The Labor Market is on the Mend

The labor market in the United State is clearly on the mend.  The number of job losses per month is now negligible (probably—see my post on the effects of the snow storm here). and unless the economic outlook deteriorates substantially we are likely to see outright gains in employment in the months to come.  Nonetheless, detailed labor market continues to point to a tepid recovery in terms of overall growth rates.

The latest JOLTS data from the BLS confirms some healing in the labor market.  The black line in the figure below shows the separation rate adjusted for quits through January 2010.  (See my description of this data here.) The separation rate has now returned to its post-2000 average.  With fewer employees losing their jobs each month, the economy is likely to continue on the path to recovery.

Lower fire rates signal economic growth for two reasons.  First, the decline indicates firms are comfortable with the current size of their labor force.  These firms, at the least, foresee stability in future output.  Clearly, the actions of firms are far more important for assessing their outlook than the responses they give to the various confidence surveys.  Second, the reduction in the fire rate indicates positive consumption growth.  The growth comes through two important channels. 

First, the reduction in the fire rate indicates lower aggregate employment risk.  The lower risk decreases the precautionary motive of households, lowers savings rates, and increases consumption.  Second, and far more important, the number of households involuntarily loosing their jobs declines.  Households with a sharp reduction in income consume less.  So, with fewer households losing their jobs this important drag to growth diminishes.  [Note:  unemployed households do not consume appreciably more.  But the drop in their consumption has already been incorporated in the National Income Accounts.  The fact that they are still not consuming has zero effect on the growth rate of consumption.]

That’s the good news.  The labor market is stabilizing and growth should resume. 

The bad news:  Weak is not strong enough of a word to describe the likely recovery.

The picture below adds the hire rate to the previous graph.  The hire rate has shown no indication of improvement.  The lack of improvement in this rate is quite interesting and is in conflict with the data from initial claims, which have improved from 700 thousand jobs per week to a little under 500 thousand per week.  The current gap between the hire rate and the average hire rate since 2000 represents a shortfall of 1.2 million jobs per month.  This is an unbelievably large number. 

To put this in perspective, the jobless recovery following the 2001 recession was characterized with the separation rate near average and the hire rate near average.  The previous “jobless recovery” had job growth of between 50 and 200 thousand jobs per month.  This leaves room for the current jobless recovery to occur in an environment with only small average job losses per month.  (Small average losses per month imply that the variability in the survey will allow some positive months.)

The low hire rate also implies that firms do not see a particularly strong recovery.  While firms are content with their current workforce, they do not, as yet, see a sufficient increase in demand to warrant an expansion of their labor force.  This bodes ill for investment going forward as well.  Firms that do not need new people also, likely, have little need for new equipment. 

So, the low hire rate implies subdued growth for two reasons.  First, the data implies little or no investment growth going forward.  Typically, during a recovery, investment grows robustly contributing substantially to growth.  Second, the data implies that movement from unemployment/out-of-the-labor-force will be extraordinarily slow relative to typical recoveries.  People who move from unemployment to employment (low income to high) increase their consumption substantially.  This channel is a drag on consumption growth. 

Takeaways

The labor market is healing but is far from normal, even when normal is compared to the weak job market of the last ten years.  The U.S. economy faces serious structural problems going forward.  The number of unemployed people is likely to stay at depression levels for a long period.  [The unemployment rate is still likely to fall.  People will exit the labor force rather than continue to report themselves as unemployed.]

The current social safety network is ill-equipped to handle large numbers of semi-permanently unemployed people.  The United States is going to have to make some hard choices on how to handle this social problem.  The solutions must balance the welfare of the unemployed against the need to maintain incentives to seek employment.  Further, these decisions are going to have to be made in an environment of slow revenue growth.

Jobs and the Jobs Bill (304,000 jobs over the next 12 months)

The economy lost an additional 36,000 jobs in February.  Interpreting the numbers is difficult because of the record breaking snow storm on the East coast during the reference period for the establishment survey.  Almost every analyst I heard on topic predicted that the storm subtracted anywhere from 30 to 80 thousand jobs from the labor report.  

It’s amazing how confident people are in their statements.  They might be right but I am less sure.  Keith Hall, the Labor Commissioner and one of the most forthright people around, said that, while it was likely the storm had a substantial impact on the jobs report, it was not possible to tell whether the jobs number was biased upwards or down as a result.  He did not know how much the storm effected the hiring and firing decisions of firms nor did he know how much the storm impacted temporary hiring, as businesses and schools hired temporary workers to help remove the snow—temporary help rose 48,000 in February.

I think there is a more general issue.  The storm effectively shut down firms in the mid-Atlantic region for a full week in February.  It seems to me that the first order effect of the storm depends on whether the impacted firms would have hired or fired in that week.  For some companies, the storm may have served as a temporary furlough, pushing back firings and perhaps delaying them indefinitely.  For others, the storm may have prevented them from hiring.  The net effect depends on the aggregate state of hiring and firing.  I don’t know the answer and neither does anybody else. 

The Jobs Bill:  Adding 230 to 370 thousand new jobs

For those of you who are regular readers, you know my opinion of economic stimulus:  I am not a believer in big multipliers.  But I like the jobs bill.  If you want to increase employment this bill is exactly the right medicine.  I suggest exactly this program in December 2008 (here scroll down to “What can the government do?”)  My plan went farther, in line with spending almost $1 trillion, but the incentives are the same.

