Felix Salmon

This chart comes from Margaret Jacobson and Filippo Occhino at the Cleveland Fed, and it’s reasonably terrifying — yet another one of those charts where the trend is down and to the right, and where it’s only gotten worse since the end of the recession.

What you’re looking at here is the share of total national income which is accounted for by labor — a measure that includes wages, salaries, bonuses and things like pension and insurance benefits. Everything else is capital income: interest, dividends, capital gains. There are two ways of measuring this, which is why there are two lines; both of them are telling the same story.

The fascinating thing to me, here, is what has happened since the crisis. Over the past three years or so, wages and salaries have been rising steadily, while interest rates have been stuck at zero. It’s never been harder to make income from capital, while incomes for people with jobs have actually kept on rising. And unemployment, while still high, has been coming down.

Given all that, it would stand to reason that the share of national income going to labor should be rising, not falling. Labor incomes are going up, the number of employed people is going up, and income from savings is going down. And yet! It turns out that people with capital are so rich, and getting so much richer, that it’s not even close. All that belly-aching about the plight of savers on fixed incomes in a zero interest-rate environment? Well, you don’t see it in these numbers. Looking at this chart, if you were given the choice between having money and no job, or having a job but no money, it’s not obvious which one to go for.

Of course, as the Cleveland Fed paper shows, a lot of the story here is about rising inequality. But the more powerful, if less obvious, story, is just how entrenched capital income has become in the US economy. As recently as 2000, it was at levels more or less in line with the historical average. And then, something big happened. During the Great Moderation — when yields fell on all capital asset classes — capital income went up sharply. Then the crisis happened, a classic case of a dog not barking: you’d expect capital income to have fallen enormously, at least for a year or two, but it didn’t, it just stopped rising. Most recently, in the wake of the financial crisis, capital income has been soaring again.

There’s a big lesson here for anybody serious about fiscal policy, too. (Paul Ryan, I’m looking at you.) As the labor share of income goes down and the capital share of income goes up, the only way that we can stop tax revenues from plunging disastrously is to tax capital income at least as much as we tax labor income. By contrast, the Ryan plan proposes taxing capital income at zero — putting ever more of a burden on working Americans, while giving unearned income a massive tax break the rich really don’t need.

There are big global forces driving this chart, most importantly the way in which labor is becoming increasingly global and fungible. Labor income has been declining for a good 25 years, and the only substantial countertrend was the dot-com bubble. The trend is a bad one, and it’s getting worse. And while I don’t see any policies, on either side of the aisle, which really try to address it, the fact is that Republican policies seem explicitly designed to exacerbate it. Think of capital income as the money flowing to “job creators”, and the chart is very clear on that front.

Back when this blog was on hiatus, I put a chart of Microsoft and Apple valuations up over at felixsalmon.com. People liked it, and so I decided to take the obvious next step, and animate it. The result is the video above, and this gif.

The data are a little bit out of date at this point, and so you can’t see Apple soaring to its latest $650 billion valuation* — but it’s easy to see where it’s going. And the big picture is still very clear: Apple basically curves up with market cap being an inverse function of p/e, as you’d expect; when Microsoft, by contrast, reached its highest valuation, it had a whopping great p/e ratio.

Today, for the record, Apple has a market cap of $650 billion and a p/e ratio of 16.4; Microsoft has a market cap of $260 billion and a p/e ratio of 15.6. As far as their earnings ratios are concerned, both are very much in line with the S&P 500, which is currently trading at a p/e of 16.5. Wherever excess earnings growth is going to come from, the market isn’t expecting it from either of these tech giants.

*Yes, I said $700 billion in the video. I meant $700 per share. Oops.

The FT’s Dan McCrum has found an interesting nugget in the data of my corporate cousins at Lipper:

Mutual funds are not performing as badly as last year, when just 27 per cent offered better returns than the benchmark they choose to track, according to research group Lipper. But, again, the majority still trail in 2012.

This was crying out for a chart, so here you go, with many thanks to Matt Lemieux:

This is a bit noisy, however, and most people (I hope) hold their mutual funds for periods of a bit longer than a year. So here’s the chart looking at what percentage of active mutual funds beat their benchmarks over three years:

And if you’re the kind of sensible person who holds their mutual funds for five years, at that point things start becoming more predictable, and we can start overlaying lines:

This is pretty much in line with the latest Spiva analysis, as of year-end 2011, which shows just 16% of equity funds outperforming the S&P Composite 1500 over 1 year to end-2011, 43% outperforming over three years, and 38% outperforming over five years. For bond funds, the Spiva numbers are much worse: if you look at page 18 of the PDF, you’ll see that over five years it’s pretty much unheard-of for bond funds to beat their benchmark, especially those funds investing in long-dated bonds.

