Opinion

Felix Salmon

The government-dominated bond market

Felix Salmon
Nov 22, 2013 22:27 UTC

JP Morgan’s Nikolaos Panigirtzoglou put a fascinating report out last week, looking at supply and demand in the global bond market in 2014. And although I consider myself something of a bond nerd, I was genuinely astonished by some of the charts he put together, starting with this one:

demand2.png

This chart alone suffices to explain why the markets care so much about the taper: central-bank buying accounts for $1.6 trillion — more than half — of the total demand for bonds in 2013. Meanwhile, private banks are taking the opposite side of the trade: while they were huge buyers of bonds in 2007 and 2008, they’re net sellers in 2013 and 2014, more or less completely negating the buying pressure from pension funds, insurance companies, bond funds, and retail investors. In 2014, it seems, substantially all the net demand for bonds is going to come from the official sector. So it matters a great deal when that demand is diminished.

What’s more, central-bank buying, overwhelmingly from the Fed and the Bank of Japan, accounts for the lion’s share of official-sector buying: sovereign wealth funds and other foreign official institutions will buy just $364 billion of bonds this year, according to JP Morgan’s estimates, down from $678 billion last year. So the heavy lifting is still going to have to be conducted by QE operations, in the face of a taper which JP Morgan estimates at $500 billion over the course of the year. (The assumption is that it starts in January, and is completed by September.) Between the taper and other sources of diminished demand, total bond-buying firepower is likely to be $750 billion smaller in 2014 than it was in 2013. Bad news, for bonds, right?

Not so fast! It turns out that even as demand for bonds is shrinking, the supply of new bonds is shrinking just as fast:

Screen Shot 2013-11-22 at 4.45.23 PM.png

Again, this chart surprised me: I knew that government debt was a very important part of the total bond market, but I wouldn’t have guessed how important it was — or how fast it is shrinking.

Panigirtzoglou puts the two charts together, and you end up with this result:

In total we expect bond supply to decline by $600bn in 2014 to $1.8tr, more than offsetting the $500bn decline in bond demand due to Fed tapering. The balance between supply and demand, i.e. excess supply, looks set to widen from $140bn in 2013 to $280bn in 2014.

That number has pretty large error bars: you could pretty much cover the entire thing just by delaying the taper for three months. So let’s not worry too much about the difference between the two estimates, here. Instead, step back and look at the big picture, which is pretty simple: as a stylized fact, the bond market is dominated on both sides by the official sector. Private participants might sit in the middle as market-makers, or try to borrow money here or there, but overall what you’re looking at, when you look at the bond market, is government issuing debt and governments buying it.

The good news is that this large transfer of money from the official sector’s left hand to its right hand is slowing down, but that’s going to take a while. In any case, there doesn’t seem to be any conceivable way that the private sector could possibly be able to fund the still-substantial government deficits which have been bequeathed to us by the financial crisis. As a result, I suspect that QE is likely going to be around for a while, just as a matter of mathematical necessity. The world’s national deficits can’t get funded any other way.

COMMENT

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How the NYT neglects business journalism

Felix Salmon
Nov 15, 2013 06:52 UTC

Brian Abelson has a fantastic post about the performance of NYT articles. The main gist is that it’s possible to predict with surprising accuracy how many pageviews any given NYT article is going to receive, given just a few variables like the amount of time that article spent on the home page, and whether or not it was tweeted by the main @nytimes Twitter account.

There’s a lot of information in the post, however, and a couple of other things jumped out at me, seeing as how I’m a business journalist for a wire service. The first is the almost hilarious way in which the NYT seems to go out of its way to ensure that readers do not read wire stories on the NYT site, despite the fact that they make up the overwhelming majority of the content on the site.