The jobs bill works on the margin.  New hires are exempt from the firm’s share of social security plus the firm receives and additional thousand dollars if the employee is kept on the payroll for 1 year.  For the average worker in the United States, this is a $3,600 payment per qualifying hire.  The maximum payment is close to twice that. 

This is not enough money to change the hiring decision of the average firm; fortunately, economics does not work on the average; it works on the margin.  Because the jobs bill is only allocates between $12 and $15 billion, the bill is perfectly designed to work on the margin. 

I estimate that the jobs bill (the employment portion only) will add between 230 and 370 thousand jobs over the next year.  These jobs are new additional jobs in the United States. 

Why is the jobs bill effective and ordinary stimulus not?  Because the jobs bill changes the real relative price of labor.  (Stimulus does too but with the wrong sign.)  Labor demand curves are downward sloping:  cheaper equals more.  The jobs bill is just like Cash-for-Clunkers and the Home Buyer’s Credit.  The change in real relative prices induces a real change in demand.  [Note, this does not necessarily boost GDP; it is only expected to change the demand for the particular product.  The bills have costs.]

Unsustainable Deficits and Growth

Just how much contraction can we expect to get the Federal budget deficit back under 3% of GDP on a trend basis if the private sector doesn't pick back up? Is the multiplier greater when cutting the deficit?  NorthGG

To answer this question, I start with the budget assumptions released by the CBO.  (In my last point, I used the much less realistic numbers published by the OMB.)  Under the CBO’s assumptions, the deficit dips under 3 percent in 2014 before unfavorable demographics push it back over 3 percent by 2020. 

We can’t adjust the deficit for growth assumptions until we pin down the relationship between revenue growth and GDP growth.  I copied the picture below from the CBOs website—ignore the scary outlays line, I only want to talk about revenues.  Between 1950 and 2007, revenues remained around 18 percent of GDP.  This implies that on average revenue growth has been the same as nominal GDP growth.  In the CBO projection (remember they are constrained by law), revenues are growing consistently faster than nominal GDP. 

So, first things first.  I adjust the CBO’s revenue line so that it matches its historical growth pattern.  This scenario, shown by the solid red line below, yields a deficit that stabilizes around 8 percent of GDP before trending negative in 2018.  If GDP growth stays low as NorthGG suggests, the deficit trends negative from 2012 forward, reaching almost 11 percent of GDP by 2020. 

Neither of these scenarios is sustainable.  Under the first scenario to reach a 3 percent deficit by 2020, total nominal spending growth can increase no more than 1 percent per year from 2012 forward.  To achieve the same goal by 2015, requires total nominal spending to fall by 2.3 percent per year beginning in 2011.  Under the slow-growth scenario, these numbers fall to zero and negative 4.5 percent.

Is the multiplier greater when cutting the deficit?

Good news.  In my view, government spending is for the most part a drag on growth.  The spending can be good or bad but it harms growth.  So, reducing the deficit by slowing the growth of outlays is positive for growth. 

Unfortunately, politicians love large government.  (Yes, this statement is true across ideologies.)  The deficit is much more likely to be attacked from the tax side rather than the spending side.  Higher taxes are the likely outcome.  This route is unambiguously bad for growth—both from distortionary taxes and distortionary spending. 

In my view, the budget doesn’t have to be balanced but spending has to be brought under control.  I don’t think it will happen, though.  At least not until, the pain of adjustment is far more difficult than mere 1 percent nominal growth of spending. 

Robert Barro on Fiscal Stimulus

Take a look at the wsj collumn by Robert Barro (here).  We are pretty much in agreement on the effects of fiscal stimulus, although I think his use of war spending provides a substantial upward bias to the spending multiplier. 

The Economic Report of the President and Fiscal Stimulus: Romer Does it Again

Chapter 3 in this year’s ERP makes the case for the positive benefits of fiscal stimulus.  The chapter brings much evidence to bear in support of the case that fiscal multipliers are large and positive.  But perhaps the most convincing is chart 3-13.  In that graph, the ERP compares forecast errors against fiscal stimulus, measured as a percent of GDP. 

Here I have replicated the chart.  The vertical axis is the deviation in 2009Q2 growth (annual rate) from private sector forecasts in the third quarter of 2008.  The horizontal axis is the level of fiscal stimulus in 2009 as a percent of GDP.  I use data from table 3-1 in the chapter.  The dates are chosen carefully:  the first date is pre-stimulus for most economies; the second date maximizes the slope of the regression line. 

The results are impressive.  The slope is positive and highly statistically significant.  Indeed, since the time of the ERP was put to bed, data revisions—especially for the Czech Republic—have improved the relationship.  For every additional percentage point additional fiscal stimulus, the economies grew 2 percentage points faster.  Notice, because we are using annual rates that is not a multiplier of 2 but rather a multiplier of 0.25. 

But the chart is deceptive.  First, the result in the chart is specific to Q2.  If we chose any other quarter in 2009 (or the year as a whole), the result is no longer statically significant.  Second, the results as shown rely entirely on the data points for Japan and South Korea.  In the absence of these two countries, the regression line has a slope of zero and the R-squared is 0.002.  Of course, one might make the argument that it would be deceptive to exclude those two data points. 

I agree, which brings me to my third and final point.  Why didn’t they include the rest of the countries included in table 3-1.  The hard part of this exercise is finding comparable measures of fiscal stimulus but they already took the trouble to include these numbers in the chapter.  With twenty minutes of effort, I tracked down both the comparable private-sector forecast for these countries and the realization of GDP growth in the third quarter. 

