Business Cycle Peaks Research Articles

Industrial Production and Capacity Utilization: The 2002 Historical and Annual Revision

In late 2002, the Board of Governors of the Federal Reserve System published a revision to its index of industrial production and the related measures of capacity utilization. The primary feature of the revision was the reclassification back to 1972 of production and capacity indexes for individual industries from the Standard Industrial Classification System to the North American Industry Classification System. The revision also reflects the incorporation of newly available, more comprehensive source data, and it introduced improved methods for measuring the annual real output of communications equipment manufacturing. Along with the updating and the restatement of the data using the North American Industry Classification System, all production and capacity indexes are now expressed as percentages of output in 1997. The new information resulted in an upward revision to the rate of increase in industrial production and capacity from 1997 to 2000. For that period, the average rate of industrial capacity utilization is 0.7 percentage point higher than previously reported. The most recent business-cycle peak is still June 2000, at 116.2 percent of 1997, with the low being the fourth quarter of 2001. The rate of industrial capacity in the third quarter of 2002, at 76.2 percent, is essentially unchanged from previously reported data. The rate is more than 5 percentage points below its 1972-2001 average and about 3 percentage points below the trough in the 1990-91 recession but 5 percentage points above the trough in the 1982 recession.

Asset Mispricing, Arbitrage, and Volatility

The originaltheoretical arguments put forward in favor of effi-cient markets were based on the notion of stabiliz-ing speculation in the form of arbitrage (Friedman,1953). Simply put, arbitrage is “the simultaneouspurchase and sale of the same, or essentially similar,security in two different markets for advantageouslydifferent prices” (Sharpe and Alexander, 1990). Intheory, a perfectly hedged trading position of thissort could be executed at no cost (as the short-saleproceeds are used to finance the long position).Vigilant traders on the look-out for just such arbi-trage opportunities would ensure that no one couldconsistently “beat the market”—the hallmark ofefficient markets theory.The academics’ logical case for efficient marketsboils down to a pair of simple rhetorical questions:Why would utility-maximizing traders leave unex-ploited any profitable opportunities (after adjustingproperly for risk)? And if no risk-adjusted “freelunches” exist, how could market prices be predict-able enough to make money? For several decades,empirical evidence piled up both for and againstmarket efficiency. As of the early 1990s, neitherside could claim total vindication. As the 1990sprogressed, however, the weight of the evidenceseemed to tip toward those who claimed asset priceswere, at least to some extent, predictable (Campbell,Lo, and MacKinlay, 1997, Chaps. 2 and 7).The academic asset-pricing literature today isdominated by attempts to explain why and to whatextent the price movements of financial assets arepredictable. One potential explanation of stock-return predictability is that markets are efficient(“no free lunch”) but expected returns are time-varying, perhaps being linked to the business cycle.For example, expected returns may be highest wheneconomic risks are perceived to be high, such as ator near the bottom of a business cycle. Conversely,expected returns may be lowest when economicrisks are perceived to be low, at or near a business-cycle peak. Thus, the simple random-walk modelof stock returns may be false, but a relevant notionof market efficiency survives because high returnsare earned only by taking large amounts of risk. Adifferent type of explanation of return predictabilityrejects market efficiency and focuses on marketimperfections of various sorts, such as incompletestock-market participation by households, signifi canttransactions costs, changes in investor sentiment,or limited wealth and liquidity resources to conductarbitrage (as in the current article).

