The Secret Economist

The Labor Market is on the Mend

2010-03-12T14:25:00.000-05:00

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)

2010-03-06T08:28:00.002-05:00

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

2010-02-24T17:25:00.001-05:00

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

2010-02-22T20:48:00.000-05:00

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

2010-02-21T18:34:00.000-05:00

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?

2010-02-05T14:32:00.000-05:00

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

2010-02-03T15:38:00.000-05:00

“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

2010-02-02T20:29:00.000-05:00

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

2010-02-01T06:35:00.000-05:00

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

2010-01-29T19:32:00.000-05:00

“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?

2010-01-19T20:59:00.000-05:00

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

2010-01-16T12:49:00.002-05:00

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

2010-01-16T07:03:00.000-05:00

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.

Low Housing Inventories? Foreclosures continue to drive the market.

2009-12-02T19:27:00.002-05:00

The inventory of new homes is in the ballpark of a new record low. Almost one year ago, I posted on the topic of housing inventories (here). Since then, the inventory of new homes has decreased to the lower end of my estimates, falling through my floor of 250 thousand; still the fall in inventories shows no sign of abating. The pace of inventory drawdown has remained between 2.5 and 4.5 percent per month all year. In every other recession, the end of the inventory cycle has been marked by gentle U-shape. If true this time as well, the end of the housing correction is still far into the future.

Why are inventories falling? They are falling because the total inventory of housing effectively on the market is much larger than in a normal downturn. Inventory is pushed up by the stock of foreclosed homes.

Foreclosed homes directly compete with new homes, and they add to inventory in exactly the same manner. A new home is empty and must be sold to maintain its value. A foreclosed home is (generally) empty and must be sold to maintain its value.

In my view, the scale of the foreclosure problem is only beginning show. The following chart shows the inventory of new homes, the solid blue line. The inventory has declined from just shy of 600 thousand homes in early 2006 to almost 200 thousand in October. Foreclosures swamp these numbers.

Fannie and Freddie alone have a stock of foreclosed houses on their books of over 100 thousand. The red diamond in the chart shows inventories adjusted for these homes. This addition moves the stock of unsold homes above the long-term average of the series.

But these homes are just the tip of the iceberg. The current number of foreclosed houses is much larger than those on the books of the two GSEs. Even more importantly, at present, another 11 million households are in a negative equity position. If even 10 percent of these households go into foreclosure, the true level of inventories is over 1.4 million, an inventory stock 2.5 times the previous record high.

Since the foreclosed inventory cannot be worked off through starts alone, the adjustment will have to come through prices. House prices are going to have to fall further. They have to get cheap enough to clear the market. The fall in prices will put more households under water; some of these houses will go into foreclosure, keeping inventories elevated.

Also of note, median months for sale has continued to rise. The graph below shows data from 1988. Two prior recessions are shown and in neither of those recessions did the time for sale even budge. The median new home on the market has now been for sale for 13.5 months, an increase of 4.4 months in the last year. Most likely, this rise reflects blocks of houses that cannot be sold or at least cannot be sold at anything near the current price.

America’s Lost Decade Already Happened

2009-11-20T20:52:00.000-05:00

Once again, the United States faces the prospect of a jobless recovery. Next month, the President is holding a conference on job creation. The President seems genuinely distressed over the jobs situation. He is seeking real answers. He is asking the wrong questions.

The President and his advisers view the job losses as a temporary cyclical problem, not as a structural problem with the U.S. economy. Quick fixes for temporary downturns in the labor market are expensive but easy. Last year (here, scroll about half way down), I suggested a temporary social security tax holiday to boost employment and output. Last week, Alan Blinder took up the call in an editorial in the Wall Street Journal. At a cost of about $100,000 per job (I am not as optimistic as Blinder), the administration can create several million temporary jobs. For far less money, the government could simply hire the same people at an average price of less than $40,000.

Ultimately all of these programs are doomed to failure. Quick fixes cannot solve structural problems, and a structural problem exists. Before a solution can be devised, we must understand the source of the problem. As of right now, the source is unknown and finding the source probably requires more data than is readily available on the structure of the job losses. But we can make a start with the data in hand.

In this post, I break out some of the key data, documenting the sectors of decline and the sectors of growth. In my next post, I will try to explain the loss. I will not succeed but perhaps we can get closer to an answer.

The Problem

Between January 2000 and October 2009, employment in the United States rose by a paltry 67,000. Including the already announced benchmark revision, the United States lost 757,000 jobs. This loss was the first over a ten-year period since the Great Depression. The post-war, record-low, ten-year job growth was 5.7 million achieved in December 1962.

The job gap (the difference between the growth of the working age population and the growth in jobs) now exceeds 16 million. The job gap in 1962 was 3.5 million jobs. As a percent of employment, the current gap is 12 percent, the 1962 gap 6 percent, and the maximum gap during the Great Depression 19 percent.

And, the job gap is not merely an artifact of the current downturn. At the peak of the labor market in December 2007, the job gap had already grown to more than 7 million jobs. The economy was producing enough jobs to eventually overcome the difference, easing concerns into complacency, but the gap was still large. The current downturn has simply exacerbated and made obvious an already existing problem.

The current gap will almost certainly still exist when my fifth-grade daughter enters the labor force. If we create 181,000 jobs per month (the average monthly job growth in the 1990s), the gap would take more than 12 years to close, given current projections of the growth of the overall workforce. Even if job growth could be sustained at the maximum annual pace observed between 1940 and 2009 (392 thousand jobs per month), the jobs gap would persist for more than five years.

Aren’t the losses “just” manufacturing jobs?

Over the past decade, job losses have been concentrated in manufacturing. The goods-producing sector shed more than 6.3 million jobs as the Services sector added a roughly similar number. In the middle of the last decade, many economists favored the idea that the loss of manufacturing jobs was a result of rapid productivity gains in the services sector. They favored the idea that the loss of manufacturing jobs was part of the long evolution of modern economies: from agriculture to manufacturing, from manufacturing to services. The job losses were nothing more than a continuation of a long-term trend.

The basic fact is verifiable. As a share of output or employment, the services sector has outpaced the manufacturing sector since the early 1950s. A shift to capital intensive technology in the manufacturing sector and rapid productivity gains in the services sector pushed and pulled workers. The services sector was pulling labor out of the manufacturing sector, offering higher wages and easier work. Between 1950 and 2000, the United States became an economic superpower and no one may question the relative change in our standard of living. We survived the manufacturing losses and became stronger because of them.

But, in the last decade, something changed: manufacturing pushed workers out faster than the services sector could pull them in. See, even though the share of manufacturing workers was falling, in absolute terms it was stable. The level of manufacturing employment was approximately equal in 1950 and 2000.

Job losses in manufacturing have been widespread. Every major sub-category of manufacturing employment has declined. Transportation, metals, computers, machinery, textiles, apparel, plastics, and printing have each lost more than 300,000 workers. (I was amazed by the textiles figures. When I went to NC State back in 1995, the textile industry was already rumored dead. How can a dead industry still be shedding jobs 15 years later?)

Manufacturing is sick: No surprise.

Won’t Services save the day?

Maybe. The services sector added almost 6.4 million jobs over the last decade. The possibility that we remain in a period of rapid transformation exists. It could be true, but the data stacks against the hypothesis.

As manufacturing losses are broad, services gains are narrow. Three sectors account for more than 100% of the service-sector gains. The following chart shows the change in employment by major sector between January 2000 and October 2009. Health led the charge. The health services industry added more than 3.5 million jobs. Part of this gain was funded by an expansion of Medicaid, but the gains look good. Health is a growth sector.

Unfortunately, the rest of the story is not as rosy. Government employment accounted for one-third of the gains, 1.8 million people. Worse, already in 2000, the government was the largest service-sector employer. Government jobs do not promote growth. Government jobs must be funded from other sectors. Government jobs seldom innovate.

Are the losses simply a symptom of an emerging new economy?

In the 1990s, the innovative services sector pulled workers in, but it did not pull just any worker. Young workers are more flexible and adapt to new industries quicker than old workers. They more flexible because they have less to lose in leaving the old industries; they have not yet built a stock of industry-specific capital. The dynamic led to an increase in participation for workers younger than 45 and a decrease in those who were older.

Over the past decade the trend has reversed and it has reversed to a shocking extent. The following graph shows the participation rate of workers by age group. In all groups younger than 55, the participation rate has fallen. In all groups over 55, the participation rate has risen.

In 2003, the social security retirement age increased by a year, and that change may explain a bit of the shift. But the change does not explain the increase in participation of the 70+ crowd. The increase in 75+ almost doubles the participation rate of the very old. The increase is not driven by better health, ten years is too short to effect that change.

No, the data indicates two things: older workers feel poorer and are working longer, and young people can’t get jobs. (I am including everyone below 55 in the young category. I will let you know when I need to change the definition again.) I am surprised by the breakdown. I had thought the picture would tilt the other direction, at least for prime-age workers (25-45).

Push and Pull

The manufacturing sector pushes workers out whenever its productivity growth is sufficiently high. The symptom of this push is rising output with or without gains in employment. Since the 1950s, manufacturing output has increased steadily despite stagnant employment. Beginning in 2000, this stopped being true. Even the surge between 2004 and 2006 was mild. The maximum growth rate of manufacturing output between 2001 and 2009 was lower than its average growth rate between 1992 and 2000.

Likewise, the services sector pulled workers with high rates of productivity growth. And, these high rates persisted over the last decade, but the average growth rate was a full percentage point lower in the last decade relative to the 1990s. The services sector is not growing fast enough on average to offset manufacturing losses.

Takeaways

The more I look at the data, the more I am convinced that the current downturn and the downturn in 2000 are related episodes. At least from the labor-market perspective, they seem closely connected. Treating the current job losses in isolation is a mistake. Whatever forces devastated the labor market in the early 2000s remain in effect today.

The President wants to solve the “jobs problem”, of this I am certain. We are lucky to have a President who genuinely wants to solve the problem. But his advisers do not seem aware of the structural issues. The first step is admitting the problem. Economists, especially those named Christina Romer or Larry Summers, need to stop thinking of the job losses as an unavoidable bad outcome associated with all recessions. This time really is different.

Resolving the Foreclosure Crisis: What Can We Do?

2009-11-16T05:05:00.000-05:00

Almost two years have elapsed since the beginning of the recession, but the foreclosure crisis continues. At the end of the second quarter, residential mortgages held by commercial banks reached a new record default rate of nearly 9 percent, and according to recent news reports, this number continued to rise through the third quarter. The high default rates do not owe to subprime borrowers alone. The majority of delinquencies are no longer subprime mortgages. That honor now belongs to prime and near-prime mortgages.

The crisis has continued despite innumerable programs, at both the state and federal level, to alleviate the crisis. The FHA has introduced a string of programs to help homeowners refinance into more affordable loans. Together, various supervisory authorities have put pressure on banks and lending companies to modify the terms of residential mortgages—more than 1.5 million mortgages have been modified in the last year. Several states have tried temporary foreclosure moratoriums. In two indirect efforts to reduce foreclosures, the Federal Reserve purchased close to $800 billion in mortgage-backed securities and Congress passed an $8,000 new home-buyer tax credit.

None of these programs has worked because none attack the source of the problem. Households owe more on their mortgage than their home is worth. They have economic incentives to default.

Background

The mortgage crisis started because too many households borrowed too much and bought houses they could not afford. Real money flowed into the housing market and residential investment increased. Because we couldn’t build enough, especially in urban areas, the price of houses rose. In this sense, we had borrowing bubble not a housing bubble. While subprime mortgages are the poster children of the borrowing bubble, prime mortgages also flowed freely.

The borrowing bubble has burst. The money that flowed into the housing market is gone. There are now too many houses, and house prices have to fall.

This adjustment process is well under way. Housing starts have fallen off a cliff, and house prices are falling. Prices are anywhere between 5 and 40 percent below their peak depending on where you live and which measure you believe. They are going to be lower.

Falling prices are the root cause of mortgage foreclosures. The more prices fall, the more households are under water (the value of the mortgage exceeds the value of the home). Under water households are more likely than any other class of borrower to default. They may continue making payments for a time, but the incentive to make payments is diminished. If their house price falls more or if they lose even a little bit of income, they are likely to default. From the household’s perspective, this default can be optimal.

The relationship between being under water and defaulting is simple and direct. Think of a household that has lost its income. Households that have sufficient equity in their homes will always, under this circumstance, choose to sell the home (even at a loss) rather than undergoing foreclosure. By selling, these households profit financially and socially—they go forward with their credit unblemished. Without sufficient equity, they do not have this option. Without income they cannot make payments. But, they also cannot sell because they owe more than the home is worth. Default is the only option.

The Plan

Any successful foreclosure-reduction plan must address under-water households first. Reducing a household’s monthly mortgage payment reduces the probability of default but only slightly (see this post). For most under-water households, a lower interest rate or an extended term does not change the default incentives.

My plan is simple: a voluntary program to purchase all mortgages with a loan-to-value ratio greater than 90 percent and issue a new mortgage with a 90 percent LTV to the household. To reduce moral hazard and capricious program take up, issue an equity claim with the new mortgage. Optimally, this claim amounts to 30 percent of the difference between the ultimate selling price and the origination value of the mortgage.

The devil is in the details, but this plan would solve the foreclosure crisis.

A Few Details

Why 90 percent? This ratio gives homeowners an immediate financial stake in their property. A household with 10 percent equity does not have a financial incentive to default. Even after paying closing costs and paying the equity claim (see below), selling is more profitable than defaulting. A few households will still default, households make mistakes, but a few defaults are not a problem.

Why issue an equity claim? The equity claim reduces the redistributive aspects of the program and reduces moral hazard. As with any government intervention in markets, this plan is a transfer between households. Responsible households are subsidizing the houses of those who over borrowed. The program also encourages future households to borrow more in the hope they will receive a bailout if things go bad. Issuing an equity claim reduces both distortions.

Why 30 percent? The equity claim recovers 30 percent of the difference between the origination value of the new mortgage and the eventual selling price of the home. With this percentage, the household stands to gain about 1 percent of the value of his home after closing costs at the time of origination. That is, even at origination, the household has an incentive not to re-default. If the claim were larger, households would remain under water, and the program would not meet with success. Moreover, with a 70 percent stake in their property, homeowners have incentives to maintain their house.

The equity claim, which is worth 3 percent of the home’s value at origination, also reduces take up of the program and provides an incentive for households in the program to increase the declared value of their property, thereby taking on a larger mortgage. No one who does not need this program will voluntarily forfeit 3 percent of their home’s value.

How do we determine home values? The most difficult part of this plan is determining the house value. But this is a macro program—macroeconomic policy is designed to care for the health of the economy not individuals—the program only has to be correct on average. The current value of the home can be estimated by using the price of the home when it was last sold combined with the average change in house prices for the metropolitan area as measured by any good house price index. The program should permit the household to increase but not decrease the current declared value.

Because no price index is perfect, especially when volumes are low, the government should reevaluate home values periodically, say quarterly. If homeowners cannot sell their houses at the declared value, the declared value is too high and must be lowered. If take up rates are high and homes are selling easily, the value may be too low.

Who would own the mortgage? The mortgages would be held, initially, by the government. But these notes do not have to be held. Since the mortgages are now above water and since the government is committed to repeating the plan if values should fall, the mortgages could be sold to private investors. If the plan is followed in its entirety, the mortgages would not even need a government guarantee.

How much would the plan cost? The cost of the program depends on take up rates, but it need not be expensive. Outstanding mortgage debt increased almost $4 trillion between 2005Q1 and 2008Q2. The average LTV for these mortgages was far less than 80 percent. Most of these households remain above water; indeed, the best guess is that 3 out of 4 of these households remain above water. With a 100 percent take-up rate amongst under-water households, the cost of the program would be roughly $100 billion. Most likely, the final bill would fall between $50 and $100 billion.

Takeaways

This is the simplest plan for resolving the foreclosure crisis. It requires little private information, and the government does not need to make an affordability determination. The household’s income does not matter. If the household can afford their payments, they will make them. If not, they will sell, making a small profit.

The plan has the added advantage of minimizing forward-looking housing market distortions. Households can freely sell their house, and housing market adjustment is not hindered by negative equity households. Labor market adjustment can also proceed. An under-water manufacturing worker in Michigan is no longer forced to look for work in his local labor market alone. He is free to sell his house and conduct a national search.

My plan is scalable and can easily incorporate the good elements of other plans. For example, with ongoing rising job losses, in some areas, too many houses could flood the market, hindering adjustment. In this case, a temporary payment holiday for high LTV households or even an outright foreclosure moratorium could be combined with the mortgage purchase plan, effectively metering the flow of houses onto the market.

No matter the bells and whistles, a successful program must ensure that households remain above water.

Households must have a stake in their property or they will default—at some point.

Separations and Hires: Has the Recovery Stalled?

2009-11-11T08:08:00.001-05:00

This post revises and updates my views from this post last March.

The JOTLS data (find the data here) produced by the BLS gives the best insight into the current state of the job market. As Robert Shimer, a professor at the University of Chicago, showed some time ago, unemployment can go up either because workers become more likely to lose their jobs (the separation rate) or because unemployed workers have a more difficult time finding new jobs (the hires or matching rate). The BLS only began collecting data in late 2000, much too late for us to compare the current downturn to previous episodes. Bob Shimer, however, has computed separation and matching rates going back to 1947 (his data is here). The data is not strictly comparable but I think we can use the lessons from Shimer’s data and apply them to the current episode.

I have spent a lot of time working with his data lately. The cyclical behavior of matching and separation rates is remarkable and should provide the key to the next level of understanding in business cycle research. The more I work with this data the more I feel like I am beginning to understand consumer behavior during recessions.

Matching rates, the probability of finding a job conditional on unemployment, begin to fall well before recessions begin and continue to fall well after the recession ends. Separation rates tend to rise at the beginning of recessions and tend to fall well before the end of the recession. Not surprisingly, the worst recessions in the post-war era (1958, 1982) are characterized by large changes in both rates.

In every post-war recession, the separation rate returned to more-or-less its long term average 4-to-6 months before the trough. The fall in separation rates also coincides with a rise in consumption. Apparently, consumption begins to rise once employed households no longer fear unemployment – a rational outcome. Consumption rises before unemployment falls. Unemployed workers continue to have trouble finding work long after the recession ends. But, their consumption is small and stable. Employed worker consumption rises.

As a result of this research, I am beginning to have more faith in the signals emitted by the JOLTS data. First, take a look at the picture below. The picture shows the number of hires each month in the JOLTS data from late 2000 to January 2009. Amazingly, the number of hires began to fall as early as January 2006, the same month the housing market turned sour. This data is consistent with the duration of unemployment calculated from the household survey. The average duration of unemployment is now at a record high, implying a record low probability of finding a job conditional on unemployment.

This is the clearest piece of data I have yet come across to indicate that the collapse of the housing market was not a random event. The decline in hire rates reduces the permanent income of households. People realize that conditional on losing their job, new work will be harder to find. Households also seem to know that this trend tends to have long cyclical properties – a decline in the series today is likely to signal a long period of increasingly lower matching rates.

Of course, I want to know if the recession is over, or if the recession has yet to end, when it is likely to end. Take a careful look at the very end of the hires graph. Hires spiked upward July but have since fallen back. Granted the fallback is only two months worth of data, but it is consistent with a labor market that tried to improve and then suffered a setback. This is consistent with employment data (discussed here) and it is consistent with the picture from the separation rate.

As I showed in March, the separation rate the total number of separations has been steadily falling since early 2007. This data alone would indicate that flows into unemployment should be falling, quite the opposite of our experience over this period. Again, note the July bobble in separations.

To understand the labor market, separations must control for the voluntary versus involuntary separations. If I quit my job today, knowing I had a new job in the bag, I would show up first as a separation then as a hire. We care only about involuntary separation. To get a better picture, subtract the number of monthly quits from total separations. The resulting picture, shown below, gives a completely different view of the state of the labor market.

The level of separations in January 2009 was 35 percent higher than its 2001-07 average level. Keeping in mind that half of that time period was during bad labor markets, this statistic is quite stunning. The labor market has improved since January. However, the recovery seems to have stalled and over the past 4 or 5 months the number of involuntary separations has achieved a plateau 17 percent above pre-recession average.

This plateau also indicates a recovery stalled. While we do not have a sufficiently long time series to know the behavior of this series in previous recessions, Shimer’s separation rates fall sharply before the end of recessions and remain low thereafter. The high level of involuntary separations is not consistent with recovery. This data is giving the same signal as initial claims data. Initial claims are down sharply from their peak but remain extremely high compared to their historic average.

Casey Mulligan, a Chicago economist, notes in his blog (and more recently here) that consumer spending is rising as is disposable income even as the job market continues to deteriorate. In particular, he has been keen on noting the ongoing increases in personal income. He does realize that personal income includes transfers (at record highs) from the government. I don’t think Casey Mulligan would really believe transfers accompanied by an increase in debt are an actual increase in income.

Nonetheless, even as current income continues to rise, the high separation and low matching rates have sharply reduced permanent income for households – they are faced with an ongoing high probability of job loss and amazingly low odds of getting a new job if they become unemployed. And, labor income is far and away the largest portion of permanent income for the vast majority of Americans.

The Unemployment Rate: Moderation through Participation

2009-11-08T12:59:00.000-05:00

Here is a long-form answer to NorthGG’s recent comment.
In October, the unemployment rate breached double digits for the first time since 1983. This number, 10.2 percent, seems bad—one out of every ten Americans is out of work but the number is deceptively benign. In this recession, more than at any other time since the early 1970s, declines in labor-market participation are moderating the unemployment rate.

The unemployment rate including all workers who have left the labor force in the last year is currently about two percentage points higher than the official rate. That is, the drop in participation is currently contributing about 2 percentage points to the unemployment rate. Under this measure, the unemployment rate is currently at a record high.

The contribution from flows out of the labor force is about the same as in the early 1970s. There is an important difference in the current situation and the difference is critical for the long-term outlook for the U.S. economy. In the early 1970s, the baby boomers were just entering the workforce. The drop in participation came as boomers left the labor market to remain in school. They went to school both for economic reasons and to avoid the draft. But, this education pool of workers has been a boon for the U.S. economy and is likely, in part, responsible for the emergence of the U.S. as an economic superpower.

