Why the Internet Knows Us Better Than We Know Ourselves


Everybody Book Cover

In this digitally connected era, all of us produce enormous numbers of data points every day. What we search. How we search it. What we buy, and what we read. What we like and dislike, whom we chose to associate with, and so much more — a steady stream of data that can be quantified, sifted and analyzed en masse with the data from everyone else to reveal patterns previously hidden, sometimes things we’re not even aware of about ourselves.

That data may offer us as a society a better way to truly understand who people really are, a theory that author Seth Stephens-Davidowitz submits for our consideration in his new book Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are. A former Google data scientist who is also a visiting lecturer at Wharton, Stephens-Davidowitz joined the Knowledge@Wharton Show on Sirius XM channel 111 to talk about what properly analyzed big data can reveal about our political views, our health, our biases and more.

An edited transcript of the conversation follows.

Knowledge@Wharton: There’s not much doubt that our digital footprints say a lot about who we are, but I get the sense that people, to a degree, still scoff at the idea that so much can be gleaned from all of this information.

Seth Stephens-Davidowitz: Yes. Some people have this traditional notion of what data is. They think of it like a representative survey: You have clear questions with check boxes that people can answer very clearly. I think they get a little uncomfortable with the wild world of the internet, where data tends to be more unstructured and a little bit different than they’re used to.

Knowledge@Wharton: Does it feel like people still believe they have a higher level of data security than they really do?

Stephens-Davidowitz: I think there are definitely concerns about the power of big data. Because data is so predictive, companies can potentially use it to really take advantage of people. I talk about it in the book. One example is if you apply for a loan, companies can predict whether you’ll pay back the loan just based on the words you use in your loan application. For example, if you use the word “God” in a loan request, you’re 2.2 times more likely to default, 2.2 times more likely not to pay it back. So a company could save money by not giving loans to people who end their requests with “God bless you,” which is pretty scary.

Knowledge@Wharton: Throughout the book, you tackle some of the bigger issues that we have in society, like racism and child abuse. And there are all kinds of data points which will lean one way or another in these areas.

Stephens-Davidowitz: Right. There’s just so much information now from the web. And there are certain sources, such as Google, which I focus a lot on. People are just really honest and tell Google things they may not tell anyone else. So when it comes to really important areas like the ones you mentioned, we can get really new insights into who we are.

Knowledge@Wharton: One of the areas you look at is sex.

Stephens-Davidowitz: I like to say that big data is so powerful that it turned me into a sex expert, because it wasn’t a natural area of expertise for me. There’s obviously a lot of lying around sex because it’s an uncomfortable, taboo area. I think we can learn a lot more from Google searches about what people like.

Knowledge@Wharton: You also looked at racism; and talk about how racism actually surfaced more, not during the presidential race in 2008, but in the immediate aftermath of President Obama being elected.

“[If you] go by conventional wisdom, racism is considered a Southern issue…. If you look at the Google search data, which is more honest, you see many of the areas with the highest racism are Northern places.”

Stephens-Davidowitz: There is a disturbing element to this data. If, in general, people lie to make themselves look good, then we’re going to have an overly optimistic perception of who people are.

But if we know the truth, in many areas, unfortunately, we’re going to learn darker things about people, and racism is one of the areas. It’s shocking. One of the most surprising things I found right away in this data was the shocking number of racist searches people make, basically looking for jokes mocking African-Americans. And yes, this was a big theme — really nasty searches about Obama as soon as he was elected.

Knowledge@Wharton: One of the long-held beliefs about this was that racism is more of a Southern phenomenon, but your data showed that is not necessarily the case.

Stephens-Davidowitz: Yes. If you ask in surveys or go by conventional wisdom, racism is considered a Southern issue. But I think that may be because in the South, there’s just less need to hide that racism. If you look at the Google search data, which is more honest, you see many of the areas with the highest racism are Northern places: western Pennsylvania, eastern Ohio, upstate New York, industrial Michigan. The real divide in racism these days is not South versus North, it’s East versus West.

Knowledge@Wharton: If people or companies were able to use this data in a more coherent, more effective manner, what do you think the impact in general would be for the country, or for society?

