Edward Renshaw
Professor of Economics
State University of New York at Albany
There is a possibility that the U.S. economy may experience a double dip recession as far as non farm payroll employment is concerned.
While no two recessions are exactly alike, they all seem to have the property of being triggered by inflation and/or unsustainable increases in one or more components of GDP.
Government spending is one variable that should not be ignored. There have been economic recessions associated with all of the major wars that the U.S. has participated in during the last century. The most interesting aspect to the Gulf War of 1990-91 is that the U.S. recession began in August of 1990, before our soldiers began to fight, rather than after the war was over. This would suggest that the mere threat of another Gulf War might have a negative effect on the U.S. economy in the year 2003.
The payroll employment contraction from March 2001 to April 2002 was moderated to a considerable extent by an increase in government employment amounting to almost half a million new jobs. Budget deficits have become such a problem at the state and local level, however, that government lay-offs might have a negative impact on employment during 2003.
Higher prices for imported oil, compared to last year, could also have a negative impact on consumer spending for other purposes this winter. See Essay 6.
The industrial recession which began in October 2000 was triggered in part by an unsustainable increase in spending by business enterprises for new computers and software. The improvements in labor productivity associated with this "technological revolution" has helped to engineer the weakest recovery in payroll employment for any recession in the post 1947 period and raises the possibility that the depression in employment will last for a very long time.
Two of the most interesting components of the Federal Reserve's index of industrial production are lumber (most of which is used for residential construction) and consumer automotive products. When the U.S. was not engaged in a prolonged war, as was the case in 1951-52 and 1966-67, year-to-year declines in these two variable have either led or been associated with recessions dated by NBER.
Lower interest rates have kept residential constrution unusually strong until recently. But with little or no growth in employment, that is not likely to remain the case forever.
The sale of automobiles has also been elevated by deep discounts and "zero percent financing". Without good jobs, however, most young people are not going to be able to afford new cars. Intense competition from foreign auto producers could also have a negative impact on auto production in this country.
The unanswered question is what should the federal government do to revive economic activity? One of the most efficient approachs would be for Congress to subsidize a prompt acceleration of expenditure for public works.
The emphasis at the present time appears to be more tax cuts. Tax cuts are not very efficient at moderating recessions, however, since many businesses and persons who are most impacted by a recession do not have any taxable income.
On August 4, 1981 Congress enacted the largest tax cut bill in U.S. history up to that time. The economic recession which began in that month did not end until 16 months later. One of the problems with large tax cuts, from a recessionary perspective, is that they encourage reductions in government employment and expenditure for other purposes.
The recession of 1990-91 was aided in part by a 26.2 percent reduction in real spending by the federal government for new structures during the trough year of 1991. This may have been one of the factors that enabled a little known governor from Arkansas to oust an incumbent president. See Table 1.5.
In the early post World War II period increases in the value of new public structures put in place during years containing a recessionary trough in business activity did help to moderate recessions. Since the recession of 1960-61, however, expenditure for public works has had a negative effect on economic recoveries, more often than not. In Albany, New York and many other capital cities in the USA construction budgets have been routinely robbed or short changed during recessions to hide budget deficits which are unconstitutional in most states.
During the great depression of the 1930s John Maynard Keynes suggested that the U.S was suffering "from a bad attack of economic pessimism." One of his proposed cures was an increase in expenditures for public works. "Public works even of doubtful utility may pay for themselves over and over again at a time of severe unemployment, if only from the diminished cost of relief expenditures." This was not a new idea. Philip Wicksteed, who died in 1927, had long recommended that the state should undertake public works in "slack periods when they can be executed at least expense, and will, at the same time, have a tendency to counteract a serious evil."
The multipliers that can be derived from models of the U.S. economy would suggest, in any event, that additional expenditure for public works have a first year effect that is at least twice as large as the multiplier for most tax cuts and will keep on yielding benefits for many years in the future. See Essay 8.
Crumbling infrastructure, moreover, is now perceived to be a serious problem in many parts of the U.S. An Associated Press computer analysis of Federal Highway Administration records found 29 percent of U.S. bridges to be deficient as of August 31, 2000.
An additional person employed in the construction of highways and bridges can be expected to put at least one other person to work outside the construction industry during recessions and generate enough additional tax revenue within one year to cover more than half of the budgetary cost.
The Federal Reserve began to lower its funds rate rather aggressively in January 2001 with the hope of preventing another recession. The life cycle hypothesis, however, implies that persons who want to even out their consumption over an entire life time should save more when real interest rates are low and save less when they are high. This type of behavior makes it easier for one to understand why personal saving rates often increase during recessions and why monetary policy has acquired the reputation of only working after a long and variable lag. For retired persons who have deposited most of their assets in savings accounts with flexible rates, monetary easing can have a very harmful affect on consumption.
Earlier comments with regaard to the current recession can be found at the end of the following index.
The outlook for the U.S. economy is now worse than was anticipated in March 2002. BEA has now revised its accounts to show three quarterly dips in real GDP during 2001 instead of only one. There has been hardly any increase in payroll employment, so far. The most prolonged stock market crash in the post World War II period has ruined many stock options and lowered the demand for pricey new homes. With many state and local governments cutting expenditures and laying off workers to help balance their budgets there is a possibility of a "double dip" recession and the most prolonged depression in employment since the great depression of the 1930s.
There is a possibility that the NBER recession which began in March 2001, with the peaking out of payroll employment, may have ended in December 2001 or January 2002. If January marks the bottom there have only been six shorter recessions since NBER began dating peaks and troughs that go back to 1854.
The recession of 2001-02, however, may be recorded in some history books as the "mildest" since BEA began calculating real GDP on a quarterly basis starting in 1947. There has so far only been one recorded quarterly dip in real GDP since 1993. The nine preceding recessions are now characterized as having experienced at least two lower quarterly values for real GDP expressed in chained 1996 dollars.
The mildness of the current recession is related in part to a very warm winter in the north eastern U.S. The savings in heating oil in conjunction with a grudging reduction in the price of oil on the part of OPEC has enabled consumers to spend more on goods and services produced in this country.
Another factor that helped to produce a mild recession was a propensity on the part of U.S. auto producers to offer new car buyers large discounts or cheap financing rather then let their sales plunge as much as was the case during previous recessions. The "zero" financing was aided to a considerable degree by an aggressive lowering of short term interest rates by the Federal Reserve beginning in January 2001 when it became clear that the industrial recession, which began in October 2000, might not be very mild. Lower interest rates also helped to keep housing and other types of construction from tumbling as much as was the case in previous recessions. The tax cuts enacted at the Federal level may have also helped to keep consumer spending from declining.
Another factor contributing the mildness of the current recession was a lot of pent up demand for workers on the part of business enterprises that were not adversely affected by the slump in demand for nonresidential structures, equipment and software. Payroll employment did not peak out until six months after the October peak in industrial production.
While more workers have lost their jobs in this recession than was the case during the recessions of 1960-61, 1969-70 and 1980, the impact has been moderated by a greater propensity to retire older workers who were eligible for social security and other types of retirement benefits.
The destruction of the World Trade Center in September 2001 may have also had a favorable effect on employment by bolstering the demand for clean-up workers and by increasing the need for security personnel, equipment and software, and weapons of various sorts to wage an on going war on terrorism.
One would hope that the employment recession is over. It should be appreciated, however, that there have sometimes been one or two upward blips in the quarterly values for real GDP during recessions that were followed by another quarterly decline. See Table 7.2.
Many states and local governments are now experiencing financial difficulties and may decide to cut spending and employment rather than raise taxes to balance their budgets. With stock prices, long term interest rates and oil prices on the rise there is also a possibility that some consumers will decide to save more by cutting back spending or use more of their income to purchase vehicles with greater fuel efficiency that are produced in other countries.
Continued growth in consumer consumption and incredibly low personal savings rates, in any event, are the primary reasons for the mild recession so far.
With many high school and college graduates finding it very difficult to obtain good jobs, however, and with the capacity utilization rate in U.S. industry at about the lowest level in recorded history, it is not likely that business investment will recover at a very rapid rate in the near future.
On Monday, November 26, the NBER dating committee finally decided that the longest expansion in business cycle history ended with the peaking out of payroll employment during March 2001. The unanswered question is how long will the current recession last?
Since the 43 month recession from August 1929 to March 1933 the recessions dated by the National Bureau of Economic Research (NBER) have ranged in duration from only six months to sixteen months for the 1973-75 and 1981-82 recessions. See Table 1.1. If the more recent range is not exceeded the current recession should end on or before June 2002.
An analysis of some leading and coincident indicators may provide at least a little ground for greater optimism. The coincident indicator with the best track record for identifying the recessionary bottoms decided upon by the NBER dating committee is the Federal Reserves index of industrial production. In the post 1947 period this indicator has (so far) always come within one month of identifying the recessionary troughs dated by NBER.
Since 1947 industrial recessions have never lasted more than 17 months. If this relationship continues to hold, the current industrial recession, which began in October 2000, should be over on or before February 2002.
Further support for this conclusion can be obtained by examining the behavior of both industrial production and payroll employment in the vicinity of the peaks identified by NBER. See Table 1.6 and Table 5.3.
It should be noted, however, that recessions with relatively small first quarter declines in real gross private domestic investment tend to be of longer duration than those that get off to a steeper start. See . The same sort of pessimistic conclusion has been applicable to payroll employment recessions. See Table 5.7.
The leading economic indicator with the best track record at identifying recessionary troughs before they occur is the S&P composite stock price index. The stock market crash of 2000-01 now has the distinction of being the second worst dip in this index in the post World War II period. It, like the worst crash of 1973-74, is also associated with a prolonged decline in industrial production.
The data in column (4) of Table 3.4 show the recessionary lead times for the S&P composite for the nine completed industrial troughs experienced since 1945. All of these lead times fall in the comparatively narrow range of from four to seven months. The lead times have been longer, on the average, for those recessions associated with U.S. participation in wars of various kinds, than for recessions which have occurred in more peaceful times.
If the current recession turns out to have characteristics similar to the preceding three war time recessions, industrial production can be expected to bottom out in the vicinity of February to April of 2002. One would hope that the current recession will end before then. The nice thing about this prediction, however, is that it can be used as a standard for helping to measure the curative properties of recent federal tax cuts and the lower interest rates that have been engineered by the Federal Reserve this year.
Optimists can perhaps take some comfort in the fact that the following lead times to an industrial trough, after a bottoming out of the stock market, have been shorter, on the average, for cases where industrial production had already been in a state of decline for at least nine months and for cases where the Fed funds rate was down 35 percent or more from its cyclical peak.
While lower interest rates have usually helped to bolster housing construction, they can also have harmful effects. For conservative retired persons who have kept most of their financial assets in money market funds a dramatic reduction in short term interest rates can have negative effects on income and consumption. It is not unreasonable to suppose, moreover, that sophisticated home owners and business enterprises that refinance outstanding debt will use a portion of the savings to purchase stock at bargain prices, rather than increase their consumption or investment in new goods and services. The same conclusion might very well be applicable to tax cuts.
There was evidence in support of an old fashioned NBER type of recession even before the destruction of the World Trade Center on September 11.
Industrial production peaked out way back in September 2000, however, and has been drifting down at a rapid enough rate to leave little doubt that the U.S. has been experiencing a manufacturing recession.
Payroll employment peaked out in March 2001 and had plunged enough in August to cause most economists to substantially lower their growth forecasts for the rest of the year. From 1947-2000 the U.S. economy always experienced an NBER peak or was headed down in that type of recession after four lower closing values for this indicator.
The civilian unemployment rate is another useful recession indicator. From 1947-2000 the U.S. economy was always mired down in an NBER type of recession after a cumulative increase in this variable of .9 percentage points. See Table 5.2. During August 2001 the civilian unemployment rate was up one full percentage point from the preceding low.
The recessions dated by NBER in the post 1947 period can all be identified on the basis of a second lower quarterly total for real GDP. The second lower total must be smaller than the first but doesn't have to immediately follow a first quarterly decline in real GDP. See Table 7.2. This hasn't happened yet but might happen before the year is over.
It should be appreciated that most forecasting systems and BEA's preliminary projections of late occurring information have an upward bias in the vicinity of recessionary peaks. Real GDP has been increasing at an annualized rate of less than two percent since the second quarter of 2000. This is the most prolonged period of stagnant growth in the history of the NIPA accounts. See Table 7.2. In August 2001 BEA reduced its estimated growth rates for 1998, 1999 and 2000 and lowered the second quarter annualized growth rate for 2001 to only .2 percent. As more and better information becomes available there is a possibility that the second quarter growth rate might be lowered into negative territory.
From 1948-99 the U.S. economy was always in the midst of an NBER recession after five lower monthly closing values for industrial production. See Table 1.6. There was a recurrence of that type of signal in February, 2001.
Since the Fed began to fight inflation in the late 1950s, by raising its funds rate by three percentage points or more during economic expansions, payroll peaks have lagged industrial peaks, more often than not, with a median lag in the three to five month range.
The good news is that employment declines which began four or more months after a peak in industrial production have been both milder and of shorter duration than those with peaks of a more coincident nature. See Table 5.3.
The bad news is that employment recessions with a very weak decline in the first three months of the recession have lasted longer than those with a more severe decline. See Table 5.7. It will be interesting to see if the Fed's rather aggressive reduction in short term interest rates and the President's tax rebates alter this relationship.
Some even worse news, perhaps, is that economic depressions are not getting shorter, on the average, if viewed from the perspective of how long it takes a "coincident" indicator such as industrial production or payroll employment, to recover to another new high. See Table 1.3. There are a number of economic and financial distortions, in any event, which might keep economic activity depressed for a long time.
On September 7, 2000 President Clinton warned Crown Prince Abdullah of Saudi Arabia that high oil prices might tip the U.S. economy into another recession. The ten poorest growth years for the U.S. economy from 1948-2000 all followed a year-year increase in the average price of crude oil of five percent or more and an anemic increase in June-December industrial production amounting to .1 percent or less. See Table 6.1. All of these poor growth rates were closely related to recessions.
During the fourth quarter of 2000 U.S. industrial production experienced the sharpest decline since the recessionary trough year of 1991 and more than wiped out a modest increase during the third quarter.
The oil price shock of 1999-2000, in any event, was amplified by an over emphasis on U.S. built trucks and SUVs that don't get very good gas milage and by a shortage of natural gas that sent home heating costs and some electrical bills soaring to the highest levels in history.
Another variable that may have helped to set the stage for a recession is a very jittery stock market. From 1928 to 1999 years with 43 or more one day declines of one percent or more for the S&P composite were all closely linked to economic recessions. See Table 3.6. Since this index was extended back to 1928, all of the cumulative new high declines lasting more than 170 trading days have been linked to recessions. See Table 3.7.
It should be appreciated that the dividends and earnings associated with the S&P composite are still over valued from an historical point of view. Sagging earnings may keep this recession indicator from recovering to another new high as quickly as was the case after the last six crashes amounting to 13.9 percent or more. To the extent that investment and consumption are linked to stock holder wealth, that could have a depressing effect on economic activity.
A soaring stock market, in any event, may have helped to raise single family housing starts in 1999 to the highest levels since the baby boomer expansion of 1977-78. If the stock market doesn't recover fairly quickly there aren't going to be as many people who can afford the pricey homes that are now being constructed.
A booming stock market may have also helped to bloat consumption and lower personal saving rates to levels not experienced since the great depression of the 1930s. This, in turn, has led to a condition where many communities are now over malled and over stored.
The consumption explosion has been facilitated in part by a rapid expansion of consumer debt. The interest payments needed to finance expanding debt could depress both investment and consumption, even if there is no precipitous decline in personal income.
An almost tripling of business investment in software and equipment, since the recessionary trough of 1990, might turnout to be another distortion that will keep economic activity in the high tech industry depressed for a long time. Over estimates of consumer willingness to buy goods on line has ruined some software companies and helped to create a surplus of office structures in some cities.
In the post 1947 period economic recessions have tended to be longer the higher the percentage of real GDP devoted to gross private domestic investment during the quarter containing an NBER peak in economic activity. See Table 4.2. If improved technology still has the potential to boost productivity at a rapid rate it could mean the loss of a lot of white collar jobs in a world where there is little or no growth in other types of employment.
The accelerator relationship which is examined in Essay 7 implies that real GDP must increase by about two percent per year just to keep private investment from falling and having a deleterious feedback effect on the growth of economic activity. The instability of the GDP growth rate in the zero to two percent range has sometimes made it very difficult for the Federal Reserve to effectively fight inflation without pushing the U.S. economy into a recession.
The employment variable with one of the best forecasting records prior to the Gulf War recession of 1990-91 is the number of employees on nonagricultural payrolls in goods-producing industries. A cumulative drop in goods-producing employment amounting to 89 thousand workers is not a healthy development. This variable was signaling a near term recession in August 2000. See Table 6.2
For some other indications of a recession click on to: Table 2.1, Table 2.3, Table 3.5, and Table 5.4.
Since the Korean War build-up year of 1952 one has only needed at least three lower totals for non agricultural payroll employment spread over a three to five month period to be confident that the U.S. economy was involved in the eight recessions dated by the National Bureau of Economic research. See Table 5.5.
When the U.S. economy is viewed from this perspective it seems clear that the next few months might turnout to be a very testy time for both leading indicators and monetary and fiscal efforts to achieve a soft landing that will not be marred by another dated recession.
One reason most forecasters are not inclined to predict a recession until after it has occurred is that it might become a self fulfilling prophesy and hurt their employers. There are enough clouds on the horizon, however, so that students, consumers, government employees, business enterprises and ordinary investors might find it advantageous to keep a close eye on policy developments taking place in our nation's capital and some of the economic and financial indicators examined in this collection of essays and tables on the business cycle.
What should students who are worried about another recession do? My advice would be to consider going on to college or graduate school. Unemployment can reduce the "opportunity cost" of a more advanced education and will sometimes result in a big payoff there after.
About 80 percent of the world's oil production comes from large fields which were discovered before the Arab oil embargo of 1973 and are rapidly being depleted. There are a number of models and forecasting approaches which suggest that the world production of conventional oil will peak out in this decade and gradually decline. Further support for this hypothesis can be found in a host of production peaks which occurred before OPEC agreed to reduce its output on March 23, 1999.
U.S. oil production, for example, peaked out way back in 1970 and at the end of 1999 had declined by about 39 percent in spite of major improvements in exploration and recovery technology and expanded oil production in Alaska and the Gulf of Mexico. During the 12 year period after this peak the U.S. economy experienced four recessions. If oil production peaks of a world wide nature are not to result in more recessions, automobile producers and consumers in this country will have to appreciate that the era of very cheap gasoline may be nearing an end and do their part to improve the efficiency of our motor vehicle population.
Big increases in oil production in other countries outside of OPEC and a 24 percent increase in the fuel efficiency of U.S. passenger cars forced OPEC to reduce its oil production by about 48 percent from 1977 to 1985.
From 1988 to 1999, however, increases in oil production in other non OPEC countries was not sufficient, on the average, to offset declining production in the United States. This, in a world of modest improvements in the fuel efficiency of passenger vehicles in the U.S. and rapid increases in motor vehicles in developing countries, enabled OPEC to increase its oil production by 78 percent from 1977 to 1998, before the Asian flu lowered the demand for gasoline in some developing countries.
More than half of the world's proven oil reserves are believed to be located in the Persian Gulf region. Oil production outside this region peaked out in December 1997 and as of June 2000 was still down almost 1.4 percent in spite of higher oil prices.
Giant oil discoveries are now rare and are usually located either in deep water or hostile regions in terms of climate and/or politics. The slow recovery in exploratory drilling from the Asian flu and the steep downward trend in reserves per exploratory well since the 1960s makes it rather questionable whether conventional oil production outside the Persian Gulf will ever recover to a very dramatic new high.
The most talked about frontier outside of the Gulf is in largely unexplored portions of the Caspian Sea. Once a pipeline is constructed to that area it might help to offset declining production in say the North Sea, but not for long, judging by the short lived increase in U.S. oil production after the construction of the Alaska pipe line.
The unanswered question is whether oil producers in the Persian Gulf area will be able and willing to increase their output enough after the current recession is over to more than offset modest increases in the rest of the world and prevent another world wide recession similar to that which occurred after the first big upward spike in crude oil prices in 1973-74.
Iran has little excess capacity at the present time and is unlikely to ever restore production to the peak level that was achieved under the Shaw in 1974. The two countries in the Persian Gulf with the most unused capacity are Iraq and Saudi Arabia. It is by no means clear that the rest of the world will be better off if Iraq, with its war like history and stored away weapons, is allowed to increase its production dramatically.
During the world wide business expansion which followed the Gulf War recession of 1990-91, and was terminated with the Asian flu in 1998, world demand for oil increased at an average annual rate of more than one million barrels per day. Saudi Arabia, which is believed to possess about a quarter of the world's proven oil reserves, might be able to increase its oil output at that rate for two or three years.
Crop production in Saudi Arabia, however, is already in a state of decline as a result of an over emphasis on ground water mining. If that desert kingdom is to feed a rapidly growing population and enjoy prosperity for the rest of this millennium it will need to be rather conservative in its exploitation of remaining oil and gas reserves. Most of the world's other major oil exporting countries are also plagued with natural resource constraints which will make it unwise for them to restore oil production to new historic highs.
There is not much doubt, in any event, that the era of very cheap gasoline is nearing an end. If another recession is to be avoided in this decade, history would suggest that the United States, which now imports almost 60 percent of its oil using money borrowed from the rest of the world, will have to do its part by taking vigorous steps to improve the fuel efficiency of its motor vehicles.
Edward Renshaw
Professor of Economics
State University of New York at Albany
Economic recessions have been studied and analyzed for more than a century. Economists, however, have not had much success at identifying recessionary peaks in business activity in close proximity to their occurrence. This in turn has made it difficult for decision makers to implement measures that might prevent or at least moderate some of the damage that is caused by recessions.
While much labor has gone into the development of models and indicators that might warn decision makers of an impending recession, the success of these efforts has been disappointing. McNees (in Lahiri and Moore 1991, chapter 9) has noted that before the two most prolonged recessions in the Post World War II period (the recessions of 1973-75 and 1981-82) forecasters were not able to identify the peak until "about the time that it was occurring".
Robert Eggert has been summarizing the economic forecasts of a noted panel of economists on a monthly basis since 1976. In July 1979 his Blue Chip Economic Indicators newsletter proclaimed, "PANEL SAYS RECESSION IS HERE--WITH NO UPTURN UNTIL SECOND-HALF 1980". While the panel was right about an upturn occurring in the second half of 1980, the business cycle dating committee of the National Bureau of Economic Research (NBER) finally decided that the actual peak in business activity didn't occur until January 1980 or six months after a majority of the economists surveyed by Eggert thought it was upon us.
The headline for the July 1981 issue of the Blue Chip news letter was "ECONOMIC EXUBERANCE ENVISIONED FOR 1982". It wasn't until November--four months after the NBER peak in business activity, that a majority of the Blue Chip panelists believed that another recession had started. "However, a big majority of the 39 who said we were in a recession agreed with President Reagan that it would be mild. Most thought it would be over by the end of the first quarter of 1982." The actual trough did not occur until November 1982.
In April 1985 forecasters were queried as to how long the on going recovery in business activity would last. The average response was that the business expansion would last 49 months, which put the next peak in business activity at December 1986, more than 3 and one-half years before the actual occurrence of another recession.
In the July 10, 1990 issue of Blue Chip Economic Indicators it was reported that, "The year-ago CONSENSUS forecast for 1990 of a "soft landing" (no recession, but sluggish growth) remains intact. The economy in 1991 is projected to be a shade better, but the prognosis is still largely 'more of the same'." This was the month that ended the longest peace time expansion of the U.S. economy up to that point in time.
During the year 2001 it wasn't until the September 11 terrorist attack on New York City that a majority of economic forecasters got around to admitting that the U.S. economy was probably in the midst of another recession.
One reason economists have not been very successful at identifying economic recessions before they occurred is the wide variation in the duration of business expansions. In the post World War II period business expansions have ranged in duration from only 12 months for the abortive recovery from the short lived recession of 1980 to more than 90 months for three expansions.
If economic recessions are expected to occur less frequently than was formerly the case, that should discourage some economists from "sticking their neck out" and trying to predict or identify them before they occur.