Now, though, the decline in participation is not, for the most part, being driven by the young. It is being driven by the old. The workers leaving the workforce are older. The largest contribution to the decline in participation is among workers between the ages of 45 and 55.

These workers are in the prime earning years. They do not leave the labor force lightly. I suspect that the majority of these workers had jobs that no longer exist. Many of these workers will have to find jobs in new industries. A lot of their industry-specific human capital has been destroyed. Almost certainly, at least for a time, the new job will pay less than the old job. Even when they eventually find work, they will be a drag on growth.

What do we do with the large mass of dislocated workers over the age of 45? They are too young, and too poor, to retire. I don’t know the answer but I have a feeling that without this answer the U.S. economy is not going to remain an economic superpower for long.

To formulate policy, we need data. We do not know why these workers are not working. We need to find out who these workers are. What jobs did they hold? Why are they no longer working? What skills do they have? What skills do they need? Are there similar workers in similar circumstance that have managed to stay working? Why did one group perform well and another poorly?

Once we have the answers to these questions then, and only then, we can begin to formulate a policy response. With funding the BLS and the Census Department could answer these questions in a few months. There is no point in throwing money desperately at job creation programs until we understand the source of the jobs problem.

The Employment Situation: Bad News for a Recovery

2009-11-07T12:56:00.000-05:00

The economy lost 190,000 jobs in October, a significant improvement from early this year, but losses remain near the peak of the last two recessions. More importantly, several pieces of the jobs report point to a possible deterioration in the labor market and are unambiguously bad for the economic outlook.

Although the labor market has improved substantially since early this year, over the past three months, job losses stabilized around 200,000. We have never had an economic recovery with job losses at this level. I find it beyond belief that the economy is in the midst of a recovery with these losses.

Most forecasters a jobless recovery has already begun. But a jobless recovery is characterized by a weak but stable labor market, a market where losses have ended but gains have yet to occur. An economy can, apparently, muddle along without job growth; it cannot grow with large job losses.

So, even at this level of losses, an economic recovery is not in the cards. But, more worrisome, other indicators of the labor market are much weaker than the establishment survey and some of these indicators point to accelerating losses.

Initial Claims Remain Weak

I wrote almost a year ago on the strong long-term link between initial claims per month and job losses per month. At the time, claims were accelerating sharply and were pointing to unheard of job losses. Initial claims have, of course, improved. But they have not fallen quickly or robustly. Initial claims seem persistently stuck above 500,000 per week.

These initial claims are consistent with job losses between 300,000 and 450,000 per month.

The Household Survey is a Disaster

Once again, job losses as measured by the household survey are outpacing job losses from the establishment survey by a substantial number. From February to June, the household survey and the establishment survey were, on a twelve-month change basis, showing essentially the same job losses. However, since mid-summer, the household survey has pulled ahead by 878,000 about 292,000 extra losses a month.

As I wrote (here), the month-to-month changes in household survey employment are not a reliable indicator of labor market conditions. The survey is subject to substantial sampling variation and month-to-month changes can be absurdly misleading. I have found, however, that whenever the household survey jumps ahead of the establishment survey, in either direction, it tends to predict both the direction of the labor market and the sign of future revisions to the establishment survey.

The household survey, consistent with the claims data, is pointing to a much weaker labor market than is the establishment survey.

A Recession Dummy in the BEDS Model?

I don’t know why the establishment survey is so much stronger than other labor-market indicators. But, I do have a suspicion. A long time ago, I wrote about the Birth and Deaths model used by the BLS to adjust the establishment data. Essentially, the BLS uses this model to control for the number of businesses being created and destroyed every month. I suspect, but do not know, that the BLS is uses a recession dummy in the model. The recession dummy, if it exists, is important and likely substantially improves the performance of the model.

I suspect the BLS turned off the recession dummy in the third quarter. Without the recession dummy in place, the model will, for any given read of the source data, produce fewer job losses. Remember, I do not know that they use one. But, if I were using a model to estimate losses, I would include one. So, I suspect that they have one.

Takeaways

We cannot have a recovery, jobless or otherwise, if the economy continues to shed jobs at a rate of 200,000+ per month. The best indicators do not at the moment point to any further near-term improvement in the labor market. Until we see some substantial improvement in the labor market (at least 0 losses), the economy cannot recover.

Doing the Math: The Fiscal Multiplier Effect of the 2009 American Recovery and Reinvestment Act

2009-10-31T08:31:00.002-04:00

Fiscal stimulus is everything I always dreamed it would be.

Any regular reader of this blog knows my opinion of fiscal stimulus and fiscal multipliers. I have shown evidence (here and here) that fiscal multipliers must be below 1 and are likely closer to zero or even negative. Pushing against this belief is the recent performance of the economy. GDP grew by a very healthy 3.5 percent in the third quarter, boosted by gains ranging from private consumption, to residential investment, to direct government expenditures. According to the Vice President, the increase in GDP is entirely attributable to the stimulus efforts by the administration.

I am inclined to agree.

I believe that in the absence of government stimulus the U.S. economy would have continued to contract in the third quarter. What’s more, I say this without changing my views on the multiplier. How is that possible? Let’s do some math.

The following table shows the GDP growth that would have occurred in the absence of fiscal stimulus under different assumptions for what counts as government stimulus using a multiplier of 1 (my maximum) and a multiplier of 3 (Romer’s base case). Because there is considerable uncertainty over the timing of the stimulus, I show the four-quarter change in GDP through the third quarter. Over this time period, GDP fell by 2.3 percent. The numbers in the table show the four-quarter change without stimulus and should all be viewed relative to the 2.3 percent fall.

The first row of the table assumes that the sum total of fiscal stimulus is the pay out from the ARRA. According to data from Recovery.gov, as of October 29, the government had actually spent $173 billion (this number includes tax relief and spending). This is the most conservative estimate of stimulus spent to date. (Romer would include both actual spending and money allocated ($310 billion). I agree with her but want to use the smallest number to start. My numbers get bigger fast anyway.) Assuming a multiplier of 1 ($173 billion spent adds $173 billion to GDP), counterfactual GDP growth is -3.6 percent. With a multiplier of 3, the counterfactual falls to -6.2 percent.

I find both of these numbers credible.

I actually believe GDP would have fallen more than 6 percent in the absence of the programs. And, if I believed that total stimulus was $173 billion, I would also have to join Romer in the multiplier is greater than 3 world. Fortunately for me (I am not the introspective type), the total amount of stimulus is much greater than $173 billion.

There are lots of ways of accounting for government stimulus, but the most time honored and method (used by the IMF, the OECD, and the Federal Reserve) is to simply examine the change in the government balance. Over the last four quarters, publically held debt rose by $1,732 billion. (Really, we should only count the increase in the growth rate; but over the previous four quarters, debt only increased by $280 billion.) This number is the correct measure of stimulus. It includes all Federal government spending and all implicit reductions in the tax burden. The number is still conservative because it does not include the increase in state borrowing, an additional $50 to $200 billion.

Using this number and a multiplier of 1, yields a counterfactual GDP growth of -15.4 percent. With a multiplier of 3, the number falls to -41.6 percent. These numbers are beyond the pale. GDP never fell by more than 40 percent in the Great Depression. Using this number and my belief of a counterfactual fall in output of 10 percent, the current multiplier is negative.

But, there is also the issue of the Fed’s balance sheet. The Fed has pumped almost $1,000 billion into the economy over the same period. This is measured by the expansion of the Fed’s balance sheet. There is no difference between receiving a tax break for $1 trillion dollars and receiving a $1 trillion dollars in cash from the Fed. We can argue about effectiveness but that is the exercise here.

Adding the Fed’s balance sheet expansion to the calculation, yields a counterfactual GDP growth of -22.4 percent. With a multiplier of 3, we have the absurd number of -62.5 percent. I believe that latter number would be a modern-era record. I suspect we would have to go back to the plague years in Europe to find an equivalent fall. I would love to see Summers or Romer stand up and make the case for this counterfactual.

So like many Banana Republics before us, we have managed to spend enough to turn GDP growth positive. But, the cost of achieving this number has been phenomenal. To achieve a paltry $212 billion increase in real GDP, we spent about $2 trillion dollars. This gives us a net return of 20 cents on the dollar.

Yes, GDP would have fallen without the spending. The probability that this recession would have scored as a depression in the absence of stimulus is high.

Was it worth it?

Housing Tax Credit: Did it boost the housing market?

2009-10-31T08:28:00.001-04:00

The $8000 first time home buyers tax credit seems to have boosted the sales of housing. The following graphic gives my estimates of the total impact on the value of homes sold since the passage of the bill in January.

Including both new and existing homes and valuing the sales at their median price, total housing sales increased by about $16 billion through September. It is, of course, very difficult to measure how much of the increase is attributable to a natural increase in demand and how much is attributable to an increase in demand derived from the tax credit alone. I estimate two distinct effects: the direct effect of the extra money pouring into the housing market and the indirect effect from the induced change in prices.
In normal times according to the National Association of Realtors, about forty percent of sales go to first time home buyers. The tax credit likely increased this number. I don’t know how much but I assume 50 percent is a conservative number. Under this assumption, total outlays under the tax credit were $14.1 billion through the end of September, slightly more than the CBO’s scoring of the program ($11 billion) and slightly less than NAR’s estimate ($15.2 billion).

I assume that the $14.1 billion has a direct one-for-one impact on the demand for housing. This impact is shown in the figure as the difference between the solid black and the dashed black lines. The estimated impact has grown through the year. The sharp rise between April and June reflects ordinary seasonal fluctuations in demand. These data are not seasonally adjusted.

In addition to the direct effect, the subsidy has an indirect effect through induced price changes. Given an estimated price elasticity of demand, the subsidy pushed average house prices up by about 4 percent (My estimate is just below that of Goldman Sachs who estimated a little more than 5 percent. They must have estimated a slightly higher demand elasticity.) This price effect induces sales of existing homes. Because of the higher price, existing home owners have an incentive to sell their house and either rent (less likely) or buy a new home (more likely). This impact is small relative to the direct effect and is shown as the difference between the dashed red and dashed black lines.

In total, I estimate that the existence of the subsidy has boosted the value of housing sales between January and September by about $17.3 billion.

There is no question that this tax break helped the housing market. An extra $17 billion likely kept some home builders in business and it must have paid the bills of quite a few real estate agents. Whether or not the program was worthwhile depends on the public policy decision of whether or not we want to subsidy the housing market. There are no macro side effects of the program as designed.

Whether or not the tax credit is extended (and it looks like this is a done deal), the housing market is likely to decline in the coming months. Because the program was expected to expire this month, most households eligible for the program and able to buy a house have already taken advantage of the tax credit—see the 9 percent surge in existing home sales in September. The majority of these households would have bought a house sometime in the next year without the subsidy and the tax credit simply changed the timing of their decision. With the extension, I expect the value of sales to fall by about $9 billion over the next several months and to fall by the remainder as the credit is phased out.

Of course congress looking ahead to midterm elections hopes that underlying demand for housing will surge by the time the credit expires, disguising the tax credit induced slump. It is possible but we are going to have to see a more robust labor-market recovery before this can happen.

Estimated Taylor Rules: A Good Fit for Monetary Policy?

2009-10-15T20:26:00.004-04:00

Several economists have recently used the Taylor rule to comment on the appropriateness of monetary policy (Rudebusch, Calculated Risk, Krugman, Altig). In some form or another, they regress the Fed Funds rate on a constant, the lagged policy rate, lagged inflation, and some measure of economic slack. They find that the fit of this equation over the last 20 years has been quite good, and that therefore, monetary policy has been appropriate.

In fact, the fit of these lines is so good that I have become a bit suspicious over the exercise. I decided to do two things: 1) I would extend the forecast back to the early 1950s and 2) I would drop any measure of economic slack.

The reason for the first is obvious: The Fed seems to have made systematic policy mistakes during the 1970s after performing admirably in the 1960s. Then, if the Taylor rule is to be a decent guide as to the appropriateness of policy, it had better deviate systematically in the 70s.

The reason for the second is both econometric and philosophical. If the lagged policy rate responds to economic slack and slack evolves on slowly, the system is over-identified: we are more or less estimating an identity. Philosophically, economic slack is impossible to measure. We have no idea how to measure it or how this measure would relate to inflation. (I know: there are a thousand ways to measure slack. I am just saying none of them are meaningful.)

The result of the exercise is shown below. I too was stunned.

The estimated Fed funds rate lies almost exactly on top of the effective funds rate, shown as a dashed line. Either this is a meaningless measure of policy or monetary policy has been equally appropriate from 1956 through 2009. I choose the former as the more reasonable explanation.

I am not saying monetary policy has been bad of late. I am saying that using a Taylor rule to evaluate policy is meaningless. (Not the theory behind the Taylor rule; just the practice of estimating such a rule.)

What gives?: The Taylor Rule is an Identity

The Taylor rule is really something of an economic identity rather than a model of economic decision making. In effect, estimating a Taylor rule is no different than estimating the national income identity and finding out that Y really is equal to the sum of its parts.

To see this, use the old quantity theory equation:

P = v(φ)*M/Y

Prices are a function of Money (M), the quantity of goods in the economy (Y), and perhaps some measure of the current state of the economy (v(φ)), where the variable φ simply stands in for the current state of everything not written explicitly.

This equation is old fashioned but is fundamentally an identity: more money chasing fewer goods leads to higher prices.

We can transform the identity by taking logs and then first difference the equation.

dln(P) = dln(v(φ)) + dln(M) – dln(Y)

Then rearrange the equation to put money on the left hand side:

dln(M) = dln(P) + dln(Y) – dln(v(φ))

This is the Taylor rule.

Wait! We started with an identity (of sorts) and we ended with a Taylor rule. Logic then insists that the Taylor rule is an identity.

Okay, I hear you. The Fed does not target the money supply; the Fed targets a nominal short-term interest rate and the Taylor rule uses a short-term interest rate not the supply of money.

To see the link, how does the Fed enforce its nominal target. The Fed buys or sells bonds until short bonds are trading at the desired price. What does it use to buy and sell bonds? Money, of course.

So, the money supply is determined by the desired interest rate. The Fed, in effect, adjusts the money supply such that the short-term bond market is in equilibrium at the desired policy rate. M = f(r) and r = G(M). Where G is the inverse function of f.

Then, we have finally derived the Taylor rule.

dln(f(r)) = dln(P) + dln(Y) – dln(v(φ))

Finally, it is a small step to transform the above equation to the more familiar:

r_t = αr_t-1 + ϐ₁Inf_t + ϐ₂Growth Rate of Output_t – ϐ₃Potential Something_t

The coefficients in the equation can be estimated to minimize the information loss in the last transformation. The final term simply reinterprets our state variable. Recall, v(φ) was just some term reflecting the economic environment. In my specification, I set this coefficient to zero. In a standard specification, the restriction sets this to some measure of potential output. We don’t have to argue over signs or magnitudes because we may freely estimate the coefficients.

The Taylor rule fits because it is an identity.

The Quantity Theory

For those of you who don’t believe in money or at least who don’t believe in the link between inflation and money. Take a look at the following picture. The graph shows the five-year change in M2 divided by output against the five year cumulative change in the CPI. If M/Y rises rapidly, so do prices.

NorthGG asked if the relationship is between core or headline. I would say: yes. The five-year changes should completely eliminate the influence of high-frequency changes in food or energy. Only prolonged increases in either food or energy prices will show through. And, prolonged increases are also known as inflation.

Is High Inflation Likely?

2009-10-12T21:04:00.003-04:00

The Statement

In his blog, Krugman dismisses the possibility of inflation. He goes farther and calls the Fed irresponsible for even considering the possibility of inflation. Krugman’s analysis is completely misleading over the prospects of inflation. He is using a backwards looking indicator that ignores changes in current policy. I too believe that high inflation outcomes are likely avoidable. Krugman seems to believe high inflation will be avoided even if the Fed leaves rates at zero forever. I believe that positive action (tighter policy) on the part of the Fed will be necessary to avoid inflation.

The Method

To see the difference in our beliefs, let’s work within Krugman’s framework. Krugman proposes the Taylor rule as his model of monetary policy and uses the parameters estimated by Glen Rudebusch at the SF Fed to judge the current, appropriate policy stance. Krugman uses the following Taylor rule:

Target fed funds rate = 2.07 + 1.28 x inflation - 1.95 x excess unemployment

Rudebusch’s weight on the unemployment gap is much higher than most other estimates. Taylor called for a coefficient of 0.5 on excess employment relative to a coefficient of 1.0 on inflation, implying the Fed should care twice as much about inflation as unemployment. Krugman believes the Fed should only care about 2/3 as much about inflation as the unemployment gap. The change in weights is quite significant.

In Krugman’s specification, with current inflation at -.02 percent (core PCE, 4-quarter change), the first two terms imply a Fed funds rate of positive 2.1 percent. The unemployment gap is then driving the current negative policy rate. With current unemployment around 9.8 percent (it was only 9.3 percent in Q2) and using the CBO’s pre-recession estimates of the NAIRU, the implied policy rate is very, very negative; indeed, much more negative than Krugman reports, negative 7.7 percent. If we had used more standard weights and a constant of 2, the implied policy rate is just barely negative, -0.5 percent.

But, to be honest, I am with Krugman and don’t see any justification for Taylor’s weights. The Taylor rule is not a model of the policy rate but was rather designed as a descriptive rule for understanding policy setting. Given this view, we are free to estimate rates and if I estimate the Taylor rule I arrive at coefficients closer to Glen’s than to Taylor’s.

The Mistake

If you think the Taylor rule was a good guide to policy in the past, the Fed shouldn’t start to raise rates until the rule starts, you know, yielding a positive number.

The first part of the quote is wrong and the second part puzzling.

The Taylor rule was not a good guide to policy in the past: The Taylor rule had been a good description of past policy. The two statements are not equivalent.

The Taylor rule is essentially a linearized equation from a specific model of Fed policy and inflation and resource slack. The Taylor rule does not describe a fundamental relationship between policy, output and inflation. There are many models of output and inflation.

In particular, we can modify Krugman’s Taylor rule to make it forward looking. An optimally behaving Fed will set policy not based on the past behavior of variables but rather on their forward-looking expectations.

Money Matters

For some reason many Fed officials seem to view it as inherently unsound to stay at a zero rate for several years running — but I’m at a loss to understand what model, or even conceptual framework, leads them to that conclusion.

Maybe some of the Fed officials remember the adage ardently espoused by Friedman: Inflation is always and everywhere a monetary phenomena.

Krugman, along with the rest of the Fresh Water economists, seem to have completely forgotten about the link between money and inflation. Lucas, Freidman, Smith, Hume, and yes even Keynes believed in a link between the quantity of money and inflation. Lots of money: lots of inflation.

Every scholar who has ever seriously examined the relationship between inflation and money has found a positive relationship. Lucas found a positive relationship using both U.S. and international data. Friedman found the same in 1960s for data sets running from the mid 1880s to the early 1960s. Both Hume and Smith believed in a positive relationship between money and inflation, although there use of data was more anecdotal and conjectural than rigorous.

The most recent study of inflation and money, of which I am aware, was presented last Thursday at a Federal Reserve conference. “Money and Inflation,” by Bennet McCallum and Edward Nelson, finds a consistent positive relationship between money supply and inflation: money raises inflation about 1-for-1 with a two year lag.

The Fed has increased the money supply substantially over the last two years. According to the Federal Reserve’s H.6 release, M1 has increased 18.6 percent over the past twelve months, while the broader M2 has risen 7.8 percent. These are extremely high growth rates. According to the work of McCallum and Nelson, this growth rate will lead to inflation one to two years from now.

If we replace Krugman’s backward-looking PCE with a reasonable forward looking expectation of inflation driven by the increase in the money supply, we find that between one and two years from now the policy rate had better be above 2 percent. Further, if we believe the results of McCallum and Nelson, the policy rate probably needs to start increasing now. That is, the money supply has to be reduced now to avoid the high inflation in the future.

This is the model policy makers likely have in mind when they call for tighter policy.

I don’t think there is any particular rush to raise rates. I think the Fed can afford to be patient and watch the data.

Conclusion

Krugman and I agree: There is very little likelihood of high inflation. Krugman believes in the Taylor rule and so a passive Fed can achieve this outcome. I believe in money and so an active Fed will achieve this outcome.

Krugman’s own Taylor series framework implies high inflation within one to two years, if the Fed is passive. Fortunately, the Fed is not passive. The Fed has the power to control inflation. It simply has to withdraw the liquidity in a timely manner.

Gold Bugs, Exchange Rates, and Monetary Policy

2009-10-10T11:43:00.002-04:00

Beware the dollar hawks says Krugman in a recent post to his blog. The dollar is depreciating quickly and many (Krugman’s many I have not fact checked) are calling for somebody to do something about it. Krugman believes there is a danger in this call. And for once, I am in unambiguous agreement with him.

Using monetary policy to control the value of the dollar would be a policy mistake.

The Fed is already trying to do too much with a single policy tool. Adding yet another criterion to their already long list is too likely to lead to policy mistakes. The Fed needs to keep its eyes on the balls already in the air and not add balls to impress like a foolish street juggler.

But, at the same time, the Fed should not disregard the exchange rate as a signal of overly loose policy. The exchange rate is perhaps the best, broadest, and most flexible dollar denominated price. A depreciation of the dollar is inflation—the dollar price of foreign goods goes up. It reflects the average relative price of dollar goods. Amongst their other price signals, the Fed should monitor the exchange rate. The value of this mechanism as a price signal ultimately lies behind the current dispute amongst various members of the FOMC, no different than the debate 4 years ago on the value of the Cleveland Fed’s median inflation rate.

The danger of the Fed ignoring these signals is exceptionally high at the moment. There is a group of economists (Roubini talks about this all the time.) that have been looking for a decline in the real value of the dollar for a long time. They view the real value of the dollar as an equilibrating mechanism to adjust the pattern of global demand. If the Fed shares these beliefs, they may confuse a nominal movement in the dollar with the long-looked-for real depreciation.

Remember, any price has two components, real and nominal. The real component reflects an equilibrium between supply and demand (of and for the good). The nominal value reflects and equilibrium between the good and money. The Fed controls the latter (to an extent) and never the former.

To tie it all together, the Fed should be careful not to confuse the real and nominal value of the dollar. From their perspective, unexplained movements in the dollar are probably nominal.

Watch the value of the dollar but don’t target it.