Stephens-Davidowitz: Well, there’s an optimistic scenario and a pessimistic scenario. I don’t know which one will come true. The pessimistic scenario is that companies would use this to take advantage of people, to get them to spend more money that they don’t have, or spend more time on their websites even though they don’t need to be on those websites. The optimistic scenario is that we would have insights into really, really important areas — health, racism, sexuality — and really learn how to improve society.

Knowledge@Wharton: The health angle of it is very interesting. The idea that we would be able to glean information that might lead to cures for diseases, or be able to take a more effective preventive approach, being able to catch diseases before they become worse — those things would have an incredible impact both on the people in this country, and also the economics surrounding health care. 

Stephens-Davidowitz: Yes. In one of my favorite studies, they used search data and found people who made searches such as “just diagnosed with pancreatic cancer.” And you know when someone makes a search like that, they probably just got diagnosed with pancreatic cancer. Then you compare those people to similar people who never were diagnosed with pancreatic cancer, and you look in the prior months what symptoms were they searching for. And they found really, really subtle patterns that are predictors of eventually getting a pancreatic cancer diagnosis.

For example, if you searched “indigestion” followed by “abdominal pain,” that’s a risk factor in pancreatic cancer. Whereas searching “indigestion” by itself is not a risk factor. That’s a really, really subtle pattern that is hard to pick up without massive data sets, and it almost suggests a new kind of medicine.

Knowledge@Wharton: One of the things that may have helped revolutionize big data and our understanding of this, which you write about, is Google Trends.

Stephens-Davidowitz: Yes, it’s interesting. Google Trends can show you where different terms are searched, what place they’re searched more frequently, and then you can also see how things are searched over time. When it first came out, it was considered a little bit of a joke. It was not considered a scholarly source; it was just more a fun kind of PR source for Google, potentially. You could play around, learn what fashions were popular, what celebrities were popular. But I think we’re learning more and more that this is no joke. This is, as I say, probably the most important data set ever collected on the human psyche, and definitely a really important tool for researchers to focus on.

Knowledge@Wharton: And in contrast to that, a lot of these surveys that come out proclaiming data may not necessarily be as accurate as they would lead people to believe.

Stephens-Davidowitz: Yes, I think surveys have big holes in them. The more I look at surveys the more skeptical I become. Even just in little things. Recently, I looked at survey data on potential car purchase behavior versus actual car purchases, and they don’t match up at all. People say they’re going to purchase cars that they don’t, or they don’t say they’re going to purchase the car that they do. So I think surveys have been dramatically overvalued, and really are going to play a much smaller role in the future as some of these new internet data sources become more accessible.
“[Google Trends] is … probably the most important data set ever collected on the human psyche, and definitely a really important tool for researchers to focus on.”

Knowledge@Wharton: That’s part of the reason why a lot more companies are really looking at analytics, and looking at data to get a truer understanding of what consumers are thinking, correct?

Stephens-Davidowitz: Yes. I think it’s also just that you have to be careful. For every data source, you have to think: What is this data source? What are the incentives that people have when they’re giving me this data? I think a lot of people, any time they see numbers or data, they say, “Oh, that’s reliable.” But a lot of data sources are crap — pardon my language. A lot of data is really unreliable, and a lot of data is reliable. But what people click on, what people purchase, what people search — that’s more valuable than many of the other sources that you might consider.

Knowledge@Wharton: Going back to the political realm, you discuss in the book that the data and what was out there on the internet did suggest that President Trump was going to be the person to win, not only the Republican primary, but also the general election, correct?

Stephens-Davidowitz: I think there were definitely clues. It’s a little tough. It’s one of the most common questions I get: “Can you use Google searches to predict elections?” And it’s a little difficult, because we’ve only had four elections in which Google search data has been around, so it’s a little challenging to predict their models.

But I think within four to eight years, we’re going to be able to use this data to predict elections very, very well. I’ve already talked about some of the clues I already had right before the election that suggested to me Trump was going to win. A couple things tipped me off. One, you can see based on whether people search for “how to vote” or “where to vote” before an election whether they’ll actually turn out to vote. You can’t really trust when people tell you in surveys that they’re going to vote. Everyone says they’re going to vote, and then many of them don’t.

