1.  Bas B. Bakker and Joshua Feldman just published an IMF working paper, "The Rich and the Great Recession," that shifts the focus from the role of the middle class to the role of the top 10% in the debate over the macro trends that led into the crisis.

They call both the inequality narrative, that stagnant wages led the middle class to over-borrow the rich's savings, and the wealth narrative, which ignores distribution and blames rollercoastering asset prices, flawed, and advance the idea that the rich, whose debt increased as quickly as that of the lower 90% in their methodology, were "active participants" in the boom that busted. Their work, including modeling, suggests that the savings rate of top 10% followed the same pattern as that of the lower 90%, and their model implies something they call, "…truly striking: between 1993 and 2003, about 55 percent of the increase in consumption came from the rich; over the last ten years, the share was even larger (71 percent)."

They focus on the savings rate from the National Income and Product Accounts, pointing out that in the early 1980s the savings rate was 10% and the share of income going to the top 10% one third, but by 2010 aggregated savings had slipped to 5%, and the upper share of income had risen to one half.

They do not believe, as none of us should, that the only process that would support the inequality scenario on its own, that the savings rate of the top 10% held steady over time while the rate of the lower 90% fell from 10% to zero, is plausible, especially as it requires both groups to have been saving at the same rate in the early eighties. They make the more likely assumption that the savings rate of the rich was well above that of all the rest in 1980, perhaps three times higher, and then fell much harder. Supporting this is the negative correlation between the share of wealth heading into cashmere pockets and the aggregate savings rate of the last 3 decades.

The Flow of Funds, the authors don't even bother with the new moniker, shows the debt of the top 10% rose as quickly as that of the less flush, and was likely a response to the increase of wealth. Wealth to income ratios rose from 721% in 1994 to 912% in 2007 for the top group, and from 373% to 404% for the bottom. With both savings rates traveling along the same curve, it makes sense that the top 10%, who enjoyed the lion's share of income and wealth gains, played a central role in both the boom and bust.

Check their models here.

2. In a study of income and spending patterns of 100,000 of its account holders (a sample drawn from 27 million accounts), the JPMorgan Chase Foundation found high levels of volatility on both sides of the income statement, with close to all experiencing changes of 5% or more from month to month. Over a quarter, 26%, experienced income changes of 30% or more over the course of a year (10% of them a decline, 16% an increase). There was surprisingly little variation by income quintile – in fact the top quintile experienced somewhat more volatility than the bottom. This results generally show more volatility than previous studies have.

Income and consumption changes didn't move in tandem: just 28% of the sample, whom the bank called "responders," showed a tight correlation between the two. Responders were slightly more likely to be in the bottom quintile, to be maxed out on their credit cards, and on a fixed income. A third of the sample were "sticky optimists," meaning consumption increases typically exceed income increases by 10 percentage points or more; most increased consumption despite drops in income. The optimists tended to be higher earners, though obviously that kind of aggressive consumption isn't sustainable over the longer term. The largest share of accounts, 39%, were "sticky pessimists," meaning that consumption increases less than income by more than than 10 points or more. Their consumption rises less than income when income rises, and they cut back on spending when income drops. For a large minority, 39%, income and consumption changes between 2013 and 2014 moved in the opposite direction. The data suggests that few individuals follow a monthly budget: 60% of the sample showed average monthly changes in consumption of greater than 30%.

Of course, it's hard to generalize from just two years of data, but the looseness of fit between income and consumption changes is striking. It'd be interesting to know how these categories have been affected by the Great Recession – are consumers more cautious than they used to be? – but we just don't have the data.

The typical household in the sample just didn't have the savings cushion necessary to weather the income and spending volatility. The bank estimates that a middle-income household should have about $4,800 in liquid assets on hand to cope with normal monthly ups and downs – but they had only about $3,000. The shortfall was present in all but the top quintile.

Both the volatility and lack of cushion found in this study are usually thought to affect lower-income households, but that's clearly not the case. Better financial planning and budgeting could help mitigate the problem, but most Americans experience quite a bit of economic uncertainty. As the report says in its conclusion, "financial insecurity and scarcity exact a mental toll, making it more difficult for people to solve problems, exert self-discipline, and have the mental bandwidth to weigh the costs of borrowing or other short-term solutions."

3. In their April minutes, the FOMC noted that state and local governments cut back on construction spending in the first quarter. Bloomberg's Mark Nicette put together a nice roundup of state finances, citing, among others, the mighty Rockefeller Institute, that shows that despite better collections 32 states face gaps in their FY 2015 and/or 2016 budgets, and that, as we and others have pointed out, adjusted for inflation state revenues have not regained pre-recession peaks. This means new cuts.

As we often mention, the Great Recession is the only one on record where public sector employment fell, and hence could not function as an automatic stabilizer. Nicette ends his piece with a quote from Alabama's two-term Republican Governor Robert Bentley, who just asked for a tax hike (gasp). "We have a real crisis on our hands." 

Like it or not, public employees are generally quite well paid, and they shop in the same stores as everyone else. The Bureau of Economic Analysis assigns a 2.22 economic multiplier to local (N.B.) governmental enterprises, just below residential construction's 2.27, and among the highest. And dead last? Federal banks, credit intermediation and related activities, 1.39. Uh oh.