The True Impact of Fiscal Stimulus

2009-09-30T19:41:00.000-04:00

There are currently a number of economists crowing over the absolute success of fiscal policy. I am still unconvinced that fiscal policy has any meaningful impact on economic output. I continue to believe that the programs to date have merely masked the underlying weakness in the economy.

“Masked the underlying weakness – Isn’t that exactly the definition of fiscal stimulus?” you ask.

The answer is not necessarily. To explain my views here, let’s turn to the case of China, the current poster child for successful fiscal and monetary stimulus and a crisis already occurring. In the second quarter, GDP increased at a rate someplace in the high teens. (China only issues real GDP on a four-quarter change basis; so, quarterly changes must be inferred.) The growth rate was phenomenal and is directly attributable to government intervention.

How did China achieve this remarkable growth? Easy. I believe that the government simply insisted that factories continue producing output—I am sure the insistence was accompanied by a promise of a fiscal transfer. Factories keep producing output, calamity averted, the stimulus is effective.

In fact, GDP gets an additional boost. The factories are producing output that nobody is buying – exports remained depressed and consumption is not picking up all of the slack. The output from the factories is accumulated as inventories, a positive contribution to GDP. But, since nobody is buying the price of the inventories also falls. Real GDP gets an arbitrarily large boost as the price deflator declines. [I am exaggerating for hyperbole. I know some of the goods were purchased but the increase in output likely owes to exactly the channels I am suggesting. If you want to see this at work in the United States, go back to the fourth quarter of 2006. Autos made a large positive contribution to GDP as the prices of new cars fell sharply and the real value of car inventories was pushed up.]

This is not real growth. It is not real growth in the present and it is a direct drag on growth in the future. Barring a sudden increase in OECD demand, China is in for some hard times.

This is the nature of fiscal stimulus.

The Fiscal Cost of Stimulus

2009-09-30T19:39:00.001-04:00

Paul Krugman and I posted almost opposite comments on fiscal spending yesterday (his; mine). (Trust me they are not dueling – Nobel Prize winning famous guy, me.) Whereas I was concerned about the long-term cost of higher government spending, he was blasé. From his article, “I’m not proposing a fiscal-stimulus Laffer curve here: it’s probably not true that spending money actually improves the government’s long-run fiscal position (although that’s certainly within the range of possibilities.)” He almost believes that fiscal stimulus (at least under certain conditions) is almost self financing.

I don’t believe it for a minute. But, Krugman’s views are amazingly internally consistent and there is no questioning his mental acuity.

He believes in large multipliers. That is, every dollar increase in government spending increases output by something much larger than a dollar. (He has publically averred to multipliers of around 1.3 but this would not be even in the ball park of self-financing. Spend $1 billion, get $1.3 billion. Spend $1 billion raise $100,000 in extra tax revenue. With our tax system, self-financing begins with multipliers greater than 3.)

If Krugman is correct on the multipliers then he is correct on the cost of the fiscal stimulus. If not, …

The Federal Deficit and the Tax Burden: We can afford the debt not the spending.

2009-09-29T20:03:00.003-04:00

Over the past year, the amount of publically held Treasuries, the current debt of the U.S. government, has ballooned. Debt held by the public has risen from 56 percent of GDP (already a near record) to over 63 percent of GDP in 2009. Importantly, this increase has occurred before most of the stimulus money from the American Recovery and Reinvestment Act has been spent; indeed, of that money, a meager $151 billion had been drawn as of the end of August. With this large debt come concerns over fiscal sustainability.
I have always taken it for granted that the debt was sustainable. After all, why would the markets lend to the government if the debt were not sustainable. Recent events (and Krugman’s excellent ariticle in the NY Times on the state of macroeconomics—Summers’ ketchup economics is brilliant.) have shaken my outright confidence in markets. So, I thought I should do my own sustainability calculations. I came to the surprising conclusion that the stock of government debt is not only sustainable but it is downright affordable.

To be conservative in my estimates, I use the entire amount of Treasuries outstanding. Publically held debt is a better measure of government debt but either will do for my purposes—funds held by the Fed and other public agencies don’t really count. In my mind, counting them is the same as counting the total sum of Treasuries that could be issued.

To check for sustainability, I take the Federal Government in hand and put it on a debt payment plan. If the Feds were a household and we were financial managers we might choose a 10 to 30 year plan depending on their age and circumstance. But, governments are special. I chose to put the gov’t on a 100-year payment plan at the end of which time debt must be zero. Of course with the payment plan the government is forbidden to acquire new debt—this feature will be the lemon in the pudding.

If real interest rates stay at their current low levels of around 1 percent (unfathomable), the payment plan costs each of the 137 million workers in the United States $1,361 per year, a paltry 1.7 percent of personal income. Even if real interest rates rise as high as 10 percent (equally unfathomable (or maybe I can picture this one)), the debt burden per year remains an affordable $8,248 per year per worker or about 10 percent of personal income. The latter number is large but feasible.

The real problem is that the government must also raise sufficient funds to keep from borrowing more. The Bush administration ran the largest deficits of any government to that time. If we add the Bush deficits to the total, yearly payments need to rise. Under the low interest rate case, this raises the payments to a little more than $3,000 per year per worker. At 10 percent, we are quickly approaching $10,000 per worker per year.

The Obama deficits are projected to be much, much larger. If we have to raise revenue to offset the average Obama deficit over his first term as calculated by the CBO, even at 1 percent interest, the debt payment is $6,326 per worker. At 10 percent interest, payments per worker increases to almost 17 percent of personal income.

This exercise reveals the national debt to be affordable in the ordinary sense of the word. The United States could pay off its debt in a mere 100 years with only a modest strain on workers. However, if government spending continues to rise (or stays at the current levels), the strain on workers is likely to extreme. Additional taxes in excess of 10 percent of total personal income are likely to have a large negative impact on the workforce.

I might be in favor of higher government spending or I might be against it. As always, the decision is one of public policy and not of economics. But, good economics should inform the decisions and we should make an informed choice on how much to spend based on an honest national dialogue.

The Strength of the Recovery: Demographics could kill it all.

2009-09-29T19:30:00.001-04:00

Mussa gave an interesting talk on the recovery the other day at the Peterson Institute. You can find his paper on their website. He makes an excellent case for a V-shaped recovery. With his forecast of U.S. growth at 4.5 percent for 2010, he makes the claim that his estimate is actually conservative. That, in reality, we could, and likely should, look for an even steeper recovery. In particular, he points to the fact that in the average post-war recession average growth has been closer to 10 percent than 5.

In my last two posts, I gave a cycle-by-cycle breakdown of the key parts of GDP. The rate of recovery in individual components was not particularly strong. Adding up the various measures, the strength of various recoveries was accomplished because most of the rising demand coming out of recessions was satisfied from domestic sources. In this recession, any rising demand is likely to be satisfied to a large extent by production outside of the United States. Just as the downturn in the United States was cushioned by a fall in imports the rebound will be pushed down by imports.

But, in my mind, this is all accounting. The potential U.S. recovery is exposed to a much more serious threat: the U.S. consumer. In percentage terms, the rise in the savings rate in this recession far exceeds the rise at any other time in post-war history. Indeed, we have to go back to the 1930s to see a similar spike. Prior to this recession but still post-war, the largest increase in the savings rate was a little more than 30 percent. In this episode, the increase has been a little more than 80 percent. Granted, the calculation is from a very low base near 2 percent of income.
However, never in post-war history has consumption accounted for such a large portion of aggregate demand. I believe the large change in savings reflects a combination of cyclical forces and the beginning of a long-term consumer retrenchment. Americans must consume a smaller portion of their income.

With 1 in 6 Americans underemployed and with wage growth falling, consumption is likely to be a substantial drag on any recovery.

Yet, I believe Mussa would retort (he makes the argument in his paper) that consumption has fallen more than in any other recovery and we are due a bounce back. Indeed, so Mussa would say, following the recession of 1980 consumer spending bounced back with a vengeance.

Take a look at the following picture. Despite the financial crisis aspect of this recession, consumer credit was unimpaired relative to previous recessions. Real consumer credit has only nudged down. Compare this to the almost 15 percent fall in 1982. In 1982, real interest rates rose to record levels. Consumers stopped spending. Following the recession, the expansion of credit played a huge role in the rapid expansion of spending. Consumer credit grew at almost three times the pace of the average recovery. With almost no impairment in this recession, the room for a bounce back is smaller.

The resumption of consumption also faces a very unfavorable demographic situation. The chart below shows prime-age workers (30 to 65) as a percent of the total population. In 1982, this group of agents increased almost 8 percent over the first five years of the 1980s. In the current recession, this percentage falls remarkably over the forecast. Indeed, it falls faster than at any other time since at least before 1901. Prime-age workers are the most productive people in the economy. They also spend the most: they buy big houses and fancy cars. A fall in this group has wide-spread implications for the economy. There is only one other country I know of with this demographic pattern: 1990s Japan. That is not a good omen for V-shaped recoveries.

Just to finish the thought, here is the path of prime-age workers as a percent of the total population from 1970 through 2014. It seems that one source of our amazing growth rates from 1980 through 2007 was the rise of this group of workers. Notice, that during the productivity slowdown in the 1970s, this group of workers was flat or falling. During the 2000 recession and long jobless recovery that followed, this group of workers was also quite stagnant. For the next ten years or so, these workers will retire out of the labor force.

Most model-based forecasts assume that the fall in population itself will subtract only a few tenths from growth. With labor shares and capital constant, this is true. However, the fall in prime-age workers is also likely to be associated with a long-term downward trend in investment. We have already almost a decade of subpar investment growth. More importantly, the loss of human capital will be severe. It takes years to produce prime-age workers. These workers embody skills that have been acquired in a life-time of work. There loss is likely to drag on labor productivity for the foreseeable future.

Part I: The Great Recession of 2009: Where are we going?

2009-09-09T20:54:00.003-04:00

Most analysts now believe the recession is either already over or at least kicking its last gasp. They might be right. But, I remember having this conversation 6 months ago; back in March when the PMIs first turned up. The end of the recession wasn’t in the data then and it is not obvious it is in the data now, although the odds have shifted towards recovery. The difference between now and then is time. We now, I believe, have sufficient data on hand to judge both the severity of the downturn and to compare this downturn to previous recessions. The comparison may shed light on both the likelihood of a near-term recovery and the potential shape of that recovery.

We examine, in turn, key elements of U.S. demand, running from investment to imports to exports to consumption. For each series, we plot data 12 quarters on either side of the end point of post-war U.S. recessions. The zero date is the last quarter of the NBER recession. The solid line is the average behavior of all post-war recessions excluding 2009. The dotted line shows the behavior of investment in the worst post-war recession defined as the recession with the largest peak-to-trough decline in the particular series being shown. All points on the dotted line belong to the same recession. The dashed line shows data for the current recession. We plot the data as if 2009Q2 is the end of the recession. All lines are indexed to 100 at last date of the recession. The indexing scales the graphs to show cumulative percent changes from the zero date. For example, a value of 120 in period -4 indicates a 16 percent fall in the series in the last year of the recession. While a value of 120 in period 4 indicates a 20 percent rise from the end date o the recession. All series are real.

We begin with machinery and equipment investment. I have long advocated that this recession is a manufacturing recession (see this post); therefore, in my view, the recession is unlikely to end without a recovery in this sector. Investment is one of the most volatile components of GDP, typically falling almost 10 percent over the last year of the recession. Post-recession, investment grows robustly recovering in level terms one year after the end of the recession.

The worst investment recession was 1958. Investment fell a little more than 18 percent with the decline occurring in a single year. Investment recovered in level terms just in time for the 1961 recession. Despite the faster-than-average growth upon exit, investment did not recover in level terms to its pre-recession peak until almost 2 years after the end of the recession.

The current recession has solidly replaced 1958 as the worst investment recession. Investment has, to date, fallen more than 20 percent from its peak 6 quarters ago. Investment does not currently show any signs of life; however, investment turns on a dime and typically turns up only once the recession is over. If history is a reliable guide, investment is likely to surge robustly in coming quarters. I believe a typical recovery is more likely than the sluggish recovery experienced after the 2001 recession. It seems a global manufacturing reshuffling is once again underfoot. Even though this reshuffling is likely to destroy capital (the shutting down of American car plants for example), it may also breed investment.
Like investment, trade has plummeted more in this recession that at any time in U.S. post-war history. Unlike investment, however, trade shows a clear, leading, end-of-recession pattern. The decline in imports tends to slow in the quarter or two before the actual end of the recession. Following the recession, imports tend to grow very rapidly, in part reflecting the strong trend in trade over the last 40 years. The worst import recession was in 1975.

In the current recession, imports have also surpassed all previous records, falling over 20 percent. As of the second quarter, imports still declined but the pace of decline was somewhat smaller than in previous quarters. This may signal the end of the recession or it may just be one of the inevitable bobbles in the data. In former case, the recession would end following the third quarter.

Of course given the above focus on investment, capital imports may be even more important than overall imports. The largest capital import recession occurred in the 2001 recession as the United States shed one-third of all manufacturing jobs and the tech expansion dissipated. The current recession has exceeded this high mark. Capital imports lead the cycle more strongly than overall imports. In past recessions, capital imports stopped falling entirely, on average, one to two quarters before the end date. Currently, capital imports have not yet truly flattened.

However, while NIPA capital imports did not stabilize as of the second quarter, capital imports have since leveled out. The following figure shows real capital imports to Japan, Canada, the Euro Area, and the United States. Only in Japan are capital imports actually rising. But, in all three other regions, capital imports have stopped falling. This may be a sign of the recession ending. If historical patterns hold true, this leveling would indicate a fourth quarter recovery rather than a third.

This post continues in Part II

Part II: The Great Recession of 2009: Where are we going?

2009-09-09T20:40:00.003-04:00

This post is part II of The Great Recession of 2009

Exports from the United States tell the tale of this recession. Exports fell in line with the worst previous U.S. exports recession (1958), but no worse. It’s a global recession to be sure but foreign demand held up better than in the United States, particularly at the beginning of the recession. As with imports, exports tend to flatten out before the end of the recession. It seems that not only Asian economies have benefited from export led recoveries. And, in the current recession exports are showing signs of stability.

Durable consumption, the most volatile component of consumption, has also fallen sharply in the current recession, although by a smaller amount than the cumulative decline associated with the 1958 recession. Durable consumption leads the end of the recession more than any other component of demand, turning up more than a full quarter before the end of the recession. In the current recession, durable consumption actually increased in the first quarter. However, this increase was more than undone in the second quarter. I have not looked at the detail but I suspect the bobble is attributable, in part, to the bankruptcy difficulties of the auto makers early in the quarter. These temporary concerns may have pushed demand for cars across quarters. Too, by the second quarter it was becoming increasingly obvious that the U.S. government would at some point subsidize car sales. Savvy consumers then may have postponed purchases to benefit from the subsidy.

In any case, there has never been a post-war recession that has not been followed by a steep surge in durable purchases. The economics behind this surge is clear. Durables are durable. The service flow from refrigerators and cars diminishes only slowly. Purchases of these items are easy to move across time. So, once the stress of the recession ebbs, particularly labor market stress see next, consumers begin to restock their durables. Interestingly, however, the speed of the recovery seems independent of the extent of the decline, implying that some consumption is forgone permanently.

Residential investment is, in reality, simply the most important component of durable consumption; however, in National Income Accounting, it is treated as investment. As I have noted before, residential investment is a leading indicator. It peaks before the beginning of the recession and troughs before the end. And, residential investment was an early hallmark of this recession; but, with the constant stream of bad news last fall, it is easy to lose track of just how far the housing market has fallen. Just over the last three years (we are missing two quarters of decline), the fall in residential investment has bested the worst recession by more than 50 percentage points, falling by well more than 50 percent. The fall in residential investment continued through the second quarter despite several massive intervention attempts, including direct intervention in mortgage markets and a $7,000 dollar subsidy to first time buyers.

I have trouble picturing a recovery that does not include a recovery in residential investment. The ongoing sharp fall is bad news on that front. However, data since the second quarter has shown some signs of hope and housing starts are turning up. If the turn is real and not temporary induced by temporary government incentives, the recession is likely to end after the third quarter.

I have discussed the importance of initial claims for identifying turning points in the data (see this post). Initial claims are a timely indicator of the firing rate in the economy. As soon as this rate stops rising, the economy begins to recover. This is both causal, employed households have less fear of losing their jobs and begin to spend, and coincidental, firms see better conditions and stop firing workers. As claims begin to drop sharply, the recession is over. This is an extra important indicator because claims data is a timely, if noisy, indicator. So, even though the change in claims is coincident with the end of the recession it is available sooner than other macro data.

Looking at the pattern of weekly claims since the end of the second quarter, the sharp downward trend is hard to spot. Claims seem stuck between 550 and 600 thousand per week, a rate I consider inconsistent with full-on recovery. Despite the horrific jobs numbers we have experienced in this recession, the rise in claims is not as big as in the worst recession (1975) nor is it as bad as the second worst, 1958.

With the debate and eventual passage of an almost $1 trillion dollar stimulus earlier this year came a barnstorm of economists either touting the benefits of fiscal stimulus or warning against its potential inefficacy. Romer was in the former camp while I fell in the latter. It turns out, however, that we were all arguing with air. Take a look at the total government outlays chart below. Total government outlays includes all government spending and all transfer programs; quite simply, it is total government spending. The level of government spending is almost unchanged during the recession (periods before the zero) and only jumps slightly above trend in the periods after. Whatever the multiplier, it is not going to influence the trajectory of the economy in any substantive (read measurable) manner with these small changes. We have no idea what fiscal spending does.

The spending in the current recession will give us our one experiment. The rise in government outlays, from an already elevated point, should if the multipliers are high lead to a faster-than-normal recovery. In any case, the rise in government spending is somewhat stunning. But one data point is still just an anecdote.

Conclusion

Among the key things I learned from these charts is that the depth of the decline has very little to do with the pattern of recovery and that it takes about two years to recover in level terms from each downturn. This makes sense to me and was also consistent with the recoveries experienced by Argentina after its 2001 collapse, the Asian economies following the Asian Financial Crisis, and Mexico following the Peso Crisis. In other words, bad things happen and then they get better. The only post-war economy that didn’t fully recover from a crash is Japan. Japan has more or less stagnated for the past 20 years.

All told, I would judge the indicators point to a recovery that will not happen before the fourth quarter. Only the most nascent signs of recovery are apparent (ignoring survey data). But, nascent signs are better than none.

Monte Carlo Simulation: The Problem is Not Skinny Tails

2009-05-14T19:19:00.001-04:00

Monte Carlo simulation is a convenient statistical tool by which an analyst can make inferences over the probability of rare events. Basically, the method takes a model with its estimated parameters and simulates possible sample paths under some distributional assumptions on the errors. The technique is particularly useful for understanding the tail risk of distributions. The method has been widely adopted by banks, investors, and financial advisors. And, because the models failed so horribly in the current downturn, the method is now under attack.

A lot of discussion over the failure of these models has centered on the use of normally distributed errors. The Normal distribution is the mainstay of statistically theory but the distribution places relatively little weight on tail events. In other words, the distribution treats rare events as rare.

The proposed solution is to work with fat-tailed distributions. Distributions that put more weight on tail events. These distributions raise the probability of very rare events, increasing the risk and raising the probability of observing rare events.

Monte Carlo simulation faces a far more severe problem than its distributional assumptions. To run a Monte Carlo simulation, the parameters of the distribution must be known. With a normality assumption, we need know only the mean and the covariance matrix. Yet, even assuming the model is correctly specified, we can never know these parameters. We have at best a good guess.

This problem of parameter uncertainty swamps any distributional assumptions. Rare events are rare. They are not observed in any reasonably-sized sample. Every sample is going to produce slightly different estimates of the model parameters and these slightly different parameters have huge implications for the probability of tail events.

I think the easiest way to demonstrate the problem is with an example. I will run a Monte Carlo simulation to find the probability of Industrial Production recessions. I assume the growth rate of IP is an i.i.d normally distributed process.

But, I estimate the variance and mean of the process over different periods. First, I choose the Great Moderation era, 1984 to 2006. Many statistical models of the economy restrict themselves to this period. This is a reasonable sample if you believe there was a regime shift in the early 1980s. Second, I extend the sample through March 2009. Finally, I use the post-war period and then the full sample from 1921.

The bars in the chart below show the probability that IP will fall below its starting value over any five year period. Essentially, the probability is the odds of a recession occurring over any five-year interval. The number above each bar gives the average interval between recessions.

Between 1984 and 2006, there is a fifty percent chance of a recession in any five year period with a recession occurring every 9.8 years on average. Extending the sample two additional years changes the probability significantly. Using data from 1984 to 2009, a recession occurs once every 7.4 years. This number is strikingly similar to the probability over the entire post-war era, the third bar. Finally using the entire IP sample, a recession occurs every 6.3 years on average and the odds of a recession in any five-year period is over 80 percent.
The average size of recession (shown in the next chart) also changes over the different samples. The ordering is preserved with the Great Moderation having the mildest downturns and the full sample, including the Great Depression, having the harshest recessions.

Again using the Great Moderation sample, the tail risk is also small. The following chart shows the average decline in output in a five percent recession. That is a recession that occurs only five percent of the time. The tail fall in output during the Great Moderation is less than 2 percent.

Takeaway: Monte Carlo simulation suffers far more from parameter uncertainty than from shortcomings over the choice of distribution. Slight changes in the sample used yield drastically different simulations. And, the correct sample is unknowable.

By the way, the problem of parameter uncertainty is endemic in forecasting models. We want to use history to guide our judgment of the future but history is a fickle guide.

The End of the Beginning? The Household Sector is a Problem

2009-05-08T06:15:00.004-04:00

In a recent post, I presented some mixed signals on the prospects for a near-term recovery of the U.S. economy. In the end, I came down against a recovery being imminent but different eyes could have made the call in the other direction; indeed, once again I am in the minority and most forecasters are seeing a preponderance of green shoots in the data.

I view the world through different eyes than most economists. At least in policy circles, economists see the current downturn as driven in its entirety by a financial crisis. I don’t think this is a financial crisis (here’s why). I think this recession was caused by a shock to the manufacturing sector but it is the bad household balance sheets that have driven the dynamics of this recession: too much debt transformed a small productivity shock into a full blown solvency crisis. The asset side of the household balance sheet (household income) has fallen slightly; the high level of liabilities transforms this to a crisis.