But what this revealed is that African-American turnout was going to be much lower than in previous elections. This really hurt Hillary Clinton in the election.

Then there is a really subtle clue that I think is fascinating: The order in which people search for candidates can give a tip off of which way they’re going to vote. If people searched “Trump/Clinton poll,” they’re much more likely to go Trump’s way. And if people go “Clinton/Trump poll,” they’re much more likely to go Clinton’s way. And there were many more searches for Trump/Clinton polls in certain key states in the Midwest.

Knowledge@Wharton: So was there an implication that could be gleaned from just having Clinton in a search, whether that included Trump with it or whether it did not?

Stephens-Davidowitz: No, I think that search by itself is not revealing, because you may search Clinton because you love her, or you may search Clinton because you hate her. You may search Trump because you love him, you may search Trump because you hate him. It doesn’t really tell you anything. It has to be a little more subtle than that. But the order in which candidates are searched does have predictive power. It may even be that people give away who they’re going to support before they realize it themselves, because people may think they’re undecided, but if they’ve been searching “Trump/Clinton debate,” “Trump/Clinton polls,” “Trump/Clinton election,” they’re very likely to be going for Trump.

Knowledge@Wharton: Do you believe, though, that we are getting to a point where people have a better understanding in general about all the data that is out there? Because it’s seemingly a fairly common story about how we really, truly don’t understand all of this data. Maybe it’s a bit of a gradual process to really get a handle on a lot of this.

Stephens-Davidowitz: I think we’re getting there pretty fast. It does need more people. I think because it initially was considered so strange that you could just understand people from their internet behavior, it hasn’t really been the subject of as much academic research as it should have been. But it’s definitely being studied more and more, and you’re seeing more and more methodologists in this area. We’re really getting there. We’re beyond the point where it’s just, “This is cool.” We’re now actually getting, real, real insights into who we are from this data.
“Going after little questions doesn’t make sense with big data.”
Knowledge@Wharton: So is this going to be a growth area for the U.S. economy: People who can do the analytics, who can understand how to use this data to really make the impact on companies and people alike?

Stephens-Davidowitz: Absolutely. But I think it’s more subtle than people realize. This came up a lot in my Wharton class. When you think “big data,” you think it’s this very technical thing, and it’s all about statistics and a left-brain, nerdy pursuit. And it definitely is a technical area — I’m not going to lie. But it’s surprising how much it is a creative process. It’s really about knowing what questions to ask, and knowing how to find the nuggets of information in that data. You can’t necessarily teach that. It’s a bit of an art that you learn and master over time.

So I don’t think it’s as simple as throw a data scientist at this question and you’re done. It’s more complicated than that.

Knowledge@Wharton: That would lead me to believe that we’re going to see more partnerships with data scientists and a variety of different business sectors over the next couple of decades to really try to get a handle on it, using it to address some of the world’s greatest problems, whether it’s access to water or fighting disease.

Stephens-Davidowitz: It’s thrilling — the possibilities are really mind-blowing, and in big areas.

Because this new data exists — and it’s honest — it makes sense to go after the big questions, to be really ambitious. Going after little questions doesn’t make sense with big data.

Knowledge@Wharton: But what about people being able to understand themselves a little bit more? We talk a lot about how this data can impact other people and impact businesses: Will people be able to understand themselves better in the future?

Stephens-Davidowitz: I think so. Data frequently understands us better than we do. For example: Netflix initially in the early days of the company asked people, “What videos are you going to watch in the coming days? We know what you’re watching now, but this weekend, what do you want to watch so we’ll cue that up when the weekend comes around?” When you ask them, people say “I’m going to watch a documentary,” or “I’m going to watch avant-garde French films.” Then Friday comes around, and you have that in the queue, and they ignore it and watch the same lowbrow comedies or romance flicks that they’ve always watched. And Netflix just realized they should ignore what people tell them, and instead focus on what they actually do, and let the algorithm tell the story.