Abelson put together a database, for this post, of 21,006 stories published on nytimes.com between July and August of this year. Of those 21,006 stories, 15,269 — or 73% — came from wire services (either Reuters or the AP); the other 5,737 were original content. But get this: any given piece of original content had a 21% chance of being tweeted out by @nytimes. A wire story, on the other hand, had only a 0.6% chance of being tweeted by @nytimes. Or, to put it another way, @nytimes tweeted out 1,273 different articles over the course of those two months — and of those articles, just 89 came from wire services.

Abelson says that these numbers make intuitive sense, on the grounds that “stories from the wire should not receive the same promotional energies as those that come from journalists working at the Times”. But I’m not sure what kind of work the word “should” is doing in that sentence. Is he saying that wire stories don’t deserve to be brought to the attention of the NYT’s readers? That, to a first approximation, only 0.6% of wire stories are likely to rise the exalted standards of @nytimes, while a full 21% of original stories do? Is he saying that the NYT has a good business reason to promote its own stories over those which originated elsewhere? Or is he just saying that news organizations should look after their own, and that it would be somehow disloyal for @nytimes to tweet out many stories which were written by wire journalists, even if those tweets were links to the NYT website?

Instead of trying to guess what Abelson means, though, we can just look at this chart that he put together instead.

pred-vs-actual.png

What you’re looking here is a scatter chart of actual pageviews, on the y-axis, against the number of views that you’d predict any given story would receive, given variables like how much time it spent on the home page, whether it was tweeted by @nytimes, the number of section fronts it appeared on, etc. By using nine such variables, Abelson is able to explain 90% of the variance in pageviews. He explains one way to read this chart:

In the graph, the straight gray line signifies the threshold of a perfect prediction. Articles that fall above this line can be thought to have exceeded their expected number of pageviews while those below the line have underperformed. Computing the error of the model, or the difference between actual and predicted pageviews, allows us to calculate the degree to which any given article has performed in relationship to its “replacement”  —  or a hypothetically similar article which received the same level of promotion.

The interesting thing for me, looking at the chart, is the large number of outliers in the bottom-left-hand corner. This grouping is overwhelmingly wire stories which, according to Abelson’s formula, should get very few pageviews — and yet, despite that, and despite the fact that they were receiving no real promotion at all from the NYT, they did surprisingly well on the actual-pageview front. Indeed, it seems pretty clear from eyeballing the chart that wire stories are, in aggregate, significant outperformers when it comes to what Abelson calls his “pageviews above replacement” metric, which is an attempt to judge how many pageviews a story gets after controlling for the amount of promotional oomph it received from the NYT.

One way of looking at this is that the readers have spoken — and they’ve shown, quite clearly, that the NYT has made the right choice to pay Reuters and the AP for the right to run their wire stories. Another way of looking at this, however, is that the NYT systematically underutlilizes the wire stories it’s paying for. To be sure, those stories are available elsewhere on the internet: they’re not exclusive to the NYT. But readers don’t care about exclusives — even if they’re genuine exclusives, which, most of the time, they’re not. Readers just want to know what’s going on in the world — and the wires can do a very good job of telling the thousands of stories that a newsroom with finite resources can’t cover.

What’s more, from a business perspective, it would make sense for the NYT to put more promotional muscle behind the stories above the grey line. When a wire story starts performing surprisingly well — something which seems to happen quite frequently, according to this chart — then that’s a pretty good sign that the NYT should start putting it on more section fronts, tweeting it out, and generally giving it substantially more prominence. Given the limited space available on section fronts and in the NYT’s Twitter feeds, doing so would help to maximize pageviews and would deliver more of what the NYT’s readership is demonstrating that it wants to read. One of the great weaknesses of news organizations in general is that they don’t give nearly enough respect to stories they didn’t write themselves. That weakness costs valuable pageviews, if you’re already paying to be able to run those stories on your own website.

And then there’s the business-news perspective. Here’s another of Abelson’s charts:

par-by-section.png

This one’s a bit harder to read, but in the first instance, just look at the blue circles. The size of the circle shows how many articles there are in each section, while the position of the circle, from left to right, shows how many pageviews the average story in that section receives. The magazine and the dining section, for instance, don’t run a lot of stories, but the stories which do run tend to get a lot of pageviews. And then there’s the business section — which runs a large number of stories, but whose articles get fewer pageviews, on average, than any other section.