The results including the omitted countries are shown in the chart below.  By coincidence, the excluded economies yield a regression with almost exactly the opposite result to the included countries.  These countries yield a line with a slope of negative 2.2—every extra percentage point of stimulus lowers GDP growth by a little more than 2.  Combining all of the data into a single line yields an insignificant regression. 

Once again, the case for fiscal stimulus does not survive the microscope.  I wish, however, that the ones responsible for deciding on the level, type, and timing of stimulus were a little more honest in their presentation of the facts.

The Deficit: On an Unsustainable Path?

The Administration published its budget this week.  I will leave others to play with the details of the budget.  (For example, this post on EconomistMom does a good job of examining the key details).  I like to look at the macro assumptions embedded in the budget. 

This administration, following the grand and old tradition of budget forecasting, makes economic assumptions that are possible but on the extreme edge of possible.  In other words, the Administration sticks to the broad outlines of the macro models but whenever there is a choice it errs in the favorable direction:  growth is a little higher, inflation a little faster, spending a little slower.  In addition, the Administration likes to cut programs in its budget that it knows Congress will reinstate.  An old favorite is threatened military programs.  A new favorite is sun setting tax provisions. 

I like to take the budget and tweak each of these assumptions back into the reasonable range.  Sometimes the result is a little worse; sometimes it is a lot worse.  In the current case, things end up a lot worse, because the jumping off point for the budget is so bad. 

I recalculate the budget forecast assuming 10 percent (not percentage points) lower nominal GDP growth, 18 percent faster expenditure growth (still faster growth than any time since the early 1990s), and 15 percent slower revenue growth. 

My forecast for government outlays as a percent of GDP is shown as the red line below.  Outlays as a percent of GDP start at a record 26 percent and grow steadily thereafter. 

The next picture shows my forecast for the deficit, using the same adjustments.  Notice, the assumptions in the OMB budget are exactly those that stabilize the deficit as a percent of GDP by 2014.  Using more reasonable assumptions, the deficit improves in the near term but then begins to explode as Social Security and Medicare expenditures rise sharply with the coming surge in retirements. 

What I have not considered is the cost of higher interest rates.  The Administration assumes almost no increase in interest rates despite the robust recovery and relatively rapid inflation path.  In itself, this is not realistic.  But it is especially not realistic if the markets come to believe the path I have written down.  If my path becomes the standard, the market will begin to demand a premium to hold U.S. debt.  (Of course, the good news is that the spread between corporate debt and U.S. Treasuries will narrow.  Oh wait …)

How (Not) to Destroy American Jobs: Bad Analysis and Misleading Statistics

“The fundamental assumption behind [the Administration’s proposals to tax U.S. multinationals] is that U.S. multinationals expand abroad only to "export" jobs out of the country. Thus, taxing their foreign operations more would boost tax revenues here and create desperately needed U.S. jobs.

Academic research, including most recently by Harvard's Mihir Desai and Fritz Foley and University of Michigan's James Hines [here], has consistently found that expansion abroad by U.S. multinationals tends to support jobs based in the U.S. More investment and employment abroad is strongly associated with more investment and employment in American parent companies.”  Slaughter, WSJ 2010  

A group of international economists have been pushing two ideas for some time:  When multinational firms expand their overseas operations, jobs in the U.S. increase; when multinational firms increase their overseas investment, they also increase their investment in the United States.  Therefore, because these firms are also productive, taxing or inhibiting the growth of these firms harms the United States and slows domestic growth.  The seeds of the idea have merit: international trade should make the United States a wealthier country, at least when the driving force for the trade is not regulatory or tax avoidance.

Estimates from Desai et al. (2008) show that a 10 percent increase in foreign investment results in a 2.6 percent increase in domestic investment.  That is, for every dollar these multinational corporations spend abroad, they drop 25 cents in the United States.  They find similar results for sales, compensation, and employment.  They conclude, “These results do not support the popular notion that expansions abroad reduce a firm's domestic activity, instead suggesting the opposite.” 

First, and most important, their conclusion, as well as Slaughter’s in other work, is based on a flawed counterfactual.  Desai et al, in concluding the firms boost output in the United States, consider a counterfactual world in which the firms do not exist.  They do not consider a world in which the firms continue to exist but are prohibited, either through taxes or fiat, from expanding overseas.  The implicit assumption is that there growth would have been zero but for the expansion. 

I agree that their expansion would have been smaller had they not been able to take advantage of the cheaper, more lightly regulated, and lower tax foreign environment.  But do Desai, Foley, and Slaughter believe that Proctor & Gamble would not have grown at all between 1982 and 2004 if they had not been able to expand internationally.  So, to find direct evidence of the gains from outsourcing production, we need to compare the outcomes of the multinational firms with other U.S. firms.  I will do so in a moment.

Second, their statistical results rely on using the full sample.  Take a quick look at figure 1 in Desai et al (here  – scroll down to page 26).  The scatter plot shows foreign sales growth versus domestic sales growth over the sample period.  A positive relationship is easy to discern but is not particularly strong.  What the picture hides is the change in the relationship late (and early) in their sample.  Data post 1999 are very different than data before 1999, particularly the data between 1994 and 1999. 