Another reason for not getting involved in predicting the end of business expansions is the turbulent behavior of real GDP growth rates in the vicinity of business peaks. Six of the ten recessions experienced in the post 1947 period were preceded by at least one quarter with a negative growth rate for chain weighted real GDP. During six of the last eight quarters followed by dated recessionary declines in real GDP, however, the growth rates for real GDP were higher than in the preceding quarter. Business expansions, it would seem, often sputter to an unpredictable end.
It should be appreciated that much of the information pertaining to real GDP is not available at the time preliminary estimates are made. The tendency for BEA to project past growth rates into the future creates an upward bias in the vicinity of recessionary peaks that makes it very difficult to know whether the economy is sinking or not. It was not until July 2002 that BEA finally decided that the U.S. economy declined in both the first and second quarter of 2001 as well as in the third quarter.
The problem of turbulence is not limited to real GDP and its components. All of the indicators that have been developed to identify cyclical turning points and provide advanced warning of a possible recession also suffer from chaotic behavior that can inspire false predictions about what will happen to the economy in the future.
One of the first lessons to be learned from the coincident indicators that are examined in the process of dating business peaks is that they seldom agree on the exact timing of the peak. None of the Conference Board's coincident indicators have consistently declined in the first month after an NBER peak. The usefulness of leading and coincident indicators in helping to identify business peaks before or shortly after their occurrence has also been tarnished in recent years by numerous dips in the indicators that did not trigger a near term recession.
The longest consistent set of forecast data available for the U.S. economy has been compiled by the Research Seminar in Quantitative Economics at the University of Michigan. Over the 35 year period from 1953-87 there were seven years when real GNP declined on a year-to-year basis. In every one of these years the forecasts which were made in the preceding November either missed the recession altogether or underestimated its severity by amounts ranging from only .1 percentage points for the short lived 1980 recession to 4.5 percentage points for the recessionary year of 1974 (McNees 1988, Table 1, p. 17). For the seven recessionary years as a whole the average bias was 1.9 percentage points.
Webb (in Lahiri and Moore 1991, pp. 113-115) has concluded that for horizons of several quarters ahead it is hard to beat a simple forecasting rule of never predicting a recession. At the four-quarter horizon he found that several forecasting services, which use econometric models, did no better than a simple no-recession forecast. "The very low number of long range forecasts of recession may be an implicit acknowledgement of the forecasting services of the low accuracy of such forecasts." The many break-downs and poor success of models that endeavor to explain the duration of business expansions is another reason for not sticking one's neck out and predicting a recession before it occurs.
Any method of trying to identify recessionary peaks before their occurrence, in any event, is highly suspect since inflationary shocks are hard to predict in advance of their occurrence and because changes in the character of the U.S. economy may have made it more recession proof. The data in Table 1.1 on the business cycle expansions and contractions dated by the National Bureau of Economic Research would strongly suggest, however, that business expansions are lasting longer than they used too and that economic contractions are shorter than they once were.
The five longest expansions in business cycle history have all occurred since the horrendous 43 month contraction in economic activity which lasted from August 1929 to March 1933.
While economists have not had much luck at developing models that do a good job of explaining the duration of business expansions there is one interesting relationship that can be used as a standard to help measure the success of monetary and government policies in preventing recessions.
In 1993 the Center for International Business Cycle Research found that the duration of post World War II expansions in economic activity have been about equal to 15 months plus 1.57 times the monthly lag for a secular rise in the prime rate after months containing an NBER trough in business activity. See Table 1.2. If this relationship had continued to hold the most recent business expansion would have ended in January 1997.
In The Longevity of Expansions Haimowitz (1998) suggests that some of the reasons for longer expansions might include: a larger government sector, the use of automatic stabilizers, financial reforms, economic policy activism on the part of both government and the Federal Reserve, structural changes in the economy (such as a reduction in the number of persons employed in the volatile goods producing sector of our economy), incomes that are more recession proof, changes in inventory behavior, increased globalization and improvements in producer and consumer confidence. To this list should be added a more forward looking monetary policy on the part of the Federal Reserve (McNees 1986) and changes in the way inflation is measured.
Rising imports have shifted some of the cost of a slump in goods demand to other countries and bolstered the demand for U.S. exports. If the economies of other countries are not fully synchronized with what is happening in the U.S. the rising share of imports and exports should have a stabilizing effect on both the U.S. economy and world wide output.
Residential construction is one of the most volatile sectors of the U.S. economy but may not destabilize economic activity as much in the future, as it has in the past. In 1950, 6.96 percent of nominal GDP was allocated to this sector. During the housing boom of 1972, when almost a quarter of a million more housing units were started than in 1950, residential construction had slipped to only 5.62 percent of GDP and during the more recent slow growth year of 2001 residential construction was only equal to 3.95 percent of GDP.
Changes in business inventories are another volatile component of GDP that have been closely linked to recessions. Just-in-time delivery of parts and computerized management of business inventories, however, have reduced some of the instability caused by wide fluctuations in inventory investment. During the economic recession of 1982 the inventory/sales ratio for manufacturing and trade peaked out at 1.67. By the end of 1999 it had sunk to 1.32.
The consumption of services is the most stable component of GDP. Since 1950 its share of nominal GDP has increased from 20.6 percent to 40.8 percent in 2001.
Gross private domestic investment (GPDI) has the distinction of being the most recession prone component of real GDP. Since the food and oil price shock recession of 1973-75, the decline in real chain weighted GPDI has drifted downward from 29.4 percent to only 17.1 percent for the more recent recession.
The good news with regard to the wage-price spiral is that cost of living escalator clauses have gone out of fashion making wage inflation less responsive to food and energy price shocks. In 1990, for the first time since 1960, the growth of average hourly earnings slowed during a year containing a peak in business activity. High unemployment in Europe and many developing countries and the globalization of manufacturing have made many industrial unions more concerned about job security than a possible resumption of wage-price inflation.
While consumers are now spending a higher fraction of their income on housing some much needed stability has been imparted to the home ownership component of the CPI cost of shelter index as a result of a 1983 shift to a rental equivalent measure of home owner costs. The old procedure was to measure the price of new houses, mortgage interest rates and other cost elements that are borne by those relatively few families that are fortunate enough to be able to afford a new home during the month in question. This made the cost of home ownership extremely sensitive to changes in monetary policy. In 1970, 1975, 1978, 1979, 1980, 1981 and 1982 the official CPI increased from one to 2.5 percentage points more rapidly than an experimental rental equivalent CPI. Tight money, instead of being a cure for inflation, probably made the official inflation rate worse in some of these years.
The bad news with regard to the inflation rate for shelter is that it is more sticky and not likely to come down as much in response to short lived recessions as other prices. This component of the consumer price index, moreover, is likely to continue to increase more rapidly, on the average, than the CPI as a whole.
Medical care services are another inflation prone component of the CPI that ought, in principle, to be more controllable in the next few years with many older persons quitting smoking, drinking more moderately, getting more exercise and about to shrink in numbers as a result of the baby bust which occurred during World War II and the great depression of the 1930s. The AIDS epidemic and many new and more costly drugs and medical procedures, however, are likely to make health care cost containment one of our most enduring and frustrating problems.
Since food and energy price shocks are less likely to be amplified now by cost of living adjustment clauses in labor contracts and by changes in mortgage interest rates it is reasonable to hope that future changes in the CPI inflation rate will be smaller, on the average, than was the case from 1945-91 and that the Fed won't have to resort to draconian increases in short term interest rates to keep inflation in check.
It should be appreciated, however, that economic recessions are the only sure cure for inflation. In the post 1947 period employment recessions have reduced the CPI inflation rate from a little less than one percentage point, for the mild recession of 1960-61, to almost seven percentage points for the prolonged recession of 1981-82. The reduction in the inflation rate one year before the employment peak to one year after the employment trough has been about equal, on the average, to the percentage point increase in the civilian unemployment rate from its cyclical low to its cyclical high. The higher the 12 month inflation rate at the employment peak, the greater the drop in the inflation rate, if the increase in the unemployment rate was about the same. See Table 1.4. Including the duration of the payroll recessions as an independent variable in the regression used to identify the parameters in this table doesn't improve the adjusted R square. This would suggest that prolonged employment recessions are not very helpful at reducing the inflation rate.
The recessions dated by the National Bureau of Economic Research from 1947- 2001 were all associated with at least two lower quarterly totals for real GDP. See Table 7.2. One of the problems with this "rule of thumb" for describing recessions is that the recessionary troughs have some times occurred before this type of information was available.
It should be appreciated that recessionary peaks are of a judgmental nature and are not easy to identify on the basis of economic and financial indicators. The most widely publicized "coincident" indicators are industrial production and non agricultural payroll employment.
From 1948-2001 the U.S economy was always mired down in or on the verge of an NBER type of recession after industrial production, which has often been a leading indicator, experienced five lower closing values.
Since 1960 non agricultural payroll employment has often been a lagging indicator at the recessionary peaks identified by NBER. The U.S. economy, however, has (so far) always experienced a peak or been mired down in an NBER recession after four lower closing values for this indicator. See Table 1.6.
The labor force variable with the best overall record for enabling one to identify NBER recessions before they were over is the civilian unemployment rate. From 1948-2001 the U.S. economy was always mired down in a recession after a cumulative increase in this variable of .9 percentage points or more. Since the strike prone labor force unrest of the 1950s, however, one has only needed a cumulative increase of .6 percentage points in the unemployment rate to identify NBER recessions. See Table 5.2.
In December 1992 NBER's dating committee finally decided that the preceding recession ended in March 1991 and had a duration of only eight months. If you believe this assessment there have only been two shorter recessions in the history of business cycle analysis, the seven month recession following World War I and the six month recession from January to July of 1980.
One of the problems with NBER's dating system is that it doesn't provide any insight as to the severity of the recessions or the time that was required for the economy to recover to a new historic high. This problem can be ameliorated, to some extent, by focusing one's attention on the behavior of gross domestic product (GDP) expressed in constant dollars and other statistical series that have acquired the reputation of being "coincident" indicators of economic activity. See Table 1.3.
Strong demand for US exports caused industrial production to peak out in September 1990, two months after the official NBER peak in business activity, and to only decline by a modest 4.2 percent at the NBER Trough in March 1991. The 25 month depression in industrial production from September 1990 to October 1992, however, was of median duration for the post 1948 period.
The 34 month depression for payroll employment, on the other hand, established a new record for depressed activity that was six months longer than the 28 month depression associated with the 1981-82 recession. The prolonged period of no growth in payroll employment probably cost President Bush his job and gave rise to the immortal political slogan, "it's the economy, stupid."
The prolonged depression in employment may have been related in part to a 26.2 percent reduction in federal investment in structures during the recessionary trough year of 1991. See Table 1.5.
The more important point to note in connection with Table 1.3 is that post World War II recessions in economic activity are not getting shorter, if we view them from the perspective of how long it takes a coincident indicator, such as payroll employment or industrial production, to recover to a new historic high. The average duration of the last four completed recessions is higher for each of these indicators than the average duration for the first five recessions since 1947.
Prolonged depressions in economic activity and our inability to develop policy oriented models that do a good job of explaining the duration of business expansions would suggest, in any event, that some attention should be paid to leading economic indicators and other forecasting approaches that might be of value in identifying recessionary peaks in business activity in close proximity to their occurrence.
Haimowitz, Joseph (1998). "The Longevity of Expansions," Economic Review, Federal Reserve Bank of Kansas city, Fourth Quarter, pp. 13-34. This article can be accessed on the bank's Website at www.kc.frb.org.
Lahiri, Kajal and Geoffrey Moore (1991). Leading Economic Indicators: New Approaches and Forecasting Records(New York: Cambridge University Press).
McNees, Stephen (November/December 1986). "Modeling the Fed: A Forward-Looking Monetary Policy Reaction Function," New England Economic Review, pp. 3-8.
------(July/August 1988). "How Accurate Are Macroeconomic Forecasts?" New England Economic Review, pp. 15-36.
Zarnowitz, Victor (1992). Business Cycles: Theory, History, Indicators and Forecasting(University of Chicago Press).
Business Cycle Reference Dates ----Duration in Months----
Trough Peak Contraction Expansion
Previous Peak Trough to
to Trough Peak
Dec. 1854 June 1857 ...... 30
Dec. 1858 Oct. 1860 18 22
June 1861 Apr. 1865 8 46*
Dec. 1867 June 1869 32 18
Dec. 1870 Oct. 1873 18 34
Mar. 1879 Mar. 1882 65 36
May 1885 Mar. 1887 38 22
Apr. 1888 July 1890 13 27
May 1891 Jan. 1893 10 20
June 1894 Dec. 1895 17 18
June 1897 June 1899 18 24
Dec. 1900 Sep. 1902 18 21
Aug. 1904 May 1907 23 33
June 1908 Jan. 1910 13 19
Jan. 1912 Jan. 1913 24 12
Dec. 1914 Aug. 1918 23 44*
Mar. 1919 Jan. 1920 7 10
July 1921 May 1923 18 22
July 1924 Oct. 1926 14 27
Nov. 1927 Aug. 1929 13 21
Mar. 1933 May 1937 43 50
June 1938 Feb. 1945 13 80*
Oct. 1945 Nov. 1948 8 37
Oct. 1949 July 1953 11 45*
May 1954 Aug. 1957 10 39
Apr. 1958 Apr. 1960 8 24
Feb. 1961 Dec. 1969 10 106*
Nov. 1970 Nov. 1973 11 36
Mar. 1975 Jan. 1980 16 58
July 1980 July 1981 6 12
Nov. 1982 July 1990 16 92*
Mar. 1991 Mar. 2000 8 120
Average, peacetime cycles
1854-1982 (25 cycles) 19 27
1854-1919 (14 cycles) 22 24
1919-1945 (5 cycles) 20 26
1945-1982 (6 cycles) 11 34
*Business expansions which may have been prolonged or shortened by wars (Civil War, World Wars I and II, Korean War, Vietnam War and Operation Desert Storm).
Source: National Bureau of Economic Research, Inc.
Date of Duration of Business Expansion Actual
--------------------- Lag in ------------------------------ Minus
NBER Prime Rate Months for Predicted Actual Predicted
Trough Trough Prime Rate --------in Months------ Duration
(1) (2) (3) (4)n (5) (6)n
Oct. 1945 Nov. 1947 25 54 37 -17
Oct. 1949 Aug. 1950 10 31 45 14*
May 1954 July 1955 14 37 39 2
Apr. 1958 Aug. 1958 4 21 24 3
Feb. 1961 Nov. 1965 57 104 106 2*
Nov. 1970 Mar. 1972 16 40 36 - 4
Mar. 1975 Apr. 1977 25 54 58 4
July 1980 Aug 1980 1 17 12 - 5
Nov. 1982 Mar. 1987 52 97 92 - 5
Mar. 1991 Feb. 1994 35 70 120 50
(4)n. The predicted duration of the business expansion is equal to 15 months plus 1.57 times the monthly lag for the prime rate in column (1). This formula was developed at the Columbia University's Center for International Business Cycle Research under the direction of its Director, Geoffrey Moore, and widely publicized by Lindley Clark in "A Slump Predictor Clinton Should Love," The Wall Street Journal, December 28, 1993, p. A10.
(6)n. Column (5) minus column (4).
Source of basic data: The Federal Reserve Bulletin.
*Business expansions which may have been prolonged by wars.
Dates of Recessionary Peaks Duration to a New High Recovery ---------------------------- ------------------------------- IP EMP RGDP IP EMP RGDP July 48 Sep. 48 48-4 21 22 5 July 53 June 53 53-2 20 23 6 Mar. 57 Mar. 57 57-3 23 25 5 Jan. 60 Apr. 60 60-1 21 20 5 Oct. 69 Mar. 70 69-3 26 18 6 Nov. 73 Oct. 74 73-4 37 16 8 May 79 Mar. 80 80-1 26 10 4 July 81 July 81 81-3 26 28 7 Sep. 90 June 90 90-2 25 34 7 Sep. 00 Mar. 01 00-4 ? ? 4 Average Duration to a New High Recovery First Five Recessions 22.2 21.6 5.4 Last Four Completed Recessions 28.5 22.0 6.5
Source of basic data: The Survey of Current Business, October 1994 and January/February 1996.
Decline in Inflation Rate
Economic % Point CPI -------------------------
Recession Increase Inflation Actual Predicted
Civilian Rate at
Unemployment Recessionary
Rate Peak
(1) (2) (3)n (4)n
1948-49 4.5 6.5 3.0 4.4
1953-54 3.6 1.0 1.5 1.6
1957-58 3.8 3.6 3.3 2.7
1960-61 2.3 2.0 1.1 .7
1969-70 2.7 6.0 2.5 2.4
1973-75 4.4 12.0 5.9 6.1
1980 2.2 14.7 4.0 4.8
1981-82 3.6 10.7 7.5 4.9
1990-91 2.7 4.7 1.5 2.0
2001-? 1.9 3.0
Average Values 3.3 6.4 3.4 3.4
(3)n. The actual 12 month CPI inflation rate at the employment peak in column (2) minus the 12 month CPI inflation rate one year after the employment trough.
(4)n. The predicted decline in the CPI inflation rate is equal to the increase in the civilian unemployment rate from its cyclical low to its recessionary peak in column (1) plus one-third of the peak CPI inflation rate in column (2) minus 2.3 percentage points.
Trough Federal State and Year Local 1949 - 2.0 38.6 1954 -15.6 20.2 1958 11.4 10.8 1961 6.5 8.3 1970 -18.0 - 7.5 1975 - 3.2 - .8 1980 15.1 - .1 1982 17.7 - .9 1991 -26.2 .6 2002?
Source of basic data: National Income and Product Accounts, 1929-94, Volume 2, Table 7.11.
*The asterisks identify recessionary declines associated with cases where
the fourth lower closing value for payroll employment occurred before the
fifth lower closing value for industrial production. These recessions have
been shorter on the average than the other recessions dated by NBER.
#The hatch marks identify recessionary declines when the lower closing value
dates for both industrial production and payroll employment lagged NBER peak
dates by five or more months.
Source of basic data: The Survey of Current Business, October 1994.
Edward Renshaw
In the last decade there was a revival of interest in leading economic
indicators on the part of academics (Lahiri and Moore 1991) and some
improvements in indicator construction and interpretation that might aid
forecasters and policy analysts to make better use of the cyclical
indicators that were pioneered by the National Bureau of Economic Research
(NBER) in the 1920s and 30s.
In 1961 the Commerce Department's Bureau of the Census began to
publish Business Cycle Developments which featured some of these
indicators. In 1968 this publication was expanded to include previously
unpublished composite indexes and its title was changed to Business
Conditions Digest. In 1972 responsibility for the cyclical indicators
was turned over to the Bureau of Economic Analysis. In April 1990 the
indicators were integrated into the Survey of Current Business. In
December 1995 the responsibility for maintaining and publicizing the
composite indexes of leading, coincident and lagging indicators was turned
over to the Conference Board.
In this essay we will provide a brief history of important changes in
the construction of the index of leading economic indicators and then
address such issues as how to reduce the number of false or highly premature
indications of an impending recession. Persons who are worried about a
possible recession should keep a close eye on the prime rate charged by
banks and one of the index of leading indicators' most interesting
components, new private housing units authorized by local building permits.
The Commerce Department's first index of leading economic indicators
was seriously distorted by a failure to deflate some of its components which
were measured in current dollars. At the December 1969 peak in business
activity the preliminary lead time for the index was only three months and
at the November 1973 peak in economic activity the index registered a new
all time high. The composite index was substantially overhauled in 1975 to
correct this problem and at the January 1980 peak in business activity the
new index had been depressed for a total of 15 months.
The success of the revised index after 1980, however, was impaired by
other objectives. Zarnowitz and Boschan noted in (1975) that there are six
criteria which are often used in selecting and assessing cyclical
indicators: (1) Economic significance--how well understood and how
important is the role in business cycles of the variable represented by the
data? (2) statistical adequacy--how well does the given series measure the
economic variable or process in question? (3) timing at revivals and
recessions--how consistently has the series led (or coincided, or lagged)
at the successive business cycle turns? (4) conformity to historical
business cycles--how regularly have the movements in the specific indicator
reflected the expansions and contractions in the economy at large? (5)
smoothness--how promptly can a cyclical turn in the series be distinguished
from directional change associated with shorter (mainly irregular)
movements? and (6) currency or timeliness--how promptly available are the
statistics and how frequently are they reported?
Many of the components in the composite index of leading indicators
are not as trendy as real GNP or GDP. In order to satisfy objective (4) and
make the index of leading economic indicators do a better job of conforming
to the upward trend in what many economists would consider our best overall
measure of economic activity, the Commerce Department added .142 percent to
the index of leading economic indicators each month to achieve an overall
targeted trend of .261 percent per month. This trend adjustment sometimes
delayed the down turn in the index of leading indicators enough to obscure
the risk of a recession. At the July 1981 business peak the new index of
leading economic indicators had been depressed for only three months and at
the July 1990 peak in economic activity it registered another historic high
at a time when six of its eleven components had been depressed for 25 months
or more.
In November 1993 the trend adjustment was eliminated. See the
discussion by Green and Beckman in the October (1993) Survey of Current
Business. This adjustment and a reweighting of the components to make
the unadjusted lead times for the composite index conform more closely with
the lead times for the median component of the revised index can be
considered a major improvement. The index of leading economic indicators
which was taken over by the Conference Board, however, was still plagued by
a problem of false signals--which led Nobel Laureate Paul Samuelson to once
quip that the index of leading economic indicators has correctly predicted
9 of the last five recessions. A further revision of the composite index
by the Conference Board in December 1996 eliminated some of the misleading
signals.
In Table 2.1 we show some adjusted lead times
at business peaks for the Conference Board's first revised index of eleven
leading economic indicators. The adjusted lead times are associated with
cumulative declines amounting to one percent or more from the index's own
peak value to those peaks identified by the National Bureau of Economic
Research. A cumulative decline of this magnitude does help to reduce the
number of false signals. Two false or rather premature signals, that are
associated with tight monetary policy and stock market "corrections" in the
midst of prosperity, can still be identified in the 1959-96 period, however.
One way to cope with the possibility of false signals is to ignore
declines in the index of leading economic indicators until after the Fed has
ceased to worry about a recession and has begun to raise short term interest
rates aggressively in an effort to prevent an acceleration in the inflation
rate. In the post 1947 period the U.S. has (so far) never experienced an
NBER peak in business activity until three months after the average prime
rate charged by banks has increased by one third or more from its cyclical
low.
In the post 1961 period there has (so far) never been a recession
until at least five months after the prime rate was up more than 50 percent
from its cyclical low. See Table 5.4. This
threshold would have allowed one to have eliminated both of the false or
very early warning signals for the Conference Board's first revised index
of leading indicators. It was not until May 2000 that this indicator began
to signal the possiblity of another recession.
Once this amount of financial stringency has been imposed on the
economy one should pay closer attention to one of the leading index's most
interest sensitive components: the number of new private housing units
authorized by local building permits. The collected data are related to the
issuance of permits and not the start of construction, which frequently
occurs several months later and sometimes not at all.
In 1989 this analyst noted that residential building permits is the
only component of the index of leading economic indicators that has
consistently declined by at least twenty percent before each of the economic
recessions in the post 1947 period. For the five cases where the number of
housing units authorized by local building permits exceeded 1800 thousand
units there was at least a 35 percent decline in the number of authorized
units before the next recessionary peak identified by NBER. This set of
relationships turned out to provide the only model or rule of thumb in
Lahiri and Moore's collected compendium on Leading Economic
Indicators that did a good job of identifying the July 1990 peak in
business activity shortly before it occurred. See the last column of Table 2.2 for an updated version of this rule of thumb.
In May 1995, after the Conference Board's revised index of leading
economic indicators declined by a little over one percent, the number of
authorized building permits was only down about 17 percent and was heading
upward in response to lower mortgage rates.
Authorized housing units peaked out at 1,762 thousand units in January
2000 and slipped about 16 percent to only 1,486 thousand units during August
2000. While comparatively modest investment in this sector and a downward
drift in mortgage rates since February 2000 prevented this variable from
being a reliable indicator of the more recent recession, economy watchers
should probably continue to keep a close eye on this component of the
Conference Board's index of leading indicators. The unadjusted lead time
for this indicator was 14 months, making it the sixth longest lead time for
the last ten recessions.