If we go back even further, the numbers, as you might at this point expect, just become worse. Over 10 years, the proportion of active mutual funds outperforming their benchmark never goes above 35%, and between 1981 and 1991, it was just 27%. Over 30 years, just 31.5% of active mutual funds outperformed their benchmark. And that’s before adjusting for survivorship bias, or the fact that investors tend to be late to the party, investing in high-performing mutual funds after they’ve had their period of outperformance, and just before they mean-revert.

All of which is to say that picking an outperforming mutual fund is at least as hard as picking stocks. And while there are some famously successful stock-pickers out there, I’ve never heard of a very successful mutual-fund picker. So, don’t bother. Throw all your money in a Vanguard target-date fund, and forget about it. It’s much easier, and you’ll end up with more money when you finally need it.

This chart comes from Uber data geek (that is, a data geek who works for Uber) Bradley Voytek. You might recognize it from a blog post of Voytek’s from back in June, headlined “Building the Perfect Uber Party City”.

What Voytek managed to do, back then, was create two “stereotyped patterns” of Uber car usage, based on something called principal component analysis. The first pattern he called “Weekend Component”, and it’s the chart you see above. The second pattern he calls “Weekday component”, and it looks very different indeed. (You can see the two overlaid on top of each other at right.)

Just by looking at these two curves — the red and the blue — Voytek can account for 93% of the way in which demand for Uber cars fluctuates over time. Some cities and neighborhoods are more Weekday; other cities and neighborhoods are more Weekend. (Most, it turns out, are more Weekend than Weekday, at least when it comes to demand for Ubers.) But just about everywhere comes very close to being a mix of the two, rather than something altogether different.

And there are some neighborhoods which correlate very strongly with the weekend curve in particular: Voytek calls these the “party neighborhoods”. In his post, he picked out the most “weekendish” neighborhoods in each of Uber’s cities: North Beach in San Francisco, Soho in New York, and so on. But I was interested in the league table. So, via Voytek, here’s the top 50:

The Weekend Index, here, is the degree to which Uber usage in the neighborhood in question resembles the red line in Voytek’s chart. Obviously, it’s not all nights and weekends, but it’s skewed that way. Sunday nights are very slow, and then each successive night picks up a bit, and goes on a little bit later, until you get big peaks on Friday and Saturday nights. And across the board, nighttime usage is much heavier than daytime usage.

Voytek also sent me a list of the least “weekendish” neighborhoods that Uber covers. They’re pretty dull, as you might expect. What you might not expect is that the top six are all on the west coast. At the top of the list is Outer Richmond, in San Francisco, followed by Roosevelt and Madrona in Seattle, Visitacion Valley in San Francisco, Greenwood in Seattle, and Leschi in Seattle. Nowhere in New York or Boston or DC even makes the top ten.

The big league table, however, of the most weekendish neighborhoods, is fascinating — just because those tend to be particularly (to use a word that Thomas Frank hates) vibrant. These are the neighborhoods that other cities aspire to; they’re the areas that cause people to want to move to a city, and make them willing to pay high rents to live there.

And if you ever wondered what were the best and worst nights to go out, this Uber chart should answer your question very simply: the later you get in the week, the more crowded any given place is likely to become. That’s pretty intuitive, but it’s always good to see intuitions backed up with empirical data — and it’s easy to see why restaurants that close one or two days a week always choose Sundays or Mondays.

The big news from the Fed this week is, in the words of the NYT headline, that Family Net Worth Drops to Level of Early ’90s. But if you look at the actual report, there isn’t any data in there on family net worth before 2001. So many thanks to Peter Coy, who actually went ahead and ran the numbers.

Now these are Coy’s numbers, not the Fed’s. But Coy uses the Fed’s data, and here’s what he comes up with:

According to these numbers, the median family net worth in 2010, $77,300, is lower than it was in 1989, when it was $79,600. And it might well even be lower than in 1983, when, according to a different methodology, it was $88,000.