The large upward revisions to August payrolls released by the Bureau of Labor Statistics this morning (with jobs data for September) set the conspiracy theorists' world on fire. And they were strong, about three times the usual revision. But, as we pointed out in a report we sent to our clients earlier this week, this is a long-standing pattern. It almost always happens, whether there's an election coming up or not. Here's the relevant text from this morning's note:

August's gain was revised upward by 46,000, and July's by 40,000. Almost all the revisions, however, came from an upward revision of 101,000 to local government education in August before seasonal adjustment – a recurrent anomaly at this time of year that we wrote about in Wednesday's report. The concurrent seasonal adjustment technique distributes large changes like that backwards, so the gain was split between July and August in the adjusted numbers. Some excitable types are attributing the upward revision to political machinations, but this pattern has been around a long time. It's likely something is amiss in the BLS's collection process, and they are working on it. There shouldn't be a recurrent pattern of error like this. (Excitable types should also note that the birth/death model subtracted 9,000 jobs in September.)

In the November 8 issue of the San Francisco Fed’s Economic Letter, Rob Valletta and Katherine Kuang look at the regional and sectoral behavior of employment and find little evidence for the mismatch employment thesis currently making the rounds. If there were a serious mismatch problem, you’d expect to find major disparities in employment across geography and industry: healthy regions or sectors would show shortages of workers, and sickly ones would show surpluses. But in fact you don’t see that: regional and sectoral variation is little different now from past cycles. Valletta and Kuang conclude that the major reason for persistently high unemployment is cyclical and not structural.

The San Francisco Fed study looks at Beveridge curves. Beveridge curves plot unemployment against job vacancies. In general, the more vacancies there are, the lower the rate of unemployment. Minneapolis Fed President Narayana Kochlerlakota’s argument, outlined in a speech he made over the summer, depended heavily on the idea that the Beveridge curve suggests that the unemployment rate should be about 1.5–2.0 points below where it is, which is what leads him to the mismatch conclusion. His curve, though, was based entirely on only the most recent decade’s data.

But there’s nothing at all stable about this relationship over time. The Valletta–Kuang paper includes a graph showing how much the Beveridge curve has wiggled around over the decades. For example, Valetta and Kuang point out that the curve shifted about 4 points to the right between 1960 and the early 1980s before shifting back about 2.5 points by 1989, and that the variation in the NAIRU during that period was considerably smaller than the movement in the curve would suggest. We thought this worth a closer look.

Unfortunately, the JOLTS data only begins in 2000. Most earlier work on the unemployment–vacancy relationship used the Conference Board’s old Help Wanted (HW) index as a proxy for openings. In an earlier paper, Valletta used the period of overlap between the HW series and the JOLTS data to estimate a consistent vacancy rate series going back to 1960. He graciously shared that with us.

In the graph below you will see a set of Beveridge curves by decade—they show that a major cause of the noisiness in the long-term relation is that the vacancy–unemployment connection shifts over time. The r2’s for the decade regressions are a lot better than for the whole series, with most in the high 0.80s/low 0.90s. But there are also some noisy decades: the r2 for the 1970s is just 0.39. The 1980s aren’t so great either, with an r2 of 0.77.

The labor market is usually thought to be functioning better the further down and to the left (i.e., closer to the origin) the Beveridge curve is on the graph. The further out the curve is, the more friction, like regional or sectoral mismatches. The graph for the 2000s (which is based on data through June 2009, when the relationship prevailing earlier in the decade started breaking down) shows a much better-functioning labor market than in earlier decades, especially the 1980s.

Until, that is, we get to recent history, as shown by the dot marking the data for November 2010. It’s about two unemployment points to the right of where the curve suggests it “should” be. But it’s actually lower than the 1980s curve would predict, and not that much higher than the 1970s curve. And those were decades of major structural change in the U.S. economy—periods of major financial and real sector shocks, far more severe than those of the following two decades.

So the recent breakdown in the Beveridge curve looks to us more like a reflection of a major financial and psychological shock (one that has left employers extremely shy about hiring) than some fresh mismatch in the U.S. labor market. The fresh mismatch theory seems especially odd in light of the fact that things seemed to be functioning so well (meaning the Beveridge curve was close to the origin) for so much of the decade until the economy fell apart. The novelty is the having fallen apart, not some recent change in the labor force.

This one from the Saint Louis Fed. They say trend lines never go straight up or straight down, but there are some dizzying graphs posted at the Eighth District’s website where the trend lines do just that.

Here’s a one-hundred year history of borrowing by depository institutions from the Federal Reserve:

And here’s the flip side, non-borrowed reserves of those institutions:

Some argue that these graphs are misleading since the Fed has turned  itself into a supplier of reserves, and has promised to offset all infusions with sales and redemptions, but that strikes us as making a virtue out of necessity. The graphs accurately reflect a Fed forced onto a path without precedent by extreme stress in the markets, and surely it’s important that prior banking crises barely register against the current situation. And if banks are using emergency funding as a substitute for regular fed funds borrowing, that in itself is major news: banks don’t trust each other, and have to turn to Washington in a pinch (and a painful pinch it is!).