An inverted yield curve for government securities implies a prediction
on the part of participants in the fixed income securities market that long
term interest rates are likely to decline. This in turn is only likely to
happen if the economic growth rate is slowing down and the economy is in
danger of slipping into another recession. There is now an extensive
literature on the use of interest rate spreads to forecast economic activity
and identify business peaks before they occur. See Estrella and Mishkin
(1996) for a review of this literature.
Since 1955 the US economy has never experienced a dated recession
until at least seven months after the yield on one year Treasury notes first
exceeded the monthly yield on ten year Treasury bonds. See Table 2.3. Except for the Vietnam War build up credit
crunch inversion of 1965-66 all of the lead times for this recession
indicator were in the seven to 17 month range. And except for the two years
(1965 and 1967) surrounding the Vietnam War build-up, all December yields
for one year Treasury notes in excess of the yield on ten year Treasury
bonds have been followed by below average growth rates for real GDP
expressed in 1987 dollars. During March 2000 the yield curve for U.S.
Treasury securities began to show signs of an inversion and during August
it became decidedly inverted.
Modern portfolio theory assumes that risk will be reward in the market
place. Since the prices of long term bonds fluctuate more widely than the
prices of notes, bills and marketable commercial paper, there is a
presumption that the slope of the yield to maturity curve should normally
slope upward. If risk is not being reward in the bond market one should
probably be cautious about investing in the stock market.
The data in Table 2.3, in any event, support
the hypothesis that it has been rather risky to have owned stock after a
yield curve inversion. The February 1989 inversion is the only case where
one couldn't have sold an index fund tracking the S&P 500 at the end of the
month after the first inversion and then been able to repurchase the index
at a lower value during the stock market crashes associated with
recessionary peaks in business activity.
It makes sense, in any event, for investors and people who are
concerned about the future of economic activity to keep a close eye on the
shape of the yield to maturity curve which has now become a component of the
Conference Board's revised index of leading economic indicators.
One of the more important lessons to be learned from the current recession,
however, is that an aggressive effort on the part of the Federal Reserve to lower
its funds rate can sometimes be more successful at eliminating inverted yield
curves and bolstering both the housing market and indexes of leading indicators
to some extent than in preventing or shortening the life of economic recessions.
See Table 9.4.
Persons who are concerned about the possibility of another recession, in
any event, should pay close attention to other types of recession indicators.
And that is what the remaining chapters in this compendium are all about.
Estrella, Arturo and Frederic Mishkin (1996). "The Yield Curve as a
Predictor of U.S. Recessions," FRBNY Current Issues, June.
Green, George and Barry Beckman (1993). "Business Cycle Indicators:
Upcoming Revision of the Composite Indexes," Survey of Current
Business, October, 44-51.
Lahiri, Kajal and Geoffrey Moore (1991). Leading Economic Indicators:
New Approaches and Forecasting Records(New York: Cambridge
University Press).
Moore, Geoffrey (1961). Business Cycle Indicators(Princeton:
Princeton University Press), Vol. 1, Table 3.2, p. 56.
Renshaw, Edward (1992). The Practical Forecasters'
Almanac(Burr Ridge, Illinois: Irwin Professional Publishing).
Zarnowitz, Victor and Charlotte Boschan (1975). "Cyclical Indicators: An
Evaluation and New Leading Indexes," Business Conditions
Digest, May, v-xiv.
(8)n. Months from the NBER business peak in Column (3) to the month when
the revised index of leading economic indicators was down one percent in
Column (2).
(5)n. Months from the next economic peak identified by the NBER after the
building permit index has been down 20 percent for two months in a row if
the permit peak was less than 1800 units and down 35 percent for two months
in a row if the permit peak was over 1800 units.
Source of basic data: An index (1987 = 100) in the October 1995 issue of
the Survey of Current Business is multiplied by 15.348 to produce
unit values for the 1947-53 period. The basic data are collected by the Bureau
of the Census, Manufacturing and Construction Division, Building Permits Branch,
Washington, DC 20233.
(1)n. The first month in a business expansion when the yield on one year
Treasury notes rises above the yield on ten year Treasury bonds.
Source of basic Data: The Federal Reserve Bulletin.
Edward Renshaw
Some of the most persuasive evidence in support of a longer run pattern to
stock prices has been provided by the National Bureau of Economic Research. In
a massive compendium which was published in 1961, Geoffrey Moore found that
common stocks were excelled as a leading indicator of business cycles by only the
net change in the number of operating businesses. Stock prices were classified
as being a leading indicator 31 times, roughly coincident 14 times and a lagging
indicator only five times.
The most publicized weakness of stock prices as an indicator of economic
activity is a propensity for them to have predicted more economic recessions than
have actually occurred. This point is illustrated in Table
3.1 where cumulative reversals amounting to 5.5 percent or more in the
monthly average daily closing prices for the S&P composite 500 stock price index
are used to define peaks and troughs in the index.
This reversal percentage was the smallest percentage that one could use and
still eliminate all of the abortive stock market rallies which occurred in the
midst of economic recessions from 1947-91. Once the economy was in a recession
and the stock market has recovered 5.5 percent on a monthly average basis the S&P
index continued up for an additional gain of at least 25 percentage points before
the index reversed itself on the downside by 5.5 percent. (See the gains in
column 5 of Table 3.1 which are marked with a double
asterisk.)
Rules of thumb, which would have worked well in the past sometimes break
down, however. During the industrial recession which began in October of 2000
the S&P composite experienced a monthly average rally of 7.1 percent from March
2001 to May of that year. This rally was probably related to a rather aggressive
effort on the part of the Federal Reserve to lower its funds rate.
After declining another 17.8 percent between May and September the S&P
composite then had another false rally amounting to 10.4 percent before declining
25.9 percent and hopefully bottoming out during October 2002. This crash may
have been related to the publicity associated with numerous accounting scandals
and a better appreciation that the stock market was still overvalued from an
historical perspective.
It will be noted that there have been 29 downside reversals of 5.5 percent
since May 1946 and only ten recessionary peaks. Seventeen of these downside
reversals occurred in the midst of a business expansion and were followed by an
upside reversal of at least 5.5 percent before the economy became mired down in
a recession. There are eight cases where a downside reversal of 5.5 percent was
followed by a recession and three cases where a reversal of this magnitude did
not occur until after the business peaks of January 1980, July 1991 and March
2001.
From January to April of 1994 the monthly values for the S&P composite
stock price index declined 5.446 percent and came very close to adding another
contraction of 5.5 percent to the downside reversals in Table
3.1. The disqualification of this contraction enabled the S&P index to
exceed the old record established in January 1966 of 43 months of sustained rise
in stock prices (without a dip of 5.5 percent or more) when the monthly values
for the S&P index recovered to a new historic high of 481.92 in February 1995.
The staying power of the 1990-2000 bull market can also be illustrated by
examining fluctuations of ten percent or more in the monthly values for the S&P
composite and the Cowles Commission indexes--which have been extended back to the
1870s. In October 1996 the S&P index exceeded the previous record of 71 months
of sustained increase without a correction of ten percent or more which was
established from October 1923 to September 1929.
Economy watchers and investors who want to distinguish between those
cumulative declines that have turned out to be the prelude to a recession and
those crashes which have occurred in the midst of business expansions are
probably well advised to ignore monthly average values for the S&P index and
focus their attention on cumulative new high declines amounting to 13.9 percent
or more.
In the 1958-2001 period cumulative dips of 13.9 percent or more which
occurred after a cyclical increase in the discount rate on new issues of 91 day
Treasury bills of 3.15 percentage points or more were all associated with
economic recessions. See the down dates identified with hatch marks in
Table 3.2. This type of tight monetary policy was
predicting another recession when the S&P dipped 13.9% on November 30, 2000.
From 1928 to 2001 there were only three recessions that began more than one
month after a cumulative dip of 13.9 percent. The first exception is associated
with the Vietnam War wind down recession of 1970. Very tight labor markets and
a problem of accelerating inflation encouraged the Board of Governors of the
Federal Reserve to increase the Federal funds rate by more than three percentage
points during the first eight months of 1968. The stock market reacted
negatively and was down more than 15 percent from its November 1968 high by the
end of July, but the U.S. economy didn't peak out until December 1969 when the
Federal Government really began to contract its defense expenditures. Pent up
demand for motor vehicles and other consumer goods by returning veterans of an
ill fated war made the recession of 1970 one of the mildest on record in terms
of the decline in payroll employment.
The second case when the S&P composite experienced a cumulative dip of 13.9
percent several months in advance of a recessionary peak occurred during the
first five months of 1973 when the Fed was also raising short term interest rates
at a very rapid rate in response to accelerating inflation.
In the Economic Report of the President which was transmitted to Congress
in February 1973, the Council of Economic Advisers noted that price and wage
controls had worked better than many had expected. The good news with regard to
CPI inflation, however, was about to end as a result of uncontrolled prices for
basis food stuffs. The world wide food shortage which followed crop failures in
the Soviet Union and a number of other countries caused the price of wheat at the
farm level to increase 124.4 percent and the price of corn to increase 62.4
percent on a year-year basis during 1973 after increases of 31.3 and 45.4 percent
respectively in 1972.
The fourth and biggest Arab-Israeli War in 25 years erupted in the
afternoon of Yom Kippur on October 6. On October 19 a total ban on oil exports
to the U.S. was imposed by Arab oil producing nations. Food inflation in
conjunction with long waits at gasoline pumps and a sizable increase in the price
of household energy caused the CPI inflation rate to more than double from 3.4
percent in 1972 to 8.7 percent for 1973 and helped to tip the U.S. economy into
another recession by December 1973.
Both of these exceptions, however, can easily be identified as the prelude
to a recessionary bear market by examining changes in the Fed funds rate in the
12 months terminating with a cumulative dip of 13.9 percent for the S&P
composite. The four cases (7/69, 5/73, 3/80 and 9/82) where the Fed funds rate
had increased by 2.00 percentage points or more on a 12 month basis are all
associated with recessionary bear markets.
The third case of a recessionary peak occurring several months after a dip
of 13.9 percent in the S&P composite is the more recent recession beginning in
March 2001. In this case the Fed funds rate was only up 1.09 percentage points
on a 12 month basis. Part of the reason for the delay, however, might have been
a stock market that was still over valued from an historical perspective.
If the S&P composite has experienced a cumulative decline of 13.9 percent
in the midst of a business expansion and the U.S. economy has not yet experienced
a recessionary peak or any indication of a near term peak based on the recession
indicators in Essays 2, 4 and 6, history would suggest that the stock market may
be experiencing a crash in the midst of prosperity.
There is not much doubt that the U.S. economy is more recession proof than
it used to be. It should be appreciated, however, that most of the completed
stock market crashes in the midst of prolonged business expansions since 1928
have occurred fairly early in the expansion or in the midst of a war time build
up like the crash of 1966.
The first exception is the celebrated crash of 1987. A big difference
between that crash and the more recent late occurring, 1998 crash in the midst
of prosperity, is the state of U.S. exports. Our exports increased 16.1 percent
in 1988 in response to a one third reduction in the real multilateral trade-
weighted value of the U.S. dollar from 1985-88.
During the third quarter of 1998 U.S. exports in chain weighted dollars
were down by about 1.8 percent from the preceding 4th quarter peak in response
to financial turmoil and economic recessions in South Asia, Japan, Russia and
numerous developing nations. Consumer demand for goods and services in the U.S.,
on the other hand, was up by about 3.9 percent in response to declining inflation
and improved employment.
In the 1929-2001 period all of the false recessionary signals occurring in
a business expansion 32 months or more after a recovery from an NBER recessionary
trough were eventually followed by a recession associated with the next dip of
13.9 percent or more for the S&P composite.
In the post 1946 period there has usually been a full recovery from a crash
in the midst of prosperity in less than two years. The only exception, so far,
is the crash which began in September 1976 when the all item CPI was about to
zoom upward from only 4.9 percent in 1976 to 13.3 percent in 1979. Respectable
earnings growth and increases in the Federal funds rate which were not sufficient
to offset increases in the inflation rate, however, did enable the S&P composite
to finally recover the ground that was lost from 9/21/76 to 3/6/78 by August 15,
1979.
January and December are the most interesting months to study the stock
market. By the mid-1970s, statisticians were beginning to conclude that there
might be some seasonality to the monthly rates of return from holding common
stock due primarily to large January returns and by the early 1980s it was
discovered that most of the exceptional returns in this month were associated
with small firms.
While much has been written about the January size effect, it is less well
appreciated that this month often sets a tone for the market that will carry
forward to the end of the year. From 1947 to 2000 there were 30 years when the
S&P composite increased by one percent or more in January and in every one of
those years the financial return for the entire year was positive.
Over the somewhat longer period from 1942 to 2000 there were 19 years when
the S&P composite declined in January. See Table 3.5.
The January 1974 decline occurred in the middle of a prolonged recession and the
January 1970 and 1982 declines in years containing a recessionary trough. Five
of the other January declines occurred in the first two years of recovery after
a year containing a recessionary trough. See those years in this table that are
identified with an "R". All of the other January declines were followed by a
recessionary peak in from three to 24 months.
Another way to eliminate some of the longer lead times associated with
January declines in the S&P composite is to pay close attention to the behavior
of short term interest rates. When the prime rate charged by banks increased by
.25 percentage points on either a January to January basis or on a preceding year
to year average basis in the 1942-2001 period, there were only two January
declines in 1956 and 1978 when the U.S. economy didn't experienced a recessionary
peak in the remainder of the decline year or early in the following year.
The 19 month lead following the January 1956 decline occurred in the second
year after the recessionary trough of 1954. The 24 month lead following the
January 1978 decline occurred under very unusual circumstances in the third year
after the recessionary trough of 1975.
The 1977-79 period is very unique in that it represented a protracted
period when the inflation rate for the all item consumer price index was not only
increasing faster than the Fed funds rate but was also in excess of most short
term interest rates. The high inflation rates relative to the cost of borrowing
provided consumers and business enterprises with a strong incentive to rush out
and purchase durable goods on credit until Paul Volcker took over the helm of the
Federal Reserve in 1979 and raised short term interest rates fast enough to tip
the U.S. economy into two recessions in less than two years.
Under more ordinary inflation fighting conditions in the post World War II
period, prolonged business expansions have ended during the year starting out
with a January decline in the S&P composite or shortly after as was the case for
the January decline in the year 2000.
History would suggest, in any event, that investors should be cautious
after a January dip in the S&P composite. Further declines to the lowest
following closing value for this index have ranged from a modest 2.6 percent
after the January 1978 case to a more monstrous 46.3 percent after the January
1973 decline. The 1.6 percent decline during January of 2002 was immediately
followed by five down days in a row. Being able to distinguish between crashes
in the midst of prosperity and those associated with recessions, however, can be
of some value to investors.
The parentheses associated with the January declines in
Table 3.5 show the number of days that the S&P composite
declined by one percent or more during the year in question. All of the years
with 25 or more one percent down days from 1947-2000 were closely associated with
economic recessions.
Five of these years contained a peak in economic activity. One year (1974)
was a year of intervening decline in economic activity. Two of the years
contained a recessionary trough and one year (1962) was a first year of recovery
from the recession of 1960-62.
The year 2000 now has the distinction of being the second most jittery year
associated with a January decline in the S&P composite since the great depression
of the 1930s.
Since the S&P composite was extended back to 1928 all of the years with 43
or more one percent down days have been closely associated with economic
recessions. See Table 3.6.
Three of these years contained recessionary peaks. One of these years,
2000, was soon followed by a recession. There were four cases of intervening
decline in economic activity, one year containing a recessionary trough and three
cases of 43 or more one day declines in the S&P composite which occurred during
the first two years of recovery from an economic recession.
The year 2000 now has the distinction of being the most jittery year for
the NASDAQ composite in its entire thirty year history and the tenth most jittery
year in the history of the S&P composite.
From 1928-2001 prolonged new high declines in the S&P composite lasting
more than 200 trading days were all associated with economic recessions.
See Table 3.7.
Recessionary crashes lasting more than 400 trading days were all of
prolonged duration.
The unanswered question as of November 2002 is whether the very prolonged
crash from March 24, 2000 to October 9, 2002 will also be associated with a
prolonged recession.
One of the best times to have sold common stock in the post 1947 period is
about eight months before a peak in business activity as defined by the National
Bureau of Economic Research. An investor with the clairvoyance to have followed
this strategy could have repurchased a portfolio similar to the S&P index two
months after the business peak at an average discount of about nine percent.
The problem with this method of refuting the random walk hypothesis is that
economists haven't been very successful at identifying business peaks before
their occurrence. The many failures of economic models, in this regard, should
make one a bit suspicious of market timing news letters. While it is easy to
devise switching models that would have generated impressive profits in the past,
the out of sample performance of these models has been rather dismal, especially
in the 1990s.
Suppose one was able to identify business peaks at the end of the month in
which NBER finally decided that they occurred. Would it then be too early to
purchase common stock? The data which are presented in Table
3.3 show that the S&P index has then proceeded to lose from 3.5 to 35.1
percent of its value on a daily closing value basis before bottoming out during
the ten dated recessions since 1947.
While much attention has been paid to the unreliability of stock prices as
a predictor of economic recessions it is less well appreciated that this market
use to be a very superior predictor of economic recoveries. If we ignore the
money supply, M-2 expressed in constant dollars, which is so trendy as to have
not exhibited a distinct trough during some recessions, stock prices can claim
the distinction of being the only component of the index of leading economic
indicators that consistently led economic recoveries from 1947-99.
In Table 3.4 we show the recessionary lead times for
the lowest daily recessionary closes for the S&P composite that have been
associated with industrial production troughs. Daily lows for the S&P are more
interesting from a speculative point of view than the monthly average lows
associated with most leading indicator indexes. Industrial troughs have (so far)
coincided with most NBER trough dates in the post 1947 period with two misses of
only one month each.
All of the S&P lead times to industrial troughs from 1948-91 fell in the
comparatively narrow range of from four to seven months. The lead times were
longer, on the average, for those recessions associated with U.S. participation
in international wars of various kinds, than for recessions which have occurred
in more peaceful times.
There is a possibility, however, that corporate accounting scandals may
have destroyed the usefulness of stock prices as a leading indicator of the
current recovery. One would hope, in any event, that industrial production won't
retreat to a new low during 2003.
If the current recession turns out to be an exception, it follows that if
investors are to maximize the benefits from stock market crashes associated with
economic recessions, they will usually have to possess the courage to buy stocks
before the economy and most statistics with leading indicator properties have
turned up.
One statistic that ought to be kept in mind by investors who want to take
advantage of economic recessions is the civilian unemployment rate. From 1947-
2001 the U.S. economy was always bogged down in another recession after a
cumulative increase in the unemployment rate amounting .9 percentage points.
For those recessions from 1947-90 the S&P composite always appreciated at
least 14.8 percent one year after the end of the month following a cumulative
increase in the unemployment rate of this magnitude. See Table 3.8.
During five of the nine completed recessions in this table the S&P
composite had experienced a bear market low before the end of the month following
a cumulative increase in the unemployment rate of .9 percent.
The behavior of the stock market in 2002 would suggest, however, that
economic and financial relationships that would have been useful predictors in
the past can break down and not be as reliable in the future.
*Stock market declines that were associated with economic recessions.
**Rises following economic recessions.
Source of Basic Data: Economic Report of the President.
# The hatch marks identify bear market declines of 13.9 Percent or more which
occurred after a cyclical increase in the discount rate on new issues of 91 day
Treasury bills of 3.15 percentage points or more. From 1958-2001 all of these
cases were associated with recessionary bear markets.
* The asterisks identify recessionary bear markets with an NBER peak before or
not more than one month after the S&P composite stock price index had decline by
13.9 percent or more.
F identifies false signals that are associated with cumulative declines of 13.9%
that occurred less than a year after the previous recessionary trough identified
by NBER.
L identifies false signals that are associated with cumulative declines of 13.9%
that occurred at least 32 months after the previous recessionary trough
identified by NBER. In the 1929-2001 period all of these false signals were
eventually followed by dips of 13.9% that are associated with economic
recessions.
(5)n. The numbers in parentheses are the percentage changes in non agricultural
payroll employment during the three months ending in the month when the S&P
composite was down 13.9 percent from the peak dates. Payroll employment was
already down at this juncture during five of the last ten recessionary bear
markets.
*Only case where the stock market peak occurred after the business peak.
Source of Basic Data: Standard and Poor's Security Price Index
Record.
#The hatch marks identify cases where industrial production peaked out at least
nine months before the S&P low. The following lead times for these cases to an
industrial trough have been shorter, on the average, than for the other cases.
*The asterisks identify lead times that may have been lengthened as a result of
U.S. involvement in international wars of one sort or another.
Source of Industrial Trough Data: BCI series 47, the Survey of Current
Business, October 1994.
"D" identifies a year of intervening decline in economic activity.
"P" identifies a year containing a recessionary peak in activity.
"R" identifies a first or second year of recovery after a trough year.
"T" identifies a year containing a recessionary trough in economic activity.
The parentheses associated with the January declines show the number of days that
the S&P composite was down one percent or more during the year in question.
*The asterisks identify changes in the prime rate amounting to .25 percentage
points or more. If the U.S. economy was not in the midst of a recession these
cases were always followed by a recessionary peak in from three to 24 months
during the 1947-2001 period. Non recessionary cases with an increase of .25
percentage points or more for the prime rate on both a January-to-January basis
and a preceding year-to-year basis were followed by recessionary peaks in from
three to only eleven months. "R" type of recovery years were all associated with
lead times of 19 or more months.
"D" identifies a year of intervening decline in economic activity.
"F" identifies a year which was followed by a recessionary peak.
"P" identifies a year containing a recessionary peak.
"R" identifies first and second years of recovery following a trough year.
"T" identifies a year containing a trough in economic activity.
The hatch marks identify recessions associated with stock market crashes with
trading day declines lasting more than 400 days. All of these dated recessions
have prolonged duration records.
*The asterisks identify modest interim declines (or gains) where the bear market
low occurred before the end of the purchase month.
Edward Renshaw
While none of the individual components of real GDP are very reliable
recessionary indicators, history would suggest that a noteworthy loss of
investment confidence in more than one interest sensitive sector of the U.S.
economy will often spread to other sectors and help to tip the economy into
a recession.
This point is illustrated in Table 4.1, which
shows the lead times from NBER peaks during business expansions for three or more
annualized quarterly growth rates for real GDP, consumer durables, non
residential equipment and software, and residential investment amounting to 1.9
percent or less during business expansions--which have occurred after or are
associated with a sustained increase in the prime rate charged by banks of a
third or more from its cyclical low.
Other investment categories with a propensity to "collapse" during
economic recessions (such as changes in business inventories and non
residential structures) are ignored on the grounds that they tend to be
lagging indicators at recessionary peaks.
The accelerator model, which is discussed in Essay 7
provides a rational for being especially concerned about real GDP growth rates
in the unstable range of from zero to two percent.
All of the recessionary peaks experienced from 1947 to 2001 were preceded
by at least one quarter with three poor growth rates for the economic variables
in Table 4.1. The longest lead time, following a
one-third increase in the prime rate from its cyclical low, is 19 months. This
lead time is associated with the Korean War build-up year of 1951 and can easily
be eliminated on the basis of no dramatic declines in either real GDP or its
components with leading indicator properties.
For the eight quarters where the growth rates for real GDP and its
components were all under 1.9 percent the lead times from the business peaks
identified by the National Bureau of Economic Research are all in the
comparatively narrow range of from seven to zero months.
For the nine quarters when the annualized decline in residential investment
was equal to 9.4 percent or more the lead times are all in the even narrower
range of from 5 to zero months.
Preliminary estimates of gross domestic product are usually available about
one month after a quarter is over. The more serious problem with using poor
growth rates for real GDP and some of its more interest sensitive components to
help identify business peaks is that some of the lead times may have been too
short to enable the Fed to take actions that might have prevented another
recession.
At the end of 2000 all four of our interest sensitive variables were
signaling a possible recession and this may have encouraged Fed Chairman Alan
Greenspan to promptly initiate a series of Fed funds rate cuts.
Once the economy has peaked out recession watchers should keep a close eye
on the ratio of real gross private domestic investment (GPDI) to real GDP. The
higher this ratio in a quarter containing an NBER peak, other things equal, the
longer the recession is likely to be. Rapid declines in real GPDI in the
following quarter, however, have tended to shorten recessions. See Table 4.2.
# The hatch mark identifies a special quarter when only two of the GDP components
declined but they were both relatively large asterisks declines.