The 1962 and 1983 numbers can be compared to each other but not directly to the rest, because the methodology changed. But the fact is that they’re just as likely to be too low as they are to be too high. And as a general guide to household net worth, I think it’s fair to say that the median US household is no richer now than it was 30 years ago.

And in case you’re wondering whether things might have gotten better since 2010, the answer is almost certainly no. In 2007, median household net worth was $126,400, while the median amount of home equity was $110,000; in 2010 net worth had dropped to $77,300, while home equity had dropped to $75,000. These days, home equity is net worth. And since house prices haven’t recovered since 2010, it’s safe to assume that net worth hasn’t recovered either.

The fact is that household net worth was pretty inadequate even at the top of the housing bubble in 2007. Families need a place to live, and if you strip out the housing component of the net-worth calculation, the median US family has barely any net worth at all. Certainly nothing they can retire on. This of course is why Social Security is so important: with the recent drop in net worth, there’s no realistic chance that the median US family will ever save up enough to live on when they’re no longer earning money.

In the wake of its fabulous report about how investors in VC funds are stupid, the Kauffman foundation has released another report, this time about IPOs. This one comes with a very bad press release, which says in breathless fashion that “nearly 1.9 million new jobs forfeited in the past decade as fewer entrepreneurial firms join ranks of public companies”. In fact, the report itself is much less alarmist, and a single chart does a very good job of debunking the idea that if we had more IPOs, we’d automagically have much more employment.

What this chart shows is that during the dot-com bubble, companies like Amazon and eBay would go public and promptly reinvest the proceeds, using them to grow as fast as they could. Both of them were just three years old at IPO, and used their equity capital to carve out dominant positions in their respective corners of the internet. As the report says, the market’s mantra during the dot-com bubble was “grow rapidly or fail”, and so all companies which went to market adopted pretty much that strategy.

With hindsight, many of those companies would have been better off conserving their capital, preserving a bunch of liquidity for a rainy day, and going for sustainability over growth. But that didn’t happen, and what you can see in the chart is a spectacular failure of public companies to create jobs after the dot-com bubble burst. The older companies certainly didn’t: total employment in companies which went public in 1996, for instance, actually fell significantly between 2000 and 2003. And even newly-public companies, if they went public in 2001 onwards, basically gained no jobs at all; the only exception was 2004, the year Google and Salesforce went public.

So yes, the number of companies going public after the dot-com bubble burst was lower than the number of companies which went public during the bubble, when equity capital was dirt cheap. That doesn’t mean that jobs were forfeited as a result: if more companies had gone public, there’s no way they would have grown their payrolls at the rate that the cohorts of 1996 and 1997 did.

Indeed, the report itself explains very clearly that the 1.9 million number is not remotely something to be taken literally:

Since the number of years in which to grow would have been shorter than for the firms that went public in the late 1990s, the jobs created through 2010 probably would be lower. Second, there is an assumption that the average quality of firms going public would remain the same as those that actually did go public. In other words, that there would have been additional eBays, Amazon.coms, and Googles if there had just been more IPOs. Third, that the people that would have been hired would not have been doing something else. In other words, there is an implicit assumption that a mass army of would-be engineers, scientists, and marketing experts is sitting at home watching television. And fourth, that the capital invested when a company raises funds in an IPO would not otherwise have been invested in job-creating activities. The average company that conducted an IPO during our sample period raised $162 million in inflation-adjusted dollars, and if there were 2,288 more IPOs of the same average size, $370 billion of capital would have been pulled from other uses.

Instead, the point of the 1.9 million jobs number is that it’s low, not high: it’s being presented in order to contrast with insanely overinflated figures elsewhere, such as Grant Thornton report which says (slide 15) that the decrease in IPOs “may have cost the United States 22 million jobs over the last decade”.

In fact, what has happened over the past decade or so is that companies have been getting older and older at IPO, and have been able to raise, in some cases, billions of dollars in venture capital before going public. As such, IPOs have not been a way of raising growth capital, so much as a way of creating an exit for VC funders. Or, to put it another way, there are still lots of hot 3-year-old technology companies raising huge amounts of equity and using it to hire loads of people. They’re just doing it in the private markets rather than the public markets.

What’s more, the fast-growing technology companies which are going public now, or which have gone public in recent years, are hiring precisely the one group of people where there’s no unemployment problem at all: computer engineers in general, and Silicon Valley computer engineers in particular.