Results

The picture below uses aggregate data from the BEA (table 1 here).  The red bars show the annual average growth rate of employment in the foreign affiliates of U.S. multinationals (MNCs), the parents, and overall U.S. employment between 1982 and 2004.  Based on this aggregate data, the relationship between job creation at home and abroad is stronger than implied by the micro data, implying almost 80 jobs for every foreign job. 

However, job creation at multinationals in percentage terms pales in comparison to overall job creation in the United States.  Employment increased 1.8 percent per year on average between in 1982 and 2004 in the United States as a whole while growing a mere 0.6 percent at parent companies.  Indeed, the growth in employment of foreign affiliates is quite similar to the growth of overall U.S. employment. 

MCNs diminished net job creation in the United States.

But the picture is much worse for Slaughter’s argument if we simply examine the last 5 years of data.  Between 1999 and 2004, the growth rate of foreign affiliate employment was almost unchanged, near 1.8 percent, but the parents shed jobs.  Indeed, over this period, parents cut 2 jobs for every job created at a foreign affiliate.  Overall U.S. employment slowed over the period but remained positive.  Continuing the sample through 2007, does not change the picture.  U.S. and foreign affiliate employment remain positive while parent employment is still negative.  Although we do not have data beyond 2007, an increase in parent company employment during the recession is unimaginable, especially given the outsize decline in U.S. trade. 

What surprises most is that neither Desai et al. nor Slaughter acknowledge this point.  Nobody can work with the data as closely as these economists have and not notice such a basic fact.  In fact, the paper by Desai et al. never uses the words decline, slowing, reversal, or drop.  They have panel data and never include a time dummy.  They almost appear to be hiding the inconsistent fact in their data. 

Other Evidence

The two main other points brought up by Slaughter and Desai et al are the investments of MCNs and the value added (which leads to measured productivity).  The picture below shows gross value added for parents, foreign affiliates, and the nonfarm business sector in red and shows annual average growth rate of capital expenditures (gross investment for the non-farm business sector) in blue.

Parent’s growth is positive both in terms of investment and value added.  However, again the growth rates are well below that of both the foreign affiliates and the non-farm business sector. 

MCNs lowered investment and value added growth in the United States. 

By the way, the growth in value added combined with a decline in employment is what gives the parents their measured productivity edge. 

Takeaways

The expansion of international trade very likely has benefits for the United States.  But the expansion of trade has also had its costs.  The net benefit to the United State may be positive or negative (likely positive). 

With true facts before us, we can discuss the taxation of multinationals.  Taxing these companies will slow their growth, reduce their investment, and lower total U.S. employment.  That’s what taxes do; they reduce the profit incentives of firms.  But taxing U.S. companies and U.S. households has exactly the same effect.  If we make a public policy decision to have a large government, we must also make a public policy decision to tax heavily – either now or in the future. 

There is no data (which I consider credible) to support the idea that taxing multinationals is any worse than taxing any other company or for that matter than taxing the household sector. 

In any event, the overall employment, value added, or investment of these companies or any other is not the metric by which we decide which sectors to tax most heavily.  Optimal tax theory relies on relative elasticities.  The least elastic sector should face the highest tax burden, thereby minimizing the overall tax distortion. 

So, I don’t know whether we should tax the multinationals.  But there is zero evidence for taking them off the table before the discussion is even begun. 

Real Estate Delinquencies, Mortgage Modification, and Two-Shock Foreclosures

Real estate delinquencies are continuing to grow.  The official delinquency rate on residential real estate at banks in the United States is nearly 10 percent.  But the official numbers, which do not include mortgages that have been modified, are currently understating the level of delinquencies by nearly 25 percent. 

Take a look at the picture below.  The black-dashed line is the official delinquency rate.  The red line uses OCC data (from the mortgage metrics report) to add modified mortgages back into the total.  As of the third quarter of 2009, there was a $41 billion gap between the two lines. 

I include modified mortgages in total delinquencies because almost all of these modifications are on track to fail.  To date, more than 60 percent of the loans modified in 2008Q3 have redefaulted.  That’s 6 times the default rate of a 2007-vintage subprime mortgage.  The modifications are doing nothing more than keeping a large set of non-performing loans off the books of banks (and the FHA). 

Why are mortgage modifications failing?

The following chart shows 12-month delinquency rates by payment modification.  The redefault rate moves from 40 percent when the monthly payments are reduced by 20 percent or more to almost 70 percent in cases where the monthly payment actually increases.

A shockingly high percentage of modifications result in higher payments to the household – who do the banks think they are kidding here?  The banks are not making a genuine effort to rework the loans.  In the third quarter of 2008, about 35 percent of modifications actually increased the monthly payment of the household.  Even in the latest data and after extreme (I am kidding) pressure from the OCC, 17 percent of modifications result in higher monthly payments.  One cannot construct an economic model in which the household has a lower incentive to default when their principal is not changed and their payments increase.  (If this fact does not make you angry, don’t forget you are subsidizing all of these modifications.)

However, even the modifications made in good faith and that substantially lower the household’s payments are seeing spectacularly high redefault rates, still four times the worst of the subprime mortgages.  And, remember that these modifications are only done in the cases where the bank believed (allegedly) that the household had a good chance of making the payments. 

Mortgage modifications are not working because the modifications are not hitting the key driver of foreclosures:  A household with negative equity has an increased incentive to default on their mortgage. 

Why do policy makers not believe this?