(1)n. The monthly average prime rate at the end of the quarter expressed as a
ratio to the monthly cyclical low value for the prime rate in the preceding
business expansion.
* The asterisks identify consumer durable declines amounting to five percent or
more, declines in non residential equipment and software amounting to three
percent or more, and declines in residential investment amounting to nine percent
or more. Since the Korean War wind down recession of 1953-54, the lead from the
next business peak identified by the National Bureau of Economic Research in the
last column have all been equal to seven months or less for quarters with two or
more asterisks.
Source of basic data: The annualized growth rates for real GDP and its
components can be obtained from the National Income and Product Account Table
8.1.
*The asterisks identify monthly durations associated with following quarter
declines in real GPDI amounting to 6.9 percent or more in the last column.
Recessions with rapid first quarter declines in GPDI tend to be of shorter
duration than those that get off to a slow start. This has been especially the
case since the Korean War wind down recession of 1953-54.
Source of basic data: The Survey of Current Business, August 2000.
Edward Renshaw
Of the twelve components in the Commerce Department's original index of
leading economic indicators, which was first released in November 1968, only
three (the average workweek in manufacturing, residential building permits, and
stock prices) have survived without substantial modification.
The average weekly hours of nonsupervisory workers in manufacturing
provides a good example of an enduring component of the index of leading
indicators that has always peaked out in advance of an economic recession but has
sometimes not declined enough to provide a very reliable warning of its
occurrence (Renshaw 1992, Table 1.20).
Evidence in support of a more stable economy that may have helped to
prolong the most recent business expansion, however, is especially apparent when
one examines cyclical fluctuations in nonagricultural payroll employment, and its
major components: employment in goods producing industries and service
industries. Since the mild recession of 1960-61 employment recessions have been
largely confined to layoffs in goods producing industries.
The good news with regard to employment in manufacturing is that automation
and other improvements in productivity have reduced the share of total employment
in goods producing industries by more than 50 percent since the post World War
II peak in 1953. From March 1980 to December 1999 there was actually a net
reduction in the number of persons classified as being employed in goods
production at a time when real GDP is estimated to have increased by more than
80 percent.
The shrinking share of nonsupervisory workers in recession prone, goods
producing industries has helped to moderate recent employment recessions in
comparison to those which occurred from 1948-58. See Table
5.5.
The expense of automating has also made it more advantageous for some
industries, such as the automobile industry, to offer rebates rather than cut
production and lay off skilled workers when consumer demand is weak. This should
help to stabilize the demand for motor vehicles and lengthen business expansions.
Since the mild and rather unexpected recession of 1960-61 employment in
goods producing industries has usually peaked at least four months in advance of
the recessionary peaks identified by NBER. The only exception, so far, is a one
month lag after the recessionary peak of November 1973 when peace time wage and
price controls were still in effect and the Fed was apparently hoping that the
inflationary shock resulting from crop failures in the Soviet Union and other
parts of the world would soon moderate. See Table 5.1.
A failure on the part of the Board of Governors to raise short term interest
rates as rapidly as the consumer price index during the last half of 1973
encouraged many people to invest in new houses and other types of durable goods
rather than have the purchasing power of their savings diminished by inflation
while on deposit in commercial banks and other types of thrift institutions.
The 18 month lead time for goods producing employment before the July 1990
peak in business activity was probably one of the factors that encouraged the
Board of Governors to lower the federal funds rate by 1.70 percentage points
between March 1989 and July 1990. While the reduction in short term interest
rates wasn't sufficient to prevent the employment recession of 1990-92, there is
a possibility that this recession wouldn't have occurred if it weren't for the
oil price shock and economic uncertainty associated with Iraq's invasion of
Kuwait in August 1990.
The civilian unemployment rate, is one of the most widely publicized
cyclical indicators. It is classified as being a leading economic indicator at
recessionary peaks and a lagging indicator at recessionary troughs. From 1947-
2001 the U.S. economy was always mired down in an NBER type of recession after
a cumulative increase in the civilian unemployment rate amounting to .9
percentage points or more. See Table 5.2. During August
2001 the unemployment rate was up one full percentage point from its 2000 monthly
low of 3.9 percent.
Increases in the civilian unemployment rate during the nine recessions from
1947 to 1994 ranged from a low of 2.2 percentage points for the short-lived
recession of 1980 to a high of 4.5 percentage points for the 1948-49 recession.
The highest unemployment rate in the post World War II period was 10.8 percent
for December 1982 and can be compared to an average unemployment rate of 24.9
percent for all of 1933.
While economists have not had much success at developing models that do a
good job of explaining the duration of business expansions, some progress has
been made in explaining the duration of employment recessions.
Since much of the reduction in real GDP during an economic recession can
be explained by a reduction in inventory investment, it is not unreasonable to
suppose that the sooner the peak in industrial production, other things equal,
the sooner an inventory recession will be over.
In the post 1947 period the duration of employment recessions has been
about equal to 15 months minus the monthly lead time for industrial production
at its peak relative to the peak in payroll employment. See Table 5.3. The implication would seem to be that a year or
more may be required to turn an industrial recession around and that lower
interest rates may not be very effective at halting a cyclical decline in payroll
employment that begins before or at about the same time as the decline in
industrial production. This formula was predicting that the recemt payroll
recession might bottom out in December of 2001.
That did not happen, however. There was a bottom in April 2002 but the
recovery has been so weak as of November 2002 that the employment recession may
not be over.
Another way to estimate the duration of payroll employment recessions is
to calculate the percent decline during the first three months of the recession.
The steeper the decline, other things equal, the shorter the recession. See Table 5.7. The gain in employment during the three months
prior to the payroll recession is not nearly as helpful. As more information
becomes available, however, it becomes increasingly clear that modest declines
in payroll employment aren't always associated with prolonged recessions. See
the last column of this table related to six month declines and the last column
of Table 5.3.
From 1946-62 the U.S. economy never experienced an economic recession until
at least three months after a sustained increase in the prime rate charged by
banks of a third or more from its preceding cyclical low. The data in Table 5.4 would suggest, however, that the U.S. economy has
become more resistant to tighter monetary policy in recent decades.
In the period from 1963-2001 our economy never experienced a recession
until at least five months after the prime rate was up more than fifty percent
from its cyclical low. During two of the last six recessions the U.S. economy
did not peak out until 16 months after a cumulative increase in the prime rate
of more than 50 percent from its cyclical low.
Once the prime rate has signaled a near term recession it makes sense for
economy watchers to pay close attention to the behavior of the average weekly
hours of production or nonsupervisory workers in manufacturing which has a long
history of being included in the index of leading economic indicators which was
pioneered by the National Bureau of Economic Research and eventually turned over
to the Conference Board. Cumulative peak related declines of .3 hours for this
variable following the prime rate signals in the 1946-2001 period were always
followed by a recessionary peak in from one to only eleven months if the economy
was not already suffering from a recession, as was the case during February 1974.
The August 2000 signal for this variable implied that the longest expansion in
business cycle history should end either in the year 2000 or the first six months
of 2001.
Once the prime rate signal has been followed by a shorter work week signal
the historical data would suggest that economy watchers should pay close
attention to associated or following back-to-back increases in the civilian
unemployment rate. Since 1959 the U.S. economy has always experienced a
recessionary peak after this type of triple whammy.
Another way to try to determine whether the U.S economy is in the midst of
a recession is to examine monthly declines in nonagricultural payroll employment.
Since the Korean War build-up year of 1952 our economy has always been involved
in a recession once payroll employment experienced at least a third lower closing
total spread over a three to five month period.
The leads and lags associated with various coincident indicators in
Table 5.6 would strongly suggest that NBER's dating of
recessionary peaks use to be heavily influenced by the behavior of personal
income less transfer payments expressed in deflated dollars. More of the lead-
lag times for this indicator are in the minus one to plus one range than for any
other component of BEA's index of coincident indicators prior to the current
recession.
It should be appreciated, however, that this indicator has experienced
numerous ups and downs on a month-to-month basis during business expansions and
has been subject to a lot of revisions spread over five or more months which may
have provided NBER with a nice excuse for not getting involved in dating business
peaks until after some recessions were over.
There is also a question as to whether personal income less transfer
payments will continue to be a fairly reliable predictor of recessionary peaks
in a new era where many corporate executives and some of the employees in start
up companies have been rewarded for a large portion of their services with stock
options (which can't be exercised immediately) rather than ordinary wages and
salaries. Another reason for ignoring this indicator is that it is no longer
computed on a regular basis.
While persons concerned about the possibility of another recession should
pay close attention to industrial production and manufacturing and trade sales,
which have often been leading indicators prior to NBER peaks, there is not much
doubt that most people (including NBER's dating committee) are now more concerned
about jobs than other indicators of a recessionary peak.
One of the problems with recessionary peaks and troughs of a judgmental
nature is that they don't provide one with a reliable indication of how severe
the recessions were and may have discouraged economic analysts from investing
more time and effort in developing indexes of leading economic indicators that
do a good job of tracking specific indicators such as payroll employment or
industrial production.
If future dips in most coincident indicators were to become shallower and
less prolonged there might be some difficult problems of a controversial nature
in deciding whether there has been a recession.
These considerations and the recent propensity for actual and prospective
recessions to become highly politicized suggest that it might not be such a bad
idea if the National Bureau of Economic Research simply abandoned the task of
trying to date business peaks.
Renshaw, Edward (1992). The Practical Forecasters' Almanac(Burr Ridge,
Illinois: Irwin Professional Publishing).
(7)n. Months from the NBER peak date in column (3) to the date in column
(2) when goods employment was down one percent or more from its own peak.
Source of basic data: The Survey of Current Business, January 1995 and more
recent issues.
(4)n. Months from the business peaks identified by NBER after the civilian
unemployment rate has reversed itself on the upside by at least .9
percentage points after experiencing a trough.
Source of Data: Economic Report of the President.
(3)n. The predicted duration is equal to 15 months minus the monthly lead
time for the industrial production peak in column (1).
Source of data: The old cyclical indicator section of the Survey of
Current Business, October 1994.
The recessionary signals for the average weekly hours of production or
nonsupervisory workers in manufacturing are based on following cumulative peak
declines of .3 hours after the prime rate was up a third or more from 1946-61 and
over 50 percent from 1962-2000.
The unemployment signals are based on back-to-back monthly increases of .1
percent or more that are associated with or follow the average weekly hours
signals.
Another interesting way to begin the task of identifying recessionary peaks since
1962 is to look for cyclical increases in the prime rate equal to 3.50 percentage
points or more. The lead times for this signal are about the same, on the
average, as an increase in the prime rate of over 50 percent.
The figures in parentheses are the lead times in months from the recessionary
peaks and troughs identified by the National Bureau of Economic Research.
Source of basic data: Survey of Current Business, October 1994 and more recent
issues.
NST identifies a case with no specific turn. Personal Income less transfer
payments was estimated to have experienced a double peak expressed in 1987
dollars on November 1969 and April 1970. It should be noted that this indicator
is no longer computed on a regular basis by BEA.
Source of basic data: Survey of Current Business, October 1994.
Source of basic data: The Survey of Current Business, October 1994.
Edward Renshaw
In 1999 total U.S. crude oil production was 38 percent less than in 1970,
in spite of large oil discoveries in Alaska and improved offshore drilling
technology which has boosted output in the Gulf of Mexico. Since the U.S.
Department of Energy began tracking production and imports in 1973, oil imports
have almost tripled, increasing from about 12 percent of total supply to more
than 57 percent in 1998.
Rapid increases in oil prices in conjunction with weak economic indicators
are of particular concern since they may indicate that the economy has already
slipped into another recession. The eleven poorest growth years for the U.S.
economy from 1948-2001, were all preceded by a year-to-year increase in the motor
fuel component of the consumer price index amounting to three percent or more and
a June-December increase in industrial production of .1 percent or less. See Table 6.1.
Since 1946 the U.S. economy was always mired down in an industrial
recession after back-to-back increases in the average annual values for the motor
fuels component of the all item consumer price index amounting to nine percent
or more. See Table 6.3.
While higher oil prices may not be the cause of economic recessions they
can lead to restrictive monetary policies that will help to terminate a business
expansion. Except for 1953 (when the consumer price index was still increasing
at a modest .7 percent annual rate), 1971 and 1976 (when the U.S. economy was
beginning to recover from economic recessions), and 1999 (when oil prices were
recovering from the Asian Flu slump), the Federal Reserve has not allowed the
conventional money supply M1 to increase as rapidly as the consumer price index
when crude oil prices were increasing at an average rate of five percent or more.
If the economy was not already in an economic recession, the Fed has often
resisted the inflationary effect of large increases in crude oil prices by
allowing short-term interest rates to rise at a rapid rate. The 1990-91
recession was different, however. Between March 1989 and July 1990 the Fed
allowed the yield on new issues of 91 day Treasury bills to decline by 1.2
percentage points, or more than 13 percent, in an unsuccessful effort to prevent
an economic recession.
It is easy to understand how a large increase in gasoline prices might
trigger a recession in the United States. It will reduce the demand for large,
gas guzzling motor vehicles produced by domestic auto makers and siphon off
purchasing power from consumers to oil companies and petroleum exporting
countries that might not be able to recycle the proceeds in a manner that will
quickly generate an offsetting demand for goods and services produced in the USA.
U.S. oil companies will use some of the extra revenue to look for petroleum
in other countries, reduce their indebtedness or repurchase some of their own
shares and petroleum exporting countries will use some of their proceeds to
purchase securities issued by the U.S. Treasury, that help to finance
unemployment benefits, but don't generate jobs in this country. An oil induced
recession that begins at the gas pump and cripples the auto industry, can then
be expected to spread to other industries as consumers cut back expenditures for
other goods and services to keep their motor vehicles operating.
Once an economic recession has begun in the United States it can easily
spread to other countries as a result of a reduction in imports for other types
of goods and services besides petroleum products. After the Arab oil embargo of
October 1973, for example, U.S. imports in constant dollars declined by 13.3
percent from the fourth quarter of 1973 to the first quarter of 1975. During the
milder recession of 1990-91 the reduction in real imports was 5.1 percent.
The 1990-91 oil related recession in the United States was unique in that
the decline in industrial production in Canada and the United Kingdom began
before the October 1990 drop in U.S. industrial output. Japan and Germany were
able to weather the storm for a while but eventually slipped into recessions that
were partly the result of a very anemic recovery from the 1990-91 recession in
the United States.
The most disturbing aspect to the 1990-91 recession in the United States
is that it occurred during a period of monetary easing without a very precipitous
increase in the price of motor fuels. The implication would seem to be that the
U.S. economy may be even more vulnerable to oil price shocks in the future as it
becomes more dependent on imported oil. Most adults now own their own cars and
in recent years many colleges and universities have had to expand their parking
lots to accommodate commuting students.
From 1973 to 1991 there was a 63 percent increase in passenger car miles
per gallon in the USA. This helped to lower the real price of motor fuels to
levels not experienced since the great depression of the 1930s. Since the
collapse of world oil prices in the mid 1980s and the relapse which occurred
after the successful completion of Operation Desert Storm, however, American
motorists have become infatuated with minivans, light duty trucks and sport
utility vehicles that do not get very good gas mileage. This can also be
expected to make the U.S. more vulnerable to another oil price shock recession.
Increases in oil production in the North Sea and other areas outside OPEC,
and a recessionary drop in oil demand in some Asian countries led to a dramatic
drop in crude oil prices in 1998. Production cut-backs by Saudi Arabia and other
petroleum exporting countries caused the price of imported oil to rise above the
1998 level in March of 1999, however. During 2000 the price of crude oil was
almost twice as high as the 1999 average price of $15.56 per barrel.
One of the problems with oil prices and most of the other recessionary
indicators in this compendium is that they do not do a very good job of enabling
one to identify recessionary peaks close to their occurrence. In
Table 6.2 we provide readers with some
adjusted lead times for five widely publicized variables with both leading and
coincident properties that have been helpful at identifying the last nine
recessionary peak months in close proximity to their occurrence. The indicator
thresholds are based on the largest cumulative changes with the property of
enabling one to identify all of these peaks with a lag of not more than two
months.
Our first indicator of a possible recession is a cumulative increase in the
civilian unemployment rate of .4 percentage points or more after its cyclical
low. This is an important variable to keep an eye on since some economists at
the Fed believe that we will be stuck with a problem of accelerating inflation
if the unemployment rate is allowed to remain below 4.5 percent.
The employment variable with one of the best forecasting records is the
number of employees on nonagricultural payrolls in goods-producing industries.
A cumulative drop in goods-producing employment amounting to 89 thousand workers
is not a healthy development and a cumulative increase of 70 thousand in the
average weekly number of initial claims for unemployment insurance has often
meant that a business expansion was over.
Manufacturing and trade sales expressed in constant dollars have a history
of being perhaps the best "coincident" indicator with some leading indicator
properties. Cumulative declines in this variable amounting to 1.0 percent or
more have been closely linked to recessions. Our last indicator of a
recessionary peak is a cumulative decline in the monthly average values for the
S&P composite stock price index amounting to four percent or more.
It should be appreciated that there have been some false or rather
premature signals associated with some of these recessionary thresholds. When
three or more of these variables are pointing in the direction of a possible peak
in business activity, however, investors should be rather cautious.
When four of these indicators were pointing in the direction of a recession
six of the last dated dated business expansions were over. When all of these
indicators were pointing in the direction of a recession every expansion was
over.
As of March 2001 all of these indicators were pointing in the direction of
a recession.
**The financial return associated with the S&P composite stock price index the
year after an increase in the price of motor fuels of 3 percent or more and a
June-December decline in industrial production of .5 percent or more.
Source of basic data: and Economic Report of the President. For an
interesting discussion of major oil price shocks see: Philip Verleger, "Third
Oil Price Shock: Real or Imaginary?" Oil and Gas Journal June 12, 2000,
pp. 76-88.
(1) Civilian unemployment rate up .4 percentage points from cyclical low.
(2) Goods-producing employment down 89 thousand from cyclical peak.
(3) Average weekly initial unemployment insurance claims up 70 thousand from
cyclical low.
(4) Manufacturing and trade sales expressed in constant dollars down 1.0
percent or more from cyclical high.
(5) Monthly average value for the S&P composite down 4 percent or more from
cyclical high.
Source of basic data: Survey of Current Business, October 1994 to January
1995.
Since 1946 the U.S. economy has always been in the midst of an industrial
recession after back-to-back increases in the average price of motor fuels of
nine percent or more.
Edward Renshaw
In 1990 three economists received the Nobel Prize for their contributions
to a relatively new branch of the dismal science that can perhaps best be
described as financial economics. The contributions of Harry Markowitz, William
Sharpe and Merton Miller have revolutionized the teaching of finance in business
schools and have helped to crowd the IS-LM framework, which was made famous by
another Nobel Laureate, to the back of most text books on money and banking.
The blurry nature of what used to be separate disciplines has led the
National Association of Colleges and Employers, which tracks campus recruiting
activity, to make no distinction between economics and finance majors when
reporting salary offers that are made to college graduates.
The ideas of Markowitz, Sharpe and Miller, in any event, were well received
by Nobel Laureates Paul Samuelson, James Tobin and Franco Modigliani, and a wider
spectrum of economists who believe that financial markets are relatively
efficient. While the efficient market hypothesis has been challenged in recent
years, the economics profession has not had a lot of success at developing models
that refute the random walk hypothesis and allow one to take advantage of
exceptional circumstances.
There is one model, however, which has withstood a test of both time, and
a major change in the way GDP and its components are now deflated, that may be
of some value in helping one to understand the bull market of 1995-99.
During the last seven recessions the cumulative decline in gross private
domestic investment has always been greater than the decline in real GDP. The
volatility of investment and its sensitivity to declines in real GDP can be
partially explained on the basis of an accelerator principle.
This principle is firmly embedded in pre-Keynesian literature. Michael
Evans (1969) has noted that its origins can be traced back to publications of
Aftalian, Bickerdike and Hawtrey in the early 1900s. Probably the best-known
early study is J. M. Clark's (1917) article on "Business Acceleration and the Law
of Demand".
An updated accelerator model which was publicized by Renshaw in (1992,
Table 1.32) is illustrated in Table 7.1 where we show that
the year-to-year growth in real gross private fixed domestic investment has often
been about equal to three times the growth rate for real GDP minus six percentage
points. This relationship can be derived from a Cobb Douglas production function
if we assume that fixed investment is proportional to the flow of productive
services from the capital stock.
Where Q is real GDP, K is the flow of productive services from the capital
stock and T is a time trend that represents technology, labor and other resources
that have been left out of the production function we have:
Since depreciation charges and the income from capital services are a
primary source of funds for investment purposes it is not unreasonable to suppose
that the aggregate amount of real fixed investment might be roughly proportional
to the flow of services from the capital stock:
Substituting equation (2) into (1) and noting that small changes in
variables that are related to each other in a multiplicative way are
approximately equal to percentage changes we can conclude that: the percentage
change in real GDP should be about equal to 2.0 percentage points plus one-third
of the percentage change in real fixed private domestic investment. One of the
implications of this relationship (when the equation is solved for the growth of
fixed investment) is that real GDP must increase by about two percent per year
just to keep private investment from falling and having a deleterious feedback
effect on the growth of economic activity.
The instability of the GDP growth rate in the zero to two percent range has
sometimes made it very difficult for the Federal Reserve to effectively fight
inflation without pushing the U.S. economy into a recession.
When the actual growth of real fixed investment expressed in chained 1996
dollars has been at least 2.2 percentage points greater than the expected
increase that is predicted by the accelerator model in Table
7.1, optimism on the part of business enterprises and developers has tended
to spread to the stock market and produce following year returns for the S&P
index (dividends plus price appreciation expressed as a percent of price at the
beginning of the year) that have been positive. The behavior of the stock market
in the year 2000 would strongly suggest, however, that a very prolonged period
of super optimism can help to get some investors in trouble.
The following year financial returns the S&P composite have also been
positive after a decline in real GDP expressed in 1996 dollars of minus .2
percentage points or more.
It should be emphasized that investment optimism on the part of business
enterprises and developers hasn't done a very good job of protecting investors
from some nerve racking "corrections" in the midst of more pronounced bull
markets. Persons who purchased an index fund tracking the S&P 500 at the end of
1946, 1947, 1969, 1977 and 1993 would have had to wait a full year to be sure of
getting their money back. All of these corrections, however, were followed by
at least one year of double digit returns before the S&P index crashed for a long
enough period of time so that its dividends were unable to compensate for lower
prices.
It should be noted that positive error terms for the accelerator model in
column (4) of Table 7.1 have a propensity to flock
together or occur in "runs" and can be considered a good example of the Keynesian
notion of "animal spirits". The seven error terms in excess of 2.2 percent from
1992-98 have now exceeded the prolonged period of super investment optimism for
the U.S. economy which began toward the end of World War II and persisted from
1944-48 before ushering in the great bull market of the 1950s.
To identify NBER recessions from 1948-2000 one needs at least two lower
quarterly totals for real GDP in chain weighed 1996 dollars. See Table 7.2.
Quarterly declines of 1.8 percent or more for real GDP have all been
closely linked to NBER recessions and are helpful at enabling one to identify
recessions before they were over.
One quarterly decline of .3 percent or more in a sequence of two or more
growth rates under 2.0 percent help to illustrate the instability of real GDP in
the zero to two percent range and will sometimes allow one to identify an NBER
recession sooner than two negative growth rates.
From 1948-2001 there was only one "growth recession" involving a sequence
of three quarterly growth rates for real GDP under 2.5 percent. They were all
positive growth rates beginning in the second quarter of 1989 and were soon
followed by an NBER recession beginning in the third quarter of 1990.
Clark, J. M. (1917). "Business Acceleration and the Law of Demand," Journal
of Political Economy, Vol. 25, No. 1 (March), 217-235.
Evans,
Michael (1969). Macroeconomic Activity: Theory, Forecasting, and
Control(New York: Harper & Row),
(3)n. The predicted growth rate for fixed investment is equal to three times the
growth rate for real GDP in column (1) minus six percentage points.
T denotes years containing a trough in business activity.
# identifies year to year declines in fixed investment in excess of two percent.
Since the Korean War wind down recession of 1953-54 all of these declines have
been associated with economic recessions.
* denotes years when the difference between the actual and the predicted growth
rate for fixed investment was equal to 2.2 percentage points or more. From
1946-2000 all of these years were followed by a positive financial return for the
S&P index.