Once upon a time, when IPOs were primarily ways for young, fast-growing companies to raise the capital they needed to continue to grow, there was a strong case to be made that they helped create jobs. Today, however, IPOs are something else entirely. If there were more IPOs, that might be a good thing, but it’s silly to believe that we’d have more jobs that way. IPOs, like leveraged buyouts, are financial tools used by financial professionals to make money. Those financial professionals surely like to think of themselves as job creators. But their real job is to make money, not jobs. And so while there are reasons to bemoan the lack of IPOs in recent years, this idea — that we’d have many more jobs right now if there had only been more IPOs — isn’t one of them.

Many thanks to Scotty Barber for putting this chart together for me. It shows the extraordinarily high correlations that we saw within the S&P 100 at the height of the Lehman crisis; at the height of the Greece crisis; and then, again, for pretty much the entire second half of 2011. At that point, high correlations really did look as though they were the new normal.

Obviously, correlations within and across different asset classes don’t always move in tandem with each other. But in general, the RORO trade, as it’s known, (risk-on, risk-off) tends to send correlations soaring across the board. And I can’t help but wonder whether that huge plunge in correlations that we see at the beginning of 2012 was related to the way in which JP Morgan’s CIO blew up.

Remember that the CIO’s main job was to make hedges: buy buying or selling one thing, the idea was that the bank would protect itself against losses on some other thing. So in order for hedges to work, those two things need to continue to be highly correlated.

But if you look at this chart, the period when Bruno Iksikl was putting on his huge CDS index trade was also the period when correlations were falling at an almost unprecedented pace.

Jamie Dimon, from the day he revealed the losses, has had nothing but the harshest possible words for the hedges in question, saying that they were flawed and should never have been put on. But that’s easy to say in hindsight. Maybe they were really great hedges, in a high-correlation world — and then correlations fell apart, and the trades started moving against JP Morgan, and they had to get bigger and bigger in order to retain any hint of actual hedging capability. Obviously we don’t know for sure that’s what happened. But it’s certainly consistent with movements in correlations this year.

In March 2010, the unemployment rate for high-school graduates 25 years or older peaked at 11.9%. Since then, it has dropped 4.2 percentage points — a pretty impressive showing, in just two years — and now stands at 7.7%.

In the same period, the unemployment rate for college dropouts 25 years or older also fell, from 9.5% to 8.0%. But that drop, of 1.5 percentage points, is much smaller. And now college dropouts have a higher unemployment rate than their friends who never went to college at all.

And these two series are very comparable: both sets include about 34 million people.

Now these numbers aren’t seasonally adjusted, and you can see that the unemployment rate for high-school graduates is pretty bumpy. As a result, it might well rise again soon. But the big picture is clear: unemployment among college dropouts is proving much more stubborn than it is among most of the rest of the population.

Jed Graham lists a number of reasons why that might be the case. For one thing, with the majority of Americans now attending college, even if they don’t all graduate, college is less of a destination for the elite than it used to be. The elite will always get jobs, but as students become less elite, they’re  less assured of having great careers once they graduate.

And for another thing, college dropouts are still significantly more likely than high-school graduates to have a job: their employment rate is higher, even if their unemployment rate is lower, since they have a significantly higher labor force participation rate. On the other hand, the reason for the higher labor force participation rate is just that fewer people used to go to college, which means that among people 68 years or older, high-school graduates vastly outnumber people who went to college. And once you’re over 68, you can be excused for no longer being in the labor force.

So what’s going on? Graham’s convinced there’s something real here:

Labor Department data show that while the jobless-rate advantage of college dropouts over high school finishers has disappeared and turned negative, the advantage of two-year-degree holders over college dropouts is way above the historical norm.

Meanwhile, the jobless rate differential between high school finishers and two-year-degree holders is right in line with its historical average. This suggests that the principal mover is a decline in employment outcomes among dropouts.

I suspect that the conjoined forces of for-profit colleges and student loans are a significant contributory factor here. College dropouts certainly have more debt than they used to, and although indebtedness doesn’t directly lead to higher unemployment rates, it does at the margin act as a constraint on the massive life option that everybody has when they leave college. If high debts force you into some crappy job when you drop out of college, your chances of getting a good long-term career diminish.