We tend to make horrible predictions about what we’re going to do in the future. Almost all of us are way too over-optimistic. I think data can ground us much better.

Knowledge@Wharton: This also could help us understand more clearly how this country may be different compared to, say, China or France or Germany. That has an impact when you’re thinking from a global perspective, whether it’s in business, politics, or on a variety of different fronts.

Stephens-Davidowitz: Definitely. It’s just really interesting to compare the differences between countries that can be revealed in this data. Then also, from a business perspective, of course, the data is just horrible from some of these countries. Nigeria, I think the biggest economy in Africa — one time, they realized there was a flaw in their GDP estimate and overnight, they changed the estimate by 90%. So traditional data in some of these countries is really, really bad. Some of these new data sources that are coming can dramatically improve our understanding of these countries.

I talk about night lights data, which can measure the economy just based on how much light is being produced. I talk about Premise, which is a company that basically just goes around taking pictures of economic activity in developing countries, and from those pictures is able to give estimates of inflation rates, interest rates and lots of other things.

Knowledge@Wharton: The potential for change from all of these different elements is massive. And they would seemingly give you much better predictive tools to use in fostering growth or avoiding pitfalls in various economies around the world.

Stephens-Davidowitz: Yes. I think I tend to be a very cynical, skeptical person, so when I hear a term like “big data,” or when I hear a buzzword, I’m just kind of like, “Ugh, these things are so silly. It’s just the latest, hottest fad.” But I’ve been studying this for five years. I’ve talked to people in the field. And I’m constantly blown away by what you can find. This one is no fad.

It’s really a revolution in our understanding of people and the world.
“We tend to make horrible predictions about what we’re going to do in the future. Almost all of us are way too over-optimistic. I think data can ground us much better.”
Knowledge@Wharton: You are a self-professed cynical person, yet your life is in data. And truly, the data is the truth, correct?

Stephens-Davidowitz: Yes. I think in some sense, it confirms my skepticism, my cynicism in that you can’t trust what people tell you. With a lot of the traditional data sources, there are incentives for people giving you that data. But I’m not cynical at all about what you can learn if you know the right data to look at.

Knowledge@Wharton: You immerse yourself in this data on a daily basis at this point. I mean, this is an open-ended sector right now, in that there is data on everything and anything you could potentially want to have an effect on. This could be something where you could literally go from business to business and be able to collect data on a daily basis, correct?

Stephens-Davidowitz: Well, yes. For the end of my Wharton class, we had a group presentation and I gave them very, very broad topics. I just said, “Think of a new business in education or think about a new business in health, think about a new business in politics, and how using the tools of new data and big data would help you with that business.” And by the end of every single presentation, all of the students said, “Why doesn’t this exist? It doesn’t make sense. This should exist.” It’s usually hard to come up with something new, because smart people have been spending their whole lives trying to find things that should exist, things that people want.

But I think with big data, it’s really surprisingly easy to come up with a new, important idea in a really big area.

Knowledge@Wharton: So you are positive about the future here? You’re working with that next generation, the students who are going to be out there in society. They understand the importance of these types of data points, and they will continue to build and grow them as we go forward.

Stephens-Davidowitz: It’s really exciting. The one concern is the ethical issue, definitely.

Businesses may almost become too powerful, and really be able to squeeze consumers for everything they’re worth, because they know more about consumers than consumers know about themselves.

That’s definitely a big concern I have.

Knowledge@Wharton: How do you guard against that?

Stephens-Davidowitz: It’s going to take a lot of work. I think most people in the areas of law and ethics aren’t quite prepared for just how revolutionary big data is in some of these arenas.  Basically, the way I like to think about it is that everything correlates with everything. There’s very little that’s 0.000 correlation. So just about anything you do will predict something else you do. Traditionally, companies have had only three or four or five variables to make these predictions on. But now they have pretty much everything anybody’s ever done to make these predictions. So it’s very powerful stuff.