** denotes financial returns following a year to year decline in real GDP in
column (1) of minus .3 percentage points or more. All of these financial returns
have been positive.
Source: This is a partially up-dated table that was first published in The
Practical Forecasters' Almanac(Burr Ridge, Illinois: Irwin Professional
Publishing, 1992), Table 1.32, pp. 131-32. The growth rates for real GDP and
gross private fixed domestic investment have been revised to correspond with the
chain weighted values published in The Survey of Current Business,
December 1999.
"G" identifies growth recessions and "R" cases which were eventually dated as
real recessions by The National Bureau of Economic Research. To identify NBER
recessions one needs at least two lower quarterly values for real GDP. Quarterly
declines of 1.8 percent or more have all been closely linked to NBER recessions
and are helpful at enabling one to identify some recessions before they were
over.
*The asterisks identify declines associated with a second lower annualized
quarterly total for real GDP.
Source of basic data: The Survey of Survey of Current Business.
Edward Renshaw
While most large scale econometric forecasting models are basically
Keynesian in character, there has been a notable reluctance on the part of
textbook writers to confront economic theory with fact. Part of the
over-reliance on pure theory may be related to the difficulty that has been
encountered in establishing plausible direct multiplier relationships between
aggregate income and the two most important fiscal policy variables, government
spending and taxes. The absence of easily verified multipliers in the early post
World War II period led Milton Friedman to conclude in the words of Paul
Samuelson (1976), "that fiscal policy per se has essentially no predictable
effect of any significance on the prospects for inflation or deflation, for high
employment or mass unemployment."
Keynesian economics is basically disequilibrium or depressionary economics.
During periods of reasonably full employment one can use production possibility
curves involving a tradeoff between guns and butter to seriously question the
notion of sizable multiplier effects for government spending. Three economic
recessions in about one decade, the monetary revolution of 1979, and the Economic
Recovery Tax Act of 1981, however, helped to produce a "window of opportunity"
where it was easier to explain changes in the national income and product
accounts on the basis of a rather simple Keynesian model which focuses one's
attention on the problem of large budget and trade deficits in the U.S. (Renshaw
1990). A large decline in the personal saving rate and some major changes in the
way real GDP is computed, though, have made it desirable to up-date that version
of the Keynesian multiplier.
In Chapter 8 of The General Theory of Employment, Interest and Money
it is stated that: "The fundamental psychological law, upon which we are
entitled to depend with great confidence both a priori and from our knowledge of
human nature and from the detailed facts of experience, is that men are disposed,
as a rule and on the average, to increase their consumption as their income
increases, but not by as much as the increase in their income."
The simplest way to describe this type of consumption function is with the
linear equation, C = a + bY. When this equation is fit to cross-sectional data
for a particular time period one obtains a value for "a" that is positive and a
value for "b" that supports the Keynesian hypothesis that the marginal propensity
to consume "mpc" is less than one.
In the analysis which follows we will assume that the marginal propensity
to consume in relation to current income is in the vicinity of .5. This number
was first obtained from empirical data by Paul Samuelson in 1941. In an appendix
to Chapter 11 of A. H. Hansen's Fiscal Policy and Business Cycles he found
that "In the period between the two world wars, the marginal propensity to
consume relative to income produced was approximately .5."
While there may have been numerous years and periods when the mpc was not
equal to .5, there are a lot of advantages in featuring this number when
confronting theory with fact and trying to reconcile textbook multipliers with
those derived from complex models of the US economy.
In deriving multipliers, however, we will ignore the Commerce
Department's estimates of disposable income and follow the usual textbook
practice of utilizing a broader and less conventional definition of disposable
income. Our starting point is the accounting identity, income equals consumption
plus investment plus government purchases of goods and services plus net exports:
Consumption is now regarded to be a function not only of current income but
also wealth which can be approximated by consumption or the value of income in
the preceding period (Duesenberry 1949, Modigliani 1947 and Friedman 1957).
Since positive amounts of net exports cannot be consumed in the home country and
are unlikely to have as big an effect on local output as domestic investment, if
they are used to finance investment in other countries, this variable will be
subtracted from GDP along with net taxes to arrive at a useful definition of
disposable income that can be simply linked to both consumption and aggregate
income:
Disregarding the constant term for equation (3), which is not very
significant from a statistical point of view, and substituting rounded values for
the remaining coefficients into accounting identity (1) enables one to obtain the
following multiplier equation:
In equation (4) we have separated the government expenditure
multiplier into two G variables to allow one to more easily assess the economic
implications of large trade and federal budget deficits in the national income
and product accounts.
In Table 8.1 all of the errors for our featured
version of the Keynesian multiplier model implied by equation (4) are assumed to
change the propensity to spend out of the previous year's income. The
coefficient for the lagged income (or GDP) variable in column (6) is expressed
in percentage points and is referred to as the "shifting propensity to spend".
While some of the error terms for this multiplier model may indeed be
related to shifts in the marginal propensity to consume out of current income,
rather than the previous year's income, a consolidation of the error terms in
conjunction with lagged GDP can serve a useful purpose in enabling one to more
easily appreciate, in relative terms, how well this version of Keynesian
economics has explained the data during different periods and at various points
in the business cycle.
The smaller the year-year changes in the shifting propensity to spend out
of the previous year's income in column (7), the more confident one can be that
the marginal propensity to consume is in the vicinity of .5 and that our fiscal
policy multipliers are in the right ball park.
The large differences in the shifting propensity to spend that emerged in
the late 1960s and continued to occur through most of the 1980s, help to explain
why Keynesian economics has fallen out of favor, in some quarters, and is now
confronted with a number of competing schools of thought (Snowdon, Vane and
Wynarczyk 1994).
During the 1990s, however, the year-year differences in column (7) have
been less on the average than was formerly the case. The implication is that
our multiplier equation now fits the data better than was the case in the 1960s,
when Keynesian economics was the most highly featured school of macroeconomic
thought.
In a world where inflation is no longer as serious a problem as was the
case in the late 1960s through portions of the 1980s it can be instructive to use
the data in Table 8.1 to better assess the success and
failure of the fiscal policy implications of Keynesian economics.
The data in column (2) on the real value of the federal deficit in the
national income and product accounts help to dramatize the importance of
automatic "built in" stabilizers in helping to offset the deleterious effect of
recessionary declines in gross private domestic investment on real GDP. From
1963-93 the real value of the federal deficit more than doubled from its
preceding cyclical low to the first year of recovery from an economic recession.
The peaks and troughs in economic activity compiled by the National Bureau
of Economic Research (NBER) suggest that the emergence of numerous automatic
stabilizers since the great depression of the 1930s may have shortened the
duration of economic recessions. From June 1857 to June 1938 the United States
experienced 21 economic recessions with an average duration of 21.2 months. In
the post World War II period no recession has persisted that long. The average
duration for the ten recessions from 1945-95 is only 10.4 months.
The longest recession recorded by NBER lasted 65 months from October 1873
to March 1879. The next longest persisted for 43 months from August 1929 to
March 1933. In the post-Keynesian era, the two longest recessions were the 16
month contractions from November 1973 to March 1975 and from July 1981 to
November 1982.
The sad thing about this success story is that it has depended a lot more
on an expansion of consumption type transfer payments rather than a contra
cyclical expansion of government investment in infrastructure which can improve
economic efficiency and yield long term benefits. One way to begin the task of
validating this point is to examine changes in the inflation adjusted value of
government consumption and investment in column (3) of Table
8.1.
During the recessionary trough year of 1961 there was a thirty billion
dollar increase in total government consumption and investment and only a lower
surplus for the federal government in the national income and product accounts.
From the recessionary peak in business activity in 1990 to the anemic
recovery year of 1992 there was a 133.6 billion dollar increase in the real value
of the federal deficit and only a 25.2 billion dollar increase in total
government consumption and investment.
Since the mild recession of 1960-61 government investment in
structures has declined more often than not during economic recessions. In
Albany, New York, Washington, D.C. and many other capital cities in the USA
construction budgets are routinely robbed or short changed during economic
recessions to hide deficits. This is a very inefficient way to cope with the
problem of recurring recessions.
Equation (4) implies a first year multiplier of only one for an increase
in the deficit that is associated with a cut in taxes. This can be compared to
a multiplier of two for an increase in government purchases that is not offset
by an increase in taxes.
The problem with a tax cut is that businesses and consumers are left with
a choice as to whether they use the cut to bolster their savings (and help
finance the deficit) or to increase their consumption of goods and services. An
increase in government purchases, other things equal, ensures an extra round of
expenditure that will help to get the economy moving again. If the increase in
expenditure is used to enhance the nation's infrastructure it can be expected to
yield even more benefits with the passage of time.
The spectacular deficit for 1992 and the slow decline in its size in the
wake of persistent cut backs in the real value of federal spending for goods and
services since 1992 inspired Chairman Alan Greenspan and economists associated
with the Federal Reserve Bank of Kansas City to host a major symposium on
Budget Deficits and Debt: Issues and Options at Jackson Hole, Wyoming
from August 31-September 2, 1995. In his summary of the symposium's conclusions
Stuart Weiner noted:
Stanley Fischer (1994) examined data for a sample of 94 countries between
1962 and 1988 and found that large budget deficits are negatively associated with
economic growth.
Large budget deficits, moreover, provide no assurance that a country's
unemployment rate will be low. Belgium, Italy, Canada, Spain and Austria were
projected to have more net governmental financial liabilities outstanding as a
percentage of nominal GDP during 1996 than the United States and were all
suffering from higher unemployment rates according to statistics in the OECD
Economic Outlook for June 1996.
Economists in recent decades, however, have tended to favor tax cuts over
increases in government purchases when the U.S. economy slips into a recession
on the theory that they can be implemented more quickly. There is little
evidence to support this conclusion so far. During the 1990-01 recession the
U.S. Congress was inundated with all sorts of tax cut proposals but none of them
were implemented.
It now seems clear that the tax cuts and rebates enacted during the early
stage of the second Bush administrtion were also unsuccessful at preventing
another recession.
One way to facilitate a contra cyclical investment policy for public
infrastructure would be to develop aid formulas for roads, sewage plants, and
other structures that are partly funded by the federal government which are more
contra cyclical in character. In the 1947-2001 period the civilian unemployment
rate increased from one to 16 months in advance of the ten economic recessions
which have been identified by the National Bureau of Economic Research. By
letting federal support for public infrastructure automatically increase in
response to rising unemployment it might be possible to prevent some recessions
and moderate those that do occur.
If Keynes were alive today I am sure that he would also be intrigued by
proposals to allow first time home, and perhaps new car buyers, to make penalty
free withdrawals from their IRAs. If these withdrawals were limited to periods
of economic weakness when real GDP is increasing at a below average rate, or when
unemployment is excessively high, it would help to stabilize the U.S. economy and
might even reduce the federal budget deficit since most first time home buyers
would have to borrow far more than they withdraw from an IRA to finance the
construction of a new home.
Does a government expenditure multiplier in the vicinity of two or less
mean that Robert Lucas was correct in suggesting in 1982 that Keynesian economics
is dead. I don't think so. There can be crowding in as well as crowding out.
His policy ineffectiveness proposition should be considered a special case that
may or may not be true depending on the circumstances. If a tax cut, or a timely
increase in government expenditure, keeps private investment from falling further
during an economic recession, that will help to moderate the recession and might
very well imply an indirect effect upon the economy that is greater than the
direct effect.
The most notable difference between the great depression of the 1930s and
recent recessions is the extent to which an increase in the government deficit
was able to offset the adverse effect of a decrease in investment spending.
During the 1929-32 recession the increase in the budget deficit only offset 9.6
percent of the assumed direct and indirect effect of the slump in investment
(Renshaw 1990). In the post 1947 period increases in the government deficit have
typically offset from 21 to more than 80 percent of the decline in investment
times its assumed multiplier of two.
When the indirect effect of a slump in investment spending is largely
offset by increases in the other variables in equation (4), a steep decline in
investment spending will apparently reverse itself in a year or so as excess
inventory is liquidated and worn out equipment breaks down and needs to be
replaced.
While the Keynesian multiplier model focuses one's attention on
fluctuations in gross private domestic investment as a primary cause of
recessions it is by no means clear that the blame for economic recessions should
be placed on business enterprises. The Commerce Department's revised data for
real GDP and its components indicate that consumer investment in automobiles and
other durable goods peaked out before producer's investment in new equipment
during five of the seven business expansions from 1958-2001.
It should also be noted that the shifting propensity to spend out of the
previous year's income in column (6) of Table 8.1 has
declined during economic recessions. At least part of this unexplained collapse
in spending should probably be attributed to consumers.
Some macroeconomic principles textbooks contain a page or two
illustrating the Keynesian notion that "by trying to save more consumers may
end up saving less". Validating the "paradox of thrift" in its strongest
form, however, was so difficult in the early post World War II period that
the basic idea is seldom mentioned in more advanced textbooks concerned with
macroeconomics.
Unemployment insurance and other transfer payment programs to aid
families with workers that have lost their jobs, however, have made it more
feasible for consumers in general to actually increase their savings during
an economic recession by purchasing government securities (directly or
indirectly) and helping to finance an automatic increase in the government
deficit.
While the peak in the Commerce Department's personal saving rate
usually comes fairly early in a business expansion, the data in Table 8.2 would suggest that there may be some truth to
the idea that the saving behavior of consumers can precipitate a recession.
During the six business expansions from 1958-90 the personal saving rate
consistently increased during the quarter containing a peak in economic activity.
The four cases from 1948-2000 with an increase of .7 percentage points or
more in the saving rate during peak quarters have longer duration records than
those cases with no increase or an increase of less than .7 percentage points.
It used to be the case that consumers could be counted on to lower
their saving rate during an economic recession and in so doing help to
moderate and shorten the recession. During four of the last five completed
recessions, however, the personal saving rate was also higher during the
trough quarter for real GDP than before the quarter containing a peak in
economic activity.
Bryant, Ralph C., Dale W. Henderson, Gerald Holthham, Peter Hooper and Steven A.
Symansky (1988). Empirical Macroeconomics for Interdependent
Economies(Washington: The Brookings Institution), 63-72.
Case, Karl and Ray Fair (1989). Principles of Economics(New Jersey:
Prentice Hall), 831-32.
Campbell, John and N. Gregory Mankiw (1991). "The Response of Consumption to
Income," European Economic Review.
Duesenberry, James (1949). Income, Saving and the Theory of
Consumption(Cambridge, Mass.: Harvard University Press).
-----, (1965). The Brookings Quarterly Econometric Model of the US(Rand
McNally), Chapter 7.
Evans, Michael (1969). Macroeconomic Activity(Harper & Row), Chapter 3.
Fair, Ray (1984). Specification, Estimation, and Analysis of
Macroeconometric Models(Harvard University Press).
Federal Reserve Bank of Kansas City (1995). Budget Deficits and Debt: Issues
and Options.
Fischer, Stanley (1994). "The Role of Macroeconomic Factors in Growth," NBER
Working Paper No. 4565.
Friedman, Milton (1957). A Theory of the Consumption Function(Princeton:
Princeton University Press).
Gallagher, G. and Associates (1989). GRE Economics
Test(Piscataway, New Jersey: Research and Education Association).
Lucas, Robert. (1982). "The Death of Keynes," in Viewpoints on Supply Side
Economics, ed. Thomas Hailstones (Richmond, Virginia: Robert Dane), 3.
Modigliani, Franco (1947). "Fluctuations in the Savings-Income Ratio,"Studies
in Income and Wealth(New York: National Bureau of Economic Research),
371-441.
Renshaw, Edward (1967). "The Future Income Hypothesis," The Southern Economic
Journal, 34(July), 40-52.
-----, (1990). "A Keynesian View of the US Budget and Trade Deficits,"Public
Finance, 45(No. 3), 440-48.
Samuelson, Paul (1941). "A Statistical Analysis of the Consumption
Function," in the appendix to chapter 11 of A. H. Hansen, Fiscal Policy and
Business Cycles.
Snowdon, Brian, Howard Vane and Peter Wynarczyk (1994). A Modern Guide to
Macroeconomics(Brookfield Vermont: Edward Elgar Publishing Company).
(6)n. The shifting propensity to spend out of the previous year's income is
equal to column (5) minus two times column (1) minus the sum of columns (2), (3)
and (4) when this Keynesian type residual is expressed as a percent of chain
weighted real GDP in the preceding year.
(-) The minus signs in parentheses following gross private domestic investment
in column (1) identify years when there was a decline in real investment. These
years are mostly associated with economic recessions. The only exceptions are
1967, 1985 and 1986 when large increases in government consumption and investment
in column (3) more than offset the reduction in real investment.
*The federal deficit in the national income and product accounts deflated by the
chain-type price index for gross domestic product in those years containing a
recessionary trough in economic activity. Built-in stabilizers have resulted in
substantial increases in the real value of the federal deficit in trough years.
**Identifies years when the absolute first difference in the shifting
propensity to spend in column (6)n was equal to 1.0 percentage points or
more. Only one of the shifts in the 1990s have been that large.
L identifies the low value for the business cycle.
H identifies the high value for the business cycle.
P identifies a year containing a recessionary peak in economic activity.
T identifies a year containing a recessionary trough in economic activity.
Source of basic data: Economic Report of the President.
(3)n. The (+) symbols identify cases of acceleration in the personal saving
rate during quarters containing a recessionary peak in real GDP.
*Identifies cases where the personal saving rate was higher during the
trough quarter than before the quarter containing a peak in real GDP.
#The hatch marks identify durations associated with an increase in the personal
saving rate amounting to .7 percentage points or more in the peak quarter. These
recessions have been longer, on the average, than those cases with no increase
or a smaller increase.
Source of basic data: The Survey of Current Business.
Edward Renshaw
Section 2A of the Federal Reserve Act as amended, requires the Board of
Governors "to promote effectively the goals of maximum employment, stable prices
and moderate long-term interest rates". Moreover, in carrying out monetary
policy the Fed is to take "account of past and prospective developments in
employment, unemployment, production, investment, real income, productivity,
international trade and payments and prices".
Robert Black, President of the Federal Reserve Bank of Richmond, criticized
this mandate in 1984 as being too sweeping and unrealistically general,
particularly if viewed as a set of objectives to be achieved in the short run.
He believes that price stability is the only sensible objective for the Fed to
try to achieve with monetary policy.
In 1989 Representative Stephen Neal sponsored a Congressional bill which
would have required the Fed to pursue policies aimed at eliminating inflation
within five years. The economic recession of 1990-91 had the effect of moth
balling that idea.
In 1995, however, Senator Connie Mack of Florida introduced legislation
known formally as the Economic Growth and Price Stability Act of 1995 which would
make price stability the primary goal of monetary policy. This bill would
require the Fed to establish an explicit numerical definition of price stability
and specify a time needed to achieve stability taking into account any output and
employment effects associated with the move to stability.
The Mack bill cites several reasons for making price stability the sole
objective of monetary policy. It argues that inflation distorts the price
mechanism, interrupting efficient allocation of resources and thereby diminishing
growth and living standards. Having a single objective would reduce uncertainty
about monetary policy and perhaps reduce some of the uncertainty surrounding a
firm's investment.
Finally, the Mack legislation argues that making price stability the
primary objective of the Federal Reserve would lead to a more stable economy.
The bill's authors are of the opinion that attempts of monetary policy to
stabilize the economy in the short run are often destabilizing since policy
changes affect the economy with long, variable, and highly unpredictable lags.
While most of the Governors of the Federal Reserve would probably support
the enactment of a Mack type bill it doesn't seem likely to happen in the near
future. The U.S. electorate has a long history of being less forgiving with
regard to an increase in the unemployment rate than with regard to a moderate
increase in the consumer price index.
Economics, if it is a science, is the science of tradeoffs. No one has
expressed this idea more colorfully than Daniel Seligman (1991): "Rule No. 1 in
thinking about environmental problems is that there are no 'solutions,' only
tradeoffs. We want a maximum of economic growth and a minimum of environmental
damage."
The recession of 1990-91 reduced the CPI inflation rate to the lowest level
in a presidential election year in over two decades and may have told us
something about the nature of the electorate's willingness to sacrifice economic
growth for a lower inflation rate. Is there a real tradeoff between inflation
and the unemployment associated with recent recessions or are they separate
problems in the minds of today's electorate?
In 1814 Broughman noted that, "A government is not supported a hundredth
part so much by the constant, uniform, quiet prosperity of the country as by
those damned spurts which Pitt used to have just in the nick of time." (Cited by
Tufte in 1978, p. 3)
The notion that economic conditions influence voters so disturbed Nobel
Laureate George Stigler in (1973) that he engaged in some econometrics to
reaffirm Kramer's finding "on the electoral unimportance of ordinary fluctuations
in unemployment." He then presented an argument based on rational voter behavior
for "the unimportance of general economic conditions in national elections."
A more careful analysis of the historical data by Fair in (1978 and an
update in 1996), however, concluded that, "Economic events as measured by the
change in real economic activity in the year of the election do appear to have
an important effect on votes for president. It does not matter much whether this
change is measured by the growth rate of real per capita GNP or by the change in
the unemployment rate, although the former gives slightly better results."
The belief that the US electorate does hold incumbent political parties
accountable for their economic performance encouraged Nordhaus (1975) and MacRae
(1977) to articulated theories of a political business cycle. Central to their
theories is a preference function or iso-vote loss curve which reflects the
willingness of the electorate to endure extra unemployment for the sake of a
lower inflation rate. The authors assume that this tradeoff is predictable and
can be approximated by a parabola or half circles. They also assume that the
electorate is myopic and that optimum partisan policy will produce a condition
of high unemployment and deflation in the early years after an election which
will be followed by an inflationary boom as the next election approaches.
Keller and May (1984) have compiled some evidence which suggests that
President Nixon understood the theory of the political business cycle and may
have been successful at influencing monetary and fiscal policy to improve his
reelection chances. Other studies, however, do not provide convincing evidence
that governments have systematically manipulated economic policy in an effort to
influence the outcome of presidential elections. It remains to be seen whether
this will continue to be the case in the future. Bush's suggestion in 2000 that
the U.S. economy might be on the verge of a recession may have helped him get
elected and revived interest in the notion of a political business cycle.
When parabolas representing a non "turnover contour" curve are fit to the
election year inflation and unemployment rates which encompass all those cases
where the incumbent political party did not lose the presidential election, the
conclusion is that the tradeoff between inflation and unemployment is not very
stable. For the elections from 1920-88 Trahan and I in (1991) obtained a
parabola which implies a preferred inflation rate of 1.66 percent and a maximum
endurable unemployment rate of about 17.6 percent. When the parabola was fit to
data for the post World War II period, however, the preferred inflation rate was
2.84 percent and the maximum endurable unemployment rate was only 9.5 percent.
Richards (1993) has examined the percentage of respondents answering "yes"
to the Gallup Poll question, "Do you approve of the way President--_____is
handling his job?" Using quarterly data on this variable for the period 1961:1
through 1992:3 Richards has concluded that voters consider an inflation rate
equal to about three percent to be optimal in the long-run.
One of the problems with these and most other models pertaining to the
possibility of a political business cycle is that they didn't do a very good job
of predicting the outcome of the presidential elections in 1992 and 2000.
The recessionary indicator with the best track record for predicting the
outcome of presidential elections (since Woodrow Wilson was elected in 1912 with
less than 42 percent of the popular vote) is changes in the unemployment rate.
Hoover in 1928 represented the last political party to get reelected after an
increase in the civilian unemployment rate during the last two years of a
presidential term. It should be noted that the average unemployment rate was
only 4.4 percent in 1928 and had been declining since shortly after the
recessionary trough of November 1927. In 1929 the unemployment rate only
averaged 3.2 percent in spite of an August peak in business activity.
All of the other presidential aspirants in the 88 year period from
1912-2000 have been defeated if the unemployment rate wasn't reduced in the last
two years before the election. (See the changes in the unemployment rate in
parentheses identified with an asterisk in column 1 of Table
9.1.)
Since 1964 there have been two cases (1968 and 2000) when the incumbent
political party lost the election when the decline in the unemployment rate in
the last two years was less than - .5 percentage points. This would suggest that
the U.S. electorate is somewhat myopic and must be reassured that the economy is
getting better before reelecting the incumbent party.