There have been several recent Fed studies that have shown that homeowners do not default simply because they are underwater; rather, homeowners only default when they suffer both income and price shocks.  This analysis however is flawed for two reasons.  First, their samples, per nature, consist of only historical data.  But in every past period, household’s expected their house to rise in value, giving them a positive value to waiting.  That is, if they could simply wait long enough (and if they could afford the payments in the meantime), the house would no longer be underwater.  Second, the house price declines in the data are small.  Households tend not to prefer default when they are barely underwater. 

The following picture shows the default lines for households taken from a simple model of housing.  In the model, households are given the simple choice between paying their mortgage or walking away from their mortgage and renting forever (again if you want details of the model email me).  When house prices fall slightly, the household prefers to keep paying their mortgage even when they are underwater. 

Take a look at the top line.  A homeowner who faces even a small fall in house prices prefers to default.  The more income the homeowner loses, the more they prefer to default.  Eventually, at an income loss greater than 60 percent they always prefer to walk away from their mortgage – if they are also underwater, they default. 

Two things made observing only one shock unlikely in historical data.  First, the average tenure was longer.  Notice the ten year line is well below the 2 or 5 year line.  Second, house price falls were all small.  Now, house price declines greater than 10 percent are not uncommon and, in large part because of the low interest rates in the last few years, the average tenure is much shorter than in historical data. 

In summary, two shocks are not now nor have they ever been a necessary condition of default.  It’s just that two shocks make default more likely especially when house price declines are small. 

Takeaway

Because banks have become (probably correctly) convinced that they cannot fail, they will take no actions to correct their balance sheets and will continue to allow the loans to fester.  Markets then cannot work out the foreclosure problem and the current round of policy-forced mortgage modifications is not working (we can debate whether or not they should have worked).  It’s time for bank regulators to get off of the fence and force principal reductions.  Or it’s time for the administration to admit that it wants the foreclosures to proceed, in which case it needs to grease the wheels of the legal system and get the adjustment underway. 

(For the record, I think foreclosures are bad for the economy.  More importantly, underwater households are reducing the flexibility of the economy, creating long-term growth problems.  More on this when I next post on the labor market.)

Boomers, Bubbles, Debt, and Dust: A Guest Post by NorthGG

The following is a comment by NorthGG on this post.  As always, NorthGG makes an excellent point and I didn’t want it to get lost in the comments sections.  The comment is posted in its entirety:  Enjoy.

Greeting SE. As always thanks for the work, it’s greatly appreciated.

There are two effects of the housing bubble, economic and financial.  I would argue that the underlying demographic demand for housing (and the coming entitlement spending) is a natural bubble and that the Fed has no control over.

The boomer bubble has passed through C and I (as well as Net Imports) and now heads to G.

In my view, the Fed exacerbated the natural C, I and Net Import bubbles in the economy making their impact financial markets much bigger (i.e allowing too much leverage in the financial sector) leaving us all with huge problems once the boom turned to bust.

Lax regulation and deflationary fears pumped up the boomer economy in financial markets. This puts the Northeast section of the US at great risk given collateral prices (housing) and dependence on Wall Street Incomes; Incomes that depend on increasing leverage that is clearly mind numbingly ridiculous already.

Please note the following data table from 1961 forward, looking only at periods where growth was positive in every quarter. This is nominal Non-Financial Debt growth regressed on Real GDP growth.  Debt grew slower than GDP prior to 73 and then it was lights out.

Period              DtGrowth      Rsq

61to69             0.37x               .98

71to73             0.59x               .98

75to80             1.34x               .96

82to90             2.8x                 .98

91to00             2.07x               .998

01to07             6.02x               .984

Since the 2001 recession, nominal nonfinancial debt has grown at almost 10x the rate of GDP to current data! 10 times!

This debt laden economy now features (end 2008 data) nonfinancial debt per household in excess of $280,000 with a median household income of $69k (end 2008).

Every $1 trillion in incremental non financial debt is roughly $8,000 per household, well over 10% of median income. Does the Fed think it can raise the rate of inflation so that it will raise free (real) cash flow in the economy? This is crazy.

This debt to income ratio is likely even higher at the end of 2009.

What I question is the Fed's role in allowing the economy to become so levered. It allowed accelerating leverage. The Fed chose to allow the bubbles to inflate but had no game plan for when they naturally peaked and deflated. The economic volatility is natural but the decision to leverage is fostered by acceptance of regulators. What is going to be the impact on this country if (when) the government debt bubble deflates like the Nasdaq or housing? For this there is no backstop. Washington has exacerbated the 2 previous bubbles and currently does on the 3rd.

As we head into the government debt bubble, will the Fed speak out against excessive leverage already in the system as we see the government red ink explode before our eyes? Excess debt is deflationary. This is a liability crisis.

The zero bound is not stimulative (private credit continue to contract) nor is the fiscal policy. We are sowing the seeds of corrosive deflation with the Fiscal policies meant to offset it.

Debt must fall relative to GDP, there is much more pain to come on the balance sheets. What is good for the economy is bad for the banks. The Fed is simply watching the federal government do what the private sector did, lever up. That I do hold them responsible for. The Fed has favored the banks over the economy for a long period of time. Allowing debt to permeate the economy so deeply and to stand by and watch the reckless debt cycle now engulfing the public sector is outrageous.