And more generally, college is slowly moving from the “things which are bought” column into the “things which are sold” column — for-profit colleges, in particular, recruit aggressively in ways that would have been unthinkable to an earlier generation of tertiary educators. As a result, people drop out of college not just because it’s statistically certain that in any college class there will be some students who drop out, but increasingly because a lot of students, especially in courses offered by for-profit colleges, really can’t and shouldn’t be in those classes in the first place.

Besides, it makes conceptual sense that if you’re going to drop out of college, you’d be better off not going to college at all. Say you drop out after two years: you could have been working hard at the beginnings of a career during those two years, earning money to get yourself started on some well-defined course. Instead, people who drop out of college (Bill Gates, Mark Zuckerberg, and a handful of other exceptions notwithstanding) generally have little idea of what they want to do next, and are well behind their high-school peers in terms of building an economically-productive life.

All of which is to say that while it’s still indubitably a good idea to go to college, the “go to college” advice has to be added to, these days, with further admonitions not to take on too much debt, and certainly not to drop out. Because as far as employability is concerned, it seems that college dropouts have never had it so bad.

Many thanks to Ben Walsh for putting this chart together for me. What you’re seeing is JP Morgan’s excess deposits — the size of the bank’s deposit base, minus the amount of its loans — both in absolute terms and as a ratio. Either way, it’s going up and to the right.

JP Morgan clearly has a certain amount of control over the amount of deposits it takes in. What you’re looking at here isn’t entirely a flight-to-quality trade: JP Morgan’s total deposits actually fell in the two years following the financial crisis. They were just over $1 trillion at the end of 2008, they dropped to $940 billion at the end of 2009, and then they fell again to $930 billion at the end of 2010.

But then, in 2011, they shot straight back up — and now they’re at a record high of $1.13 trillion, with JP Morgan having failed to lend out more than $400 billion of that amount. That’s a record not only in absolute terms but also in relative terms: for every dollar that JP Morgan has on deposit, it has managed to lend out just 64 cents.

To put it another way, JP Morgan has $9,900 on deposit per US household — and of that, $3,600 per US household is “excess deposits” which are mostly being farmed off to London rather than being invested in helping US individuals and businesses grow.

The ostensible purpose of JP Morgan’s Chief Investment Office is to take the bank’s excess deposits and invest them in a way which manages to hedge the rest of the bank’s exposures. But if you’re spending 57 cents on hedging operations for every dollar you’re making in original loans, which is the case here, then something’s clearly very wrong. JP Morgan’s loan book isn’t that risky, or difficult to hedge. And if it is, JP Morgan needs some new loan officers.

The real story here, of course, has nothing to do with the difficulty of hedging JP Morgan’s loan exposures. Rather, the hotshot CIO traders in London were managing to get a higher return on their “hedging” operations than the loan officers were getting on their bread-and-butter loans. And so Jamie Dimon started taking in all the deposits he could find, and sending them straight to London, where they could be “hedged” to the tune of billions of dollars a year in profits.

It’s easy for JP Morgan to bring in huge amounts of deposits, of course: corporate balance sheets are bloated with cash, and those corporations want to deposit their funds with a too-big-to-fail institution. But if those deposits are being attracted by JP Morgan’s implicit government backstop, then it’s incumbent upon JP Morgan to lend them out into the US economy, to get it moving again. Rather than sending them off to London to be gambled away by the likes of Bruno Iksil, even as JP Morgan’s total loan base remains lower today than it was in 2008.

Here’s the chart you’ve all been waiting for, courtesy of Reuters’s very own Scotty Barber: the spread on the CDX NA IG 9 index — the synthetic index on which JP Morgan’s Bruno Iskil was selling enormous amounts of protection — minus the spread on the index’s constituent bonds.

Three things jump out here. Firstly, the basis is negative, not positive. That means that the obvious trade was to buy the underlying bonds and hedge by buying protection on the index. That obvious trade, if held to maturity, should always make money. Iksil was funding that trade, by selling protection on the index.

Secondly, the chart is going up and to the right. Since Iksil was selling protection, that means the market was moving against him. Or, to put it another way, the obvious trade makes money when it expires at zero, and as the chart moves towards zero, Iksil loses money on a mark-to-market basis.

Finally, the move doesn’t seem to be all that huge — only about 30bp in this quarter. Which doesn’t seem remotely enough to cause a $2 billion loss. Still, Iksil managed it somehow.