The data in Table 9.1 are not inconsistent with the
idea that the U.S. electorate dislikes inflation, however. Since President
Wilson kept the U.S. out of World War I in 1916 no political part has been able
to get reelected when the annual inflation rate for the CPI in the election year
was in excess of 4.6 percent. Harding in 1920, Nixon in 1968, Carter in 1976 and
Reagan in 1980 were all successful at ousting the incumbent party from the White
House when the CPI inflation rates were in the 4.7 to 15.8 percent range.
Some caution should be exercised in interpreting this finding, however.
Three of the four cases of high inflation in column (2) of Table 9.1 are associated with recessionary increases in
unemployment in the last two years of the presidential term. The only recent
case of a high inflation rate that was not associated with a recession is 1968,
when George Wallace and his American Independent party split the Democratic vote
and allowed Richard Nixon to defeat Hubert Humphrey with only 43.4 percent of the
popular vote. Voters in many other countries have learned to live with inflation
rates that are greater than 4.7 percent. One wonders, however, whether higher
oil and natural gas prices may have helped Bush defeat Gore in the year 2000.
The more important risk that an incumbent political party faces in failing
to control inflation is the possibility that the Fed, which dislikes inflation
and has a long history of "leaning against the wind", so-to-speak, will tighten
credit to the point of tipping the economy into an "ill timed" recession. In the
post 1947 period there has only been one year (1977) when the Fed allowed the
growth of M1 in constant dollars to increase when the CPI inflation rate was over
five percent.
Since the reelection of President McKinley in 1900 no political party has
been able to retain control of the White House if the economy was experiencing
a recessionary decline in economic activity at the time of the election. Harding
in 1920, Roosevelt in 1932 and Kennedy in 1960 all seem to have benefitted from
recessions that did not end until after the election was over.) A very slow
growth rate for real GDP in the second half of 2000 and the fear that Bush raised
about a near term recession may have helped him defeat the incumbent party by a
small margin.
The electorate's distaste for economic recessions appears to have increased
with the passage of time. Since the election of Herbert Hoover in 1928, no
president has been reelected if there was a recessionary trough in economic
activity during the last two years of the presidential term. Carter in 1976,
Reagan in 1980 and Clinton in 1992 all benefitted from a reluctance on the part
of voters to quickly forget the most recent recession.
Another variable of concern to economists which may help to explain the
outcome of presidential elections is the economic growth rate. In this century
no political party has been reelected after a four-year increase in civilian
employment of less than 3.5 percent. This is the only economic variable, that
this analyst has discovered, which can explain the presidential turnover of 1952,
when a very popular general from World War II promised to get the U.S. out of an
unpopular war.
In constructing their voter preference curves involving a tradeoff between
inflation and unemployment Nordhaus and MacRae both assumed that price stability,
other things equal, is preferred by the electorate to either inflation or
deflation. In 1989 Alan Greenspan, indicated that he and other governors of the
Federal Reserve shared this view and were willing to support a bill sponsored by
Representative Stephen Neal (D-NC) which would have required the Fed to pursue
polices aimed at eliminating inflation within five years.
The attitude at the Fed, however, appears to have changed as a result of
the recent slow down in the inflation rate and the weak recovery in employment
from the recession which begain in March of 2001.
Seignorage is the revenue that governments receive from inflation when they
issue currency and do not pay interest on mandated bank reserves at the Fed.
Printing money can be a very rewarding business. If it only costs a dime to make
a ten dollar bill the profit to the U.S. Treasury will be 9900 percent. In 1988
the Federal Reserve acquired free and clear, at the stroke of a pen, an
additional $16 billion of U.S. government securities in the process of expanding
the money supply. If it were not for seignorage governments would have to raise
other taxes or borrow more money from the public.
It has been estimated that about two thirds of the more than $516 billion
of U.S. currency in circulation is circulating outside the United States. Much
of this currency has been "exported" to pay for the "importation" of illegal
drugs. And a sizable portion of the currency in use in the United States is also
held by people who are either engaged in illegal activities or involved in
underground activities that would otherwise go untaxed.
Rao Aiyagari, a research officer at the Federal Reserve Bank of
Minneapolis, has suggested that the elimination of inflation might encourage more
wasteful transactions that are designed to avoid taxation. He has concluded that
the benefits to be expected from a zero inflation rate would be small and might
even be negative (1990).
Now that the United States has ceased to be a creditor nation and has
become the world's largest debtor nation there may be another good reason for not
reducing the inflation rate to zero since it would burden U.S. tax payers and
make U.S. companies less competitive in the world market place by having to pay
off outstanding bonds with uninflated dollars.
Fairness is another consideration. In a world where there are differential
gains in productivity and where unions in the more progressive sectors have a
strong preference for obtaining their share of such gains in the form of higher
wages there may be a need for some positive inflation, on the average, to fairly
distribute higher income and wages to persons employed in those sectors where
productivity improvements are harder to achieve. The old fashioned notion of
money illusion may also have some validity, when productivity is low and unable
to support an accustomed increase in wages.
It should be noted that the CPI is not a perfect measure of inflation. To
the extent that consumers respond to individual price increases by switching to
comparable items that cost less, it may overstate inflation. The CPI can also
misrepresent inflation by not being fully adjusted for improvements in the
quality of goods and services. Some studies have suggested that these factors
may have caused the CPI to overstate the true inflation rate by as much as two
percentage points (Kahn 1994). Hershey (1994), however, has reported that
researchers at the Bureau of Labor Statistics (BLS) believe that the CPI only
overstates annual inflation by up to six-tenths of a percentage point. The
December 1994 issue of The Monthly Labor Review is a good starting point
for learning more about the various biases associated with the CPI. Since this
overview was published BLS has taken a number of steps to reduce the inflationary
bias in the CPI (Garner 1999).
The two longest recessions in the history of US business cycle
analysis--the 65 month recession from October 1873 to March 1879 and the 43 month
recession from August 1929 to November 1927--both followed inflation rates that
were either zero or negative. The great depression of the 1930s led John Maynard
Keynes to invent the notion of a liquidity trap which implies that a zero
inflation rate might make it very difficult, if not impossible, for the Fed to
cope with an unwanted recession. Low inflation may have also helped to keep the
Japanese economy in a recession throughout much of the 1990s.
In seven of the ten U.S. employment recessions from 1945-92 the Fed allowed
the yield on new issues of 91 day Treasury bills to fall below the preceding 12
month inflation rate for the CPI for one or more months. It doesn't seem likely
that this method of encouraging a revival of consumer spending would be available
to policy makers if the inflation rate were zero. Why would consumers ever pay
a premium for Treasury bills (which are sold at a discount since they don't pay
interest) when they have the alternative of holding demand deposits that are
fully insured by the federal government.
While the Fed will no doubt continue to lower short term interest rates in
response to recessions, it should be appreciated that a prompt decline in rates
may have scary implications and encourage some consumers and producers to wait
for even lower rates before reversing their purchasing and production plans. The
three shortest recessions dated by NBER in the post 1947 period are all
associated with increases in the Fed funds rates in the two months after the
peak. See Table 9.4.
In 1969 Jan Tinbergen of the Netherlands was awarded a Nobel prize for his
work in econometrics and public policy analysis. In the absence of wonder drugs
which are effective against more than one disease or problem and very special
interrelationships between variables which can sometimes exhibit the wonder drug
property, Tinbergen was able to show that there is a need for at least as many
instruments of control as there are goals or important dimensions to a problem.
The validity of this conclusion can easily be illustrated in connection
with demand and supply growth equations. To achieve the twin objective of a zero
or stable inflation rate and a growth rate for real GDP that is equal to the
economy's longer run potential growth rate one must have policy instruments that
are effective at not only controlling the growth of aggregate demand but also the
growth of aggregate supply.
Monetary and fiscal policy can be used in an effort to help control the
growth of aggregate demand. It is less clear whether free market economies have
any instruments that are very effective or efficient at ameliorating supply
shocks. The dearth of acceptable instruments for controlling the growth of
aggregate supply in the short run can lead to a situation where the nation's
monetary authorities are confronted with conflicting objectives.
An increase in the federal funds rate, for example, might help to slow the
growth of aggregate demand and lower the inflation rate but at the expense of
rising unemployment and a slower growth of output. If the primary goal is to
boost the growth of output, on the other hand, the funds rate should be lowered
to accelerate the growth of aggregate demand but that might aggravate the problem
of inflation.
One of the more interesting ways to resolve the problem of conflicting
objectives is to assume that increases in the inflation rate and declines in the
growth rate for real GDP are equally undesirable. Acceptance of the proposition
that the goals of stable growth and inflation should be treated in a balanced
way, leads one inevitably to the conclusion that the Fed should endeavor to
stabilize the growth of nominal GDP, which in turn will be about equal to the
growth of real GDP plus the growth of its implicit price deflator.
Nobel laureate James Tobin (1980) was an early advocate of nominal GDP
targeting. He noted that over short periods of time changes in nominal GDP
growth give rise to similar changes in real GDP growth with relatively little
impact on the inflation rate.
After the Volcker inspired monetary revolution of 1979 the Board of
Governors of the Federal Reserve often favored a policy of leaning against the
wind in a manner that might have helped to stabilize the year to year growth
rates for nominal GDP by letting short term interest rates rise when the GDP
growth rate was accelerating and decline when the growth of nominal GDP was
falling.
One of the more encouraging aspects to the data in Table
9.2 is that we seem to be moving in the direction of more stable and less
radical changes in the growth rate for nominal GDP. From 1961-92 there were 18
years when the four quarter growth rate for nominal GDP accelerated or
decelerated by 2.0 percentage points or more. from 1993 to 2001 there was only
been one year (2001) when the change in the 4th quarter to 4th quarter growth
rate for nominal GDP was more than 1.9 percentage points. See column (6) of Table 9.2.
The unanswered question is whether the improved stability of the nominal
GDP growth rate is the result of structural changes in the U.S. economy or a more
concerted effort on the part of the Federal Reserve to stabilize this rate.
One development which has changed the character of business forecasting in
recent years is the discovery that a consensus prediction obtained by polling a
number of different professional forecasters is cheaper and theoretically
superior to the majority of forecasts. A median forecast has to be equal to or
better than all forecasts if it turns out to be correct. And if it is way off
the mark, the median will still be at least equal to or better than half of the
forecasts.
In a world where careful evaluation of past forecasts is seldom
under-taken, where much of the data is subject to revision, where models and
forecasting procedures are also being revised and where better than average
forecasts may have simply been the result of dumb luck, rather than a superior
forecasting technique, busy decision makers are probably well advised to buy into
the consensus rather than incur the expense of maintaining their own forecasting
unit or purchasing the reports of many different forecasting organizations which
no one has time to read.
If everyone subscribed to the consensus and no one invested any effort in
trying to improve the art of forecasting, however, the value of the consensus
itself would surely deteriorate and might even end up being less reliable than
the sometimes hard to beat assumption that this year's growth or inflation rate
will be equal to last year's preliminary four quarter growth rate.
While the flowering of consensus forecasting has reduced the need for
economists, it has also opened up some new and relatively simple, yet reasonably
sophisticated procedures, for quickly checking-up on the predictions of
professional forecasters by polling statistical indicators rather than the people
who are in the business of making forecasts.
Since the invention of electronic computers the art of economic forecasting
has been heavily influenced by equations which assign a set of fixed weights to
different indicators. If the tradeoffs and relationships between the various
indicators were well behaved and quite stable this would be an unbeatable
approach. In a world where most time series with leading indicator properties
are rather volatile and where economic and financial innovation, bad weather,
fits of speculative enthusiasm and political disturbances can distort statistical
relationships, however, it will sometimes be better to examine pertinent
indicators on an individual basis and simply count the number of indicators that
seem to be pointing in a particular direction. Disturbances that are peculiar
to one or just a few of the relevant indicators will drop by the wayside and not
distort the entire forecast.
In some cases there may be a dominantly superior indicator. There are many
situations, however, where one is more likely to be right about the future if a
majority of the more reliable indicators are pointing in the same direction.
One of the advantages in polling the indicators is that it does allow one
to focus on a very specific objective such as the identification of a peak in
business activity before it occurs. In pursuing such a narrow objective it may
be possible to use some indicators that are too volatile, specialized or
otherwise misbehaved to be included in a composite index of leading indicators
or a general purpose forecasting model.
Interest rates, for example, are generally classified as being a lagging
indicator but show some promise of being a useful predictor of business peaks if
they increase at a very rapid rate or rise to too high a level in relation to
previous lows.
In a presentation before the House Banking Committee on February 6, 1975,
Chairman Burns noted that the Board of Governors and its open market committee
do pay close attention to monetary aggregates but do not confine their attention
to one particular definition of the money supply, namely demand deposits plus
currency outside banks: "The reason is that this concept no longer captures
adequately the forms in which liquid balances--or even just transaction
balances--are currently held. Financial technology in our country has been
changing rapidly. Corporate treasurers have learned how to get along with a
minimum of demand deposits, and to achieve the liquidity they need by acquiring
interest-earning assets. For the public at large, saving deposits at commercial
banks, shares in savings and loan associations, certificates of deposit, Treasury
bills and other liquid instruments have become very close substitutes for demand
deposits".
In the same year (1975) this analyst examined the three most common
definitions of the money supply (at that time) and found that they were all of
some value in forecasting nominal GNP. Even better predictions were obtained,
however, by selecting a median forecast rather than by narrowing the forecasting
process down to the one monetary aggregate with the best forecasting record.
While financial innovations have distorted the growth rates for the more
conventional monetary aggregates and led the Board of Governors to downgrade them
as reliable indicators of what might happen to the economy it might still be a
mistake to ignore their behavior altogether.
The quantity theory of money assumes that the velocity of money will remain
fairly constant. If this assumption is correct the growth of nominal GDP will
be about equal to the growth rate for the money supply. In Table 9.2 the December-December growth rates for currency,
M1 and M2 can be compared to the following year growth rates for nominal GDP in
column (6). The average value for the Federal funds rate during December is also
included in this table.
In macroeconomic modeling and forecasting it has become standard practice
to include the lagged dependent variable in most structural equations. In
keeping with this tradition we also show the fourth quarter to fourth quarter
growth rate for nominal GDP. The median value for this growth rate and the four
monetary variables is then used to explain the following year to year growth rate
for nominal GDP in column (6) of Table 9.2. The actual
minus the predicted error terms for this type of consensus forecast are shown in
parentheses.
The consensus forecasts have a mean absolute error (MAE) of only 1.22
percentage points for the 1979-99 period. This can be contrasted to MAE's in
excess of two percent for each of the monetary variables and 1.51 percentage
points for the preceding four quarter growth rate for nominal GDP. For the 13
cases where the median forecast is associated with the four monetary variables,
the MAE is only .94 percentage points.
Four of the largest forecasting errors are associated with the economic
recessions of 1981-82, 1990-91 and 2000-2? In all of these cases our consensus
over estimated the growth of GDP. One of the more interesting ways to try to
identify this type of bias since 1978 is to examine yearly changes in the S&P
composite stock price index which is included in most indexes of leading economic
indicators.
There have been six years (including 2002) since 1978, when the S&P
declined. See those error terms identified with a question mark in column 6 of
Table 9.2. For the five more or less resolved cases we
can conclude that our consensus approach always over estimated the actual growth
of GDP. It will be interesting to see if this conclusion turns out to be
applicable to what happens to GDP during 2003.
Economic forecasters have not had a lot of success at predicting changes
in inflation rates. Over the 23 year period from 1978-99 there were only six
years (1978, 1982, 1987, 1988, 1999 and 2000) when the President's Council of
Economic Advisers' fourth quarter to fourth quarter forecasts of the inflation
rates for GNP or GDP turned out to be superior to a no change in the inflation
rate assumption. See Table 9.3.
The CEA has a better track record with regard to forecasts of real GDP.
The superior forecasting record would suggest that leading indicators are more
useful at explaining changes in the real economy than the inflation rate.
In the parentheses associated with the following year growth rates for real
GDP in column (7) of Table 9.2 we compute error terms for
this variable based on a subtraction of the preceding fourth quarter to fourth
quarter growth rate for the implicit price deflator for GDP from the median or
consensus forecast for nominal GDP in columns (1) to (5).
The mean absolute error associated with this approach to real GDP
forecasting is 1.12 percentage points for the 1979-99 period. The predicted
growth rate of 4.5 percent for the year 2000 was so high, however, as to have
probably encouraged the Board of Governors to continue a process of raising its
funds rate.
Aiyagari, Rao (1990). "Deflating the Case for Zero Inflation," Federal Reserve
Bank of Minneapolis Quarterly Review, Summer, 2-11.
Alesina, Alberto (1988). "Macroeconomics and Politics," NBER
Macroeconomics Annual, 3: 13-52.
Clark, Todd (1994). "Nominal GDP Targeting Rules: Can They Stabilize the
Economy," Economic Review, Federal Reserve Bank of Kansas City, 3rd
Quarter, 11-25.
Fair, Ray (1978). "The Effect of Economic Events on Votes for President," The
Review of Economics and Statistics, 60(May), 159-73.
Fair, Ray (1996). "The Effects of Economic Events on Votes for President, 1992
Update," Political Behavior, June, 119-39.
Fischer, Stanley (1994). "The Role of Macroeconomic Factors in Growth," NBER
Working Paper No. 4565.
Garner, Alan (1999). "Progress Toward Price Stability: A 1998 Inflation Report,"
Federal Reserve Bank of Kansas City Economic Review, First Quarter, (84),
5-20.
Hershey, Robert (1994). "An Inflation Index Is Said to Overstate the Case,"
The New York Times, January 11, D1 and D15.
Hotelling, Harold (1929). "Stability in Competition," Economic Journal,
(39), 41-57.
Kahn, George (1994). "Achieving Price Stability," Federal Reserve Bank of Kansas
City Economic Review, First Quarter, 5-6.
Keller, R. and A. May (1984). The Presidential Political Business Cycle of
1972," Journal of Economic History, (44), 265-70.
Kramer, Gerald (1971). "Short-Term Fluctuations in U.S. Voting Behavior,
1896-64," The American Political Science Review, 65(March),
131-43.
MacRae, Duncan (1977). "A Political Model of the Business Cycle,"
Journal of Political Economy, 85(April), 239-63.
McNees, Stephen (1988). "How Accurate Are Macroeconomic Forecasts?" New
England Economic Review, July/August, 19.
Nordhaus, William (1975). "The Political Business Cycle," Review of Economic
Studies, 42(January), 169-90.
Renshaw, Edward (1975). "Economic Activity and Alternative Definitions of the
Money Supply," Journal of Money, Credit, and Banking, November, 507-19.
Richards, Daniel (1993). "What Inflation Policy Do American Voters Want, and Do
They Get It?" New England Economic Review, September-October, 33-44.
Seligman, Daniel (1991). "Cuddling Up to Mother Earth,"
Fortune, 123(July 29), 177.
Stigler, George (1973). "General Economic Conditions and National
Elections," American Economic Review, 63(May), 160-67.
Tinbergen, Jan (1996). Economic Policy: Principles and
Design(North Holland).
Tobin, James (1980). "Stabilization Policy Ten Years After," Brookings Papers
on Economic Activity, No. 1, 19-71.
Trahan, Emery and Edward Renshaw (1990). "Presidential Elections and the Federal
Reserve's Interest Rate Reaction Function," Journal of Policy Modeling,
12(1), 29-34.
-----, (1991). "A Note on the Electorate's Most Preferred Inflation Rate,"
Public Choice, 70, 95-97.
Tufte, Edward (1978). Political Control of the
Economy(Princeton: New Jersey: Princeton University Press).
(1)n. Average rate for the years 1916-44 and the December rate for the
years 1948-96. The figures in parentheses are the changes in the
unemployment rate during the last two years or 24 months of the presidential
term. An increase in the unemployment rate is used to predict an election turnover.
(2)n. Average annual rates 1916-44 and annual rates 1948-92. An inflation
rate in excess of 4.5 percent is used to predict an election turnover.
(3)n. The misery index is equal to the sum of the unemployment and
inflation rates in columns (1) and (2). The change in the misery index is
from one presidential election year to the next. An increase in the index
is used to predict an election turnover. The all item CPI has not been
extended back to 1912. From 1911-12, however, the Bureau of Labor
Statistic's food at home only index increased 6 percent. This would suggest
that there may not have been an increase in the misery index from 1912-16.
(4)n. Thousands of employed civilians 14 years and older 1912-48 and
employed persons 16 and over 1948-92. A four year percentage increase in
civilian employment of less than 3.5 percent is used to predict an election
turnover.
*Cases where a presidential election turnover is predicted. The asterisks
associated with 1924, 1968 and 1992 represent years of electoral unhappiness
when third party candidates were able to corral ten percent or more of the
popular vote for president. Since James Buchanan was able to get the
Democratic party reelected in 1856 with only 45.3 percent of the popular
vote, the incumbent political party has usually lost the election after this
strong a showing on the part of third party candidates. The only exception
is Calvin Coolidge in 1924 when there were no economic variables pointing
in the direction of a presidential turnover. Lincoln in 1860, Cleveland in
1892, Wilson in 1912, Nixon in 1968 and Clinton in 1992 may have all been
the beneficiaries of third party candidates who split the incumbent party
vote and allowed themselves to win with only 39.8 to 46.1 percent of the
popular vote. In 1968 George Wallace, running as a third party candidate
split the democratic vote and allowed Richard Nixon to defeat Hubert
Humphrey with only 43.4 percent of the popular vote. In 1992 Ross Perot ran
as a third party candidate and allowed Bill Clinton to defeat George Bush
with only 43 percent of the popular vote. In 1980 an independent candidate
by the name of John Anderson was able to capture 6.6 percent of the popular
vote. We have ignored this marginal case in assigning asterisks to the
outcome of presidential elections on the grounds that Reagan was able to
capture 50.7 percent of the popular vote and that there were enough economic
variables pointing in the direction of a turnover so that Carter might have
lost the election anyway.
*The asterisks identify the median rate in columns (1) to (5).
?The question marks identify error terms when the S&P composite index declined
in the preceding year.
The parentheses associated with the following year to year growth rates for
nominal GDP in column (6) are the error terms obtained by subtracting the median
rate for the variables in columns (1) to (5) from the actual following year
growth rate for nominal GDP.
The parentheses associated with the following year to year growth rates for chain
weighted real GDP in column (7) are the error terms associated with the median
rate for the variables in columns (1) to (5) minus the fourth quarter to fourth
quarter growth rate for the implicit price deflator.
The forecasted growth rates for real GNP or GDP and its implicit price deflator
are from the Economic Report of the President. The figures in parentheses
are the error terms obtained by subtracting the forecasted growth rate from the
actual preliminary growth rate in columns (2) and (4) from the next Economic
Report. In those cases where a forecast range was presented, a mid point is
used to evaluate the CEA's forecast.
(2)n and (4)n. The actual preliminary growth rates in the following year's
Economic Report of the President. The figures in parentheses are the
first differences in these preliminary growth rates. The evaluations for 1995
and beyond are based on chain-type indexes.
B identifies years when the Council of Economic Adviser's forecasting error
turned out to be smaller absolutely than the "no change in the growth rate
forecasts" implied by the first differences in the preliminary growth rates.
*The Changes in the Fed funds rate for the 1948 and 1953 peaks are approximated
by the change in the discount rate on new issues of 91 day Treasury bills.
Return to Some Featured Highlights
Return to A Tabular Index to the Essays and Tables
Table 1.6
Using Industrial Production (IP), Non Agricultural Payroll Employment (EMP)
and the Civilain Unemployment Rate (U) to Identify NBER Recessions Before
They Are Over.
------------------Dates---------------------- --Duration in Months--
NBER 5th Lower 4th Lower U up .9 % Shortest NBER
Peak Close IP Close EMP Points Lag Recession
Nov. 1948 Jan. 1949 Feb. 1949 Jan. 1949 2 11
July 1953 Dec. 1953 Oct. 1953 Nov. 1953 3 10*
Aug. 1957 Dec. 1957 Aug. 1957 Nov. 1957 0 8*
Apr. 1960 June 1960 Aug. 1960 Oct. 1960 2 10
Dec. 1969 Apr. 1970 Aug. 1970 Mar. 1970 3 11
Nov. 1973 Dec. 1974 Feb. 1975 July 1974 8 16#
Jan. 1980 May 1980 July 1980 Apr. 1980 3 6
July 1981 Dec. 1981 Dec. 1981 Nov. 1981 4 16#
July 1990 Dec. 1990 Oct. 1990 Nov. 1990 3 8*
Mar. 2001 Feb. 2001 Aug. 2001 Aug. 2001 -1 ?