Monetary Policy and House Prices: Interest Rates and Timing

“The beginning of the run-up in housing prices predates the period of highly accommodative monetary policy.”  Bernanke January 2010

“Economists who have investigated the issue have generally found that, based on historical relationships, only a small portion of the increase in house prices earlier this decade can be attributed to the stance of U.S. monetary policy.  This conclusion has been reached using both econometric models and purely statistical analyses that make no use of economic theory.”  Bernanke 2010

At the AEA meetings earlier this month Chairman Bernanke gave a speech disputing the Fed’s role in the recent sharp rise in housing prices.  His arguments ran along three key lines:  the timing of policy accommodation and the run-up in house prices do not match, models of interest rates and house prices imply little or no relationship between the two variables, and the international relationship between house prices and interest rates is inconclusive.  The last point I addressed in a previous post (here).  In this post, I will address the first two points. 

Timing

On timing, Bernanke’s main defense is that the beginning of the run-up occurred well before monetary policy became especially accommodative.  While house prices did start to rise in the late 1990s, house price appreciation accelerated sharply in late 2001.  The cumulative rise up to that point was consistent with previous U.S. house price cycles. 

The graph below shows 5-year cumulative real house price appreciation between 1980 and 2009.  The price series is the FHFA house price index deflated by the CPI.  On this basis, house prices began to increase in 1997.  That is in real terms in 1997, real house prices had finally recovered to their 1992 levels.  Between 1995 and 2000, house prices increased slightly more than 10 percent, or an average increase of about 2 percent per year.  High growth but no bubble. 

The Fed began its easing cycle in early 2001.  By the end of that year, the Fed funds rate breached 2 percent, hitting its lowest level since 1960.  By any measure, monetary policy was loose.  But policy was especially loose when compared to the advice given by a Taylor Rule.  Take a look at this picture from Bernanke’s speech.  Whether using backwards looking data or real-time forecasts, the Taylor Rule indicates an increase in policy rates by the end of 2001.  Appropriately, the Fed lowered interest rates substantially following the attacks of 9/11.  The Fed was responding to risks posed by the attacks rather than to incoming economic data. 

The timing of the lower interest rates corresponds to a sharp increase in the growth rate of real house prices.  Beginning in 2001, the five-year growth rate of real house prices rose from 13 percent (the red horizontal line) to a maximum of over thirty percent, a rate more than double its previous maximum. 

Timing does not eliminate the Fed’s culpability. 

Interest Rates and House Prices

“Economists who have investigated the issue have generally found that, based on historical relationships, only a small portion of the increase in house prices earlier this decade can be attributed to the stance of U.S. monetary policy.  This conclusion has been reached using both econometric models and purely statistical analyses that make no use of economic theory.” 

Notice, the Chairman slipped quietly from economic theory guiding the choice of Taylor Rule in the previous section to statistical analysis in this section.  This switch seems odd given the robust theoretical relationship between monetary policy and house prices.  If monetary policy controls the real interest rate, then lower interest rates translate directly into higher long-term house prices. 

To test this thought, I plug the path of interest rates into a completely standard model of house prices and consumption.  (If you want details, post a comment or email me.)  Using data on the housing stock, I find the change in interest rates accounts for most of the change in house prices over the period in question.  One cannot dismiss the possibility that looser monetary policy led to the increase in house prices.  And, it’s this point in its many guises that lead many critics to blame Fed policy. 

However, the same standard model of housing does a very poor job of matching house prices between 1980 and 2000.  The historical relationship between house prices and real interest rates in the United States is quite weak.  Take a look at the longer picture below.  Mortgage interest rates fell from 18 percent in 1981 to less than 10 percent in 1987.  The cumulative rise in house prices was less than 10 percent.  Likewise, between 1990 and 1995, interest rates fell about 2½ percentage points; house prices were about unchanged, on balance, over this period.  I would also note here that VAR models are also unsuccessful in reproducing, out of sample, house prices in any 10 year period between 1980 and 2009. 

A recent paper (here), builds a statistical model that explains the discrepancy.  They show that the interest rate negatively covaries with the risk premium on housing.  In other words, falling interest rates are always exactly offset by some other factor.  The authors give no reasonable explanation of the negative covariance. 

Nonetheless, across countries and across time, house prices have risen when long-term rates are low.  A 2005 Fed Study found a strong, lagging relationship between interest rates and house prices.  Take a look at Chart 3.7 in the linked paper.  There, a decline of about 1 percentage point was associated with a 15 percentage point increase in real house prices, on average.

So, the evidence is mixed:  theory is clear, empirics less so.  Lower interest rates should raise house prices and the lower rates observed in the 2000’s are exactly consistent with the rise in house prices.  But, their historical relationship in the United States belies this effect.  The historical relationship is why the VARs reject policies role in the house price run-up.  I wonder what a cross-country VAR would produce. 

But the Fed believed in the link between housing and interest rates

The Fed tried to bring down long-term interest rates.  During the period of loose policy, the Fed had the specific intention of pushing down long-term interest rates by promising to keep its policy rate low for an extended period.  Even when the Fed began to raise rates in 2004, it emphasized that the increases would be measured (their word, generally taken to mean a slow increase over a long period of time).  As we saw above, this policy seems to have been at least partially successful. 