Update: Many thanks to Sally Kohn for making the chart infinitely better by putting whales on it.

The Epicurean Dealmaker has been watching global equity markets metastasize:

Over the past few decades, the public equity markets have evolved from a relatively staid and selective backwater, a playground for pension funds, insurance companies, and the idiot sons of wealthy men, into a gigantic global pool of capital, driven and supported by huge amounts of money from literally everybody… I will leave it to an enterprising PhD student to research the data, but I suspect the aggregate amount of equity market capitalization as a percentage of GDP has swelled tremendously over the past three decades. Equities have gone mainstream, and as they did, the size of equity markets ballooned.

To illustrate his point, TED talks of a fund manager who invests in no more than 100 stocks at a time. When he was managing $100 million in the 1980s, he could easily invest $1 million in a company; by the time his portfolio had grown to $10 billion in the 1990s, his average investment was north of $100 million per stock. That, in turn, makes it very hard for institutional investors to buy small-cap stocks, and helps to explain why small companies can’t IPO any more.

But that’s just anecdote; I wanted data. So I found an enterprising PhD student Ben Walsh, and asked him what’s happened to equity market capitalization as a percentage of GDP. And he, in turn, found Google:

There’s definitely an up-and-to-the-right trend here, but it’s very noisy, and we’re not talking orders of magnitude: global equity capitalization is probably about 50% higher now, relative to the size of the global economy, than it was in the 1980s. That’s an interesting trend, and a welcome one. But I don’t think it explains much about the IPO market. After all, the effective minimum size for IPOs has gone up much more than 50% since the 1980s.

I do think that maybe the distribution arms of the big sell-side equity underwriters have reached the point at which it just isn’t worth it any more for them to deal with any but the biggest accounts; I have a feeling that retail investors had much more access to IPOs in the 1980s and even during the dot-com bubble of the 1990s than they do now. But this is much more a function of the consolidation of the investment-banking industry than it is a function of the size of the equity markets as a whole.

Which opens up an intriguing possibility: is there some way in which the internet might be able to spawn a small, light broker-dealer which could underwrite IPOs aimed at retail, rather than institutional, investors? Think of it as fully SEC-compliant crowdfunding, without any need for a JOBS act. And if not, why not?

I was hoping someone would do this — and now The Daily Stag Hunt has come to the rescue. All I can say is, thank you. It turns out that if you’re on the FOMC, then being in a credit bubble is really funny!

(h/t Elfenbein)

This is a chart of corporate income tax as a percentage of total corporate profits, and it’s the main thing you should bear in mind when people start saying that the US corporate income tax is too high. And while you’re at it, you should remember this chart, too, showing corporate income tax as a percentage of GDP.

Once upon a time, the corporate income tax generated a significant share of tax revenues; now, it’s bumping along in the 2%-of-GDP range. Yes, the marginal rate of corporate income tax is high, at 35%. But US companies are extremely good at not paying that.

But at least we know the aggregate amount that corporations pay in taxes. What we don’t know — because they won’t say, and no one’s forcing them to say — is how much any given public company pays.

Allan Sloan has a very good column on this today. Companies already report 16 different tax metrics; they should simply be required to add a 17th — the amount they pay the IRS in taxes — which in many ways is most important. The companies already file tax returns; the number’s right there, on lines 31 and 32. They just refuse to say what it is.

Here’s Sloan:

During the past few months I’ve repeatedly asked three big companies in the tax-wars cross hairs — GE, Verizon, and Exxon Mobil — to voluntarily disclose information that would refute allegations that they incurred no U.S. federal income tax for 2010. All have refused, saying they won’t disclose anything not legally required. They still manage to complain about the allegations, however. I suspect that if I called the rest of the Fortune 500, I’d get 497 similar responses.

As a society, we need the “taxes incurred” information to inform our current tax debate. Investors, too, would benefit; knowing the tax that companies actually incur would be a useful analytical tool.

Once the taxes-paid number was public, we could start dividing it into the company’s GAAP profits, to get an idea of what kind of tax rate companies are really paying right now. And of course, companies would be more than welcome, if they were so inclined, to reveal how much tax they were paying in other jurisdictions as well. But for the time being, all we can do is look at the aggregate numbers. Which are showing, very clearly, that corporate income taxes in America are very low and falling.