Essay 2:
Economic Recessions and Those Sometimes Misleading Indicators
Professor of Economics
State University of New York at Albany
An Historical Perspective
Coping with the Problem of False Signals
Inverted Yield Curves
References
Fluctuations in the Conference Board's First Revised Index of Leading Economic
Indicators Amounting to 1.0 Percent or More.
Dates of Index of Leading Indicators
---------------------------------- ------------------------- Adjusted
Index Index Down NBER Index Index NBER Index Lead Time
Peak 1.0% Peak Trough Peak Peak Trough in Months
(1) (2) (3) (4) (5) (6) (7) (8)n
May 59 Oct. 59 Apr. 60 Mar. 60 75.1 73.8 73.6 - 6
Mar. 66 June 66 ----- Feb. 67 85.1 ---- 83.2 ---
Apr. 69 July 69 Dec. 69 Apr. 70 87.4 85.7 83.9 - 5
Feb. 73 June 73 Nov. 73 Feb. 75 92.4 90.6 83.9 - 5
Oct. 78 Dec. 78 Jan. 80 Apr. 80 93.0 90.1 87.0 -13
Apr. 81 June 81 July 81 Mar. 82 90.9 89.8 88.7 - 1
June 88 Mar. 89 July 90 Jan. 91 100.8 99.6 97.8 -16
Dec. 94 May 95 ----- May 95 101.6 ---- 100.4 ---
Cyclical Peaks for New Private Housing Units Authorized by Local Building
Permits and Recessionary Peaks in Business Activity.
Thousands of Housing
Peak Months for Units
----------------- -------------------- Lead Time in Months
Housing Economic Permit Economic Percent -------------------
Permits Activity Peak Peak Decline Unadjusted Adjusted
(1) (2) (3) (4) (5)n
Oct. 47 Nov. 48 1476 1076 27.1 -13 - 1
July 50 July 53 2282 1248 45.3 -36 -28
Feb. 55 Aug. 57 1946 1187 39.0 -30 -11
Nov. 58 Apr. 60 1729 1232 28.7 -17 - 5
Feb. 69 Dec. 69 1689 1306 22.7 -10 0
Dec. 72 Nov. 73 2688 1557 42.1 -11 0
Jun. 78 Jan. 80 2065 1333 35.4 -19 - 1
Sep. 80 July 81 1545 974 37.0 -10 - 1
Feb. 84 July 90 2043 1108 45.8 -77 - 3
Jan. 00 Mar. 01 1762 1627 7.7 -14 No Signal
Using an Inverted Yield Curve for the One Year Treasury Note Rate and the
Ten Year Treasury Bond Rate to Identify Business Peaks Before They Occur and
Risky Times to Have Owned Common Stock.
--------Date of--------- Lead -----Values for the S&P 500-----
Yield NBER Time After After Recessionary
Curve Business in Yield NBER Closing Low
Inversion Peak Months Inversion Peak S&P 500
(1)n (2) (3) (4) (5) (6)
Dec. 1956 Aug. 1957 - 8 46.67 45.22 38.98
Sep. 1959 Apr. 1960 - 7 56.88 54.37 52.30
Dec. 1965 Dec. 1969 -48 92.43 92.06 69.29
Mar. 1973 Nov. 1973 - 8 111.52 95.96 62.28
Sep. 1978 Jan. 1980 -16 102.54 114.16 98.22
Sep. 1980 July 1981 -10 125.46 130.92 102.42
Feb. 1989 July 1990 -17 288.86 365.15 295.46
Apr. 2000 Mar. 2001 -11 1452.43 1160.13 776.76
Essay 3:
The Stock Market and the Business Cycle
Professor of Economics
State University of New York at Albany
Did The Stock Market End the Longest Expansion in Business Cycle History?
Economic Recessions Associated with a Jittery Stock Market
Prolonged New High Declines in the S&P Composite
Stock Market Declines After Business Peaks
Monthly Lead Times for Stock Prices at Industrial Troughs
Duration and Amplitude of Monthly Average Stock Market Fluctuations
Amounting to 5.5 Percent or More.
Date of Cycle S&P Stock Index Months Duration % Change in Index
-------------------- --------------- --------------- -----------------
Peak Trough Peak Trough Rise Decline Rise Decline
(1) (2) (3) (4) (5) (6)
May 1946 Nov. 1946 18.70 14.69 30 6 65.0 21.4
Feb. 1947 May 1947 15.80 14.34 3 3 7.6 9.2
July 1947 Feb. 1948 15.77 14.10 2 7 10.0 10.6
June 1948 June 1949 16.82 13.97 4 12 19.3 16.9*
June 1950 July 1950 18.74 17.38 12 1 34.1** 7.3
Jan. 1953 Sep. 1953 26.18 23.27 30 8 50.6 11.1*
July 1956 Feb. 1957 48.78 43.47 34 7 109.6** 10.9
July 1957 Dec. 1957 48.51 40.33 5 5 11.6 16.9*
July 1959 Oct. 1960 59.74 53.73 19 15 48.1** 10.1*
Dec. 1961 June 1962 71.74 55.63 14 6 33.5** 22.5
Jan. 1966 Oct. 1966 93.32 77.13 43 9 67.8 17.3
Sep. 1967 Mar. 1968 95.81 89.09 11 6 24.2 7.0
Dec. 1968 June 1970 106.48 75.59 9 18 19.5 29.0*
Apr. 1971 Nov. 1971 103.04 92.78 10 7 36.3** 10.0
Jan. 1973 Aug. 1973 118.42 103.80 14 7 27.6 12.3
Oct. 1973 Dec. 1974 109.84 67.07 2 14 5.8 38.9*
July 1975 Sep. 1975 92.49 84.67 7 2 37.9** 8.5
Sep. 1976 Mar. 1978 105.45 88.82 12 18 24.5 15.8
Aug. 1978 Nov. 1978 103.92 94.71 5 3 17.0 8.9
Feb. 1980 Apr. 1980 115.34 102.97 15 2 21.8 10.7*
Nov. 1980 July 1982 135.65 109.38 7 20 31.7** 19.4*
Oct. 1983 July 1984 167.65 151.08 15 9 53.5** 9.9
Aug. 1987 Dec. 1987 329.36 240.96 37 4 118.0 26.8
Jun. 1990 Oct. 1990 360.39 307.12 30 4 49.6 14.8*
July 1998 Sep. 1998 1156.58 1020.64 93 2 276.6** 11.8
July 1999 Oct. 1999 1380.99 1300.01 10 3 35.3 5.9
Aug. 2000 Mar. 2001 1485.46 1185.85 9 7 14.3 20.2*
May. 2001 Sep. 2001 1270.37 1044.64 2 4 7.1 17.8*
Mar. 2002 Oct. 2002 1153.79 854.63 2 7 10.4**? 25.9?
Recessionary Bear Markets and Stock Market Crashes in the Midst of Prosperity,
1928-
% Peak- Months from Previous
S&P S&P S&P NBER S&P Trough Recession Trough to
Peak Down Trough Peak Peak Decline S&P S&P Down NBER
Date 13.9 % Date Date Value S&P Peak 13.9 % Peak
(1) (2) (3) (4) (5)n (6)
9/ 7/29 10/23/29 6/ 1/32 8/29* 31.92 -86.2 22 23 21*
7/18/33 7/21/33 3/14/35 ---- 12.20 -33.9 4 4F --
3/ 6/37 4/28/37 3/31/38 5/37* 18.68 -54.5 48 49 50*
8/ 6/38 9/10/38 9/27/38 ---- 12.81 -14.2 2 3F --
11/ 9/38 1/26/39 4/28/42 ---- 13.79 -45.8 5 7F --
5/29/46 9/ 3/46 5/17/47 ---- 19.25 -28.8 7 11F --
6/15/48 2/10/49 6/13/49 11/48* 17.06 -20.6 32 40(-1.45) 37*
6/12/50 7/17/50 7/17/50 ---- 19.40 -14.0 8 9F( 2.49) --
1/ 5/53 9/14/53 9/14/53 7/53* 26.66 -14.8 39 47(- .32) 45*
8/ 2/56 2/11/57 10/22/57 8/57* 49.74 -21.6 27 33( .48) 39*
8/ 3/59 10/25/60# 10/25/60 4/60* 60.71 -13.9 16 30(- .43) 24*
12/12/61 5/22/62 6/26/62 ---- 72.64 -28.0 10 15( .90) --
2/ 9/66 8/18/66 10/ 7/66 ---- 94.06 -22.2 60 66( 1.23) --L
11/29/68 7/23/69# 5/26/70 12/69 108.37 -36.1 93 101( 1.03) 106
1/11/73 5/21/73# 10/ 3/74 11/73 120.24 -48.2 26 30( .80) 36
7/15/75 9/16/75 9/16/75 ---- 95.61 -14.1 4 6F( 1.05) --
9/21/76 12/19/77 3/ 6/78 ---- 107.83 -19.4 18 32( 1.01) --L
2/13/80 3/24/80# 3/27/80 1/80* 118.44 -17.1 59 60( .42) 58*
11/28/80 9/ 4/81# 8/12/82 7/81* 140.52 -27.1 4 14(- .10) 12*
10/10/83 7/24/84 7/24/84 ---- 172.65 -14.4 11 20( 1.06) --
8/25/87 10/16/87 12/04/87 ---- 336.77 -33.5 57 59( .91) --L
7/16/90 8/22/90# 10/11/90 7/90* 368.95 -19.9 92 93(- .19) 92*
7/17/98 8/31/98 8/31/98 ---- 1186.75 -19.3 88 89( .50) --L
3/24/00 11/30/00# 10/09/02 3/01 1527.46 -49.1 108 116( .28) 120
Stock Market Declines Associated with Business Peaks in the Post World War
II Period
Date of Closing Values S&P Index % Decline
---------------------------- ------------------------ -----------------
Market Business Market Market Business Market M. Peak B. Peak
Peak Peak Low Peak Peak Low B. Peak M. Low
6/15/48 11/30/48 6/13/49 17.06 14.75 13.55 13.5 8.1
1/05/53 7/31/53 9/14/53 26.66 24.75 22.71 7.2 8.2
8/02/56 8/30/57 10/22/57 49.74 45.22 38.98 9.1 13.8
8/03/59 4/29/60 10/25/60 60.71 54.37 52.30 10.4 3.8
11/29/68 12/31/69 5/26/70 108.37 92.06 69.29 15.1 24.7
1/11/73 11/30/73 10/03/74 120.24 95.96 62.28 20.2 35.1
2/13/80* 1/31/80 3/27/80 118.44* 114.16 98.22 3.6 14.0
11/28/80 7/31/81 8/12/82 140.52 130.92 102.42 6.8 21.8
7/16/90 7/31/90 10/11/90 368.95 356.15 295.46 3.5 17.0
3/24/00 3/30/01 10/09/02 1527.46 1160.13 776.76 24.0 33.0
Some Monthly Lead Times for the S&P Composite at Industrial Troughs
Date of Closing Industrial S&P Lead --Percentage Change S&P--
Recessionary Value Low Month Time In Preceding Next Year
Low Value S&P S&P Months Peak to Three After
Composite Composite Low Value Months Low
(1) (2) (3) (4) (5) (6) (7)
6/13/49 13.55 10/49# - 4 -20.6 16.2 42.1
9/14/53 22.71 4/54 - 7* -14.8 8.7 37.1
10/22/57 38.98 4/58 - 6 -21.6 5.7 31.0
10/25/60 52.30 2/61# - 4 -13.9 15.7 30.7
5/26/70 69.29 11/70 - 6* -36.1 17.2 43.7
10/03/74 62.28 3/75# - 5 -48.2 13.5 38.0
3/27/80 98.22 7/80 - 4 -17.1 18.1 37.1
8/12/82 102.42 12/82# - 4 -27.1 36.2 58.3
10/11/90 295.46 3/91 - 5* -19.9 6.7 29.1
10/09/02 776.76 12/01# +10 -49.1 ? ?
Recessionary Peaks Following January Declines in the S&P Composite Stock Price
Index.
Preceding Change Prime
Year January Rate Charged by Banks Lead Time in Months
% Decline Jan.-Jan Yr.-Yr. from Economic Peak to
S&P Comp. January Dip in S&P
1948P - 4.0(25) .25* ---- - 10
1953P - .7(15) .00 .44* - 6
1956R - 3.6(21) .50* .11 - 19
1957P - 4.2(25) .50* .61* - 7
1960P - 7.1(17) 1.00* .65* - 3
1962R - 3.8(34) .00 - .32
1968 - 4.4( 9) .04 .00
1969P - .8(18) .95* .65* - 11
1970T - 7.6(33) 1.55* 1.67*
1973P - 1.7(43) .82* - .45 - 10
1974D - 1.0(68) 3.73* - 2.77
1977R - 5.1(12) - .75 - 1.02
1978 - 6.2(24) 1.68* - .02 - 24
1981P - 4.6(30) 4.91* 2.60* - 6
1982T - 1.8(37) - 4.41 3.60*
1984R - .9(15) - .16 - 4.07
1990P - 6.9(42) - .39 1.55* - 6
1992R - 2.0(12) - 3.02 - 1.55
2000 - 5.1(53) .75* - .35 - 14
2002 - 1.6 - 4.30 - 2.32
Jittery Years When the S&P Composite Experienced 43 or More One Day Declines of
One Percent or More.
Year Number of One
Percent Declines
1929P 52
1930D 79
1931D 106
1932D 107
1933T 84
1934R 64
1935R 45
1937P 64
1938T 83
1939R 56
1973P 43
1974D 68
2000F 53
2001P 54
Prolonged New High Declines in the S&P Composite Lasting More than 167 Trading
Days and Economic Recessions.
Dates of S&P Closing Values S&P Percent Trading Days Duration NBER
Peaks Troughs Peaks Troughs Decline to Trough Recessions
in Months
09/07/29 06/01/32 31.92 4.40 -86.2 811 43#
08/02/56 10/22/57 49.74 38.98 -21.6 307 8
08/03/59 10/25/60 60.71 52.30 -13.9 311 10
11/29/68 05/26/70 108.37 69.29 -36.1 369 11
01/11/73 10/03/74 120.24 62.28 -48.2 436 16#
11/28/80 08/12/82 140.52 102.42 -27.1 430 16#
10/10/83 07/24/84 172.65 147.82 -14.4 199 Recovery Relapse
03/24/00 10/09/02 1527.46 776/76 -49.1 637 ?#
Buying the S&P Composite at the End of the Month Following a Cumulative Increase
in the Civilian Unemployment Rate of .9 Percentage Points.
Purchase Closing Percentage Change S&P to
Month Value Interim One Year
S&P Low Later
Feb. 1949 14.62 - 7.3 17.8
Dec. 1953 24.81 - .0* 45.0
Dec. 1957 39.99 + .9* 38.1
Nov. 1960 55.54 - .4* 28.4
Apr. 1970 81.52 -15.0 27.5
Aug. 1974 72.15 -13.7 20.4
May 1980 111.24 - .7* 19.2
Dec. 1981 122.55 -16.4 14.8
Dec. 1990 330.22 - 5.7* 26.3
Sep. 2001 1040.94 - 25.4 -21.7
Essay 4:
Economic Recessions and Interest Sensitive Components of GDP
Professor of Economics
State University of New York at Albany
Recessionary Lead Times for Cases of Three or More Quarterly Growth Rates for
Real GDP, Consumer Durables, Non Residential Equipment and Software, and
Residential Investment Equal to 1.9 Percent or Less During Business Expansions
that Are Associated with or Have Occurred after a Sustained Increase in the Prime
Rate of a Third or More from Its Cyclical Low.
------Annualized Percentage Changes-----
Quarter Prime Real Consumer Equipment Residential Lead Time in
Rate Up GDP Durables Software Investment Months from
Ratio Bus. Peak
(1)n (2) (3) (4) (5) (6)
1948-3 1.33 1.7 --- - 5.2* -11.9* - 2
1951-4 1.42 .9 - 3.0 - 3.7* --- -19
1952-3 1.50 --- -23.6* -47.7* - 4.1 -10
1953-2 1.62 --- - 2.3 - .9 1.3 - 1
1956-3 1.33 - .5 - 7.6* --- - 7.5 -11
1957-2 1.33 - .9 - 9.8* - 1.2 - 9.3* - 2
1959-4 1.43 1.3 -18.7* - .8 -11.2* - 4
1969-2 1.83 1.0 - 1.3 --- - 4.2 - 6
1969-4 1.89 -1.9 - 2.1 - 2.0 -26.2* 0
1973-2# 1.58 --- - 6.3* --- -18.8* - 5
1973-3 2.08 -1.6 - 3.1 --- -15.9* - 2
1979-1 1.88 1.0 - 4.1 --- - 9.0* -10
1979-2 1.86 .3 - 8.9* - 4.4* - 6.9 - 7
1979-4 2.45 1.3 -10.2* - 5.4* -14.8* - 1
1981-2 1.80 -2.8 -15.2* --- -14.6* - 1
1989-4 1.40 1.4 -12.6* - 7.2* - 6.5 - 7
1990-2 1.33 .9 -13.1* - 8.5* -17.2* - 1
2000-4 1.58 1.1 - 5.3* - 5.4* .0 - 3
Gross Private Domestic Investment (GPDI) as a Percent of GDP Expressed in Chained
1996 Dollars During Quarters Containing an NBER Recessionary Peak in Economic
Activity and the Duration of the Recessions Identified by NBER.
Peak GPDI % Monthly Duration % Decline GPDI
Quarter of GDP of Recession Following Quarter
2001-1 18.00 ? - 4.7
1973-4 14.82 16 - 5.9
1981-3 14.51 16 - 3.4
1980-1 14.35 6* - 9.0
1990-3 13.58 8* - 6.9
1948-4 13.37 11* -15.1
1969-4 12.67 11 - 3.2
1957-3 11.62 8* - 8.4
1960-2 11.52 10 - .6
1953-3 11.26 10* - 8.0
Essay 5:
Employment Recessions and Their Duration
Professor of Economics
State University of New York at Albany
The Duration of Payroll Employment Recessions
A Sequential Approach to Recession Forecasting
The Dating of Recessionary Peaks
Reference
Recessionary Lead Times for the Number of Employees on Nonagricultural
Payrolls in Goods Producing Industries, Business Cycle Indicator Series 40.
Date of Goods Employment Lead Times From NBER
------------------------- ------------------ Peaks in Months
Goods Goods NBER Own NBER --------------------
Employ. Employ. Peak Peak Peak Unadjusted Adjusted
Peak Down 1% ----Thousands-----
(1) (2) (3) (4) (5) (6) (7)n
Sep. 48 Dec. 48 Nov. 48 18,916 18,794 - 2 + 1
Apr. 53 Sep. 53 July 53 21,304 21,266 - 3 + 2
Dec. 56 July 57 Aug. 57 21,291 20,942 - 8 - 1
Feb. 60 Mar. 60 Apr. 60 20,903 20,716 - 2 - 1
July 69 Jan. 70 Dec. 69 24,495 24,354 - 5 + 1
Dec. 73 July 74 Nov. 73 25,264 25,214 + 1 + 8
July 79 Mar. 80 Jan. 80 26,606 26,448 - 6 + 2
Mar. 81 Nov. 81 July 81 25,654 25,630 - 4 + 4
Jan. 89 May 90 July 90 25,361 24,949 -18 - 2
July 00 Apr. 01 Mar. 01 25,774 25,602 - 8 1
Footnotes for Table 5.1
Fluctuations in the Civilian Unemployment Rate of .9 Percentage Points or
More and U.S. Business Cycles
Unemployment Rate Lead Time
Unemploy. U up .9% Unemploy. Business ---------------- (in Months)
Trough Points Peak Peak Trough Peak Total Adjusted
(1) (2) (3) (4)n
Jan. 1948 Jan. 1949 Oct. 1949 Nov. 1948 3.4 7.9 -10 +2
Jun. 1953 Nov. 1953 Sep. 1954 July 1953 2.5 6.1 - 1 +4
Mar. 1957 Nov. 1957 July 1958 Aug. 1957 3.7 7.5 - 5 +3
Feb. 1960 Oct. 1960 May 1961 Apr. 1960 4.8 7.1 - 2 +6
May 1969 Mar. 1970 Aug. 1971 Dec. 1969 3.4 6.1 - 7 +3
Oct. 1973 July 1974 May 1975 Nov. 1973 4.6 9.0 - 1 +8
May 1979 Apr. 1980 July 1980 Jan. 1980 5.6 7.8 - 8 +3
July 1981 Nov. 1981 Dec. 1982 July 1981 7.2 10.8 0 +4
Mar. 1989 Nov. 1990 June 1992 July 1990 5.0 7.7 -16 +4
Oct. 2000 Aug. 2001 Dec. 2001 Mar. 2001 3.9 5.8 - 5 +5
The Duration of Employment Recessions in Months Ranked in Order of the
Monthly Lead time for Industrial Production
Date of Peak for Peak-Trough
---------------------- Monthly Lead Time Duration of Recession % Decline
Industrial Payroll Industrial Actual Predicted Payroll
Production Employment Production -----in months----- Employment
(1) (2) (3)n (4)
Nov. 73 Oct. 74 -11 6 4 -2.9
May 79 Mar. 80 -10 4 5 -1.4
Sep. 00 Mar. 01 - 6 13? 9 -1.5?
Oct. 69 Mar. 70 - 5 8 10 -1.5
Jan. 60 Apr. 60 - 3 10 12 -2.3
July 48 Sep. 48 - 2 13 13 -5.2
Mar. 57 Mar. 57 - 0 14 15 -4.3
July 81 July 81 - 0 16 15 -3.0
July 53 Jun. 53 + 1 14 16 -3.5
Sep. 90 Jun. 90 + 3 20 18 -1.6
A Sequential Approach to Recession Forecasting Based on the Behavior of the Prime
Rate Charged by Banks and Some Employment Indicators.
Recessionary Signals Lead Times in Months from NBER
----------Monthly Dates--------- -------Recessionary Peaks-----
Prime Weekly Unemployment Prime Weekly Unemployment
Rate Hours Rate Rate Hours Rate
Cases Where the Prime Rate Was Up a Third or More from its Cyclical Low
Aug. 48 Sep. 48 Dec. 48 - 3 - 2 + 1
Nov. 51 June 53 Aug. 53 - 20 - 1 + 1
Sep. 56 Mar. 57 June 57 - 11 - 5 - 2
Sep. 59 Feb. 60 July 60 - 7 - 2 + 3
Cases Where the Prime Rate Was Up More than 50 Percent from Cyclical Low
Jan. 69 Nov. 69 Feb. 70 - 11 - 1 + 2
June 73 Feb. 74 Feb. 74 - 5 + 3 + 3
Sep. 78 Apr. 79 Jan. 80 - 16 - 9 0
Dec. 80 Feb. 81 Sep. 81 - 7 - 5 + 2
Mar. 89 Aug. 89 Aug. 90 - 16 - 11 + 1
May 00 Aug. 00 Apr. 01 - 10 - 7 + 1
Nonagricultural Payroll Recessions and How They Compare to the Recessionary Peaks
and Troughs Dated by the National Bureau of Economic Research.
Peak Employment Employment Trough Percentage Declines In Employment
Months Months Peak to Peak to Third
Trough Lower Monthly
Total
Sep. 1948(- 2) Oct. 1949( 0) - 5.2 - .3
June 1953(- 1) Aug. 1954(+ 3) - 3.5 - .3
Mar. 1957(- 5) May 1958(+ 1) - 4.3 - .2
Apr. 1960( 0) Feb. 1961( 0) - 2.3 - .9
Mar. 1970(+ 3) Nov. 1970( 0) - 1.5 - .6
Oct. 1974(+11) Apr. 1975(+ 1) - 2.9 - 1.8
Mar. 1989(+ 2) July 1980( 0) - 1.4 - 1.1
July 1981( 0) Nov. 1982( 0) - 3.0 - .4
June 1990(- 1) Feb. 1992(+11) - 1.7 - .4
Mar. 2001( 0) Apr. 2002 - 1.1? - .2?
Cyclical Leads (-) and Lags (+) for Some Coincident Indicators at the Business
Peaks Identified by the National Bureau of Economic Research.