Markets worry about central Banks

Will there be a sudden tightening in policy?
IN JANE AUSTEN’S novel, “Sense and Sensibility”, Henry Dashwood’s death plunges his wife and two daughters, Elinor and Marianne, into financial distress, because his heir grants them only a meagre allowance. Bond-market investors have started to worry that something similar is about to happen to them.

Since 2009 central banks have been incredibly supportive of the financial markets—keeping short-term interest rates at historic lows and buying trillions of dollars worth of bonds. But in recent weeks, several of them have been hinting at reducing their largesse.
The Federal Reserve has been slowly pushing up interest rates and has talked about reducing the size of its balance-sheet, by not reinvesting the proceeds of bonds when they mature. There have been suggestions that the Bank of Canada might push up rates when it meets on July 12th. Both Mark Carney, the governor of the Bank of England and Andrew Haldane, its chief economist, have hinted that a rate rise may be on their agenda.

But the biggest shock to markets came on June 27th, when Mario Draghi, the head of the European Central Bank, remarked that “deflationary forces have been replaced by reflationary ones.” The result was a sudden rise in bond yields (see chart). “Super Mario” carries great weight with investors; he was widely credited with halting the euro crisis back in 2012 with his vow to do whatever it took to save the single currency.

The ECB tried to calm investor nerves in the aftermath of the statement. Mansoor Mohi-uddin, a strategist at Royal Bank of Scotland, thinks the markets overreacted to Mr Draghi’s words. The ECB is not about to stop its stimulus. He thinks that, in September, the bank will merely indicate that it will be reducing its monthly rate of purchases from €60bn ($68bn) to €40bn at the start of 2018. Mr Draghi is just preparing the ground.

There was some speculation that central banks had deliberately co-ordinated their comments. But the simpler explanation is that they were reacting to similar factors. First, global growth seems have picked up in the second half of 2016, allowing banks to withdraw some stimulus. Second, Fed tightening gives other central banks cover; any bank tightening on its own would probably see its currency strengthen strongly, risking overkill.

Caution is essential in calling the turn in the bond market, an event that has been predicted many times before. Bond yields have merely reversed some of the declines seen earlier in the year. Inflation in most economies remains subdued; Britain is an exception because of the decline in the pound following the Brexit referendum. British bonds may also be less attractive to international investors because of signs that the budget deficit will widen under the current Conservative government, and even more so if the Labour opposition takes power.

There are also signs that the global recovery may not be that robust. Commodity prices, an indicator of global demand, have fallen since the start of the year. China’s economy is showing signs of a loss of momentum, according to Capital Economics, a consultancy. David Owen of Jefferies, an investment bank, says that global trade and industrial production are both growing at an annualised rate of less than 2%, based on the past three months. “This is not consistent with a strong recovery in investment,” he adds.

Central banks will have to tread very carefully. Global debt is higher as a proportion of GDP than it was before the financial crisis started in 2007. Ultra-low interest rates have made borrowing sustainable but have also encouraged companies and consumers to take on more debt. The annual report of the Bank for International Settlements, released on June 25th, warned of elevated credit risks in a number of emerging economies and smaller developed economies. “Financial-cycle downturns could weaken demand and growth, not least by dampening consumption and investment,” the report said. The BIS also worries that a return of trade protectionism could sap the global economy’s strength.

It is a lot easier to begin monetary stimulus than to end it. More than a quarter of a century has passed since the Japanese bubble burst in 1990, and the Bank of Japan is still pumping money into the economy and trying to keep ten-year bond yields close to zero. By the end of the novel, Elinor (sense) and Marianne (sensibility) find contentment with a vicar and a retired colonel respectively. Unlike Austen, central banks cannot always arrange a happy ending.

Candour from central bankers is overdue

When did you last hear mandarins speak clearly about radical monetary policy?

By: James Grant

Mario Draghi at the ECB forum on central banking in Sintra, Portugal, in June © Reuters

The central bankers’ foggy words obscure the how and the why of modern monetary management. They likewise conceal the right and the wrong of it.

The US Federal Reserve is trying to extricate itself from the methods of crisis management it improvised when there was a crisis. Ben Bernanke was chairman when the Fed began to promise to reverse its emergency policies — to raise interest rates and shrink its ungainly balance sheet. This was in 2011. Janet Yellen, his successor, reiterated that intention last month. Talk is the new action.