Update: Kevin Drum creates the chart I should have led with: corporate taxes as a percentage of pretax profit.

The world officially hit 7 billion people today, and so to celebrate I decided to take a look at what’s happened to poverty in the world as its population has increased — many, many thanks to Nick Rizzo and to Laurence Chandy and Homi Kharas at Brookings, two Englishmen who provided him with unpublished data and were extremely generous with their time.

The big picture can be seen in the chart at left: the number of poor people hasn’t been growing nearly as fast as the number of people. And indeed over the past 24 years, as the world’s population increased by 40% from 5 billion to 7 billion, the total number of people living in poverty has actually gone down. (One small note I should make about these charts: the dollar-a-day figures from 1987 onwards actually measure the population living on less than $1.25 a day, so a jump in the absolute-poverty numbers between 1974 and 1987 is partially a function of the fact that we’re raising the bar from $1 to $1.25.)

In fact, there’s pretty much the same number of people living in absolute poverty today — about 890 million, or 12.7% of the global population — as there were all the way back in 1804, when the world’s population hit 1 billion and 84% of them were living in absolute poverty.

Indeed, back in 1804, only 5% of the world was living on more than $2 a day. (All these numbers, of course, are real, and adjusted for purchasing power.) Today, that number is 4.7 billion, or 67% of the world’s population. The number of people in the world living out of poverty has been growing faster than the world’s population as a whole for pretty much all of recorded history.

And the “global middle” — people living on somewhere between $10 and $100 per day — is growing particularly fast. It was 1.14 billion in 1987; it’s 1.96 billion today. That’s an increase of 72%, even as the population of the world as a whole has gone up by just 40%.

A huge amount of what we’re seeing here is the effect of China, of course. Here’s what’s happened in East Asia over the past 24 years:

Absolute poverty in East Asia has gone down to 142 million today from 822 million in 1987, even as the population as a whole has risen from 1.5 billion to 1.9 billion. 24 years ago, more than half of East Asia lived in absolute poverty; today, it’s just 7%.
The other big global population center is South Asia, which includes India, Pakistan, and Bangladesh; its history of poverty reduction is less heartening but still substantial.

There’s a lot of work to be done, here, with two thirds of the South Asian population still living in poverty. But absolute poverty has declined quite dramatically in the past dozen years, which is certainly a start.

You won’t be surprised to hear that the most depressing story is in AIDS-ravaged sub-Saharan Africa.

Even here, however, the percentage of the population living in poverty is no higher than it is in South Asia, and the number of people living in absolute poverty hasn’t actually gone up over the past 12 years.
Here are the other regions:

It should be noted that all of these figures, and especially the African ones, come with massive error bars and caveats; Shanta Devarajan is very good on this. But the big picture is clear: there’s nothing Malthusian going on here. As the world’s population grows, we’re taking people out of poverty, rather than consigning them to it. Which is heartening news in a world of limited resources.

Jacob Harris is absolutely right to hate word clouds. You take a long and complex text, and then you boil it down to a group of individual words, with the most-used words being the biggest? That’s just silly. “Reporters sidestepping their limited knowledge of the subject material by peering for patterns in a word cloud,” he says, is “like reading tea leaves at the bottom of a cup”. Word clouds are crude, inaccurate, misapplied, and place the onus of understanding onto the reader.

But there’s one place where word clouds are I think both useful and accurate — and that’s when a pollster has asked a group of people to say the one word they would use to describe X. Here, for instance, is the word cloud generated when a Reuters/Ipsos poll asked Republican voters for the first word that came to mind after watching the weird Herman Cain “smoking ad”:

And here’s the word cloud from the latest Kauffman poll of econobloggers:

Here the size of the words is interesting, but more germane is the overwhelming negativity of the vast majority of words used. There’s a couple of tiny good ones in there — “rebounding” us up by the Canadian border, and “bounceback” is in the Bay Area somewhere — but they’re in a distinct minority.

Incidentally, that Kauffman poll has some fascinating responses elsewhere, too. Check out the sudden enormous popularity of NGDP targeting:

There’s also a very high degree of skepticism when it comes to how good colleges are at teaching kids useful stuff.

The bar charts here are again an effective way of communicating information. Things like chart types and word clouds are tools, and you have to know which tools are best used in various different circumstances. And while word clouds are usually stupid, sometimes they can be exactly right.