NBER Peak Employees Personal Industrial Manufacturing
Dates Non Ag. Income Less Production and Trade
Payrolls Transfer Sales
Payments
Nov. 1948 - 2 - 1 - 4 + 1
July 1953 - 1 - 1 0 - 3
Aug. 1957 - 5 0 - 5 - 6
Apr. 1960 0 + 1 - 3 - 3
Dec. 1969 + 3 NST - 2 - 2
Nov. 1973 +11 0 0 0
Jan. 1980 + 2 0 + 2 -10
July 1981 0 + 1 0 - 6
July 1990 - 1 - 3 + 2 - 4
Mar. 2001 0 ? - 6 - 6
The Duration of Employment Recessions in Months Ranked in Order of the Percentage
Declines in Nonagricultural Payroll Employment During the First Three Months of
the Recession.
Employment
Date of Three Month % Change Employment Duration of Six Month
Employment Before After Recession % Change
Peak Peak Peak in Months After Peak
Oct. 1974 .07 -1.81 6 -2.89
Mar. 1980 .42 -1.12 4 -1.04
Apr. 1960 .83 - .77 10 -1.19
Mar. 1970 .31 - .62 8 - .69
June 1990 .39 - .39 20 - .84
June 1953 .01 - .32 14 -1.52
Sep. 1948 .63 - .31 13 -2.27
Mar. 1957 .29 - .23 14 - .46
July 1981 .21 - .21 16 -1.05
Mar. 2001 .22 - .12 13? - .63
Essay 6:
Can There Be Another Recession
Without an Oil Price Shock?
Professor of Economics
State University of New York at Albany
How an Increase in Oil Prices Can Cause a Recession
Using Year-to-Year Increases of Three Percent or More for the Motor Fuel
Component of the Consumer Price Index and Anemic Increases in Industrial
Production Amounting to .1 Percent or Less to Forecast Poor Growth Years for the
U.S. Economy.
Year ---------Growth Rates for---------- Following Year
Motor Industrial Following Financial Return
Fuel Production Year Real for the S&P
Price June-Dec. GDP Composite
(1) (2) (3) (4)
1948 13.2 - 2.5 - .6 17.8**
1953 6.0 - 6.9 - .7 51.2**
1957 4.4 - 5.9 -1.0 42.4**
1960 3.0 - 5.0 2.3 26.6**
1969 3.0 - .5 .2 3.5**
1973 9.9 .1 - .6 -26.0
1974 35.3 - 8.0 - .4 36.9**
1979 35.3 - .6 - .2 31.5**
1981 11.4 - 2.3 -2.0 20.4**
1989 9.4 - .3 1.8 - 3.1
1990 14.4 - 2.1 - .5 30.0**
2000 28.4 - .4 .3 -11.8
Footnotes for Table 6.1
Lead Times in Months from the Business Peaks Identified by the National Bureau
of Economic Research for Five Economic Indicator Thresholds with Both Leading and
Coincident Properties.
NBER Civilian Goods Initial Manufact S&P Median
Peak Unemploy Producing Unemploy Trade Composite Lead
Date Rate Employment Claims Sales Time
(1) (2) (3) (4) (5) (6)
Nov. 48 - 9 - 1 - 6 2 - 3 - 3
July 53 2 1 -24 - 1 - 3 - 1
Aug. 57 - 3 - 3 1 - 4 -10 - 3
Apr. 60 - 1 - 1 1 1 - 7 - 1
Dec. 69 1 - 1 2 - 1 -11 - 1
Nov. 73 2 2 2 1 - 8 2
Jan. 80 - 5 - 5 - 9 - 9 2 - 5
July 81 2 - 2 2 - 2 - 5 - 2
July 90 - 8 -13 - 9 - 3 1 - 8
Mar. 01 0 - 2 - 4 0 - 5 - 2
Yearly Increases in the Average Annual Values of the Motor Fuels Component of the
All Item Consumer Price Index of Nine Percent or More and Their Relation to
Recessionary Peaks in Industrial Production.
Year Growth Rate Date of Recessionary
CPI Index for Peaks in Industrial
Motor Fuels Production
1947 13.3
1948 13.2 July
1973 9.9 November
1974 35.3
1979 35.3
1980 38.9 March
1981 11.4 July
1989 9.4 April
1990 14.4 September
1999 9.2
2000 28.2 September
Essay 7:
The Accelerator Principle Revisited
Professor of Economics
State University of New York at Albany
2 1/3
Q = T K (1)
I = vK (2)
Using Quarterly Percentage Changes in Real GDP to Distinguish Between Growth
Recessions and the Real Thing
References
Stock Returns and the Accelerator Relationship Between the Year to Year Growth
Rates for Chain Weighted Real GDP and Gross Private Fixed Domestic Investment.
Year Real Actual Predicted Actual Minus Following Year
GDP Investment Investment Predicted S&P Financial
Investment Return
(1) (2) (3)n (4) (5)
1960 2.5 .9 1.5 - .6 26.6
1961 2.3T - .3 .9 - 1.2 - 8.8
1962 6.0 9.0 12.0 - 3.0 22.5
1963 4.3 7.7 6.9 .8 16.3
1964 5.8 9.7 11.4 - 1.7 12.3
1965 6.4 10.2 13.2 - 3.0 -10.0
1966 6.6 5.7 13.8 - 8.1 23.7
1967 2.5 - 1.9 1.5 - 4.0 10.8
1968 4.8 6.9 8.4 - 1.5 - 8.3
1969 3.0 6.2 3.0 3.2* 3.5
1970 .2T - 2.1# - 5.4 3.3* 14.1
1971 3.3 7.5 3.9 3.6* 18.7
1972 5.4 12.0 10.2 1.8 -14.5
1973 5.8 9.1 11.4 - 2.3 -26.0
1974 - .6 - 6.3# - 7.8 1.5 36.9**
1975 - .4T -10.7# - 7.2 - 3.5 23.6**
1976 5.6 9.8 10.8 - 1.0 - 7.2
1977 4.6 14.4 7.8 6.6* 6.4
1978 5.5 11.5 10.5 1.0 18.4
1979 3.2 5.6 3.6 2.0 31.5
1980 - .2T - 6.4# - 6.6 .2 - 4.8
1981 2.5 2.2 1.5 .7 20.4
1982 - 2.0T - 7.0# -12.0 5.0* 22.3**
1983 4.3 7.5 6.9 .6 6.0
1984 7.3 16.8 15.9 .9 31.1
1985 3.8 5.3 5.4 - .1 18.5
1986 3.4 1.2 4.2 - 3.0 5.7
1987 3.4 - .0 4.2 - 4.2 16.3
1988 4.2 3.6 6.6 - 3.0 31.2
1989 3.5 2.7 4.5 - 1.8 - 3.1
1990 1.8 - 1.8 - .6 - 1.2 30.0
1991 - .5T - 6.9# - 7.5 .6 7.4**
1992 3.0 6.5 3.0 3.5* 9.9
1993 2.7 8.1 2.1 6.0* 1.3
1994 4.0 9.1 6.0 3.1* 37.1
1995 2.7 6.0 2.1 3.9* 22.7
1996 3.6 9.3 4.8 4.5* 33.1
1997 4.4 9.6 7.2 2.4* 28.3
1998 4.3 11.4 6.9 4.5* 20.9
1999 4.1 7.8 6.3 1.5 - 9.0
2000 3.8 6.1 5.4 .7 -11.8
2001 .3 -3.8# -5.1 1.3 ?
Footnotes for Table 7.1
Quarterly Dips and Back to Back Changes in Real GDP of Less Than 2.5 Percent,
1948-2000.
Date First --Quarterly Percentage Changes in Real GDP--
Slow Quarter 1st 2nd 3rd 4th Fifth Sixth Seventh
1948-3R 1.7 .8 -5.5 -1.1* 4.6 -4.0
1953-3R -2.5 -6.3* -2.0 .6
1955-4G 2.1 -1.7
1956-3 - .5
1957-1R 2.3 - .9 4.0 -4.1*-10.3 2.4
1959-3G - .2 1.3
1960-2R -2.0 .7 -5.0* 2.3
1967-2 - .3
1969-2R 1.0 2.3 -1.9 - .6* .8 3.6 -4.2
1973-3R -1.6 3.4 -3.0 1.1 -4.4* -2.2 -5.0
1977-4G .5 1.1
1979-1G 1.0 .3
1979-4R 1.3 1.3 -7.9 - .6*
1981-2R -2.8 4.9 -4.6 -6.5* 1.7 -1.9 .3
1989-2G 2.2 1.9 1.4
1990-2R .9 - .7 -3.2* -2.0 2.3 1.0 2.2
1993-1 - .1
1995-1G 1.5 .8
2000-3R .6 1.1 - .6 -1.6* - .3
Essay 8:
Rehabilitating the Keynesian Multiplier
Professor of Economics
State University of New York at Albany
Y = C + I + G + NX (1)
C = aY + b(Y - NX - T) (2)
-1
Setting Y equal to nominal GNP in billions of current dollars and fitting
this type of consumption function to U.S. data for the 1952-86 period allowed
this analyst to obtain the following least squares regression (Renshaw 1990):
C = 1.59 + .25Y + .5(Y - NX -T) (3)
-1
Y = .5Y + 2I + G + (G - T) + NX (4)
-1
"Chronic government budget deficits and escalating government debt have
become major concerns in both developed and developing countries. Concern arises
because fiscal imbalances siphon funds from private sector investment, retarding
growth and ultimately reducing standards of living. Fiscal imbalances also
create potentially large burdens on future generations, as workers may be forced
to finance unfunded social programs for rapidly expanding elderly populations.
And, fiscal imbalances can trigger disruptive movements in interest rates and
exchange rates, as highly indebted countries become increasingly vulnerable to
global market forces. Few economic issues have such far-ranging implications as
excessive deficits and debt."
Fiscal Policy: The Possibility of Indirect Effects
Who Leads the Economy Into a Recession? Consumers, More Often Than Not.
References
The Shifting Propensity to Spend Out of Previous GDP When the Government
Expenditure Multiplier Is Assumed to be Equal to Two and the Basic Data Are
In Billions of Chained 1996 Dollars, 1960-2000.
Gross Change Following
Private Federal Gov. Shifting Shifting Return
Domestic Gov. Cons. Net Actual Propensity Propensity S&P
Year Investment Deficit Invest. Exports GDP to Spend to Spend Index
(1) (2) (3) (4) (5) (6)n (7) (8)
1960 272.8(-) -51.0L 659.5 -21.2 2357.2 53.2 ---P 26.6
1961T 271.0(-) -30.1* 691.3 -19.1 2412.1 52.1 - 1.1**T - 8.8
1962 305.3 -33.1 732.9 -26.5 2557.6 52.8 .7 22.5
1963 325.7 -47.0 750.2 -22.7 2668.2 52.2 - .6 16.3
1964 352.6 -30.6 764.8 -15.9 2822.7 52.4 .2 12.3
1965 402.0 -40.5 788.6 -27.3 3002.8 52.4 .0 -10.0
1966 437.3 -42.2 859.3 -40.9 3199.5 51.6 - .8 23.7
1967 417.2(-) 5.1H 924.1 -50.2 3279.5 48.9 - 2.7** 10.8
1968 441.3 -23.4 953.4 -67.2 3435.6 51.5 2.6** - 8.3
1969 466.9 -63.3L 950.0 -71.2 3543.2 52.2 .7P 3.5
1970T 436.2(-) 24.9* 928.6 -65.0 3549.4 50.5 - 1.7T** 14.1
1971 485.8 66.2H 909.7 -76.1 3660.2 50.4 - .1 18.7
1972 543.0 21.4 909.8 -89.6 3854.2 52.6 2.2** -14.5
1973 606.5 -13.2L 902.6 -64.3 4073.1 52.8 .2P -26.0
1974 561.7(-) 12.4 921.3 -37.6 4061.7 50.1 - 2.7** 36.9
1975T 462.2(-) 165.7H* 939.3 -9.2 4050.3 50.0 - .1T 23.6
1976 555.5 106.8 938.6 -43.0 4262.6 53.1 3.1** - 7.2
1977 639.4 70.4 947.4 -68.1 4455.7 52.2 - .9 6.4
1978 713.0 16.8 977.6 -69.2 4709.9 52.9 .7 18.4
1979 735.4 - 3.0L 997.6 -48.0 4870.1 52.1 - .8 31.5
1980T 655.3(-) 78.4H* 1018.6 8.0 4872.3 50.4 - 1.7PT** - 4.8
1981 715.6 73.7L 1027.9 3.3 4993.9 50.4 .0P 20.4
1982T 615.2(-) 202.8* 1044.5 -16.0 4900.3 48.8 - 1.6T** 22.3
1983 673.7 244.2H 1078.9 -65.5 5105.6 51.0 2.2** 6.0
1984 871.5 201.0 1116.3 -130.3 5477.4 49.9 - 1.1** 31.1
1985 863.4(-) 209.0 1188.4 -150.9 5689.8 49.6 - .3 18.5
1986 857.7(-) 226.4 1253.2 -166.9 5885.7 50.2 .6 5.7
1987 879.3 174.3 1290.9 -157.6 6092.6 51.4 1.2** 16.3
1988 902.8 151.4 1306.1 -113.5 6349.1 52.5 1.1** 31.2
1989 936.5 132.5L 1341.8 -81.2 6568.7 52.0 - .5 - 3.1
1990 907.3(-) 196.2 1385.5 -58.6 6683.5 50.9 - 1.1P** 30.0
1991T 829.5(-) 248.6* 1402.8 -16.4 6669.2 50.5 - .4T 7.4
1992 899.8 329.8H 1410.7 -18.7 6891.1 50.5 .0 9.9
1993 977.9 289.5 1398.1 -59.9 7054.1 50.4 - .1 1.3
1994 1107.0 211.9 1399.4 -87.6 7337.8 51.0 .6 37.1
1995 1140.6 180.0 1405.9 -79.2 7537.1 51.1 .1 22.7
1996 1242.7 136.3 1421.9 -89.0 7813.2 51.2 .1 33.1
1997 1393.3 52.3 1455.4 -113.3 8159.5 50.9 - .3 28.3
1998 1558.0 -42.4 1483.3 -221.1 8508.9 51.1 .2 20.9
1999 1660.5 -106.9 1540.6 -320.5 8859.0 52.0 .9 - 9.0
2000 1762.9 -193.6L 1582.5 -398.8 9191.4 52.8 .8 -11.8
2001 1574.6 -65.8 1640.4 -415.9 9214.5 53.4 .6P ?
Footnotes for Table 8.1.
The Cyclical Behavior of the Personal Saving Rate
Date of Personal Saving Rate at Duration
------------------------ ------------------------------------ of NBER
Saving Real Real Saving Quarter Real Real Recession
Rate GDP GDP Rate Before GDP GDP in Months
Peak Peak Trough Peak GDP Peak Peak Trough
(1) (2) (3)n (4) (5)
46-1 48-4 49-2 12.8 8.6 8.1 5.0 11
51-2 53-2 54-1 9.7 7.8 8.5(+) 8.5 10#
57-2 57-3 58-1 8.7 8.7 8.5 8.3 8
58-4 60-1 60-4 8.9 7.4 7.6(+) 7.1 10
67-1 69-3 70-4 9.6 7.2 8.5(+) 9.9* 11#
73-4 73-4 75-1 11.7 10.5 11.7(+) 9.9 16#
75-2 80-1 80-3 12.5 9.2 9.8(+) 10.0* 6
81-4 81-3 82-3 11.9 9.8 11.3(+) 11.1* 16#
84-3 90-2 91-1 11.3 7.8 7.9(+) 8.3* 8
92-2 00-4 01-3 8.9 2.9 2.9 4.0* ?
Essay 9:
The Political Business Cycle,
and
The Fed's War on Inflation
Professor of Economics
State University of New York at Albany
Presidential Elections and The Possibility of a Political Business Cycle
Is Zero Inflation Worth the Cost?
Seignorage
Liquidity Preference and the Duration of Economic Recessions
The Tinbergen Principle of Public Policy Analysis
The Targeting of Nominal GDP
Using Monetary Variables to Help Forecast the Growth of Nominal GDP
References
Using Economic Variables to Explain the Outcome of Presidential Elections
in the US, 1916-96.
Presidential Civilian CPI Change Civilian President Elec.
Election Unemployment Inflation Misery Employment and Political
Year Rate Rate Index Increase Affiliation
Cases Where the Incumbent Political Party Was Re-elected
(1)n (2)n (3)n (4)n (5)
1916 4.8(-3.2) 7.4* --- 6.1 Wilson (D)
1924* 5.5(-2.1) .3 -14.0 7.2 Coolidge (R)
1928 4.4( 2.5)* - 1.2 - 2.6 7.3 Hoover (R)
1936 16.9(-4.8) 1.0 4.5* 15.6 Roosevelt (D)
1940 14.6(-4.4) .8 - 2.5 8.0 Roosevelt (D)
1944 1.2(-3.5) 1.6 -12.6 13.6 Roosevelt (D)
1948 4.0(- .5) 2.7 3.9* 10.0 Truman (D)
1956 4.2(- .8) 2.9 3.5* 5.9 Eisenhower (R)
1964 5.0(- .5) 1.2 - 1.9 5.4 Johnson (D)
1972 5.2(- .9) 3.4 .5* 8.2 Nixon (R)
1984 7.2(-3.6) 4.0 - 8.5 5.7 Reagan (R)
1988 5.3(-1.3) 4.4 - 1.5 9.5 Bush (R)
1996 5.3(- .1) 3.3 -1.6 6.9 Clinton (D)
Cases Where the President Was Elected From an Opposing Party
1920 4.0( 2.6)* 15.8* 7.6* 3.1* Harding (R)
1932 23.6(14.9)* -10.2 10.2* -15.7* Roosevelt (D)
1952 2.7(-1.6) .9 - 3.1 3.3* Eisenhower (R)
1960 6.6( .4)* 1.5 1.0* 3.1* Kennedy (D)
1968* 3.4(- .4)* 4.7* 1.9* 9.5 Nixon (R)
1976 7.8( .6)* 4.8* 4.0* 8.0 Carter (D)
1980 7.3( 1.3)* 12.4* 7.1* 11.9 Reagan (R)
1992* 7.3( 1.2)* 2.9 .5* 2.3* Clinton (D)
2000 4.0(- .3) 3.4 - 1.2 6.7 Bush (R)
Footnotes for Table 9.1
Using Monetary Indicators to Help Forecast Following Year Growth Rates for GDP.
Annual Growth Rates December 4 Quarter Following Year Growth Rate
Year --------------------- Fed funds GDP Growth --------------------------
Currency M1 M2 Rate Rate Nominal GDP Real GDP
(1) (2) (3) (4) (5) (6) (7)
1978 9.8* 8.0 7.5 10.0 14.5 11.8( 2.0) 3.4( .6)
1979 9.2* 6.9 7.9 13.8 10.2 8.9(- .3) .0(- .8)
1980 10.0 6.9 8.5 18.9 9.6* 12.0( 2.4) 2.5( 2.4)
1981 6.2 6.9 9.7 12.4 9.7* 4.1(-5.6)? -1.9(-3.2)
1982 8.2 8.7* 8.8 9.0 3.5 8.5(- .2) 4.2( .4)
1983 10.3* 9.8 11.3 9.5 11.3 11.3( 1.0) 7.3( .6)
1984 6.8 5.8 8.6 8.4* 9.3 7.1(-1.3) 3.9(-1.0)
1985 7.6 12.4 8.0* 8.3 7.1 5.7(-2.3) 3.4(-1.7)
1986 7.6* 16.9 9.5 6.9 5.1 6.5(-1.1) 3.5(-2.0)
1987 9.0 3.5 3.6 6.8* 7.8 7.7( .9) 4.2(- .5)
1988 7.8 4.9 5.8 8.8 7.5* 7.5( .0) 3.5(- .3)
1989 4.9 .8 5.5* 8.4 6.3 5.7( .2) 1.7(- .2)
1990 10.9 4.0 3.8 7.3 4.6* 3.2(-1.4)? - .2(- .7)
1991 8.3 8.7 3.1 4.4* 4.0 5.6( 1.2) 3.3( 1.7)
1992 9.3 14.3 1.6 2.9 6.4* 5.1(-1.3) 2.4(-1.8)
1993 10.0 10.3 1.6 3.0 5.0* 6.2( 1.2) 4.0( 1.8)
1994 10.0 1.8 .4 5.4* 6.2 4.9(- .5)? 2.7(- .7)
1995 5.1 -2.0 4.1 5.6 4.3* 5.6( 1.3) 3.6( 1.5)
1996 5.9 -4.1 4.8 5.3* 6.0 6.5( 1.2) 4.4( .8)
1997 7.7 - .7 5.7* 5.5 6.2 5.6(- .1) 4.3(- .2)
1998 8.2 2.2 8.8 4.7 6.0* 5.6(- .5) 4.1(- .3)
1999 12.6 2.5 6.1* 5.3 5.9 5.9(- .2) 3.8( .0)
2000 4.4 -3.3 6.1 6.4 4.6* 2.6(-2.0)? .3(-2.3)
2001 9.5 8.3* 10.4 1.8 2.0 ?
Footnote for Table 9.2
Evaluating the 4th Quarter to 4th Quarter Growth Rate Forecasts of the
President's Council of Economic Advisers.
Year Forecasted Preliminary Forecasted Preliminary
Growth Rate Growth Rate Growth Rate Growth Rate
for Real GNP for Real GNP for the Price for the Price
or GDP or GDP Deflator Deflator
(1) (2)n (3) (4)n
1978 4.8(- .5)B 4.3(-1.4) 6.0( 2.3)B 8.3( 2.4)
1979 2.2(-1.4)B .8(-3.5) 7.4( 1.6) 9.0( .7)
1980 -1.0( .7)B - .3(-1.1) 9.0( 1.0) 10.0( 1.0)
1981 1.8(-1.1) .7( 1.0) 10.2(-1.6) 8.6(-1.4)
1982 3.0(-4.2) -1.2(-1.9) 7.2(-2.6)B 4.6(-4.0)
1983 3.1( 3.0)B 6.1( 7.3) 5.6(-1.5) 4.1(- .5)
1984 4.5( 1.1) 5.6(- .5) 5.0(-1.5) 3.5(- .6)
1985 4.0(-1.5)B 2.5(-3.1) 4.3(-1.1) 3.2(- .3)
1986 4.0(-1.8) 2.2(- .3) 3.8(-1.6) 2.2(-1.0)
1987 3.2( .6)B 3.8( 1.6) 3.6(- .3)B 3.3( 1.1)
1988 2.4( .2)B 2.6(-1.2) 3.9( .0)B 3.9( .6)
1989 3.5(-1.1) 2.4(- .2) 3.7( .1) 3.8(- .1)
1990 2.6(-2.3) .3(-2.1) 4.2(- .2) 4.0( .2)
1991 .9(- .7) .2(- .1) 4.3(-1.3) 3.0(-1.0)
1992 1.9( 1.0)B 2.9( 2.7) 3.2(- .8) 2.4(- .6)
1993 2.9(- .1) 2.8(- .1) 2.6(- .4) 2.2(- .2)
1994 3.0( 1.0)B 4.0( 1.0) 2.7(- .4) 2.3( .1)
1995 2.4(- .9)B 1.5(-2.5) 2.9(- .4) 2.5( .2)
1996 2.2( 1.2)B 3.4( 1.9) 2.8(- .7) 2.1(- .4)
1997 2.0( 1.9) 3.9( .5) 2.5(- .7) 1.8(- .3)
1998 2.0( 2.1) 4.1( .2) 2.0(-1.1) .9(- .9)
1999 2.0( 2.2) 4.2( .1) 1.9(- .3)B 1.6( .7)
2000 2.9( 1.2) 4.1(- .1) 1.9( .5)B 2.4( .8)
2001 3.2(-2.8)B .4(-3.7) 2.0(- .2)B 1.8(- .6)
2002 2.7 1.9
Mean Absolute Forecasting Errors 1978-98
1.35 1.63 1.01 .84
Footnotes for Table 9.3
Changes in the Fed Funds Rate Two Months after NBER Peaks in Economic Activity
and the Duration of Economic Recessions.
NBER Peak % Point Change Duration of
Dates Fed Funds Rate Recession
in Months
Jan. 1980 3.37 6
Aug. 1957 .26 8
July 1990 .05 8
Nov. 1948 .02* 11
Dec. 1969 .01 11
July 1953 - .22* 10
Nov. 1973 - .38 16
Apr. 1960 - .60 10
Mar. 2001-- -1.10 10?
July 1981 -3.17 16