The global economy cannot return to health until households have worked off at least part of their excess debt. So far they have made little progress.

The picture below shows the ratio of total consumer credit outstanding to personal income. From the late 1950s through the early 1990s, this ratio was stable on average between 0.14 and 0.16. Then in the 1990s the ratio took off, increasing to 0.19 by early 2000. The fall in income and no decline in debt drove the level even higher through the summer of 2003 where it peaked at 0.23.

Like the current account balance, like consumption’s share in GDP, this ratio is too high and will have to adjust before the economy can return to health. It may have to adjust to its 2000 level or it may need to go all the way back to its long-run average, but it has to move down.
But, household debt alone does not give a sufficient accounting. Government debt is essentially an off-balance sheet liability of the household sector. And government debt has grown apace over the last 10 years. In January 2001, the federal debt was $5.7 trillion. An increase in the growth rate of government spending, combined with slower revenue growth raised the growth rate of government debt. By December 2008, federal debt had reached $9.2 trillion. Since then, the debt has increased more than 20 percent, exceeding $11 trillion dollars and approaching the value of nominal GDP.
Adding the federal debt to consumer debt raises the household debt to income ratio to more than double is pre-1990 level. As bad as these numbers seem, they are actually much worse. Household demographics do not support the increase in debt. Over the next five years, the proportion of American households over the age of 65 hits a record. There is a reason people retire at 65; beyond that age morbidity rates rise sharply, implying lower productivity. Social pension programs are a side show. The true problem is the fall in labor input and the loss of human capital and making people work longer is not the answer: there is a reason people tend to retire at 65 (morbidity). The household sector is living beyond its means and its means are not going to grow.

Long-term Adjustment: Short-term growth?

So, over the long run, the household sector needs less debt (see the previous post). What about short-term indicators? Is the household on the brink of recovery despite my dire predictions? No, but let’s take a look.

Surveys provide the timeliest insight on the health of the consumer sector and consumer confidence has ticked up. Both the Michigan survey and the Conference Board are at or near record lows, but both series have stopped falling and may be poised for a rebound. But consumer confidence is volatile and does not strongly lead the cycle. The small increases in the series so far this cycle are indistinguishable from random noise.

A better direct indicator of consumer-sector health is the state of the housing market. Residential investment leads the cycle by one to four quarters and it tends to lead vigorously, falling rapidly from the peak and rising robustly from the floor. Housing starts have shown no inclination to rise and it looks like they still want to fall.

We will hit a floor in housing starts in the next few months. (There is a binding zero nominal bound for starts.) Rather than being good news for the housing market, hitting the floor in starts may trigger a more virulent phase of the downturn. If the economy can no longer adjust through quantities, it must adjust through prices. If starts can no longer absorb part of the adjustment, house prices must fall faster. If house prices fall faster, more households will opt for foreclosure, adding an independent source of pressure on prices.
And of course, household incomes are not going to support recovery. The graph below shows a stunning (and familiar) picture of continuing claims for unemployment. Continuing claims for unemployment insurance are at a record; claims as a percent of the workforce are rapidly approaching the earlier peak as well.
So, long-term fundamentals are negative for consumption and near-term indicators have yet to turn. I just don’t see hope of a full-blown recovery. What I see right now is a short-term respite from the dreariest days followed once more by a period of contraction.

Testing the Ice: The End of the Beginning

2009-05-04T06:48:00.004-04:00

"Now this is not the end. It is not even the beginning of the end. But it is, perhaps, the end of the beginning." Winston Churchill, 1942

The evidence is mounting and an increasing number of people are seeing signs of recovery, green shoots. The consensus amongst forecasters is that the worst is behind us and that the economy will return to growth in the second half, maybe even by the second quarter. And, there are some promising signs in the data. I’d say I see a glimmer of hope, but no more than that.

By far the strongest evidence of a turnaround is the most recent initial claims data. Initial claims fell 3.4 percent in the month of April. The fall itself was not large, a one standard deviation event. But the implications of the fall may be immense. Initial claims always turn down at the economic trough. In this post, I discussed the ins and outs of employment. I showed that a fall in the separation rate was far and away the timeliest indicator of recoveries. And initial claims are the closest we have to a direct measure of this rate in real time data.
If the trend in unemployment continues, this recession is over (at least for the moment—see 1981). Of course, the movement was quite small by historical standards and does not yet point to a true recovery. False dips in initial claims are quite common and in ¾ of recessions initial claims moves down by at least 4 percent before rising once again.

The picture below puts the fall in perspective. If claims fall at their April rate for the rest of the year, the level of December claims remains far above its recent levels and far above its level during the 2001 recession. Claims have to start falling a lot faster for us to be sure of recovery, but the sign is positive. This is my single favorite indicator of upward turning points.

The second most positive piece of news is the manufacturing PMI. As shown in the picture below, the manufacturing PMI for the United States has now risen three months in a row and the increases have been sizable. Any number below 50 indicates a contraction in the sector, but the upward movement is encouraging. Historically, changes in the PMI are tightly linked to both the manufacturing sector and the broader economy. This series is consistent with a return to growth as early as the end of the second quarter (June).

Working against the manufacturing PMI is the nonmanufacturing sector. The PMI for nonmanufacturing is falling again after bouncing off its end of year lows. This series looks like it wants to head down. And since the nonmanufacturing sector accounts for around 70 percent of economic activity, the series cannot be ignored.

More importantly, the data from the manufacturing sector is not nearly as positive as the PMI would indicate. Over the three months that the PMI has risen, shipments and new orders have continued to fall, albeit at a slightly slower pace than in previous months. Still, the level of both of these series remains near the peak prior to the last recession. Like unemployment insurance claims, shipments tend to turn up before the official trough of the recession.

All told, I still do not see a recovery even in the manufacturing sector, although I think there are many hints that the sector is no longer collapsing. That’s good, if it kept collapsing at the rate of the last six months, we would reenter the dark ages before two years passed by. But an end of the collapse is not the same as the beginning of a recovery.

To stick with the WWII theme, the year is 1942; the war is not yet won. But, the Germans have been stopped at the shore, and while we are a long way from total victory, absolute defeat no longer approaches.

The Recovery and the U.S. Consumer

2009-04-14T20:17:00.004-04:00

I wrote some time ago (here) about the over-leverage of the U.S. household. As we think about a possible rebound in activity, I think it is important to keep this fundamental misalignment in mind. IF I am right and this is the recession where we begin to make progress on these imbalance THEN no recovery can occur while the U.S. household still consumes a disproportionate amount of GDP

In my mind, the easiest way to understand the over-leverage of the U.S. household is to look at the ratio of consumption to GDP. This number is essentially the amount of U.S. income devoted to consumption as opposed to investment or exports. The higher is this number the more households have borrowed to support their consumption.

To make a stronger link with leverage, one can glean similar insights from the current account deficit of the United States. Over exactly the same period that consumption as a share of GDP has been rising, the current account deficit of the United States has been falling. The match is close even to the point of matching the three percentage point jump since 2000. The current account deficit represents real indebtedness to foreign economies. It does not matter whether this debt is incurred because oil prices have risen or because we buy a lot of TV’s.

The share of income allocated to consumption in the United States has risen from 62 percent in 1980 to over 70 percent in 2008. Over 40 percent of the rise has occurred since 2000. This level of consumption is way too high. It is high by historical post-war U.S. standards and it is high by contemporary international standards.

The share of consumption to GDP in the United States must fall and it seems that we are, finally, at the turning point. There are only two ways for this adjustment to occur: either consumption must fall or the other components of GDP must rise.

How far does consumption have to fall? It depends on how far the ratio of consumption to GDP must move. I believe the ratio is likely to return to 1980 levels but a strong case can be made that the ratio need only fall back to the level of the late 1990s.

Assuming no growth in other components of GDP, consumption would have to fall 3.2 percent per year to bring the ratio back to its 2000 level within five years. For the ratio of consumption to GDP to fall back to its 1980’s level within five years, consumption would have to fall 7.4 percent per year. If we allow 10 years for the ratio to return to its 1980’s level, consumption only needs to fall 3.2 percent per year.

These falls are huge and well beyond anything in the modern historical record for industrial economies with the possible exception of the Great Depression. Only Argentina (and perhaps some of the Asians during the Asian Financial Crisis, though I have not checked) suffered falls of this magnitude. In 2001 and 2001, Argentina experienced repeated double-digit falls in consumption.

Of course the consumption numbers are all relative to the other components of GDP. Consumption does not have to fall as much if other parts of the NIPA accounts are growing. However, because consumption has such a large share of output, the other components would have to grow very fast indeed to keep consumption from having to fall at all. In addition, I have trouble imagining components like investment expanding when consumption is falling at these rates.

These numbers are scary.

And these numbers are just as scary as when I first posted this idea. The U.S. consumer has a long way to go and I do not believe we can have a sustainable recovery before they have at least begun to move along this path.

The Effectiveness of Japanese Stimulus

2009-04-12T23:06:00.001-04:00

David Bath in a question to this post asked about Japanese stimulus. “The package announced last week "mamizu" of ¥1.1 trillion, looks to lift the forecast for Japanese GDP growth in 2009 by around 1.1-1.3%. That is, it will improve growth from -3% in 2009 from the -4.3% expected before the package was announced.”

My response: I saw the monumental change in some forecasts for 2009 once the stimulus was announced. But, these must just be knee-jerk reactions to the stimulus that assume effective fiscal stimulus. I already knew I was in the minority on multipliers so these numbers don’t surprise. But, we can use these numbers to get assumed paths for GDP under effective stimulus and for my case. Then at the end of the year, once the data is in, we can compare and judge the effectiveness of this particular package.

Let’s think about what a reasonable path for Japanese GDP during 2009 might be and let’s think about when the stimulus might reasonably be expected to come online.
The stimulus is expected to be passed this month; given the slow pace of implementation with the previous two packages, I think mid-summer is an optimistic date for the actual spending to begin. So, all of the stimulus from this round must fall into the third and fourth quarters.

Pre-announcement, most analysts seemed to be thinking something like the following for Japanese GDP: -17% Q1; -4% Q2; -1% Q3; and between 0 and 2 for Q4. I am assuming from your 4.3 percent figure that you were expecting an average of positive two percent growth in both Q3 and Q4 at least if your forecast for the first half is similar to others. (All figures annual rate.)

Working from my assumption of your baseline, the stimulus would have to boost Japanese growth to 5.4 percent in both Q3 and Q4 to reach -3 percent growth in 2009. That’s a boost to GDP in the second half of 1.6 percent over the baseline. Assuming that half of the stimulus is spent this year (about 1 percent of GDP), the assumed multiplier is 1.6. This seems high to me.

But now we have a baseline of comparison. If the multiplier is large, we expect very fast GDP growth in the second half, something on the order of 5 percent. I expect that if the Japanese government does try and spend all 10 percent of the money they say they have allocated (they have not spent much yet), the second half will remain tepid. I would expect zero or negative growth in the second half. Two predictions, very different. Let’s wait and see what the numbers are later this year. Then we can do a retrospective on the effectiveness of this stimulus. We still won’t have the counterfactual but at least we have our forecasts.

Big positive growth numbers  more likely fiscal stimulus is effective
Zero or negative growth numbers  more likely that it is not

That’s the main question: Here are a few other misc. questions from his post (in italics).

Surely if there were limits to the overuse of consecutive stimuli and monetization we would be seeing it in Japan? Long term rates remain very low.

So, we don’t yet know what the limits in Japan are. While they have announced large packages, as you noted about 10% of GDP, only a tithe of this has been spent so far. And virtually nothing is showing up in real government spending. I would expect that neither the costs nor the benefits of government stimulus would happen until the money is actually spent, not just allocated.

The Bank of Japan plans to soak up a lot of the issuance that will facilitate the extension of stimulus.

This is monetizing the debt. I have no problem with the action, although I would place higher odds on effective monetary policy if they were not simply providing funds to the private sector. Japan certainly has room to print money if they desire. Yet, printing money is an inflation tax and it lowers the real value of bond holdings.

Why does Japan seem so relatively stable?

I would argue that it does not. Japan’s banks entered this recession with almost no exposure to subprime assets and very little exposure to asset-backed securities. At the start of the crisis, the bank’s were very well capitalized and indeed provided a substantial amount of capital to U.S. and European banks. Yet, at least until the rebound, Japan’s economy seems to be suffering more than any other industrial economy with the possible exception of Iceland and Ireland.

Already in the fourth quarter (after the first round of stimulus), GDP fell more than 12 percent. Most analysts expect a fall at least that bad in the first quarter.

The New Plan to Save the Auto Sector

2009-04-11T22:32:00.001-04:00

In a press release dated April 9, the Administration announced a new plan to help the auto sector. By June 1, they plan to purchase 17,600 new vehicles for the Federal fleet. The purchase has two purposes. The first is stimulus. By accelerating the purchase of the cars, the administration hopes to support the auto industry. And since the contract is for purchase of GM, Chrysler, Fords, it helps American manufacturers. The second is fuel efficiency. By trading in less efficient cars for more efficient, the government can reduce both its gasoline consumption and its carbon emissions. It’s not clear from the release how quickly this replacement would have happened in the absence of the stimulus plan. So, it is difficult to measure the full benefit of the plan.

Cars are currently selling at a pace of about 9 million per year. The federal purchase then provides a boost of about 0.4 percent in the first six months of 2009, helpful but not a solution to all of the car companies’ woes.

This, by the way, is exactly the type of stimulus spending of which I approve. The spending brings forward government demand to the period where it is most needed. The additional costs of the program are small and consist mostly of reducing the average age of the government’s fleet. And, the scale of the program, while significant, is unlikely to have a first order impact on prices. That is, the crowding out effect is likely to be quite small. If only the government could spend the full trillion in such a manner.

Of course, the plan does not stop here. The administration wants a tax credit for anybody who wants to trade in an old car in exchange for a new one. This plan would be sold as a fuel efficiency plan but would of course have the primary intent as stimulus.

What are the impacts of a broader plan?

Many countries around the world are currently implementing similar plans. The German government offered a €2,500 incentive for anybody who traded in a 9 or more year-old car for a new vehicle. I don’t have the numbers in front of me but the plan was apparently quite successful. New car sales surged in Germany. Similar programs with similar success rates were implemented in France, Italy, and, I believe, Brazil.

To pro-stimulus economists, these programs are a glowing success. The government implements a rebate program designed to boost demand and it works. We can easily measure the number of new cars sold, compare it to some baseline, and we can judge the program a success.

What we can’t measure is the costs of the program. I don’t mean the fiscal cost, that is easy. I mean the economic costs. The government has introduced a real relative price change in the economy—new cars are cheaper relative to other goods.

Real relative price movements change the allocation in the economy. People substitute towards cars. The substitute away from …

That is the problem. We don’t know what good or service households stop consuming because of the cheaper cars. It has to be something. The government may be flexible about its budget constraint the household cannot be. (The government can raise taxes or print money; the household cannot simply demand higher wages.)

The German new car sales numbers are for March. Retail sales around this time are likely to be smaller than they would otherwise have been. Once again, we have no counterfactual and so cannot estimate the crowding out effect. I suspect, though, that we will find German and French retail sales disappointing relative to Dutch, Danish, or Spanish sales.

Takeaway: The administration’s plan to purchase new energy efficient cars is a good plan. The scale is right and the plan is likely to help the car companies some. Rebates to induce sales seem ideally suited to replicating the program only on a bigger scale. These programs, however, create important distortions in the economy; distortions we cannot fully measure.

Moreover, even the hint or the rumor of these programs distorts the economy today. If you have not yet read Casey Mulligan’s blog on this topic, do so now (put the link here). The rumor causes households to postpone the purchase of cars. Because my trade in might be worth substantially more tomorrow, I will wait to purchase a car until tomorrow. Since we do not know the parameters of the program, this wait and see effect will influence a large cross section of potential new car purchasers. Just as now, many taxi drivers in Chicago are waiting to purchase their new cars because the city council might subsidize them at some point in the future.

Here the German’s beat us to the punch in terms of efficiency. Almost before German citizens knew the policy was coming, the German government passed the rebate.

How will the Japanese economy recover, whenever it actually reaches a bottom?

2009-04-09T20:12:00.006-04:00

Japanese production is at a 30-year low. The picture below looks like a data error. Modern countries do not erase 30 years worth of growth, however tepid, in four months. Really, modern countries don’t just collapse. This fall in IP is larger than any other fall on record—and I don’t just mean for Japan.

But, there was no war, no industry destroying earthquake. In four months output has fallen, Japanese industrial capacity is more or less unchanged. The factories are still there they just aren’t running. Indeed, according to the employment statistics, the factories are still more or less fully staffed. Japan could bounce back and produce at 2008 levels tomorrow.

I hope this happens. I think it is an open question as to what happens next.

Generally, when a country has a large fall in production, IP falls for a while, reaches a low point, and then starts to grow from that new jumping off point. Generally at a faster pace than before the downturn, but importantly, IP also does not return instantly to its previous level. In the data, it seems to take between one and two years for every 10 percent fall in output.

Here is a picture of Mexican IP surrounding the Peso Crisis. During the crisis, IP fell about 13 percent; 17 months after the collapse IP returned to its pre-crisis level and within 24 months had returned to its previous trend.

The 2001 crisis in Argentina is perhaps the closest comparison to Japan’s current situation. IP fell almost 20 percent from peak to trough. The economy hit bottom and grew robustly. Nonetheless, IP took 32 months to return to its pre-crisis level.

During the Great Depression in the United States, IP fell about 50 percent and took ten years to return to its pre-crisis level. A cross-section of Asian economies experienced large falls in IP during the Asian financial crisis. My brief scan of that data indicates the same rule of thumb, although they seem to have been on the faster end returning in levels in about 1 year per 10 percent fall.

Japan’s Path to Recovery

In the rosiest of scenarios, when the economy is ready to recover, Japan bounces back to its old level. Let’s say this happens this month. Then the world goes on. Japan as a country takes a 15 percent pay cut for the year, not enjoyable but livable. This could happen. As I said above, Japan has its industrial capacity intact.

But, output does not just fall. Despite Kevin Warsh’s dismissal of the current downturn as a Panic, output does not fall without cause. I continue to believe that the downturn is at its heart a manifestation of the world’s need to restructure (read this post).

Rosy scenario number two is an Asian Financial Crisis style bounce back: one year for every 10 percent fall in output. Take a look at the next picture. To bounce back in 4 years, Japan’s economy has to grow at almost three times its average growth rate of the 1980s—remember when Japan was taking over the world. That bounce back looks no more believable than the drop.

The gloomy scenario is that Japan grows at its average rate of the 1980s. In this case, it takes 11 years to recover to its pre-crisis level. This is a Great Depression scenario.

The nightmare scenario is the growth rates since 1990. In this case, Japanese output does not return to pre-crisis level until the year 2064. Today’s babies will be worrying about retirement.

Bad News for the Fed’s Plans: Mortgage Modification is not Working

2009-04-08T19:51:00.003-04:00

The Federal Reserve wants to lower long-term consumer interest rates. To this end, it has cut its policy rate to zero; engaged in any number of asset swaps with lending institutions; and, recently, begun outright purchases of GSE debt to put more direct pressure on mortgage interest rates. These policies, particularly purchases of GSE debt, seem to be working: mortgage interest rates have recently fallen to near record lows.

Of course, the Fed does not want to lower interest rates just for the sake of having lower rates. It wants to increase economic activity. And, in particular, it wants to help the housing market. The link is clear: lower mortgage interest rates raise housing affordability. Cheaper housing boosts demand. We observed a tepid increase in housing activity in February, whether a statistical fluke or a real turning point we shall see as the spring data comes in.

The average homeowner, through refinance, can take advantage of the Fed’s program and lock in for 30 years very low interest rates. A one percentage point reduction in interest rates will reduce the payments of an average homeowner about 10 percent per year. This cost reduction will push some households to buy a first home or to move up to a larger home. [Of course, this savings comes at an upfront cost of about 3 percent of their outstanding mortgage (personal survey of 10 mortgage issuers based on median house price and 80% down – prime borrowing only), raising the average debt level of the household sector.]

The Fed’s program, however, is likely to help the marginal homeowner only slightly. It seems to me that, at this moment in time, the most important marginal homeowner is the household that can no longer afford their house. When they stop making payments, their house eventually contributes to the inventory of unsold homes, lowering house prices and pushing more households to foreclosure.

It might be hoped that the lower interest rate would ease the payment burden of at risk households and reduce the number of foreclosures; however, new data from the OCC and the OTS indicate that interest rate reductions are not sufficient to substantially reduce the number of delinquent mortgages.

A reduction in mortgage interest of 1 full percentage point reduces the payment burden of households by about 10 percent. Data released in the OCC’s Mortgage Metrics report, reveal that more than 30 percent of mortgages modified such that the payment is reduced by less than 10% re-default within a few months of the modification.

Mortgage Metrics

The picture below is taken from the Mortgage Metrics report. The data are surprising: mortgage modification does not work at reducing mortgage delinquencies. After 9 months, more than 60 percent of modified mortgages are in default. And, we don’t know how many of the modified loans would have continued making payments in any event: not all delinquent loans go into foreclosure.
I think this number, by itself, goes a long ways toward understanding the low level of workouts from banks. Banks were likely aware of the low success rate and are therefore hesitant to undertake the effort needed to modify a loan. [I hope the State of Florida is paying attention. This applies directly to their new forced mediation programs.]
As striking as the relatively high level of defaults, the percentage of loans defaulting increased over the first three quarters of 2008. Over this time frame, the government, in the form of moral suasion, pushed lenders to increase the number of modifications. They responded; the number of modifications rose from 208 thousand in Q1 to 301 thousand in Q3. Apparently, however, to get this increase the lenders had to seek a broader pool of modification candidates and the deterioration of that pool is then predictable.

Perhaps one of the main problems with loan modification is that the majority of actions do not reduce the household’s mortgage payments. A whopping 32 percent of modifications leave the household with higher payments after the action. It’s clear to me why loan modification does not work if the payments don’t go down.

But what’s perhaps more surprising is that even loans with a reduction of 10 percent or more in payments still default at very high rates. After 9 months, 26 percent of loans in this category are delinquent. This number is going to worse over time because later vintage modifications do not perform well.