Now it’s not that the NYT’s readers don’t want to read business stories. The left-right positioning of the red circles shows how well each section’s stories are doing, given how much promotion they receive. On this basis, the magazine outperforms; the dining section does the worst. And the business section is right there in the middle, performing just as well as any other section. Give business stories a bit of promotion on the home page and on Twitter, in other words, and they’ll get you just as many pageviews as anything else, on average. But it turns out that the business section is systematically shortchanged by the people making those promotional decisions. Maybe (I’m not sure) because it has a higher concentration of wire stories.

Again, this looks like strategic short-sightedness. Business-news pages are some of the most valuable on the website, in terms of the amount that the NYT ad-sales team can charge for them. (They’re so valuable, in fact, that the entire Dealbook section remains outside the NYT paywall, in an attempt to garner it as many pageviews as possible.) By promoting more business stories, even if they are (horrors!) wire stories, the NYT could make more money, and everybody wants that — including the readers, who have shown that they have more interest in such things than the NYT’s editors think that they do.

What would be lost by such an approach? Very little: a few dining and metro stories might get viewed less often, if their promotional muscle started getting transferred to the business section. And maybe a few NYT egos might get a little bruised, if they discovered that their snowflakes weren’t quite as precious, to the outside world, as they liked to think, at least in comparison to the wire. But the website should be run for readers, not for journalists. And improbable as it might sound, it looks very much as though those readers would be best served if the NYT made it significantly easier to find wire stories, business stories, and — especially — business wire stories.

COMMENT

Btw, this snapshot analysis also missed the interdependence (over time) between disproportionately popular wire stories and the promoted exclusive content. I think it not debatable that readers like the NYT for its exclusive content. While they’re there, they check out interesting stories that happen to be wire stories. Take away the promotion that draws them to exclusive content and soon enough the so-called high-performing wire stories will also fall…

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Bond-fund charts of the day, rising-rates edition

Felix Salmon
Oct 22, 2013 21:31 UTC

I’ve been a bit obsessed with trying to get a feel for exactly how much money bond funds might go down if and when interest rates start to rise. And now, thanks to the wonderful Jake Levy at BuzzFeed, I can show you, in animated, rubbable-GIF form!

Jake put together two GIFs for me. Both show what happens to a $1,000 bond fund over time: the first one shows the value of the fund at various different durations, and assumes that rates are rising at a modest 0.5% per year; the second one shows the effect of the speed with which rates rise, assuming a constant duration equal to that of the Barclays US Aggregate. If you view these charts at BuzzFeed, on a touch device, then you can click the little hand in the top right corner, drag your finger across them, back and forth, and see how things change.

anigif_enhanced-buzz-11920-1382467024-46.gif

These charts ultimately come out of a conversation I had with Emanuel Derman, and this clever tool from skewtosis. They’re also purely about interest-rate risk, rather than credit risk: the duration points and initial yields for the first chart correspond to the figures for the 1-year, 2-year, 3-year, 5-year, 7-year, and 10-year Treasury bonds.

As for the lesson to be drawn from the charts, one interpretation is that the big risk to bond funds isn’t rising rates so much as it’s rapidly rising rates. Sure, if rates rise slowly and your fund has substantial duration, then you could lose a bit of money. But if rates rise quickly, you could lose a lot of money. In the BuzzFeed version of these charts, which you can see here, I say that in a rising interest rate environment, it’s possible that bonds could actually be more risky than stocks. But I’m not sure what the implications are for asset allocation. Simon Lack, for one, would say that now’s the time to pretty much get out of bonds entirely, given their large downside and small upside. But I remember that Larry Summers managed to lose a billion dollars using the argument that “rates can’t fall any further”.

So maybe the real lesson here is simply that there just isn’t such a thing as a safe investment. Not even if it’s a Treasury bond.