The sheer volume of monetary verbiage deadens the central bankers’ message, if a message there be. In 2007, when the financial rains began to fall, the policy statements of the Fed’s rate-setting committee ran to an average length of 214 words. This year, they weigh in at 892 words.

Not a little of the extra content is devoted to vacillation. Last month’s “Addendum to the Policy Normalisation Principles and Plans” concluded with a lengthy escape clause. “Normalisation” will cease if the American economy encounters difficulties, said the central bank in so many words.

If only the mandarins spoke so clearly. “Interest rates are prices,” as they are not in the habit of saying but should certainly admit. “Suppressing them, we distort perceptions of risk and flatter the value of future cash flows. On form, this will blow up in our faces.”

Or, as not one central banker has ever been heard to say, “Really, what we’re doing has never been done before. There’s no way of predicting how it will turn out. The negative nominal yields attached to a certain number of sovereign securities in Europe and Japan today are a 5,000-year first — never before seen in history. We couldn’t tell you exactly what quantitative easing would do. And we certainly can’t predict what the withdrawal of that so-called stimulus will achieve. The future is a closed book.”

It would be useful if the central bankers were held to the same standards of truth-telling as those required of pharmaceutical manufacturers. “Here is a therapy, and here are its side effects,” the law requires the drugmakers to say. Pending adaptation of that enlightened rule to monetary affairs, the public will have to translate central banking patois as best it can.

Inflation is a subject requiring special textual exegesis. Minutes of its June meeting reveal the Fed’s preoccupation with rising prices — it wants them to rise a little faster. The central bankers worry that the dollar commands an undesirably high quotient of purchasing power. They wish to weaken it. Yet — as the minutes also reveal — the committeemen fear that a weaker dollar will serve to levitate the already sky-scraping prices of stocks, bonds and real estate.

Candour would require a clean breast of the fact that inflation data are hard to interpret and that the margin for error in measuring prices is perhaps greater than the target range at which the Fed and its like-minded central banks continue to take aim.

Anyway, if “price stability” is the great desideratum, why do the monetary authorities strive for a 2 per cent rate of inflation? And are they not aware that the consequences of credit formation are complex and unforeseeable? Will they not admit that trillions of dollars of new credit might lift prices of stocks and bonds instead of those of petrol and groceries (as indeed they have)? Or that, from 1960-65 in the US, consumer prices never showed a year-on-year rise of as much as 2 per cent, yet the country grew?“

Risk, like energy, tends to be conserved not dissipated, to change its composition but not its quantum,” said Andy Haldane, chief economist of the Bank of England, in 2014. Bearing those wise words in mind, we should expect trouble to flare up in unexpected places. And we should expect the central banks to respond with main force: still lower interest rates and more quantitative easing.

The truth, yet unspoken from on high, is that radical monetary policy begets more radical monetary policy.

The writer is editor of Grant’s Interest Rate Observer

Consumers and Businesses Buckle under their Debts

by Wolf Richter

Bankruptcies surge as the “credit cycle” exacts its pound of flesh.

Commercial Chapter 11 bankruptcies – an effort to restructure the business, rather than liquidating it – jumped 16% year-over-year in June to 581 filings across the US. Total commercial bankruptcies of all types, by large corporations to tiny sole proprietorships, rose 2% year-over-year to 3,385 filings, according to the American Bankruptcy Institute. This was up 39% from June 2015 and up 18% from June 2014.

Commercial bankruptcies topped out at 9,004 in March 2010. By that time, credit conditions had been easing for a year, and liquidity was chasing yield. Not much later, even zombie companies – if they were large enough – were able to refinance their debts and borrow more to fund their operations and keep creditors happy. Bankruptcies fell sharply: In September 2015, they bottomed out at 2,217 filings.

Then the energy bust hit. Oil-and-gas companies along with coal companies began toppling, and bankruptcy filings surged. But in 2016, oil prices more than doubled off their lows. New money began pouring into the sector again. And drillers that had been cash-flow negative for two decades and had lost dizzying piles of money were able to refinance their debts and get new money to drill into the ground and live another day. And the waves of energy bankruptcies receded.