Here are my thoughts: I suspect, but do not know, that most of the loan modifications either reduce the interest rate faced by the household or extend the term of the mortgage. I suspect that very few of the modifications try to adjust the principal and that almost none of them ensure the household is actually above water following the modification. Households with negative equity positions are going to default in disproportionately large numbers.

By the way, for the first time in this cycle, the number of prime mortgage serious delinquencies is greater than that of any other category and the default rate on prime mortgages more than doubled between the first quarter and the fourth. I don’t think we are done with this housing cycle yet. Remember, the labor market did not take its nose dive until the fourth quarter. The serious delinquencies induced by this fall will not show up until at least the first quarter and more likely the second.

Have the Asian Economies Hit a Bottom? Yes and No and Yes and No …

2009-04-07T20:37:00.002-04:00

There has been a lot of talk of bottoms and second derivatives lately. Most of the discussion is related to March PMI data. In almost every country, PMIs have bounced off of their record low levels in January. Of course, they remain quite close to their record low levels and are (with the exception of China) continuing to point to sharp contractions. Nonetheless, if PMI data are to be believed, we might see an easing of the pace of decline.

But, I am always suspicious of survey data and, at least to me, the PMIs are subject to some of the same whims as consumer confidence: when purchasing managers feel good about the world they report higher numbers. In general their feelings are a good indication of the weather, but sometime they are not. I continue to believe that PMIs are most useful for their timeliness rather than their unfailing accuracy. I prefer, when I have a choice, to wait for data.

Trade data is my favorite: it’s timely, well-measured, and theoretically sound. Take a look at the picture below. Korean exports seem to have leveled off. The dramatic fall in January seems to have been over stated and probably had more to do with the nadir in the auto sector than anything else. I take this as a sign that Korea’s external sector is not as weak as I inferred from the January fall.
Korean imports, however, tell a different story. Imports continue to fall unabated. The pace of the decline may have slowed slightly but even these slower falls are huge by historical standards. I read this as an indication that Korea is still falling and has not hit bottom. That probably means Asia is not at a bottom; a Korean decline would be felt across the region.

We also have March trade data for Taiwan. In this case, both imports and exports are up from their January lows. These numbers give hope of stabilization but at an extremely low level: imports remain 50 percent below their August peak.

In all countries, the pace of imports gives insight on the growth rate of domestic demand. In the Asian economies, not surprisingly, imports are tilted toward production rather than consumption, at least relative to the G7. Imports consist of intermediate goods, both capital and commodities.

Of course, the timing of the change in production and the timing of the fall in imports do not have to coincide, inventories play a large role. Korean IP shown below bounced off its December lows, rising in both January and February. However, the given the import data shown above this resurgence does not seem sustainable. I expect Korean IP to continue its downward trend. With luck the downward trend will look like an ordinary recession rather than a free falling collapse.

The fall in Taiwanese IP is already consistent with the overall fall in imports. In this case, there is a possibility that production has found a floor. I will be quite interested to see the March production numbers to be released later this month.

Even if exports, imports, and production have reached a floor, the recession is not over. Production and trade have, if anything, stabilized at a very low level. The domestic economies have made little progress adjusting to this new level of activity. In particular, the level of employment is not consistent with the level of production. Barring a true recovery in production and trade (a recovery in levels not growth rates), employment will have to fall, maybe by as much as 20 percent.

This fall in employment will lead to a second round of production and trade cuts. These falls will (likely) be smaller than the ones seen to date. Consumption accounts for a relatively small percent of GDP in these economies. Nonetheless, the production and trade falls to date are so large that these second round adjustments are likely to themselves look like an ordinary recession.

Takeaway: I believe we are beyond the freefall. The falls in Asian and European production were unprecedented: The Asian falls were faster and more extreme than the decline in production observed during the Great Depression in the United States.

Central Banks and Credit Loosening Policies: Misguided Actions

2009-04-03T19:30:00.002-04:00

Today, both the Chairman and the Vice Chair of the FOMC gave speeches in which they clearly state that a credit crisis led to the downturn in real activity. The statements are not even qualified. From the Vice Chair: “A defining characteristic of the crisis has been a deepening adverse feedback loop in which financial strains have caused economic weakness, which has in turn led to credit losses and heightened financial strains, which then contribute to further economic weakness, and so on.”

I have shown previously that the key dates of the financial crisis were all pre-dated by turning points in the macro data. (Take a look at this post.) In my mind, then, the financial crisis itself was caused by a real deterioration in the real economy rather than the other way around. But central banks and central governments continue to implement policy as if there is nothing wrong with the world but a simple banking crisis. Indeed, Bernanke’s speech is full of measures by which the Fed’s programs have helped resolve the crisis.

Despite all of these actions and all of the rhetoric, no central bank in the world has been able to prove that a credit crisis even exists, let alone whether it started the downturn or resulted from the downturn. In October, Chari, Christiano, and Kehoe published this paper calling into question virtually every fact of the financial crisis. They acknowledge only that the asset-backed commercial paper market and the securitization market failed. A Boston Fed paper seeking to undo their results made very little progress. In their analysis, they show that some subclasses of loans were falling; but, subclasses are not the same as a credit crunch. Indeed, that only subclasses of assets are adversely impacted points much more to a real shock than a financial shock.

I could retrace all of the data in the above publications; but it seems to me that anyone who wants to claim that a financial crisis is of first order importance in the current downturn must first explain the following picture.
This picture shows the level of commercial credit from January 2001 through February 2009. Credit did not fall until November 2008, almost 18 months into the crisis and 11 months into the official recession. Even then, the fall is miniscule and likely has more to do with Federal Reserve lending programs than actual credit (think China). Indeed, credit continued to grow robustly until March 2008 when it turned flat.

Credit does not grow robustly during recessions. People are poor. People think they will be poor for a long time. They do not want to borrow.

For those of you still inclined to quibble, take a look at the same picture during the Great Depression. Credit should actually fall before we start calling it a credit crisis.

The auto sector has felt the impact of the recession more than perhaps any industry outside of the housing sector. New car sales slumped sharply beginning in the very tail end of the 2007. There were lots of speeches given about the lack of credit in the auto sector. The problem is that none of the usual indicators of credit tightening are apparent in the data.

Here is chart of auto loan interest rates at auto finance companies. Interest rates on new car loans did not rise in any meaningful way until the fall of 2008, 9 months after sales dropped. (There is a downward movement in rates at commercial banks.)

Other measures of tightness, such as months to maturity and loan-to-value ratios also remained flat.

Series by series, across many different classes of consumer and business credit, they all look essentially the same, except where evidence of falling prices can be seen. Spreads may have risen in this recession but interest rates did not rise.

Takeaway: There may be a credit crisis. But that crisis is not showing through to either business or consumer lending. Therefore, that crisis cannot explain the large drop in consumption, the large drop in auto sales, of the collapse of the housing market.

Policies designed to resolve the recession via credit intermediation are therefore misguided and destined for failure.

More on the Ins and Outs of Employment

2009-04-03T19:23:00.002-04:00

I posted a while back on the ins and outs of employment (here). Today, I took a look the CPS Labor Force Status Flows, a publication of the BLS. This is basically the data Shimer used to compute his employment exit and entrance statistics. These data are timely and are released with the household survey.

The picture below shows the percent of unemployed agents finding a job each month. Think of the number on the vertical axis as giving the job finding probability for an unemployed agent. Using this data in January 1990, about 1/3 of unemployed persons found jobs each month. In the height of the ensuing recession, the odds dropped to about 0.22. During the expansion of the late 1990s, the rate reached 35 percent, only to fall once again in the 2001 recession. In March, this number hit a series low of 16 percent. Not only are people losing their jobs rapidly during this downturn, exiting unemployment is getting increasing difficult.
The next picture shows the number of months an agent can expect to be out of work conditional on unemployment. For most of the series, the average unemployment spell lasted between three and four months. The series reached a high in the 1991 recession and a low just before the end of the tech boom in 2000. The series reached a new record high in March: 6.2 months.

At the moment, one out of every four unemployed persons has been out of work at least six months. This statistic is going to get worse. There are currently 13 million unemployed persons. At current rates, that means 7.5 million of these households will still be unemployed next September. How high the unemployment rate rises depends on how many more people lose their jobs and how many of these workers leave the workforce.

The Relationship with Consumption

Imagine an unemployment spell of 6.2 months. How would you pay your mortgage? How would you feed your family?

High unemployment has a direct effect on consumption because the affected households are poorer than they were. They are poorer today because they don’t have jobs and they are poorer in the future because they are likely to reenter the workforce with a lower wage. The income effect is the obvious channel.

There is, however, a second and likely larger channel through which the jobs numbers effect consumption: there is a precautionary savings motive. Households face both an increased probability of job loss, and conditional on job loss, an extraordinary spell of unemployment. In response to this dual risk, employed households save.

The decrease in consumption is not what Keynes would call animal spirits. Rather, the decline in consumption is an optimal reaction to an increase in risk. The government should not work to change this response.

The March Jobs Report: No Big Surprises, But No Recovery In Sight

2009-04-03T19:12:00.001-04:00

No surprises in the March employment report. Payroll employment fell 660,000 and revisions to previous months subtracted another 80,000 from the level of jobs. The same pattern we have seen over the past five months. Combine this data with the likely revisions from the benchmark survey (the direction is down because firms are failing faster than they are being created), and we are losing something in the ballpark of 1,000,000 jobs per month. Shocking, but no longer surprising.
Of minor interest, in percentage terms, we have finally surpassed the downturn in the early 1980s. Indeed, with the revisions, we surpassed the 1980 downturn in January, we just didn’t find out until today.

As I stare into my crystal ball, I don’t see any signs of a labor-market recovery. I could not find a subsector that showed any indication of rebounding. Further, unemployment insurance claims remain high and continuing claims are still rising. Survey data tell the same story. April does not seem to be on a better jobs track than March. And, the economy cannot expand while the labor market is contracting by 0.5 percent per month.

An Economic Turning Point? It’s not in the data yet.

2009-03-26T20:46:00.005-04:00

Earlier this month Trichet, President of the ECB, said central banks are beginning to see the signs of an economic turning point. If true, they are certainly not alone. An increasing number of economists are reading the tea leaves and calling for a bottom.

I’ll admit there has been some good news, especially in the household sector. Retail sales in January and February were both better than expected with the January data being downright good. My estimate of real retail sales puts the level of sales in February up 1 percent from December. Whatever happens going forward, two months of respite from plummeting sales and abysmal consumption is a relief.

Housing also bounced back somewhat from January’s horrific freefall. According to the FHFA house price series (formerly known as the OFHEO house price index), quality adjusted housing prices rose 1.7 percent in February. Housing starts, especially multifamily starts, rose nicely and this week we learned that new home sales rose 4.6 percent. Each of these series has measurement issues and volatility issues but I think we can safely say that February was a good month in the housing market.

Enough cheer. My (self-appointed) lot in life is to put good news in perspective.

In my view, while we may be at a bottom, there is no indication of it in the current data. The labor market has not turned around, indeed it seems to be getting steadily worse. We now have initial claims data through the 21st of March. Claims are hovering around 650,000. And worse, while initial claims have hit a plateau, continuing claims have continued to grow, they have increased almost 10 percent in the last four weeks alone. Continuing claims are now 20 percent above their previous-episode highs. That continuing claims are outpacing initial claims indicates falling matching rates: if you lose your job, it is much harder to get a new one.

Plain and simple, the household sector cannot recover with this level of job losses and the business sector cannot recover without a healthy households sector. Again we saw some upticks (from record low levels) in most business survey data. None of it pointed to expansion and none of it is sustainable.

Nonetheless, if household and employment data were all I had in hand, I might believe the bottom is near. But, in addition to bad IP numbers in the United States, global production appears to remain in freefall. IP in Europe fell at a record pace in January and the February fall in the German IFO survey indicates it is not done yet. And, of course, Asian data continues to be absolutely horrific.

Krugman posted a picture of U.S. IP in the current recession against IP in the Great Depression. No surprise, the Depression won by a landslide. I replicated his plot using Japanese IP in the current episode against U.S. IP data in great depression: Japan is almost 20 percentage points ahead.

February will be even worse in Japan. Take a look at this picture of Japanese exports. At the peak, exports accounted for 15 percent of Japanese GDP. The decline in trade is unbelievable. Macroeconomic data series simply do not look like this.
As I have said before, most of Japan’s exports go to the United States, Europe, and China. This decline is an extremely negative indicator for forward looking demand in these regions. By the way, this decline is not driven by motor vehicles—they contribute, but in nominal terms every major category of Japanese exports is down 40 percent (yoy).

In an even worse indicator for Japanese production, real imports are now falling at a comparable rate. Without inputs, factories cannot operate.

Remember the Asian Financial Crisis? It was 1997 and Asia was falling to pieces. With the data over the last few months, the Asian Financial Crisis barely even shows up as noise on charts.

Asian (and lots of other) data is trying to tell us we are not done with this recession yet. By not listening, we only forestall adjustments that need to be made.

Fiscal Stimulus and Christina Romer: Once More into the Breach

2009-03-24T21:53:00.002-04:00

The debate over the effectiveness of fiscal stimulus continues to rage. I have previously given my views on the likely efficacy of fiscal stimulus (Does the multiplier have to be one? and Another stab at fiscal neutrality): the multiplier is almost certainly less than one, is likely close to zero, and may even be negative.

Christina Romer, the head of the President’s Council of Economic Advisers, disagrees and she gave a forceful defense of fiscal stimulus in a recent speech. She has many reasons supporting her view, but basically she believes that any increase in government spending boosts output. This belief is predicated on the notion that the government can increase its demand for goods and services without any negative effect on other parts of the economy: she believes changes in government spending do not change prices, neither goods prices nor interest rates. Further, she appears to believe that the composition of government spending does not matter for this result to hold: any fiscal expenditure boosts output.

There is a broad literature on this topic. Many economists have attempted to estimate the multiplier. And, the vast majority of these studies, including several by the IMF, have found very small and often statistically insignificant effects. That is, most studies have found fiscal multipliers close to zero.

Romer is aware of this literature but attributes the empirical findings to omitted variable bias. In simple terms, fiscal policy is only implemented when the economy is expected to weaken. The results then simply fail to account for how much worse the economy would have been in the absence of the fiscal stimulus. Omitted variable bias is an endemic problem in empirical studies; I lack Romer’s faith in the sign of the bias.

Romer takes a three-pronged approach to defending her position: she highlights the magnitude of the increase in government demand, she draws inferences from her recent work with David Romer, and she uses the results of large-scale macro models. I take each of these in turn.

The Magnitude of Government Spending

Romer notes that the administrations stimulus plan is the large (unless noted, all quotes are from Romer’s March 3 speech:

It is simply the biggest, boldest countercyclical fiscal stimulus in American
history. One way to see this is to compare it with Franklin Roosevelt’s New Deal. In the biggest year of the New Deal, 1934, the fiscal expansion was about 1½% of GDP. And this expansion was followed the very next year by a cutback of almost the same size. In contrast, the act that was just passed provides fiscal stimulus of close to 3% of GDP in each of 2009 and 2010.

And her assessment of the program includes none of the financial rescue packages. With the Fed spending about $1.3 trillion (having already spent close to 2) and the Treasury implementing a new trillion dollar toxic asset program, total spending on stimulus is remarkably large, arguably closer to 25 percent of GDP rather than 3. In Romer’s view, this government spending cannot help but stimulate economic output.

Romer clearly believes that all government spending is stimulus. In her introduction, she makes the case over the timing of the bill rather than composition. I have already spoken on the content of the bill. But, here I find an inconsistency in Romer’s views. If all government spending increases output more than one-for-one, rebate checks should also have a larger multiplier.

After all, what is the difference between sending somebody a check for $1,000 and hiring them for 10 hours at $100 an hour? Both cases entail the same transfer of resources. Romer, however, differentiates these cases. In the former, case she believes the multiplier is close to 0.3 in the latter it is close to 1.5 (Bernstein and Romer 2009). Yet, while both examples entail a transfer of $1000 from the Federal government to private individuals, in the latter case, the government has reduced the total labor supply available to the private sector by 10 hours, placing upward pressure on wages. Higher wages mean less private employment.

Take this another way, Romer states “all of an increase in government purchases goes into spending, whereas only some fraction of a tax cut is spent.” This is a partial equilibrium statement. Think of the government’s budget constraint. To spend money the government must either raise current taxes or it must borrow money; either way, it must take cash from the private sector to spend (either in the form of a bond or in the form of taxes). Assume it is debt finance, the private investor (who has his own budget constraint) must either reduce consumption or reduce investment in some other project: He has less money net of his purchase of the government bond. This indicates a multiplier no greater than one.

Now think, the government uses the case to purchase something. Where does the government get the item? It must have bought the good and it must have paid a positive price (otherwise spend away, I don’t care). But this increase in demand for the particular good must raise its price (as the government has to bid away the good from some other user). This is a real change in relative prices. If the economy is subject to any frictions at all, the multiplier must be less than one.

In this case of the current situation, the price effects are likely to be much larger than in the historical record. There is never enough slack in the economy to spend 3 percent of output and not see a difference. And if the CBO’s new estimates are anything close to accurate the crowding out is going to get a lot bigger before it gets any smaller.

Large Scale Macro Models

In her speech, Romer states the “policy multipliers [derived in large scale macro models] are surely more accurate than the simple calculations Barro suggests because big macro models try to take into account other factors driving output.” The suite of modern macro models used by and monetary policy influence output: the models are hard-wired to find beneficial effects of policy. This is an assumption in modeling not a result driven by data. Forecasting models without these features perform just as well in forecasting output but find no effect of policy.

Nevertheless, even taking the models as an accurate tool, Bernstein and Romer force the models to produce even greater effects of fiscal policy. I have already written on Bernstein and Romer’s misuse of the macro models (here). In summary, they force the Fed’s interest rate to remain at zero forever. And, since relative prices are all relatively fixed in these models, they force all prices to remain unchanged forever. Since there is no budget constraint in the models (they absorb differences in income in consumption in an external sector), they have essentially assumed that the government does not displace any other source of demand.

[By the way, whenever you hear someway say they have produced results from a large-scale macro model assuming no monetary policy response, they are effectively fixing prices in the model. So, if you hear these words, use caution in interpreting the results.]

A new paper by Cogan, Cwik, Taylor, and Wieland shows this argument using the same suite of models. Cogan et al assume that the Fed’s interest rate remains zero for two years and then responds optimally via a Taylor rule. They use exactly the same models used by Bernstein and Romer. This one change pushes the fiscal multiplier down to 1 in the first quarter (prices are sticky in these models) and pushes the multiplier all the way down to 0.2 after four quarters. Imagine if they were able to let all other prices in the model adjust at the same time. I am thinking the model would be close to 0.2 in the first quarter.

The Romer and Romer Results

Christina Romer’s work with David Romer uses a narrative style to discuss the likely effectiveness of fiscal policy through the historical experience with tax cuts. (I discuss the details of this paper here) In short, I find their results just as biased (in an omitted variable sense) as other studies in the literature.

I object to two characterizations she makes in reference to this work. First, she states that doing a narrative analysis for government spending would be difficult. I refer her to this post and this post. In the first post, I discuss the rise in defense expenditures in the late 1970s in the United States and in the second I discuss Japan’s experience. Combine these with Barro’s wartime narrative and I think we have done her work for her.

Second, she says “the usual relationship between tax and spending multipliers would be maintained. That is, measured correctly, I would expect eh spending multiplier to be larger than the tax multiplier.” But, this sentence comes right after she tells us that a one percent decrease in taxes boosts GDP by 3 percent. That means she actually believes the multiplier is greater than 3. I think we should be able to measure a multiplier of that size.

Summary

Nothing I have said is proof that I am right. I do, however, feel that the preponderance of evidence is tilting sharply in my favor. The current economic cycle should once and for all settle the debate on the efficacy of fiscal policy. If the economy is indeed currently in the midst of recovery or if it recovers over the next few months, I will take that as evidence in favor of stimulus; if, on the other hand, the economy continues to fester, I will take that as contrary evidence. I do not believe we can say, as Krugman has argued, that the response is too timid.

But that is for future research.

Modern Macro Policy: Does it accelerate recoveries?

2009-03-24T20:43:00.002-04:00

David Bath in a comment to this post asked if the fiscal stimulus package is likely to accelerate the recovery. I don’t know the answer for sure. So, I did what I always do: I pulled historic data.

The United States in the 19th century was a haven of Laissez Faire economic policy. There was, most of the time, no central bank and there was no coordinated counter-cyclical fiscal policy. Event the automatic stabilizers, such as unemployment insurance, were virtually nonexistent prior to the 1930s.

I thought about comparing recoveries in the 19th century to recoveries in the 20th century. If macroeconomic policy is effective then I would expect to see faster and more robust recoveries in the 20th century. (You won’t believe me, but I really did expect to find this result.)

The picture below shows the acceleration of GDP growth following a recession. The bars represent the average growth rate over the four years after a downturn in industrial production divided by the average growth rate over the preceding four years. A number of 15 percent implies that growth was 15 percent faster in the four years after the recession. (The results do not change substantively if two or three year intervals are used instead.)
The first two bars use the Federal Reserve’s IP series aggregated to annual frequency. Over the entire series, 1921 to 2008, GDP grew 30 percent faster following a recession than in the years immediately preceding the slowdown. This number is driven by the 1930s. If the sample is shortened to examine only data from 1960 forward, the increase in growth is closer to 15 percent.

The second data series use available on the NBER website (here) and were compiled for Joseph H. Davis An Annual Index of U.S. Industrial Production, 1790-1915 Quarterly Journal of Economics (November 2004). This industrial production data set runs from 1790 to 1915. The average increase in growth was almost 20 percent over the entire sample and around 26 percent in the post Civil War sample.

I often use the NBER Total Physical Production series (here). The last column shows the results from this data set: around 15 percent.

Another way to cut the data is in the average length of recessions. The chart below shows the average duration, in years, of the each downturn in IP. There is startlingly little difference in average duration, less than 1/3 of one year over all of the different cuts at the data. Of course, this is annual data and I cannot necessarily tell the difference between a 13 month and a 23 month recession.

It’s amazing that in the era of modern macro policy recessions do not end in more robust growth than in the era before macro stabilization. Of course, this result could itself owe to successful stabilization policy. If downturns are less severe, the rebounds might be less robust. Nonetheless, the similarities across time raise questions in my mind.