The Fed also believed in the link between the movements in long-term interest rates and the increase in house prices.  Here is the Fed speaking in the voice of then Gov. Bernanke:

The decision to purchase a home is probably the most interest-sensitive decision made by households … I expect residential investment to continue strong this year. Mortgage rates have risen in the past month but remain low relative to historical experience, while new household formation, improved job prospects, and income growth should ensure a continued healthy demand for housing.  Bernanke April 22, 2004  

These two ideas lead me to believe that the Fed thought it was influencing the housing market. 

My Last Word

I don’t know if loose monetary policy caused the run-up in house prices.  My instincts, however, continue to tell me that monetary policy is not the culprit.  (I tend not to ascribe so much power to monetary policy.) 

Nonetheless, the circumstantial case is somewhat persuasive. 

The Fed was loose; interest rates fell; house prices rose. 

Simply dismissing these facts is not productive.  I would expect a more robust

Are Moratoria Ever Needed? Can the market do its own workouts?

The following post is a response to BCG81 who commented on this post:

The distinction between temporary and permanent income shocks ought to lead a workout guy to give you restructurings where that is the higher-NPV alternative [relative to foreclosure]. 

First, to be clear, I do not believe foreclosure moratoria are the best policy instrument at the moment.  I continue to believe my foreclosure plan (see it here) is the best permanently workable plan.  Note, in particular, that it has the benefit of not needing a temporary versus permanent distinction and induces no forward-looking market distortions.

But, to your point and thinking solely about moratoria, I agree.  Banks are good (not perfect but good) at taking actions that are best for them.  If/when banks find it in their interest to do wide scale workouts, they will do so.  But with foreclosures there is likely an important macroeconomic externality.  A foreclosure depresses local property values (see my post here), increasing the probability of further foreclosures, and potentially devastating neighborhoods.  .  The bank, correctly, does not take this fully into account.  Accordingly, it may be socially optimal to prevent foreclosures even when workouts yield lower bank profits. 

In addition, in depressed neighborhoods, banks should rush to foreclose.  The sooner they take possession and dump the property the higher their profits are likely to be.  It’s a poor equilibrium but each bank should follow this strategy.  And, the most efficient foreclosure bank will indeed likely perform the best.  This rush to foreclosure may speed adjustment where it is needed but may also put neighborhoods, which would have been only temporarily impaired, over the brink. 

I am not saying banks can’t do the workouts.  I am only saying the policy and banking motivations are not aligned. 

One problem is the lending that drove house prices and pretty much the rest of the economy through 2007, income just didn't matter.  Nobody paid any attention to whether the borrower had enough income to repay. So a big—maybe the biggest—part of the problem is probably automatically equivalent to a permanent shock.

In early 2007, we could have argued over your motivating fact.  Poor lending standards might have been the culprit.  I think, though, that with the acute perception of hindsight the evidence now leans in favor of an income shock not poor lending standards.  Be that as it may, your point stands.  Many households borrowed more than they could afford with their realized income.  And these households are likely the biggest part of the current problem.  Blanket moratoria are likely preventing adjustment in these dimensions. 

Also, don’t moratoria in response to temporary shocks also keep the market from adjusting "in a good way"? Couldn't this build inventory, potentially increasing supply relative to demand and causing prices to fall during the time it takes to resolve the temporary shock. And depending on how you restructure the loan during this period (e.g., is interest paid? capitalized?), it may offer lenders a lower recovery than foreclosure, thus impeding the deleveraging process.

Too true.  Moratoria, even temporary ones, distort markets.  Without short-term price adjustment, supply may increase, pushing house prices down once the moratoria is lifted.  But the moratoria, assuming the shock was correctly identified, do not impede the deleveraging process.  Households who face a temporary shock do not need to deleverage; they may want to delever but by definition they can afford their current debt level. 

The model seems to be the LDC debt crisis of the 1980s. Personally I think this overstates the importance of the banking system (beyond its ability to clear payments and provide working capital) in an environment like this and over prioritizes it in recovery policy/strategy.

Touché.  The LDC crisis resolution is exactly the moratoria model.  Those economies suffered temporary shocks and moratoria allowed them to repay most of their debt.  And, in the process, turned them into permanently indebted, serial defaulters. 

Defeated, I can only repeat my first point.  I do not believe foreclosure moratoria are the best policy tool available.  I like my plan—still feel free to have at it if you are so inclined. 

Foreclosures and the Housing Market

This note is in response to a comment by BCG81.

To your point here, I read recently (Mark Hanson's blog) that one of the principal unintended consequences of the state foreclosure moratoria, loan modifications and other government "keep people in their homes initiatives" has been to deprive the housing market of foreclosure sales, and thus of its main driver.

First, thanks for pointing out Mark Hanson’s blog.  I read a few of the posts and the work there is quite good.  I recommend the site. 

It’s true that foreclosure moratoria reduce the volume of houses on the market.  But is this a good or a bad thing? 

Let’s focus on the income side of the household’s balance sheet.  Consider a household that experiences a combination of temporary and permanent shocks to income.  Without getting overly technical, think of a temporary shock as an unemployment spell followed by reentry into the same industry at the same wage and a permanent shock as an unemployment spell followed by reentry into a low wage industry. 

Foreclosure policy must consider the type of shock hitting the household.  If the shocks are permanent, the household entering foreclosure likely needs to move to more affordable housing.  In this case, a foreclosure moratorium does nothing but slow the adjustment of the housing market.  It also slows adjustment of the labor market as the household will be hesitant to give up their free housing to conduct a job search outside of their local area.  With permanent shocks, foreclosure moratoria should not be used. 