Current Unemployment, Historically Contemplated

Current Unemployment, Historically Contemplated Chinhui Juhn, Kevin M. Murphy, and Robert H. Topel Eleven years ago, our Brookings Paper "Why Has the Natural Rate of Unemployment Increased over Time?" analyzed long-term changes in joblessness among American men.1 We documented the dramatic rise between 1967 and 1989 in both unemployment and nonparticipation in the labor force among prime-aged males. Our main conclusion was that a steep and sustained decline in the demand for low-skilled workers had reduced the returns to work for this group, leading to high rates of unemployment, labor force withdrawal, and long spells of joblessness for less-skilled men. We found that time spent out of the labor force and time spent unemployed accounted in roughly equal measure for the long-term growth in joblessness. We concluded that structural factors, primarily the decline in the demand for low-skilled labor, had dramatically changed the prospects for a return to low rates of joblessness any time soon. After that paper was published, things appeared to change. The 1990s opened with a brief recession that was followed by the longest sustained decline in unemployment in modern U.S. history. By the end of that expansion, the unemployment rate had reached its lowest level since the late 1960s, falling below 4 percent for the first time since 1969. Some macroeconomists argued that the so-called natural rate of unemployment [End Page 79] had permanently shifted to 5 percent or below.2 Because we had emphasized changes in the structure of labor demand that had made a return to low rates of joblessness unlikely, these facts presented a challenge to our 1991 framework. Maybe we were just wrong-maybe the demand and supply framework of our previous work is inconsistent with rates of joblessness in the post-1990 period. If so, we would join a distinguished group of social scientists who have drawn attention to a significant empirical phenomenon only to watch that phenomenon disappear immediately thereafter.3 As it turns out, however, the framework that we developed for thinking about pre-1990 patterns of joblessness also does fairly well in helping to understand jobless time in the post-1990 period. In this paper we look in some detail at employment data from the 1990s, revisiting issues raised in our earlier work. Specifically, we ask: -Have the trends we identified in our earlier paper-the concentration of nonemployment among the less skilled, the growth of nonparticipation in the labor force, and the increased duration of joblessness-been reversed with the fall in aggregate unemployment? -Did the expansion of the 1990s really return the U.S. labor market to conditions of the late 1960s, as unemployment statistics seem to indicate? -Does the economic framework of supply and demand we utilized a decade ago still help in understanding long-term developments in unemployment, nonemployment, and labor force participation? Our answers are surprising. First, the basic trends toward longer spells of joblessness and rising nonemployment have continued in spite of the prolonged expansion of national output and the concomitant fall in unemployment rates. Long jobless spells and labor force withdrawal were more important in the 1990s than ever before. Second, the fall in unemployment to levels close to historical lows is very misleading. Broader [End Page 80] measures of joblessness show that the labor market of the late 1990s was more like the relatively slack labor market of the late 1980s than like the booming labor market of the late 1960s. Finally, the basic forces of supply and demand identified in our previous paper continue to have explanatory power. The theory does a reasonably good job of explaining those trends that have continued, as well as those that have changed. Recent data also provide considerable insight into what has happened in the labor market over the past decade. Over the 1990s, even as unemployment was falling, time spent out of the labor force was rising. In fact, the increase in time spent out of the labor force was so large that total joblessness-which combines the unemployed with those who have withdrawn from the labor force-was as high at the business-cycle peak in 2000 as it had been at...

The behaviour of stock returns and interest rates over the business cycle in the US and UK

The paper studies the dynamic behaviour of the conditional mean and volatility of weekly financial variables in relation to the business cycle for the USA and UK economies. The mean US S&P stock returns steadily increases before a recession, then declines approximately six weeks prior to the trough date. Volatility reaches a local maximum 6 weeks prior to the recession, then peaks with the business cycle peak, but falls prior to the trough where the minimum is reached three weeks before. In the UK the FTSE volatility also falls before a recession but reaches its maximum 10 weeks after the peak date and has its minimum after the trough. Similarly, US interest rates are falling before a recession but there is no clear effect in the UK. The volatility of UK interest rates increases before and after the recession date. Overall more leading indicator information is provided by US stock returns and short interest rates whereas the respective UK variables seem to lag the business cycle phases.

A Note on New Estimates of the Distribution of Income in the 1920s

period from 1916 through 1929 is important to anyone interested in the history of income inequality in the United States. Current estimates suggest that income inequality increased in the antebellum period and reached a plateau of high income inequality from the Civil War to 1929.'' Inequality seems to have risen from the turn of the century to 1916 and then declined sharply during the First World War. 1920s brought a new surge of income inequality which, by 1929, pushed inequality to about the 1916 level.2 From 1929 to 1950 there was a significant decrease in income inequality in the United States followed by a period of relative distributional stability. Thus, 1916 and 1929 appear to have marked peaks in income inequality in the history of the United States. Because it directly preceded the Great Depression the surge of income inequality duning the prosperous 1920s has generated considerable interest. In 1946 Arthur Bums developed data which provided preliminary measures of this growing disparity, but the most iniportant evidence on changes in the distribution of income during the 1920s was presented in 1953 in Simon Kuznets's pathbreaking study, Shares of Upper Income Groups in Income and Savings. Kuznets carefully constructed income shares for upper-income percentiles of the total and nonfarm population. These shares provided evidence of increasing income inequality. For example, the share of disposable income ofthe top 1 percent of the nonfarm population rose from 13.13 percent in 1919 to 19.07 percent in 1929. From the business-cycle peak in 1923 to the cyclical peak in 1929 this share rose from 12.78 to 19.07 percent. Similar, though less pronounced, changes were found for the second and third, fourth and fifth, and sixth and seventh percentiles. share of disposable income for the lower 93 percent of the nonfarm population fell from 71.00 percent in 1919 to 69.74 percent in 1923 to 61.29 percent in 1929.4 In 1977 Charles Holt used Kuznets's data to estimate real per capita disposable incomes for nonfarn income percentiles. Holt found that the real per capita disposable incomes of the lower 93 percent of the nonfarm population fell 4 percent and concluded that, The inference is clear. Consumption increased markedly during the New Era in the face of little or no improvement in the consumption habits of the bulk of the nonfarm population.5 Recent research has shown that Kuznets's share estimates, and Holt's real per capita income estimates, overstate the rise in inequality during the twenties because no adjustments were made for changes in tax-avoidance behavior. This article draws upon new