COMMENT

Hey Felix. You stumbled on something your friendly neighborhood quantitative analyst has known for some time. Often the best scenario for financial intermediaries is the slow rise. It allows the bank, insurer, or S&L to build income while not getting smacked with large unrealized capital losses.

Because so many intermediaries would benefit from it, is part of the reason why it almost never happens. Rates moving in anticipation of tightening Fed policy is almost never gradual.

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Chart of the day, employment edition

Felix Salmon
Nov 2, 2012 14:09 UTC

jobs.tiff

There’s lots of good news in today’s employment report: payrolls rose substantially in October, and the already-great numbers for August and September were revised upwards to boot. Even the uptick in the unemployment rate, from 7.8% to 7.9%, was actually positive in many ways. Americans are back looking for work, which bodes well for the next few months.

And then there’s the technocratic good news, too: the BLS isn’t just releasing numbers any more, it’s also releasing charts! Along with the standard payrolls press release this morning, there was also a PDF file which included the chart above.

This chart I think tells the big-picture story very well. Firstly, jobs aren’t political, they’re economic. They rise when the economy is doing well, and fall during recessions: the name or party of the president can only affect things at the margin.

Secondly, we’re still in very bad shape, employment-wise: there’s a long way to go before we get back to where we were before the crisis. And that’s in absolute terms: remember that the US population has been growing all along.

And thirdly, in case you needed any reminder, this recovery is long and painful. Look at the rate of employment growth from 2002 to 2006, and extrapolate it upwards to get an idea of what the capacity of the US economy is: where we could be, if we hadn’t been hit by the crisis. Then, look at the gradient of the current recovery: it’s not noticeably steeper than the trend-growth gradient. Which means, to a first approximation, that we’re just as far below capacity as we were when the recession ended.

This isn’t entirely bad news. For one thing, the recession has ended, thanks in no small part to the 2009 stimulus and to the unprecedented monetary operations being carried out by the Fed. And compared to, say, any country in Europe, the strength of the US recovery is decidedly impressive. But if you aspire to full employment — and that is one of the Fed’s two mandates, after all — then in one sense we’re just as far away from that goal as we were two years ago.

Markets and economists will and should react positively to today’s report, which is significantly better than most of us expected. But it doesn’t change the fact that the biggest problem facing the US economy is unemployment and underemployment. We need to get the unemployed and underemployed working again, and the longer it takes to do that, the harder the job becomes, given the well-documented ways in which long-term unemployment erodes skills and morale.

How much can politicians really do, on that front? I don’t know. But a front-loaded fiscal employment push would be great right about now, while massive spending cuts are precisely the opposite of what we need. And in a country where millions daily face the misery of being unable to find work, it would really be unconscionable to vote for anybody who thinks it would be a good idea to cut food stamps by $134 billion.

Every little vertical notch on that BLS chart represents 2 million employed Americans. We all want to see the lines go up and to the right, so that millions more Americans get jobs. But we also need to keep in mind just how depressed those lines are. The number of Americans without work is absolutely enormous right now. And so while we’re trying to create new jobs, it’s even more important that we ensure the well-being of those who don’t have employment yet.

COMMENT

Auros, that wasn’t what he was saying though.

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Chart of the day: The long decline of labor

Felix Salmon
Sep 26, 2012 10:21 UTC

2012-13-1w.gif

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.

COMMENT

More @mlnberger than Felix, note that 1) individuals have continued to see their wages increase over the period of their working lives; each cohort is seeing lower wages with the requisite delay, and 2) since the guy before you mentioned demographics, it’s interesting to note that the incomes of different racial and ethnic groups have gone up faster than the overall level of income as the lower-income race/ethnicity groups have increased their share of the population and whites in particular have decreased their share of the population.

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Animated chart of the day, Apple vs Microsoft edition

Felix Salmon
Sep 18, 2012 18:19 UTC

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.