What about temporary shocks?  It’s true; during the period of unemployment the household cannot afford their house.  But the situation is temporary.  In a year or possibly two years, the household will be able to afford their current house.  In a frictionless market, the household should move anyway.  During their period of unemployment, the household should consume less housing; just as they are likely consuming less of other goods.  But the housing market is far from frictionless.  A foreclosure moratorium, in this case, prevents the market from adjusting, in a good way. 

So, foreclosure moratoria are good or bad depending on the nature of the shocks facing households.  I think the economic situation calls for nuanced policy.  In some areas, places where the downturn is clearly temporary, a foreclosure moratorium is likely helpful.  In other places, Detroit, a moratorium is likely to prevent needed long-term adjustment. 

On a side note, none of the moratoria that have been used to date are long enough to be helpful.  Three to six months is simply not long enough to allow a reasonable adjustment.  I think moratoria should be in place for at least a year to have any hope of being effective.  Further, I think moratoria should be considered on a case-by-case basis rather than wholesale for entire regions.  Households should justify their need.  We cannot measure whether or not they have a permanent or temporary shock, lacking a crystal ball, but we can tell if they have an income shock.  A household that can’t afford their mortgage with their existing mortgage needs to move on.

Monetary Policy and House Prices: Evidence from the States

Did the Fed cause the housing bubble?  I don’t know the answer but the issue has received renewed interest since Governor Bernanke gave a talk at the AEA meetings categorically denying any culpability on the part of the Fed.  I too question the ability of monetary policy to create a bubble in housing (or stocks) without, over a long period of time, also creating a high level of inflation.  However, if we examine evidence across U.S. states instead of across countries, where the data may or may not be comparable, there is a statistically significant relationship between the easiness of monetary policy and the rate of house price appreciation.

Bernanke’s Comparison Across Countries

In addition to a weak model-based defense, Bernanke showed the following picture.  The scatter plot shows the relationship between the average residual in the Taylor rule between 2001Q4 and 2006Q4 against the cumulative change in real house prices.  The relationship shows a weak but positive relationship between the change in house prices and the looseness of monetary policy.  The lack of statistical significance I cannot debate; however, Bernanke also referred to a lack of economic meaning, which seems an absurd statement given the Fed’s long history of speaking on the relationship between house prices and monetary policy.  Indeed, in the early 2000s, the Fed’s favorite transmission mechanism for monetary policy was through the housing market. 

Although the lack of relationship may be real, cross-country comparisons of this nature are plagued by data and comparability problems.  The measurement of house prices (as well as the measurement of the deflator) differs tremendously across countries.  Consider the data for the United States.  If the slide had used the Case-Shiller index instead of the FHFA index, the data point for the United States would be much higher.  As well, if the house price data had been deflated by the PCE deflator instead of CPI the data point would move. 

Comparison Across States

To eliminate a host of comparability problems, I replicated Bernanke’s picture using U.S. states in lieu of countries.  The BEA produces comparable annual estimates of state GDP and the FHFA produces comparable house price indices for every state.  Using these two sources, I calculate Taylor-rule residuals and average house price growth across the states and plot the results below.  This exercise is very much akin to Bernanke’s picture considering euro area countries alone (an exercise which yields a statistically significant relationship). 

States with the “loosest” monetary policy are exactly those with the highest house price appreciation.  Unlike the picture across countries, here the relationship is statistically significant.  Indeed, those States that are “looser” than the U.S. average (-2.5) experienced more than 30 percent more house price appreciation on average than those states “tighter” than the U.S. average. 

By the way, the intercept of the line, 23.2, is only a touch below the average U.S. house price growth over the last 30 years.  In other words, as the simple regression above predicts, when monetary policy is neither too loose nor too tight, house prices should grow about 4 percent per year (or 23.2 percent over five years).  If we believe this result, then the Fed’s loose policy stance over this period added 32 percentage points to the growth rate of house prices between 2001 and 2006.  Since total growth was 48 percent, the Fed’s policy accounted for two-thirds of the total increase in house prices, according to this calculation. 

House Prices vs. GDP and Inflation

In his speech, Bernanke pointed out that the relationship might be spurious:  those economies with the highest growth might be precisely those with the loosest policy.  I am not sure the logic follows for certain; the statement seems to imply some additional unstated constraints on monetary policy. 

In any case, the picture below shows the scatter plot for the states where average annual GDP growth is substituted for the Taylor residuals.  Note that the R² is actually zero.  The slope is not only statistically insignificant, the point estimate is zero. 

Likewise, no substantial relationship can be found plotting state average annual inflation.  Indeed, the statistically insignificant slope goes in the wrong direction:  states with the highest inflation had, on average, the lowest nominal appreciation of house prices. 

Conclusion

These pictures are far from proof that monetary policy caused the house price boom.  But, they can also not be easily dismissed.  Although I personally do not believe monetary policy is capable of producing these types of long-term effects, the case is far from closed. 

I find the statistical fit across states disturbing.  The simple Taylor model yields not only a believable distribution, out of sample, it nails the long run average growth of house prices.  I know most economists reject this exercise within the United States but I would like to hear Bernanke explain why it is okay for the euro area but not for the U.S. 

In my next post, I will explore the theoretical link between house prices and monetary policy.  Conditional on the Fed having some control over the long-term rate, the link is direct and large.  The post will show that Bernanke's interest rate chart in his speech was likely misleading.