Christina Romer: Tax Cuts and GDP Growth

2009-03-24T06:05:00.002-04:00

I am posting out of order. This post is part of a longer piece discussing Christina Romer’s views on the efficacy of fiscal stimulus.

One of the difficult elements of studying fiscal influences on growth is the lack of counterfactuals and exogenous variables. Large changes to government spending or to taxes almost always occur for a reason and most of the time these reasons are cyclical: the changes are made with the explicitly intent of boosting output. [The big exception to this is spending and tax changes during wars. Krugman, Romer, and other Neo Keynesians wish to exclude war spending and taxation from the current analysis. I disagree but let’s play by their rules.]

In Romer’s words (see her speech here), all studies of fiscal stimulus are plagued by omitted variable bias. Therefore, she does not find it surprising that (almost) all previous empirical studies of relationship between fiscal policy (both spending and tax) have found very small multipliers. In her view, the omitted variables are biasing the results down. Of course, since they are both omitted and unknown they could also be biasing the results up.

To bypass the problem, Romer and Romer (2008) use the narrative record to divide large tax changes into exogenous and endogenous pieces. They use the Congressional record and Presidential speeches to separate tax policy actions taken for reasons related to economic activity and those taken for non-economic reasons. They have the right idea: if we can identify exogenous tax moves, and if there are a sufficient number of them, we can identify the impact of tax changes on economic growth.

Romer and Romer find that a tax cut equivalent to 1 percent of GDP will boost output by 3 percent over ten quarters. While I believe tax cuts can stimulate growth, I do not find their results at all convincing. The tax cuts they identify as exogenous are not: the majority of their tax cuts occur in the depth of recessions. Since economies tend to bounce out of recession very quickly, their model mixes the effects of tax cuts with growth recoveries. In particular, their inclusion of a series of tax cuts in 1981 and 1982 significantly bias their results.

While this paper is an interesting academic exercise, to use these results as the justification for fiscal stimulus is wrong.

The Details

Figure 1 on page 46 in the linked paper shows their time series for exogenous tax changes. The majority of tax changes identified as exogenous are tax cuts, apparently tax hikes are less likely to be exogenous. The largest tax cut occurs in 1947 and is almost 2 percent of GDP. Then, there are clusters of tax cuts beginning in 1964 and 1981. Each of these clusters contains 4 cuts and the cuts are made over about 2 years. The final two significant cuts occur under Bush and are implemented in 2001 and 2003.

Although I applaud their attempt and they do have a good methodology, the dates they have chosen do not seem random in an economic sense. Before we turn to a general look at their dates, let’s take a look at the last two cuts: 2001 and 2003. These I know a bit more about.

Independent of the reasons given in speeches at the time of their passage, these tax cuts were proposed and enacted to counter the 2001 recession. I realize Romer and Romer divide the multitude of tax cuts over this two year period into different categories, some exogenous and some endogenous; and, it is true that the Bush administration was ideologically pro-tax cut; nonetheless, these were all implemented to boost economic output. In the absence of the recession in 2001 and the weak labor market in 2003 these tax cuts would not have occurred.

This highlights the fundamental difficulty Romer faces: the content of speeches and the congressional record, do not always reveal the full story.

Take a look at the picture below. The picture shows GDP growth over different horizons surrounding the key tax cuts in the Romer-Romer data. The first bar in each set shows average GDP growth over the 8 quarters before the tax cut. The second bar shows GDP growth for the four quarters ending at the date of implementation. The third bar is GDP growth for the 8 quarters following the cut and the last two bars show average growth for the 10 years before and after, respectively.
The first thing to notice is that with the exception of 2003 and 1971 growth in the year prior to the tax cut was slower than growth on either side of the cut. That is, whether or not there is a recession, these tax cuts only seem to occur when economic growth is relatively slow. My interpretation of this finding (especially when combined with specific knowledge of the 2001 and 2003 cuts), is that the majority of the tax cuts in the post war period are not completely exogenous in the sense Romer and Romer need them to be.

In particular, take a look at the bars labeled 1981. Reagan, like Bush, was philosophically pro-tax cuts. He would likely have tried to implement tax cuts independent of the economic situation. Whatever the counterfactual, these tax cuts were made in the midst of the (at least until now) deepest downturn of the post-war era.

The four tax cuts which occurred in 1981 likely provide much of Romer and Romer’s identification. The four tax cuts were followed by a monumental acceleration in growth: from almost -2 percent to almost plus 5 percent. Their statistical model attributes all of this acceleration to the tax cuts, not to the fact that the economy was bouncing back from a recession.

So, while the tax cuts may have helped boost growth, the model attributes to much growth to the tax cuts and not enough to the cyclical state of the economy. In other words, Romer and Romer suffer from omitted variable bias just as other studies do. To use these numbers as the basis for fiscal policy is wrong.

Housing Permits: Good news or a problem with the Seasonal Factors?

2009-03-17T21:31:00.003-04:00

February housing starts and permits bounced off their January lows. The news is positive: after 8 months of large declines, we finally get a bounce. And at least some analysts are calling for the bottom on starts. (See this post from Calculated Risk.) To me, it does not mean the end, but at least it is not bad news.

At least that’s what I thought this morning. However, this evening I delved a little deeper into the data. It seems the uptick in housing permits is an illusion caused by a larger than normal change in the seasonal adjustment factor.

Take a look at the picture below. It shows the seasonally adjusted permits data as released by the Census (the black line). This data shows a slowing and maybe even an inflection point near the end of 2008. (Even if this data were true, it would not signal the bottom – just take a look at early 2008 figures to see this.) However, the inflection and the rise in February owes entirely to this year’s seasonal factors. If instead, I use last year’s factors for December, January and February, I find a line that is still moving down (the blue extension).
Here is a picture of the seasonal factors themselves between 2005 and February 2009. Take a look at their sharply lower level (this boosts permits) and their odd shape relative to previous years. I am not a seasonal adjustment expert; but, aren’t seasonal factors supposed to be stable? Shouldn’t they adjust the monthly data in a consistent manner across time? Otherwise, aren’t they simply introducing noise into the seasonal series?

So, now I am at a loss on how to interpret the permits data. I was really thinking that the 10 percent rise in single-family permits might (just might) mean something. It was just one month but it was still better than nothing. Now, it seems we should not be too excited about the uptick.

Changes in the seasonal factors do not foretell good times ahead.

By the way, I am not sure I understand why the seasonal factors are so unstable. Is it because the downturn is throwing off the estimation procedure? If somebody has a good explanation of these factors, please post a comment or email me at secreteconomist@gmail.com.

Capacity Utilization: Bad times ahead for investment?

2009-03-17T20:29:00.005-04:00

Industrial production fell 1.5 percent in February. The fall is bad news but compared with Japan’s 10 percent fall for January released last week, the number was almost heartening. In level terms, IP is now near its lows in the 2001 recession. (Again heartening compared with Japan whose IP is at its 1984 level – anybody remember Foot Loose or Time After Time?)
More interestingly, manufacturing capacity utilization, 67.4 percent, fell solidly below its previous post-war low. This is the largest fall in utilization rates in the series. This number is consistent with the large manufacturing job losses over the past three years.

Total capacity utilization was exactly tied with its previous post-war low of 70.9 percent, achieved in December 1982. This number is boosted by both utilities and mining, neither of which has fallen to any significant degree so far in this downturn. Both series likely benefit from a decline in relative capacity. Nonetheless, I expect mining capacity utilization to fall going forward, as low metals prices is likely to close an increasing number of mines.

That capacity utilization is so low is bad news. It means that between 10 and 15 percent of our factories are idle, relative to normal, expansionary utilization rates. To many (see for example this post at Calculated Risk), the low rate of capacity utilization implies ongoing contractions in business investment. The intuition is simple: if factories are under-utilized today, why would firms invest in new capacity going forward.

And it’s true, falling capacity utilization tends to predict investment contractions. The rate, however, seems to be a good indicator of bad times today and not an independent predictor of bad times in the future.

To see this, take a look at the previous post-war low for total capacity utilization: December 1982.

Despite the record low utilization rates, at the end of 1982, 1983 was the best year, in terms of investment growth on record. This statement is true for both equipment and software spending (shown above) and for nonresidential structures.

Does this mean I am calling the end of the recession? Of course not. I am only making the simple point that low capacity utilization is not inherently linked to low investment. We are still in the midst of the downward leg of this recession. The only good news I see is the occasional lack of bad news. For example, housing starts for February bounced off their January lows. This is good news relative to the decline most people expected; however, it does not signal the end of the housing correction. Inventories are still too high (though adjusting). The economy can’t recover while initial claims for unemployment remain in the 600s.

Larry Summers and Federal Reserve Independence

2009-03-13T21:22:00.003-04:00

Warning: This post is speculative. It contains my thoughts and concerns only. It is not economic analysis.

Larry Summers, one of the President’s chief economic advisors, gave a speech at Brookings today. The speech was mostly exactly what one would expect: lots of talk about how the administration’s bail out is going to work and a few assertions that it might already be working. We could go through these but you would not find my views surprising.

Two paragraphs in the introduction to the speech, however, are of note. Here is the first:

Economic downturns historically are of two types. Most of those in post-World War II-America have been a by-product of the Federal Reserve’s efforts to control rising inflation. But an alternative source of recession comes from the spontaneous correction of financial excesses: the bursting of bubbles, de-leveraging in the financial sector, declining asset values, reduced demand, and reduced employment.

Two causes of recessions? The Fed and bubbles bursting. You do not have to be a real business cycle economist to think there might, just might, be other reasons. For example, Hamilton seems certain that at least some of the 70’s and 80’s downturns were a result of oil shocks.

Ignore the bubbles part. Why is he saying that the Fed has caused the majority of the post-war recessions? Notice, he says this without any of the usual hedge words or qualifiers. He states it categorically as if it is fact. This is not an accident. It is the written text of a speech and the written text of speeches of White House officials is vetted. Even if Summers truly believes the statement, why put it in the speech.

Before I go on, here is the second paragraph.

Our single most important priority is bringing about economic recovery and ensuring that the next economic expansion, unlike it’s predecessors, is fundamentally sound and not driven by financial excess.

“unlike it’s predecessors” The phrase jumps out. Is he saying that all previous economic expansions have been fundamentally unsound and driven by financial excess? All of them? Or does he just mean the economic expansion since say 1987 when Greenspan took over the Fed?

He is very carefully and almost in so many words saying that not only does the Fed cause recessions but the periods where the Fed has seemed to shepherd the economy along robust expansion paths, are actually the precursors to bubbles, making the Fed responsible for all recessions.

Here is what I fear. (Let me emphasize again: this is not analysis it is just my own personal thoughts on the matter.) These statements are intended to subtly begin the process of undermining the Fed. I have a hunch the administration would like a Fed with even more expansionist views on monetary policy. Perhaps one that is a bit less independent and a bit more willing to print.

I hope I am misreading the statements: I don’t think I am. I will be watching Summers’ speeches a bit more closely from now on. This is a dangerous path they are walking.

Alan Greenspan and Bad Statistics

2009-03-13T20:46:00.003-04:00

In a Wall Street Journal editorial earlier this week, Alan Greenspan categorically denied any complicity on the part of the Fed in the run-up of housing prices between 2002 and 2006.

Accelerating the path of monetary tightening that the Fed pursued in 2004-2005
could not have "prevented" the housing bubble.

While the Fed may have failed in its role as prudential regulator and may, through that channel, have contributed, I completely agree with Greenspan: the monetary policy stance of the Fed was not at fault. Greenspan attributes the run up to home mortgage rates rather than to “easy money” policies on the part of the Fed.

There are at least two broad and competing explanations of the origins of this crisis. The first is that the "easy money" policies of the Federal Reserve produced the U.S. housing bubble that is at the core of today's financial mess.

The second, and far more credible, explanation agrees that it was indeed lower interest rates that spawned the speculative euphoria. However, the interest rate that mattered was not the federal-funds rate, but the rate on long-term, fixed-rate mortgages. Between 2002 and 2005, home mortgage rates led U.S. home price change by 11 months. This correlation between home prices and mortgage rates was highly significant, and a far better indicator of rising home prices than the fed-funds rate.

At first glance, Greenspan seems to be quibbling over subtle changes in the term structure. After all, the Fed implements monetary policy through control of the shortest end of the yield curve. Indeed, over at least part of this time period in question, the Fed was actively trying to influence the long end of the curve through its monetary policy statements: recall the language such as “measured pace” in the statements.

Greenspan, however, is clearly aware of these issues and instead sites a fall in the correlation between the Fed Funds rates and mortgage interest rates as an explanation. He states

The Federal Reserve became acutely aware of the disconnect between monetary policy and mortgage rates when the latter failed to respond as expected to the Fed tightening in mid-2004. Moreover, the data show that home mortgage rates had become gradually decoupled from monetary policy even earlier -- in the wake of the emergence, beginning around the turn of this century, of a well arbitraged global market for long-term debt instruments.

U.S. mortgage rates linkage to short-term U.S. rates had been close for decades. Between 1971 and 2002, the fed-funds rate and the mortgage rate moved in lockstep. The correlation between them was a tight 0.85. Between 2002 and 2005, however, the correlation diminished to insignificance.

The picture below shows the Fed Funds rate and the 30-year mortgage rate from 1974 through February 2009. On average, the two rates do indeed move closely together—arbitrage arguments limit the differences between the two rates. The long-term average hides frequent deviations in the two series. The spread between the two rates increased sharply in 1974, 1992, and 2002.
Greenspan uses the correlation in the two rates as his proxy for the efficacy of monetary policy. He notes that the correlation between the two series dropped sharply over the three-year period between 2002 and 2005. It’s true: the correlation was abnormally low.

But, the fall in the correlation was neither unique nor was it long-lasting. The picture below shows the three-year backward-looking correlation between the Fed Funds rate and the 30-year mortgage rate. The correlation bounced immediately back to its 2002 level and the average correlation between 2004 and 2009 was only a shade below the average from 1974 to 2002. The temporary change in the correlation does not appear to have been caused by the shift in global savings patterns—else it would have remained low. The correlation was actually at its lowest point in the late 1990s.

Moreover, Greenspan is careful, throughout his editorial to refer to long-term mortgage rates. He avoids mentioning ARMs in their entirety. Over this period, the number of ARMs issued was at an historic high. And, the fall in correlation, as shown in the picture below, is not evident between ARM rates and the Fed Funds rate. The increase in the Fed Funds rate seems to have pushed up the ARM rate.

Greenspan is famous for knowing data inside and out. He knows these numbers; I don’t know why he would misuse them. And, he is misusing statistics to make a case that need not be made. The Taylor rule is descriptive not proscriptive. Only a foolish central bank would implement policy with the rule. It is a useful guide for understanding central banks and their actions, nothing more.

Separation and Hires: The key to understanding labor force dynamics.

2009-03-11T22:13:00.002-04:00

The JOTLS data (find the data here) produced by the BLS gives insight into the recent job losses. As Robert Shimer, a professor at the University of Chicago, showed some time ago, unemployment can go up either because workers become more likely to lose their jobs (the separation rate) or because unemployed workers have a more difficult time finding new jobs (the hires or matching rate). The BLS only began collecting data in late 2000, much too late for us to compare the current downturn to previous episodes. Bob Shimer, however, has computed separation and matching rates going back to 1947 (his data is here). The data is not strictly comparable but I think we can use the lessons from Shimer’s data and apply them to the current episode.

I have spent a lot of time working with his data lately. The cyclical behavior of matching and separation rates is remarkable and should provide the key to the next level of understanding in business cycle research. Matching rates, the probability of finding a job conditional on unemployment, begin to fall well before recessions begin and continue to fall well after the recession ends. Separation rates tend to rise at the beginning of recessions and tend to fall well before the end of the recession. Not surprisingly, the worst recessions in the post-war era (1958, 1982) are characterized by large changes in both rates.

In every post-war recession, the separation rate returned to more-or-less its long term average 4-to-monhts before the trough. The fall in separation rates also coincides with a rise in consumption. Apparently, consumption begins to rise once employed households no longer fear unemployment – a rational outcome. Consumption rises before unemployment falls because unemployed workers continue to have trouble finding work long after the recession ends.

As a result of this research, I am beginning to have more faith in the signals emitted by the JOLTS data. First, take a look at the picture below. The picture shows the number of hires each month in the JOLTS data from late 2000 to January 2009. Amazingly, the number of hires began to fall as early as January 2006, the same month the housing market turned South.
This is the clearest piece of data I have yet come across to indicate that the collapse of the housing market was not a random event. The decline in hire rates reduces the permanent income of households. People realize that conditional on losing their job, new work will be harder to find. Households also seem to know that this trend tends to have long cyclical properties – a decline in the series today is likely to signal a long period of increasingly lower matching rates.

Of course, I am looking for indications of turning points. I want to know when the economy is going to recover, in which case we need to look at the separation rate. The picture below is puzzling at first. It shows that the number of separations has been steadily falling since early 2007. This data alone would indicate that flows into unemployment should be falling, quite the opposite of what seems to be happening.

The problem with the data on total separations is that it does not control for the voluntary versus involuntary separation. If I quit my job today, knowing I had a new job in the bag, I would show up first as a separation then as a hire. But this type of turnover is not actually of interest. We care only about involuntary separation. To get the real picture, I subtract the number of monthly quits from the total. The resulting picture, shown below, gives a completely different view of the state of the labor market.
The level of separations in January 2009 was 28 percent higher than its 2001-07 average level. Keeping in mind that half of that time period was during bad labor markets, this statistic is quite stunning. More importantly, however, for those looking for a near-term recovery, the series was still rising in January. If the separation rate fell sharply in February, I would expect a recovery in mid-2009. It’s possible, but we don’t see any evidence of that yet. If anything, the data indicate a worsening in the separation rate: Through the first week of March, initial claims for unemployment insurance were still rising.

Casey Mulligan, another one of those Chicago economists, notes in his blog that consumer spending is falling even in the face of rising disposable income. He attributes the fall to the sharp fall in asset values. I believe in wealth effects but most estimates are actually quite small. So, while I agree with his assessment and think the change in asset prices is playing a role, I believe the decline in consumption can be more directly attributed to changes in the labor market.
Even as current income continues to rise, the high separation and low matching rates have sharply reduced permanent income for households – they are faced with greater probability of job loss and lower odds of getting a new job if they do lose their job. And, labor income is far and away the largest portion of permanent income for the vast majority of Americans.

The Employment Report: Will the bad news never end?

2009-03-06T20:22:00.009-05:00

Today’s employment report was bad, although not surprising, news. Job losses across sectors continued as they have for the past several months. The only mild surprise in the report was a drop in the number of people self reporting out of the labor force. This combined with the relatively minor losses in the household survey might give us an inkling of hope for the bottom. It’s too soon to draw any conclusion but if that continued for a couple more months I might get optimistic.

One of the key features of the employment report is the ongoing downward revisions to previous month’s reports. I took the time to download the real time data from the Philly Fed. The dashed line shows the number of job losses reported with the initial report. The sold line shows the monthly job loss as reported in the March 6th employment report. These revisions are unusual. The BLS’s methods are quite good and to get systematic revisions are unusual.

In particular, these revisions combined with the benchmark revision reported last month, have substantially lowered the level of December employment. Let me give one statistic in particular (and I only bring this statistic up because I was right). On December 11, I wrote a post predicting a loss of 933,900 jobs in December. Well, and this is completely meaningless, the difference between level of November employment as I knew it then and the level of December employment as we know it now is 938,000. Not bad for a Chicago-trained economist. Enough of that! Just keep in mind that we are still one benchmark away from knowing the job losses in the last 9 months of 2008. I suspect we will have another big round of markdowns to sort through next February.
Back to the employment report and its implications for the state of the economy. The next figure shows the level of manufacturing employment from 1939 to February 2009. In level terms, manufacturing employment is now below its post-war low (One exception, February 1946.) This is not the result of a long-term secular trend. The level of manufacturing employment rose, on average from 1939 to the late 1960. It was then reasonably stable from the 60s through 2000. (If I fit a line, I find the slightest downward trend.)

In 2000, the world changed. Between January 2000 and January 2004, the United States lost 20 percent of its manufacturing labor force and it never got it back. As of this report, we have lost almost 30 percent of our manufacturing workforce since January 2000. I hope these jobs come back but at the moment it does not look promising.

Of course, manufacturing as a share of employment has fallen steadily since the end of WWII. It is now the service sector that dominates the U.S. economy. The share of the service sector has risen steadily from around 60 percent at the end of the war to 85 percent today. This trend does not have to be bad for the United States. Many economists believe that it is the natural progression of economies: from agriculture to manufacturing to services.

In particular, the service sector has proven to be a source of stability over time. Service-sector job losses have tended to be much smaller than manufacturing losses. (We can argue about deviation from trend but I only care about outright losses at the moment.) Unfortunately, this recession has not confined itself to the manufacturing sector.

Take a look at the picture below. Job losses in the service sector are staggering. The service sector accounts for about half of the job losses to date, 2.1 million jobs in the last twelve months. In percentage terms, the twelve-month loss of jobs is 30 percent higher than the next biggest loss (recorded in 1949).

The cumulative job losses in this recession are stunning. That the losses are accelerating is worrisome. These job losses will push house prices (and other asset prices) down even further and push ever more households into foreclosure. More bad debt will put extra pressure on bank balance sheets. Bad bank balance sheets … It's Friday; let’s just call it a week.

Is it really a financial crisis?

2009-03-04T20:33:00.003-05:00

This particular downturn in economic activity has become known as The Financial Crisis. It’s called that because most people—economists, policy makers, and commentators alike—took very little notice of the slowdown in economic activity until mid-to-late summer 2007. In its Monetary Policy Report to Congress in July 2007, the Federal Reserve confidently states, “the U.S. economy appears likely to expand at a moderate pace over the second half of 2007, with growth then strengthening a bit in 2008 to a rate close to the economy’s underlying trend.” This is Central-Bank-speak for “all is well with the world.”

By August 17, 2007, merely a month later, the Fed’s tone had changed. In the press release following the August FOMC meeting, the Fed stated “Financial market conditions have deteriorated, and tighter credit conditions and increased uncertainty have the potential to restrain economic growth going forward.” The Fed was apparently blindsided by the crisis.