Charts of the day, mutual-fund outperformance edition

Felix Salmon
Sep 11, 2012 21:40 UTC

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:

beat1yr.png

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:

3yr.png

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:

5y3.png

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.

COMMENT

hello
and thank you for your excellent post.
can you please confirm that those performance figures are including the funds’ direct management commissions?

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Chart of the day, party neighborhood edition

Felix Salmon
Sep 6, 2012 13:45 UTC

Uber_weekend_model.jpg

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”.

Uberdata_PCAdemandcurve.jpgWhat 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:

City Neighborhood Weekend Index
Chicago Near North Side 89.51
San Francisco North Beach 88.75
Boston South Boston 87.59
Boston Back Bay-Beacon Hill 86.37
NYC Soho 86.03
DC Dupont Circle 85.80
San Francisco South Of Market 85.67
San Francisco Potrero Hill 85.67
Chicago Near West Side 85.62
DC Au-Tenleytown 85.44
DC Downtown 85.08
DC Georgetown 84.90
NYC Greenwich Village 84.81
NYC Tribeca 84.71
DC South West 84.63
NYC Financial District 84.53
DC Foggy Bottom 84.48
Los Angeles Santa Monica 84.38
DC Capitol Hill 84.30
NYC Clinton 84.27
NYC Chelsea 83.59
Boston East Cambridge 83.29
NYC Gramercy 82.85
Los Angeles Sawtelle 82.84
San Francisco Glen Park 82.62
Los Angeles Beverly Hills 82.58
Boston Central 82.25
Boston South End 82.09
San Francisco Chinatown 81.98
Seattle First Hill 81.51
San Francisco Financial District 81.29
Seattle Pioneer Square 81.27
NYC Midtown 81.12
DC Logan Circle 81.06
San Francisco Mission 80.96
Los Angeles Westwood 80.89
NYC Murray Hill 80.88
DC Brentwood 80.83
San Francisco Russian Hill 80.62
San Francisco Inner Sunset 80.48
DC Woodley Park 80.38
NYC Little Italy 80.34
Seattle Downtown 80.32
Chicago Lincoln Park 80.24
Seattle Capitol Hill 80.12
Los Angeles West Los Angeles 80.03
Los Angeles Mid City West 80.02
NYC Williamsburg 80.01
Los Angeles Mid Wilshire 79.73
Boston Fenway-Kenmore 79.62

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.

COMMENT

I can only speak for San Francisco, but the inclusion of some of those neighborhoods (for instance, Potrero Hill) speaks only to the lack of availability of cabs.

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Chart of the day: Median net worth, 1962-2010

Felix Salmon
Jun 12, 2012 21:36 UTC

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:

worth.png

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.

COMMENT

It just doesn’t get any

better than seeing the gorgeous “Mrs. Anita Pelaez” over at her and her

husband “Captain Kutchie’s” place..Some Folks Also Call Him..”The

KutchMan!”….(Anita and Kutchie’s Key West, Key Lime Pie Factory and

Grill)…Just watching the lovely couple baking together all those Yummy

Key Lime Pies at their Key Lime Pie Factory and Grill in Asheville.

…It’s always worth the trip to visit them in they’re Historic Key Lime

Pie Factory and Grill…It should be on everyone’s bucket list for

sure..And The World’s Best Key Lime Pies!..YUM-YUM-YUM…..”Talk About

World Class” What An Understatement!…….AAHHHHH!….The Magic Of The

Lovely..”Mrs. Anita Pelaez” And Her Delicious Key Lime Pies Baked With

Pure Love…Always……40 Years And They’re Still Going Strong….

….May GOD Continue Blessing “Anita And Kutchie Pelaez” and They’re World

Famous Key Lime Pie Factory And Grill Where The Personalities, Ovens And

Smiles Are Always Warm And Inviting. “Kutcharitaville” We Love You!…..

…Now You Know Who Is The Hottest!…And Baby Let Me Tell You, Mrs. Anita Is No Act…She’s The Real Thing Baby!…

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