But now the next wave is building, with large and small retail operations at the forefront. I’ve covered only the largest chains of the brick-and-mortar meltdown, but there are many smaller operations, mom-and-pop stores, fashion shops, and the like that have quietly given up.

Bankruptcies are very seasonal, with peaks around the end of tax season and sharp declines in the following months. The data, which is not seasonally adjusted, gives a raw and noisy impression of how businesses are faring in this economy.

This chart shows the total number of commercial bankruptcy filings of all types. Note the strong seasonality. Hence, the year-over-year comparisons in red:

Consumer bankruptcy filings followed a similar pattern, but the turning point occurred a year later – in December 2016 and January 2017 – and was less obvious. Bankruptcy filings rose year-over-year in both months, the first back-to-back increase since 2010. I called it an “early red flag” at the time.

But consumer bankruptcy filings are volatile on a monthly basis, and turning points can take a while to be confirmed. In February, filings fell year-over-year. In March, they surged. In April they fell. But contrary to prior seasonal patterns, they surged in May. And in June, they ticked up 0.6% year-over-year to 63,372.

To filter out some of the monthly noise, I’ve created this chart using a three-month moving average of the year-over-year changes in the number of filings:

The chart shows the trend since 2013: Sharp year-over-year decreases in bankruptcies as consumers recovered from the Financial Crisis. The decreases gradually tapered off, as would be expected – bankruptcies are not going to fall to zero. But now a new phase has commenced: year-over-year increases in bankruptcies.

Clearly, consumers aren’t yet all of them together collapsing under their debts in one fell-swoop, but the “early red flag” is being confirmed as more and more consumers are buckling. Data on consumer delinquencies and defaults, particularly in subprime auto loans, has been cropping up for a year. Now it is filtering into bankruptcies.

“The economic challenges weighing on the balance sheets of struggling consumers and companies, especially retail businesses, have them seeking the financial shelter of bankruptcy,” observed ABI Executive Director Samuel Gerdano.

So the credit cycle has turned for both, businesses and consumers. This was inevitable. Credit cycles always turn. Easy money has much to do with it. It encourages borrowing for consumption or to fund business losses or unproductive investments. Years of too much borrowing lead to difficulties in servicing these debts and eventually to big losses for creditors.

Consumers seeking bankruptcy protection are those with piles of debt they can no longer handle, given their stagnating or declining real incomes, or perhaps the loss of income. This data shows that more consumers are facing these conditions, and the first wave is throwing in the towel.

Mortgages are not the cause. Home prices have been surging for years. By now, most homeowners can sell the home and pay off the mortgage. And if they can’t, and the bank forecloses on the property, it will rarely try to obtain a deficiency judgment in the 38 or so “full recourse” states where it is allowed. And in the dozen “non-recourse” states, the bank cannot even try.

The $1.4 trillion in student loans, though they now have sizzling default rates, are not the cause for bankruptcy filings either because they cannot be discharged in bankruptcy. But they contribute to driving people into bankruptcy.

Medical debts play a role – but not in the increase in bankruptcy filings. Some bad luck and one major emergency-room type event, without insurance, followed by a six-digit rip-off bill will do the job. But unless unemployment surges, the level of medical bankruptcies doesn’t move with the credit cycle and remains fairly constant.

The primary causes for the increase in filings are the $1.12 trillion in auto loans, the $1 trillion in credit card debts, and other consumer loans. Those loans fired up consumer spending in prior years.

Now the bill is coming due.