We can weave a similar timing sequence for the financial stress that occurred in September 2008. In the summer of 2008, the Fed, once again, felt that the economic situation was improving. The takeover of Bear Stearns was far behind them and many indicators of financial-market stress were easing. In September, the world was jolted awake by the takeover of Fannie and Freddie and the ensuing failure of Lehmen. Many policy makers attribute the rapid fourth-quarter economic deterioration to the failure of Lehman. A growing chorus (see this WSJ editorial) attributes the deterioration to the takeover of Fannie Mae and Freddie Mac.

I will restate the timing. Deterioration in the performance of U.S. subprime mortgages came to a head in August 2007, initiating the financial crisis. In September 2008, the failure of Fannie Mae, Freddie Mac, and Lehman sent a financial shock through an already fragile system causing a rapid decline in economic activity. The shock runs from the financial sector to the real sector and maybe back again.

With at least the perspective of hindsight, however, the timing and location of changes in economic activity belie the financial crisis story.

Housing led this crisis so let’s look at the housing market first. The picture below shows housing permits for the United States. The series shows a clear peak in January 2006. This decline is too early to have been caused by the financial crisis. At that time, most people were still unclear on the difference between subprime and prime mortgages and subprime originations were proceeding apace. The default rate on both classes of loans was low and stable.

Housing markets do not simply turn. They respond to the economic environment. Residential investment strongly leads the cycle. That permits turned south so early indicates that some shift in the economy was already apparent to the population if not to economists.

Further evidence of the shift in the economic environment can be gleaned from the yield curve. January 2006 is also the month that the yield curve inverted. Inversions of the yield curve, on both empirical and theoretical lines, predict recessions.

Of course, the Federal Reserve, and most forecasters, did not believe that either the decline in permits or the inversion of the yield curve were a cause of concern. Federal Reserve staff at the time published papers using econometric models to dismiss inversions of the yield curve as a statistical anomaly rather than a predictor of recessions. (See this paper by Jonathan Wright.) This was a mistake. The theory is quite clear and I trust theory before econometric models.

The Fed also remained convinced that housing only interacted with the rest of the economy through house prices, and house prices were still rising. They still seem to not understand the concept that housing is a long-lived durable good. As such, the behavior of housing investment should give a very accurate read on household’s views on near-term growth prospects.

Of course, we still don’t know what the underlying shock that hit the households sector, and thereby moving the bond and housing markets. I can speculate, however.

The following picture shows the level of hourly real wages between 2000 and 2008. Real wages began to fall in early 2004. Perhaps households in 2004 and 2005 continued to believe the forecasts of economists, that the economy would grow at a robust pace for the foreseeable future. And, perhaps these households believed they would share in this prosperity. Then the households likely borrowed assuming they would be able to afford their payment stream with their rising wages. When rising energy prices made this assumption wrong, the most fragile, the subprime household, began to default. This would be an oil shock. I don’t know. I am just speculating.

I find this convincing but there is more. The story of the financial crisis has the shock running from the United States to Europe beginning in the summer of 2007. However, as is shown in the picture below, housing permits in the United States and in the European Union turned South at exactly the same time, January 2006. Remember, this is way too early for the financial shock to have been transmitting U.S. subprime problems across the Atlantic.

What’s more the coincidental timing of the downturn does not just rest with housing; it extends to other important macro series as well.

Real retail sales, shown in the picture below, also turned soft with similar timing in both economies. In both economies, real retail sales were both growing solidly when they hit a wall in June 2007. At that time, both series turned flat and did not grow on average for the next year. June 2007 predates the initial wave of financial turmoil. In May 2008, U.S. retail sales turned sour, while EU sales stayed flat. I don’t know why but perhaps the foreclosure crisis in the United States or perhaps the weaker social safety net was starting to show through. In any case, May 2008 predates the increase in turmoil which began in September.

Industrial production, shown next, in the United States and the European Union has not historical moved at the same pace, although business cycles have been somewhat synchronized. In this episode, however, the downturn in IP happens at exactly the same time in the two economies. December 2007 is the peak date in both places. This data is after the initial increase in financial turmoil but the coincident timing still points to a common shock.

In August 2008, IP started to collapse in earnest. August 2008 predates Lehman. August 2008 predates Fannie and Freddie. Indeed, as you can see from the pictures in this post, IP turned South in most economies before September 2008.

It seems amazingly obvious to me that there was a real economic-based reason that all of these financial companies came under stress in September 2008. The economy was weakening and the value of their portfolios was dropping. Although none of us knew enough to price them ourselves, the magic of market was at work and collectively we knew. (By the way, that’s how it’s supposed to work. I am supposed to be able to infer the state of the economy from asset prices using the market as an information aggregator.)

Why has the Fed with all of its resources come to a different conclusion? In September 2008, nobody knew that IP was collapsing. At the time, the latest “real” data on the economy was for July and everything looked fine. The Fed (and many, many others) jumped to the conclusion that the financial turmoil was occurring independent of any disruption in the macro economy: a classic Panic. The low market value of the assets on the institutions balance sheets must reflect fire-sale prices rather than economic fundamentals.

I don’t know the true nature of the original shock. But I do know that that original shock does not appear to have been a financial shock. The timing and global coordination of the downturn do not support a financial shock alone.

We are still debating the cause of the Great Depression, and however this episode turns out, we will be debating its causes for a long time as well.

The Unemployment Rate: A Poor Economic Indicator

2009-03-04T19:38:00.002-05:00

People seem curious about the unemployment rate. Among the most common questions I am asked is “How high do you think the unemployment rate will get?” Most askers seem obsessed with the 10 percent barrier.

They are asking the wrong question.

The unemployment rate is calculated from the household survey. It is the number of unemployed persons divided by the labor force. Simple enough and an easy standby measure.

The problem is, of course, in the definition of an unemployed person. To be unemployed, one must be out of work and actively seeking employment. If you are not actively seeking employment, then you are out of the labor force. The problem is that many people give up looking when there are no jobs to be had (a logical course of action, but statistically problematic).
Think of a mill town. In a typical mill town, most of the employment is related to the operation of the mill. The mill workers are directly employed, but even the grocer is simply there to provide groceries to mill workers. When the mill shuts down, there are no jobs. So what answer does a household from this mill town give, if surveyed by the BLS. Most likely, they respond that they are not in the labor force. They would work if the mill were open but it is not.

This is a wide-spread and typical example. Although not every discouraged is from a mill town, the principle is widespread. When the unemployment rises, this rise is likely associated with a large number of discouraged workers. Hence, the increase in the unemployment rate is muted. The more people out of work; the greater the effect.

Take a look at the picture below. The bottom line is the unemployment rate as measured by the BLS. In the top line, I have added to the pool of unemployed persons the change, over the previous two years, of the persons out of the labor force. This swath is too broad, but without delving into microdata I cannot do systematically better.

Notice, the Adjusted Unemployment Rate (AUR) is not always above the Unemployment Rate (UR). In 1997, when people were reentering the workforce to join the tech boom, the two rates coincided. Then in the 2000 recession there is a large divergence as discouraged workers leave the workforce.

Notice, these workers did not permanently leave the workforce. As soon as labor market conditions improved in 2004, they rejoined the workforce en masse (this can be seen by the narrowing gap between the two lines). And, by the summer of 2006, the two rates almost coincided once again. These workers should have been included in the unemployment rate. (By the way, I have long suspected, but never examined, that one of the reasons European unemployment rates are higher than in the U.S. is a differential treatment of out of the labor force individuals.)
Using, the adjusted unemployment rate, we are already approaching 10 percent, a two percentage point gap over the unadjusted rate. When the economy reaches 10 percent and above in terms of the reported unemployment rate, the gap may widen sharply as it did during the 1970 recession or the gap may narrow (as UR rises faster) as it did in 1974. It is a horse race between a falling workforce (driven by discourage workers) and the unemployed. Either could win the race. The UR could move dramatically (1974) or level out at a low level (1970, 2001).

Do not focus on the unemployment rate. If you want to know the state of the labor market, either look at initial claims for unemployment insurance or the total number of jobs lost. Don’t look for a short cut summary statistic.

GDII or Just Another Recession?

2009-03-02T14:41:00.007-05:00

Do you remember when WWI was called the Great War or the War to End All Wars? Probably not unless you were of age before 1940, which makes you 90 something now, an unlikely demographic to be browsing the web. I know the terms thanks to Colonel Potter and his fond memories of the even-then outdated cavalry charge.

Most macroeconomists believe that the Great Depression was the depression to end all depressions. We learned a number of policy lessons during the Great Depression. With these lessons in hand and our ever expanding understanding of the global economy, depressions were a thing of the past. Indeed, a large group of economists began to believe that severe recessions themselves were a thing of the past. They believed that monetary policy had become so sophisticated in managing expectations that the normal cyclical swings in economic activity could be almost completely avoided. In papers published a recently as January (this paper also contains a good overview of the moderation literature), economists continued this debate.

I don’t know all of the answers, but I do know that if this recession ended today, if the global economy returned to 3½ percent growth rates, the certitude over the efficacy of policy should be over. With Canadian GDP data now in hand (-3.4 percent), the fall in fourth-quarter global GDP was a post-war record. This recession happened under the watch of the new and improved macroeconomic policy.

And as I have said before, there is a growing risk that we are now entering a period of global depression. That depression is imminent is not, of course, certain: We may escape with a deep and prolonged recession.

But, depressions are not that uncommon. In four episodes in the latter half of the 19th century, manufacturing output in the United States fell more than 20 percent. By my definition, four depressions. In the first half of the 20th century, manufacturing output met this definition 6 times, four if we don’t count 1938 as separate from 1930-32 and if we don’t include the draw down following WWII. Between 1950 and 2008, there were exactly zero episodes. In their broadest interpretation, those papers I referenced above on the Great Moderation are really discussing this phenomenon, although they believe they are discussing the post-1980 world.

Why do we believe depressions cannot happen any longer? Because they haven’t happened lately? Because economic policy can solve all of our problems? Because the world is different?

A lot of my concerns have been driven by the collapse of the Asian economies. Across Asia, industrial production has fallen more than 30 percent. The decline in production has been mirrored by falling trade volumes and is now showing through to domestic consumption. In most countries, production has now fallen farther than it did during the Asian Financial Crisis. But Asia is not falling alone. Every major economy (with the previously discussed exception of China), is experiencing a major fall in manufacturing IP. Countries as diverse as Brazil, Poland, and South Africa are all contracting.

Take a look at the following pictures:

Industrial production in the United States reached a peak in December 2007. In August 2008, the series began its nosedive, six months of not just falls but historically large falls. For comparison, look at the fall in IP during the 2000 recession. That recession was a manufacturing recession, recall that 20 percent of manufacturing employment disappeared forever at that time.

But the fall in production in the United States is small relative to the declines currently occurring in Asia. In Taiwan, production peaked in April 2008 and has since fallen 38 percent. Taiwan’s economy is completely dependent on its manufacturing sector.

Production in Europe has fallen roughly the same as production in the United States, down just over 10 percent from the peak. The decline is not confined to countries like Ireland or Spain that had experienced fast growth in recent years, both Germany and France are seeing dropping production levels.

Of course, with Europe in recession, Eastern Europe could not be expected to thrive. But many people had high hopes that Poland with its relatively good external balance might do well.

Likewise, the downturn in Brazil is sudden and startling. Brazil is a relatively closed economy. For a developing country, its growth was supported to a large extent by an expansion of domestic demand rather than by exports. Brazil’s IP peaked in September and has since fallen 20 percent.

Even South Africa has not escaped the downturn. South African IP peaked in June 2008 and has since fallen 14 percent.

The falls in production look like nothing less than a global collapse in manufacturing. Of course, manufacturing has become a smaller and smaller share of the economy over the past 30 years. So, a decline in manufacturing does not have to correspond to a decline in overall output. Wait! Yes it does, at least when the declines are this large.

Let’s think about the other sectors of the economy that might support growth. The services sector, in the advanced economies, is the first candidate. But which parts of services can grow. The financial sector is in disarray. Retail and wholesale services are about distributing manufacturing goods. Transportation services are primarily (thinking shipping and railroads) designed to move goods and manufacturing inputs, especially coal and oil for energy hungry manufacturing plants. Agriculture production should continue to grow, people still need to eat. But, it could also shrink as people move to cheaper sources of food. I can’t think of any combination of sectors that can support growth in the absence of a manufacturing sector.

We shall see. Nobody thought there would be another world war after the Great War was over.

My Advice to Governments: Give up on fiscal stimulus. The stimulus is unlikely to help and will place country balance sheets in an untenable position if the economy continues to deteriorate. Instead, governments should look to the solvency of their social support programs.

For example, in the United States foreclosure is already a significant problem. House prices are likely to fall a lot further as the economy deteriorates over the next year. Low house prices combined with high unemployment will induce a large number of additional foreclosures. At some point, these foreclosures will cause a rise in the number of homeless families. The time to plan for this contingency is now, not when it actually happens.

Japanese Trade: These are depression-ready numbers

2009-02-25T19:27:00.001-05:00

Japanese trade data came out today. The results were stunning. With an almost 16 percent fall in January, Japanese exports have fallen 32 percent in the last three months. This is not an annualized number. Macro data series in industrialized countries (or anywhere else for that matter) simply don’t move like that.

Any optimism I may have had on the global outlook has now fled. Japan exports goods and services primarily to the United States, Europe, and China. Since Japanese exports are foreign imports, this also means that domestic demand in those economies is falling.

The fourth quarter was monumentally bad across the globe. Asia was by far the hardest hit but most economies experienced a decline in GDP and the declines were all bigger (or at least comparable to) their largest declines in the post-war era.

Worse, the deterioration in activity accelerated toward the end of the quarter. The acceleration was most evident in trade and industrial production. Countries as diverse as Brazil, Taiwan, and Germany now have annual falls in IP many times larger than the largest previous downturn on record.

The decline in January Japanese trade means the collapse continued beyond the fourth quarter. That the decline in trade was the biggest monthly fall yet indicates that we have not yet reached terminal velocity. The story is further supported by January capacity utilization for the United States, 68 percent a series low.

To add to this gloom, we have February survey data for the United States and Europe. All of these surveys, after a brief pause in December and January, resumed their downward trajectory. The range of surveys at their record lows

Are we already in a Depression? I said we wouldn’t get close. Then I said, we would probably see one from where we ended up. Now, there is a chance, just a small chance, that we are already there. I don’t know.

Here is why I am worried. There are large portions of output we don’t measure well. And, it seems to me that a lot of the data that has been supporting growth is in these areas. For example, if you look at a close breakout of U.S. trade, goods trade (measured very well) is falling rapidly. Services trade (I don’t even know why we think we can measure this) is holding up very well.

The way forward: Chairman Bernanke testified before Congress this week. Providing the stimulus measures are effective, he expects the economy to turn around in 2009 or at the latest in 2010. You know my views on fiscal stimulus. I believe the policy actions which will be taken across the globe over the next several months are much more likely to push the global economy further into recession than they are to help. (I know I am in the minority.)

There is only one way out of this quagmire and that is forward. It is a painful path: There are no easy answers.

The world’s industrial structure is oriented towards the U.S. (and to a lesser extent the European) consumer. We supported that structure with ever increasing levels of debt. The increase in debt was not supportable. Maybe it would have worked out if productivity growth had stayed in the threes, maybe not.

NorthGG would say we need a large fall in prices to clear these markets. He is right; that would have been an easier path, but it’s not going to happen. Instead, we are going to have to suffer through a long period of adjustment. The adjustment period will last until we have worked off our excesses.

The adjustment won’t be all bad. There will be times when the economy grows well. In the Great Depression 1935 was a pretty good year. In Japan’s lost decade, 1995 and 1996 were pretty good years. I don’t know how long the adjustment will take. Right now I think it is 50/50 between two years and ten. I don’t think there is room for a middle ground.

The Fiscal Multiplier: Another Stab at Fiscal Neutrality

2009-02-24T20:59:00.003-05:00

Apparently, using Japan as an example for the study of fiscal multipliers carries baggage. There seems to be a thousand reasons why Japan in the 1990s was not a good laboratory for fiscal experiments. I am sympathetic: Fiscal stimulus tends to occur at economic turning points and causality is difficult to determine.

I tried to think examples of large changes in fiscal expenditures that were not intended as stimulus (and where quotas were not imposed). The best example I could think of was the rapid increase in government spending associated with the ramp up in cold-war military spending in the early 1980s.

Of course, 1980 is also convenient because it is home to the largest post-war decline in output in the United States. I think once again the timing of changes in fiscal spending versus the timing in changes of GDP is informative.

Take a look at the picture immediately below. The picture shows real GDP for the United States and real federal expenditures on government consumption and government investment. I have placed a black vertical bar at what I believe is the break point in Federal spending. Both series are indexed to 100 at this point, the fourth quarter of 1979.

Importantly, this spending is not down (at least directly) for economic reasons. All of the 1979 and almost all of the 1980 increase in government spending is defense spending. [As an interesting aside, this ramp up is associated with Reagan. But, Reagan was not elected until November 1980 and did not take office until January 1981: the beginning of the buildup predates him.]
Just as we saw in the Japanese case, GDP did not fall until after the increase in spending. The fall in GDP could not have caused the increase in G. And, G did not cause GDP to stagnate forever, eventually, around the first quarter of 1983, the economy began to grow. Perhaps the economy adjust to the faster growth rate of G or perhaps the relative growth rate of G fell.

In the next picture, I show the same picture on the same scale except that I now scale government expenditures by aggregate GDP. The red line then depicts the ratio G/GDP. In this space, the story is easier to tell. Government spending, particularly defense spending, was decreasing in relative terms from the wind-down following the end of our involvement in Vietnam. In 1979 as our embassy was overrun in Tehran and as Soviet tanks rolled through Afghanistan, defense spending increased sharply and continued to grow relative to the rest of the economy through the end of 1982 where it leveled out. In 1984, defense spending fell and G began once again to grow at the same pace as overall GDP.

I have placed a dashed black vertical line at the leveling off point for government spending. I find it remarkable that the two series, GDP and G, turn in the exact same quarter. Again, G is not responding to the upturn in growth, the turning point is too quick, the timing too tight for fiscal policy.

A multiplier calculated over this interval would either be zero or negative depending on the exact start and end points. That is, government spending increased and private demand decreased by an exact equal and opposite amount. Remember, there were no quotas; this is simply a result of private agents responding to changes in real relative prices. (We will deal with Volcker below.)

I find this example completely convincing. I have a feeling it will not move pro-fiscal policy advocates. What we need to compute the multiplier is the counterfactual: what would private demand have looked like in the absence of fiscal spending. Again, if you simply know the multiplier is 1, then you can compute the path of private demand in the absence of the expansion of government spending.

Fortunately in this case, we do have a counterfactual: our neighbor to the North. Canada is a very similar country in terms of manufacturing structure and customs to the United States. It is not a perfect counterfactual; Canada is rich in commodities and during this period there were large swings in commodity prices. Nonetheless, we press on.

This picture below adds real Canadian GDP also indexed to the fourth quarter of 1979. Before we look at the time between the vertical black bars, look how similarly the growth rates of GDP in the two countries behaves outside of this interval. On this scale, I have trouble seeing any systematic differences.

Now, look at the relative behavior during the ramp up in defense spending in the United States. Canadian GDP continues to grow and grow robustly until the third quarter of 1981. The picture below shows the ration of Canadian to U.S. GDP. The gap between U.S. and Canadian GDP reaches 5.3 percent in the second quarter of 1981. I cannot think of a better counterfactual.

There is one final issue to deal with during this episode. Most economists know that the 1980 recessions were caused by monetary policy: the Volcker Stabilization. In order to bring inflation under control in the early 1980s Volcker cranked real interest rates up to nearly 10 percent. The increase in real interest rates pushed inflation down at the expense of economic growth.

Monetary policy may have caused the increase in interest rates. I believe it was the time path of government spending itself. If we pretend the United States is a closed economy, we can use the intertemporal Euler equation to generate a path of interest rates consistent with GDP and government spending. In particular, 1 = (1+r) β E[U’(Ct+1) / U’(Ct)] and in the closed economy (ignoring investment for simplicity only) Ct = Yt - Gt. In the tradition of macro finance, we can plug in observed values of Y and G and produce a series for r.

The following picture gives the results. I assume utility is CRRA with modest risk aversion. (In a standard utility function σ=1.5, a very low value for macro.) I use the three year forward growth rate of GDP.

Notice how well the model-based interest rate matches the real interest rate, over this time period. In particular, the timing of the large increase in interest rates in late 1979 and early 1980 is nearly identical. The model accounts for more than 60 percent of the Volker tightening. And it does so using only the simplest form of the intertemporal Euler equation.

This result is not consistent with the monetary policy story. In the Volker story, interest rates rise today and GDP falls in the future. What the picture is telling us is that during the Volker Stabilization, GDP was low today and rose in the future.

China: A Gloomy Outlook

2009-02-23T22:55:00.003-05:00

I have written several times now on the likely adjustment of consumption in the United States and to a lesser extent Europe. The United States has supported its unsustainable consumption growth through ever increasing imports. And, China has been a willing provided of both the goods and the financing. As a result, China has experienced an era of amazingly high economic growth.

This era has come to an abrupt halt. Or, more accurately, this era came to an abrupt halt in the summer of 2008.

There are many indicators of Chinese malaise. The picture below, however, is all I need to tell me China is in big trouble. Since 2000, China has emerged as an export-import economy. China imports primarily commodities and unfinished goods, adds value through processing, and then exports the finished goods abroad.

Nominal exports have fallen 18 percent over the last twelve months, incorporating about a 10 percent decline in prices and 7.5 percent in volume. The fall in nominal exports is a decline in income for China. The fall in volume shows the decline in units processed. Nominal imports have fallen 43 percent. The bulk of this decline reflects a decline in prices but 12.5 percent is a decline in volume.

The fall in exports reflects the global fall in demand. This is bad for the Chinese economy but is not unexpected. Whatever happens in China, we expect their external demand to fall. To me, the fall in imports is far more important as an overall indicator of the health of the Chinese economy.

As an axiom in economics, if a fall in price corresponds with an increase in volume, it’s a supply shock. If a fall in price corresponds with a decrease in volume, it’s a demand shock. China’s demand has fallen. And, since China’s imports are almost all intermediate inputs, this means manufacturing output in China is falling. No country, let alone a country with complete reliance on the external sector, could withstand such a large fall in exports. (Can anybody find a country that experienced such a large decline without a contraction? Email me at secreteconomist ‘at’ gmail.com.)

