardest concepts for Principles students, politicians and pundits, oh heck, just about everyone to fully grasp is inflation.  A big part of the reason is because inflation is an abstract concept that is not directly measurable.  We can conceive of it, but we can’t measure it.  I’m no physicist (and open to correction) but it strikes me that it’s a bit on par with “momentum” or “latent energy” in physics.   We don’t have direct-measuring energy-o-meters.  We measure the effects and infer the energy.  Inflation is similar.  We can conceive of a generalized, across-the-economy, sustained trend pushing all/most prices upward such that the unit of money is losing real value in general terms.  Inflation is the sustained push behind all prices. We can’t measure that directly. But we can measure the effect it has: rising prices. The problem comes in that not all prices will be rising at the same time or by the same amount.  Further, during any time period, at least part of the change in price for any good is it’s change in real price relative to all other goods (supply and demand as taught in micro).

We try to deal with this measurement issue by creating a price index – an index that tracks the changes in shopping list of goods over time.  But any price index is a just a subset of all the prices.  Even the Billion Price Project index at MIT admittedly misses most services and lots of consumer goods that aren’t available online.  Price indices are very imperfect beasts.  They have many faults, not the least of them being that they often tend to be volatile in nature.  Since we’re looking for an estimate of inflation which means sustained increases, we need to massage the data further by creating some kind of “core inflation” measure or “trimmed means” type price index.  I’ll explain those some other time.

What prompted today’s post is an article in Bloomberg and a post by Krugman about it.  Together they illustrate one of the reasons so many people want to believe we have greater inflation than we really do.  Companies like to disguise price changes.  They don’t want to be known that prices could be cut in response to demand. Example: auto company offers $2000 rebate on $20,000 car but won’t cut price by 10%, or a firm offers a “value meal”, or they offer a freebie bundled product.  Similarly they often disguise price increases by reducing sizes or portions or by changing the financing.  From Krugman:

Good article in Bloomberg:

Procter & Gamble Co.’s failure to raise the price of Cascade dishwashing soap shows why investors are buying Treasuries at the lowest yields in history, giving the Federal Reserve more scope to boost the economy.

The world’s largest consumer-products company rolled back prices after an 8 percent increase lost the firm 7 percentage points of market share. Kimberly-Clark Corp. (KMB) started offering coupons on Huggies after resistance to the diapers’ cost. Darden Restaurants Inc. (DRI) raised prices at less than the inflation rate as patrons order more of Olive Garden’s discounted stuffed rigatoni than it anticipated.

This is basic economics; prices tend to fall, or at least slow their rise, when there is vast excess capacity and weak demand.

As both the article and Krugman’s excerpt show, we’re closer to deflation than most people realize.  They don’t see the failed attempts to raise prices.  They don’t see the shifts in portions or increase in coupons that reduce effective prices.  What they do see and remember is the $.50 increase in a loaf of bread or the $.70 increase in a gallon of gas.  But even with the gas, they selectively remember the $.70 price increase in summer, but forget the $.75 price drop in autumn.  Inflation and deflation are tricky things to measure.