Why The Next Recession Will Be A Doozie For Consumers

by: Wolf Richter

Tougher for workers, rougher for the economy
The employment data released today beat expectations nicely. In June, the economy added 222,000 civilian jobs. April and May numbers were revised up. In total, over the past three months, nonfarm payrolls rose by 581,000 Jobs.
This data will do nothing to deter the Fed from proceeding with its tightening plans. The Fed should never have cut its policy rate to zero, or kept it down that long, and it should have never engaged in QE. However, acting as lender-of-last-resort when credit froze during the Financial Crisis - when even GE (NYSE:GE) and IBM (NYSE:IBM) had trouble borrowing to meet payroll - was essential to keep the system from collapsing. These short-term loans were not part of QE and were paid back. But the hangover of QE is still on the Fed's balance sheet.
So I support whatever "normalization" efforts the Fed might undertake. They should have happened years ago.
Among the reasons the Fed wants to "normalize" policy now is to put aside some dry powder for the next recession or crisis. And it will come. Recessions are an essential part of the business cycle. If allowed to proceed, they'll blow the cobwebs from the system, remove excess debts, and clean out the misallocation of capital - at the expense of creditors and investors. It's a fresh start for the economy.
But here is the thing about employment and recessions: Something big changed since 2000. It can be seen in the employment-population ratio, which tracks people over 16 years of age who have jobs, as defined by the Bureau of Labor Statistics. From the 1960s until 2000, the ratio fell during recessions, but then during the recovery regained all the lost ground plus some, ratcheting up to new records after each recession. Some of this had to do with women entering the workforce in large numbers.
But since the ratio's peak in April 2000 at 64.7%, a new pattern has developed. As before, the ratio drops before the official recession begins and keeps dropping until after the recession has ended. But when employment recovers, the ratio ticks up only slowly, recovering only a fraction of the ground lost, before the next recession hits. This has happened over the last two recessions.

For the 2001/2002 recession, the ratio started falling in May 2000 and continued falling until September 2003. During those 3.5 years, it fell 2.7 percentage points from 64.7% to 62%. Over the next three-plus years of the "recovery," the ratio rose to 63.4% by December 2006, having regained only half of the lost ground, before the next downturn set in.
This time, the ratio plunged from 63.4% to 58.2% in November 2010 and again in June and July 2011. It plunged 5.2 percentage points in 4.5 years. During that time, nonfarm payrolls plunged by 8.7 million jobs. Over the seven-plus years of the jobs recovery since then, the economy added 16.7 million jobs (146.4 million nonfarm payrolls, as defined by the BLS).
But the employment-population ratio only made it to 60.1%. It regained only 1.9 percentage points, after having plunged 5.2 percentage points. In other words, after seven-plus years of jobs recovery, it has regained less than one-third of what it had lost:
And now the Fed is preparing for the next recession.
There are all kinds of factors that move this equation one way or the other. Baby boomers are not retiring to the extent prior generations did. Millennials have fully entered into the working-age population (16 and over by this definition), though many are still in school. And according to Census Bureau estimates, the overall US population has surged by 16.7 million people from April 2010 through "today," to 325.4 million.
Since the bottom of the employment crisis in February 2010, the economy has created 16.7 million jobs as measured by nonfarm payrolls. During the same time, the population has grown by 16.7 million people. Not all of this population growth is working age. But this is the problem that the employment-population ratio depicts: jobs are being created, but not enough for the dual task of absorbing the growth in the working-age population and in putting people back to work who lost their jobs during the recession.

And these are the good times! What happens during the next recession?
However this works out, one thing we know from the past two downturns: During the next employment downturn, the employment-population ratio will get crushed - from a much lower base than during the prior recessions. And even as the economy recovers afterwards and generates 150,000 to 220,000 jobs a month, the employment-population ratio will barely budge higher and will recapture only a fraction of the ground lost during the recession.
Automation in the service sector and in the goods-producing sector, offshoring, downsizing, the corporate mania for cost cutting, the shift to a "gig economy," whose jobs are not fully captured in the employment data, the growth of the working-age population…. Whatever caused the downward ratcheting of the employment-population ratio with each recession, we can assume one thing: the next recession will be even tougher on people who want to work, on consumers overall, and by extension, much rougher on the economy that is so dependent on these consumers.
And this happens when the whole construct is burdened with more debt than ever before, thanks to the Fed's eight years of experimental scorched-earth tactics to load up the economy with debt and to achieve the "wealth effect" though clearly, those tactics have not had any visibly positive impact on this employment equation.