So far, domestic Chinese statistics have held up better than the picture implied by exports and imports. Both IP and GDP have slowed, but do not indicate contraction. Taking a stab at converting the year-on-year growth into quarterly changes, both series grew in the low single digits at the end of the year. Retail sales growth continues apace.

There may be a timing issue. The contribution from net exports may be sufficient to offset the decline in production; however, this cannot last. Alternatively, there may be forthcoming revisions to the GDP and IP data that will give a clearer picture of the economy. (I am not saying they are miss-stating their statistics. National income accounting is hard and many countries, including the United States, have large revisions to the data.)

In any event going forward, the Chinese economy will follow the path of its neighbors. I know that I am in the minority. Every China analyst seems bullish on China. They see signs of an imminent recovery around every corner. They are wrong. China will contract and cannot recover while the rest of the world remains in freefall.

China may, in the long run, reorient its economy and grow without external demand, but we are a long way from the long run.

Fiscal Stimulus: Does the Multiplier Really Have to be 1?

2009-02-23T17:52:00.004-05:00

The “stimulus” plan has now passed. So, this discussion is purely academic. We shall see what if any effect the legislation has. But, I heard over and over again during the debate that “of course, the multiplier on government spending must be at least one”. This statement is based on a false assumption.

A multiplier of one means that an increase in government spending increases output dollar for dollar. That is, the government can increase its spending without any change in consumption, investment, or net exports. Take a look through the lens of the national income identity:

Y = C + I + G + Ex - Im

Y is output, C is private consumption, I is private investment, G is government consumption and investment, Ex is exports, and Im is imports. A multiplier of one means an increase in G leads to an equal increase in Y. That is,

↑Y = C + I + ↑G + Ex – Im.

Taking the identity seriously, how exactly did G increase? G is real government consumption and investment; it does not include transfer payments. If the government increased G, it must have increased its purchases of inputs: labor, capital, and materials. Where did the government purchase these resources? The answer to this question determines the amount of stimulus embedded in government spending.

The idea behind stimulative Keynesian government spending is that there are idle resources during recessions and the government (following a different objective function than the private sector) is best positioned to use these resources. I actually have no problem with the underlying thought experiment: high unemployment and rising inventories certainly implies underutilized resources. And, the government certainly has the capability of quickly hiring workers and purchasing inventories.

However, while these resources may have been idle, they likely still had positive prices. If they had positive prices before the government spending program, then the increase in demand from the increase in government expenditures must have pushed the price up: the government had to outbid the current holder of the resource. This rise in price, no matter how small, reduces private-sector demand.

It is easiest to think of the government hiring labor from the pool of unemployed workers. The existence of unemployment (let’s leave aside temporary frictions) implies that at current wages firms are unwilling to hire all available workers. Equivalently, at current wages workers, in aggregate, are unwilling to supply more labor. If the government steps in with a jobs program, wages increase. [Take this as an axiom, where the wage is actually the shadow price of labor. I don’t want to get into a discussion on LaGrange multipliers.] With higher wages, some marginal firm in the economy will reduce its demand for labor. This reduction private-sector employment prevents the multiplier from being automatically one: the increase in government spending resulted in a decrease in private employment. We can do the same thought experiment for any other input.

Of course, the multiplier could still be one; I have only shown that it is not automatic or obvious. The bar for large multipliers is quite high. For the multiplier to be one or higher, the government spending must increase output itself and somehow spur either private investment or private consumption. In the spirit of Keynes, the government spending must reignite otherwise dampened Animal Spirits.

The question is then an empirical question. Last month, Robert Barro examined the case of the United States during WWII. He found a multiplier on government spending of 0.8. Krugman inaptly points out that WWII does not count because of the consumption quotas. To me the existence of the quotas are the proof of Barro’s point.

Why did the government need quotas during the war? Apparently, the government believed that its acquisition of private resources would drive up prices. For either budgetary or social reasons, the government found these price increases unpalatable and instead imposed quotas. With the quotas in place, they effectively created a supply of goods with zero price (this is where we need the LaGrange multipliers). By my grandmother’s account, these quotas were binding. At existing prices, people wanted to consume more. In the absence of the quotas, the government would have had to consume less.

But, WWII is Barro’s example. For me, I like Japan’s experience in the 1990s.

The Case of Japan: In the early 1990s, asset prices in Japan collapsed. Most famously the collapse was in equity prices but bond prices also fell sharply as companies stopped making payments. In many ways, that crisis is similar to that faced today. In particular, the government was forward looking and wanted to implement the stimulus early, to get ahead of the curve.

The following picture shows private demand, government demand, and in light bars four-quarter GDP growth for Japan between 1989 and 2001.
In 1991, Japan enacted a substantial stimulus plan. By the time the plan ended in 1994, the stimulus amounted to about 3.5 percent of Japanese GDP. The first thing to notice is that neither in terms of private demand nor in terms of GDP growth was Japan’s economy faltering at the time the stimulus was put in place. In the first quarter of the stimulus, marked by the first vertical line, four-quarter GDP growth remained above four percent. Private demand growth was robust.

Therefore, while the government may have simply been forecasting the drop in GDP two full years later, it does not seem we can make the case that the economy was contemporaneously weak. What is striking is that in the same quarter in which government spending began to increase, private demand stagnated.

The government could not have been reacting to fall in private demand for two reasons: First, government spending takes a long time to implement. No government in the world is that fast. Second, and much more importantly, the government did not know at the time. Demand data is released with a considerable lag. We do not learn current quarter GDP growth until about 6 weeks after the end of a quarter.

Further, private demand growth was positive both before and after the stimulus. (The second bump in 1996 is the government’s spending response to a surge in revenue and is not an announced stimulus.) Taking the data series as given, I find a multiplier on government spending of 0.38, positive but well below one. I will go one step farther. If I take as the counterfactual rising private demand, as opposed to flat in the first experiment. If for example, I use the slowest annual growth rate of private demand two years on either side of the stimulus, the multiplier becomes a large negative number.

Of course, many economists believe that government spending in this period is all that kept Japan going. They take private demand as given and calculate the path of GDP that would have occurred had the government spending not been in place. Some go farther, comfortable in their knowledge that the multiplier is greater than one, they compute quite abysmal paths for GDP in the absence of government spending.

We do not have the counterfactual. I cannot directly falsify their statements. The picture above is just a picture. In the end, the debate comes down to one of philosophy. And, as with every adherent to his chosen philosophy, I believe my views are the correct.

Government spending crowds out private demand.

Krugman’s Excellent Column: Who’ll Stop the Pain

2009-02-21T17:35:00.004-05:00

As regular readers of this blog know, I often disagree with Krugman. He believes a raft of things about fiscal and monetary policy that I simply don’t. I still think he is one of the best economists of our century and yesterday he proved once again that he understands the world quite well, maybe better than anybody else.

Take a look at this column. You have to read the column yourself to get the full force of his ideas. Basically, he manages in a little over 800 words to summarize both the depth of the current downturn and the seeds of the eventual recovery. Spoiler Alert: It’s not fiscal policy.

Go read the column. Then come back and read my thoughts below.

Since, I trust, you have already read his column, I am going to take Krugman’s points somewhat out of order and rearranged. All of the words belong to him.

To appreciate the problem, you need to know that this isn’t your father’s recession. It’s your grandfather’s, or maybe even (as I’ll explain) your great-great-grandfather’s. Your father’s recession was something like the severe downturn of 1981-1982. Your grandfather’s recession, on the other hand, was something like the Great Depression, which happened in spite of the Fed’s efforts, not because of them. The closest 19th-century parallel [your great-grandfather’s recession] I can find to the current slump is the recession that followed the Panic of 1873. That recession did eventually end without any government intervention, but it lasted more than five years, and another prolonged recession followed just three years later.

This certainly puts the current downturn into perspective. Like me, Krugman believes that the current recession is much worse than 1980 but not nearly as bad as the Great Depression. He picks the recession of 1873 as the appropriate historical comparison.

I am a fan of macro data but I don’t quite have a good feel for 19th century data. So, I went to the NBER and pulled their Index of American Business Activity (you can find it here). In my mind, this series is an excellent proxy for industrial production. The series is monthly and runs from 1855 through 1970 and, over the years where it overlaps with the Fed’s industrial production series, the correlation in the 12-month growth rate of the two series is 0.97.

Plotting the whole series is too noisy. Instead, I plot the cumulative loss in output during every major downturn between 1855 and January 2009. I define a major downturn as any period in which the 12-month growth rate of IP is negative for a sustained period. Cumulative output loss is defined as the trough (the lowest point locally in the series) over the highest level achieved in the previous two years.

The 19th century was not a fun time. There were 15 major downturns between 1855 and 1900 compared with only 8 between 1955 and 2000. On average, in the last half of the 19th century a major decline in output occurred once every three years: when did they find time to grow! Output fell an average of 17 percent in each of these episodes. This is half the output decline experienced in the last half of the 20th century.

There are many reasons for the higher volatility in the 19th century. First, the data is likely not as accurate as in the later period. Although, the long term averages should eliminate a lot of the noise in the data. Second, the economy was much more agriculturally oriented. Agriculture by its nature is more volatile and this volatility would automatically feed into industrial output. Third, the Civil War was a rather major event and may have had a large influence. Finally, the late 19th century was a time of rapid expansion, particularly of geographic expansion. The fits and starts of settling the West may also have had a large influence on the timing and size of downturns. Nonetheless, the mere fact that we are now comparing the current downturn to data from the 19th century is scary.

Returning to Krugman’s editorial, I plot just the four recessions he discusses: 1874, 1930, 1980, and 2009. The Great Depression occurred on an unimaginable scale, simply shocking. I think what surprises most people (as opposed to people reading economics blogs on the internet) is that the current decline in production is already much larger than the declines in the 1980 and 1982 recessions. The cumulative decline in output through January is 13 percent.

Thirteen percent is a large fall but I now believe the odds are exactly even on a cumulative fall in output in this downturn exceeding 20 percent. (Note: In production terms, this meets my definition of depression. However, this does not mean the economy as a whole would meet the definition. So far the overall decline has been moderated by a fall in imports and a relatively stable services sector.)

After thoroughly depressing us, Krugman offers us a glimmer of hope. He points out that the rate of adjustment is indeed sufficient to bring us (eventually) out of the recession. Economies tend to recover (not always – think of the middle ages – but they tend to) and ours is likely to recover as well.

What, then, will actually end the slump? The seeds of eventual recovery are already being planted. Consider housing starts, which have fallen to their lowest level in 50 years. That’s bad news for the near term. It means that spending on construction will fall even more. But it also means that the supply of houses is lagging behind population growth, which will eventually prompt a housing revival. Or consider the plunge in auto sales. Again, that’s bad news for the near term. But at current sales rates, as the finance blog Calculated Risk points out, it would take about 27 years to replace the existing stock of vehicles. Most cars will be junked long before that, either because they’ve worn out or because they’ve become obsolete, so we’re building up a pent-up demand for cars. The same story can be told for durable goods and assets throughout the economy: given time, the current slump will end itself, the way slumps did in the 19th century. But recovery may be a long time coming.

The 2001 recession was characterized by a capital overhang. The capital stock, particularly in things like fiber optics networks, was too high. Investment fell and eventually the stock fell sufficiently that investment recovered, although the recovery was tepid and investment growth never returned to its late 1990s levels. This adjustment was aided by the very high depreciation rate on high-tech capital goods.

This recession, in my mind, is characterized by an overhang of durable consumption goods. There are too many houses, cars, and flat-screen TVs. Just as investment fell in 2001, consumption has to fall to work off this overhang (see my thoughts on consumption here). The fall in building and motor vehicle production are part of this adjustment. We are closer to the bottom now than we were before the last month’s worth of data. Overhangs take a long time to work out.

We are still a long way from the bottom and once we get there, if I am right about the path of consumption going forward, the recovery will be more likely slow and gradual rather than the sharp growth acceleration one typically sees in recoveries.

Who’ll stop the pain? We will. The pain will end when we have worked off our excess durable consumption goods and repaired our balance sheets. Then we will move forward a bit more chastened a bit more cautious and a bit less ready to finance our consumption.

Homeowner Affordability and Stability Plan: Finding a way forward.

2009-02-19T20:53:00.002-05:00

I like the idea behind this plan. (You can find the fact sheet here) The Administration clearly wants to help homeowners and is taking steps to do so. The plan is creeping closer to the foreclosure mitigation plan I put forward in this post. I think the plan is net positive for the housing market and that it will help, on the margin. However, the initiative does not solve the fundamental problem that is leading to foreclosure: A large (and growing) number of homeowners are underwater. That is, the amount owed on the mortgage exceeds the market value of the house net of transaction costs.

Underwater households are by far the most at risk of foreclosure. They are at risk for two main reasons. 1. At some point as the value of the home falls, the homeowner is better off defaulting on the mortgage than continuing to make payments. Households in this category are clearly at risk for imminent default. 2. Underwater households are very sensitive to income shocks. If they lose their income for any reason (health, layoffs, wealth losses), they have to default. Had they not been underwater they would have had the preferable option of selling the house. (Preferable because they lose the house either way and with the sale they may recoup some losses and avoid a default judgment.)

Rewriting mortgages such that the payment is affordable (under the household’s current income) solves neither of these problems. A household that cannot afford their current house should be encouraged to move to a house they can afford. It’s not pleasant, it’s not popular, but it is still true. And, there are very few cases where a 1 or 2 percentage point change in the mortgage interest rate will make a true difference between foreclosure and affordability. (Think of the success rate of mortgage modification to date. See my comment here.)

Here is my comment on the plan point-by-point. (Actually, I just comment on the parts that strike me as particularly good or particularly bad.):

Neighborhoods are struggling, as each foreclosed home reduces nearby property values by as much as 9 percent.

I would really like to know the source for this statement. I believe foreclosed properties have a negative impact on house prices. However, empirical studies have difficulty identifying such large effects. Take a look at this recent study by Calomiris, Longhofer, and Miles. This paper is carefully done and is representative of the literature. They find a statistically significant but small independent effect of foreclosure.

Enabling Up to 4 to 5 Million Responsible Homeowners to Refinance

I have no problem with this provision. The Administration wants to allow homeowners who already hold loans guaranteed by Fannie and Freddie to refinance at today’s rates. They are willing to relax the 80 percent down payment rule to make this happen. The only cost to this program is a potential reduction (and a possible increase) in the profitability to the two GSEs. On paper, the institutions are taking on more risk because they now hold low down payment mortgages, but in reality the risk was already there.

This aspect of the plan should have a modest positive effect. Essentially, we are transferring $2300 (the Admin’s number) from Fannie and Freddie to each of the 4 million homeowners. For the absolutely most marginal borrower, this might make the difference between foreclosure and ongoing payments.

A Shared Effort to Reduce Monthly Payments: … the lender [is] responsible for bringing down interest rates so that the borrower’s monthly mortgage payment is
no more than 38 percent of his or her income. Next, the initiative would match further reductions in interest payments dollar-for-dollar with the lender to
bring that ratio down to 31 percent.

Again, reducing payments does not necessarily lead to a reduction in foreclosures. Plus, I suspect, without having the data in hand, that most foreclosed households have had a bigger shock to their income than can be accommodated by an adjustment in interest. Unemployment insurance is not going to cover a very big mortgage payment.

Lenders will also be able to bring down monthly payments by reducing the
principal owed on the mortgage, with Treasury sharing in the costs.

Here is the one sentence in a 2300 word document on principal reduction. The details of the implementation are critical here. Reducing principal is nothing if it is only done to help make payments affordable. Reducing principal is only effective if it moves households back above water. Again see my comment here.

"Pay for Success” Incentives to Servicers: Servicers will receive an up-front fee of $1,000 for each eligible modification … They will also receive “pay for success” fees … of up to $1,000 each year for three years.

The payment reduction part of the plan is intended to serve 4 million households. $4,000 times 4 million households is $16 billion or 21 percent of the $75 billion allocated.

Incentives to Help Borrowers Stay Current: To provide an extra incentive for borrowers to keep paying on time, the initiative will provide a monthly balance reduction payment that goes straight towards reducing the principal balance of the mortgage loan. As long as a borrower stays current on his or her loan, he or she can get up to $1,000 each year for five years.

This is just a transfer and a poorly designed transfer at that. I plan to pay my mortgage (unless my house value falls a lot). I would be glad to get $5000 for doing what I do now. But keep up with the math. This is an additional $20 billion, we are up to 48 percent of the total allocated money in just these two “throw away” lines.

Home Price Decline Reserve Payments: ... The insurance fund – to be created by the Treasury Department at a size of up to $10 billion – will be designed to discourage Lenders from opting to foreclose on mortgages that could be viable now out of fear that home prices will fall even further later on. Holders of mortgages modified under the program would be provided with an additional insurance payment on each modified loan, linked to declines in the home price index.

$10 billion dollars is not even in the right ball park to actually insure these loans.

Supporting Low Mortgage Rates By Strengthening Confidence in Fannie Mae and
Freddie Mac

Here I will simply note that the Administration is spending more than 3 times its total homeowner initiative to work a backdoor support of the housing market.

So, in the end, although I think the plan has many positive aspects. It will not help the mortgage market much.

The Financial Stability Plan: A Recipe for Failure

2009-02-11T21:40:00.002-05:00

This plan, as with TARP before it, is fundamentally flawed. From the takeover of Fannie and Freddie, to the bailout of AIG, the government has treated this crisis as one of liquidity. With a liquidity crisis all the government needs to do is essentially backstop the financial institutions. With the backstop in place, the liquidity crisis ends and the financial sector returns to normal and eventually the economy recovers.

If the crisis is not a liquidity crisis, if instead the crisis is one of fundamental solvency, government intervention does not necessarily resolve the issue. Indeed governments around the world have a poor track record of resolving solvency issues. According to a recent study by Ela Glowicka, only 40% of European companies receiving government bailouts survive 10 years. This is not the first study to come to such a conclusion.

I wonder how many of those bailouts began because the company claimed a temporary shortfall of liquidity.

What would it take for the Financial Stability Plan to be effective? The federal government has large resources available to it. These resources could be used to bailout the financial system. A sufficiently large transfer of resources from the government to the banks would ensure their viability. But these resources cannot be transferred in a manner that protects “taxpayer equity”.

If a firm is insolvent, the value of its liabilities exceeds the value of its assets. For the firm to become solvent, it must receive a transfer equal to the shortfall. Since all firms and especially financial companies need capital to operate, the transfer must be even larger: the transfer must be sufficiently large that the firm is a viable ongoing concern.

Bailing out all of the financial companies is likely not feasible. Therefore, if the government wants to make the public policy decision to bailout the financial sector, it must first decide which companies should continue and which firms should fail. There is no clear cut criterion by which to choose: nobody said public policy was easy (that’s why I do economics).

Treasury Remains on a Bad Path. Despite its size (I am still reeling over the $2 trillion initial price tag), the Financial Stability Plan does not actually bailout the banks. Rather, the plan provides only enough capital at any one time to keep the firms solvent. The plan is designed to continuously increase the government’s position in the banks as the economic situation worsens or as the banks experience greater-than-expected losses. No firm can survive waiting hand-to-mouth for the next check to arrive.

Japan’s Banking Intervention in the 1990s Should Serve as a Warning: The Japanese government’s intervention in its financial sector is an excellent example of how not to bailout the financial sector. Also as a result of over-leverage on the part of firms, households, and financial companies, Japan’s banks developed a non-performing loan problem (NPL) in early 1990s—in the current jargon, they had toxic assets on their balance sheets.

As a result, credit slowed, banks balance sheets were under pressure, and inter-bank lending became more difficult. The Japanese government also used its “full arsenal of financial tools” to deal with the problem. It guaranteed deposits, it injected capital, and assured investors that it would not let the banks fail.

None of these efforts were successful. Japanese banks did not fail, but they also were unable to operate as efficient financial intermediaries. It was not until late 2001 and early 2002 that the Japanese government finally forced the banks to remove the NPLs from their balance sheet, injecting enough capital to get the banks lending. I do not know the total size of the intervention but it was substantial. Only after this all out solution, did the Japanese economy begin to grow once again like a “normal” industrial economy.

The United States has a Bigger Problem: One of the main reasons for the current crisis is over leverage. Households borrowed too much and so did banks. Neither the households nor the banks can afford their balance sheets. Both must work to correct the excesses.

Unfortunately for financial sector, the work out in the household sector means that the financial intermediation sector needs to shrink. In principle, this adjustment could occur with every financial company simply getting smaller. That is not the way adjustment tends to happen. Typically, in these downturns, some firms must disappear. If the markets are left to their own devices, the strongest best managed (likely most risk averse) firms will survive. If the government decides, the adjustment is much more likely to be simply random.

The Jobs Report was Worse than You Think

2009-02-06T21:05:00.002-05:00

The jobs situation is bad. Whether you look at the payroll losses or the household survey, the decline in jobs is greater now than at any time since before WWII. In the household survey, the number of jobs lost over the last twelve months is double the loss at any time in its history. That’s right, double. The payroll survey is not quite that bad and is simply flirting with the worst of its post-war losses. Either way, the job losses are making 1982 or 1974 look like mild down-shifts in activity. Note as well, that the level of jobs in December was revised down by 333,000.
Also, this month’s report made it clear to me that the household survey is currently giving a much better picture of the employment situation than is the establishment survey. Usually, the establishment survey gives a better read on the monthly variation in employment; the structure of the survey gives less month-to-month volatility.

Over the past year, the household survey has behaved quite consistently. This tells me that the monthly changes in the sample are not moving the overall jobs numbers. This stability tells me that the deterioration in the jobs situation is widespread: any random sample gives the same picture.

In the fourth quarter, every major macro series took a nose dive. From housing to orders to industrial production, from exports to imports, to private consumption, to car sales, every series was falling at or above record rates. Yet somehow magically, the establishment survey stabilized. Yes, 500,000+ in monthly job losses are bad, but they did not continue to deteriorate over the quarter. The household survey took exactly the path I would have expected given the other indicators. And, the household survey is coming in (month after month) closer to my jobs model. I believe there will be major downward revisions to the establishment survey in the months to come.

What is your over/under for GDP in the first quarter?