Forecasting industry turning points: the US hotel industry cycle model

This study developed for the US hotel industry a business cycle that would cover hotel activity as broadly as possible and one that would represent the magnitude of growth of the industry. For the 28-year period (from 1966 to 1993), the hotel industry demonstrated three cycles (peak to peak or trough to trough). The hotel industry peaked in 1967, 1973, 1980, and 1989. The industry troughed in 1969, 1974, 1982, and 1991. The mean duration of the hotel industry cycle is 7.3 yr, calculated either by peak to peak or trough to trough. This means the next peak would be 1996 or 1997 if the past trends take the same track. According to the mean expansion figure (5.7 yr) the next peak would be also 1996 or 1997. The hotel industry declined sharply after it reached the peaks. The mean duration for the contraction is about two years (1.7 yr) and the mean duration for the expansion is about six years (5.7 yr). The hotel industry cycle was reformed with the hotel industry growth cycle based on year-over-year growth rate. The hotel industry experienced high growth (boom) every four or five years. The average expansion period is about three years and the average contraction period is about two years. The hotel industry led the general business cycle peaks by about 0.75 yr on average and also led at troughs in the general business cycle by roughly 0.5 yr. The results of this study provide useful guideposts for taking every possible advantage of the cycle study to the practitioners and researchers in the hotel industry.

Market timing and share returns

The relationships between business cycle peaks and troughs and turning points in two value indices based on the JSE All Share Index and Industrial Index are examined. It is shown that ex post and ex ante estimates of cyclical peaks and troughs based on share value indices lead the business cycle in an inconsistent way. However, timing strategies, based on forecasting the business cycle and switching between equities and treasury bills or equities and bonds around cyclical turning points have produced better than average returns.

Recognizing business cycle turning points by means of a neural network

The latest, 1990–91 recession marks the ninth downturn in the U.S. economy during the past fifty years. There is scope for adding extensions to the methodology of monitoring such major economic fluctuations. The use of artificial neural networks is proposed here. For demonstration a case study is included. In it four key economic indicators are examined; viz., sales, production, employment and personal income. The growth rate movement common to these variables is represented by a state space model of dynamic systems theory. Their monthly time series data over 1965–1989 are simultaneously analyzed. The dates of business cycle peaks and troughs identified in the analysis agree closely with the official chronology.

Stock Returns, Inflation,and the Business Cycle

This study examines the effect of inflation on stock returns in the context of the policy reaction function theory. This theory contends that the nature and extent of the government’s policy reaction to inflation will depend upon the current level of economic activity. A contractionary policy, which will depress stock returns, is more likely when the economy is a ta business cycle peak than at a trough. Therefore, the effect of inflation on stock returns varies with the stage of the business cycle. In order to test this theory, monthly consumer price indices, capacity utilization indices, and stock returns were examined. The results of using the returns of both a market index and a sample of individual companies between 1962 and 1988 support the theory.

The Political Economy of the Tariff Cycle

How can protectionism and “free” trade succeed one another? Our answer focuses on the changing balance of private actors' political demands. These actors acquire interests in tariff policies because their assets are spatially concentrated, and trade in these assets is subject to various limitations. Actors in regions experiencing no new investment in an established industry (“old” regions) have interests that sometimes differ from those in regions where there is new investment. We show that old regions have no reason to be involved in tariff politics at business cycle peaks; during troughs, whether a state becomes more or less protectionist depends, ceteris paribus, on the relative political strength of old import-competing and old exporting interests. If old import-competing industries outweigh the old exporters, then protection will tend to increase at the trough and decrease at the peak of a business cycle; the opposite result occurs when old exporters are more influential.

Sequential Signals of Recession and Recovery

This paper describes a sequential procedure for identifying the beginning and ending dates of business cycle recessions as promptly and accurately as practicable. Its origin lies in a study in progress for the Economic Development Administration (EDA), U.S. Department of Commerce, which deals with the problem of designing and testing an efficient trigger formula for public works expenditures with the aid of a system of cyclical indicators. However, the proposed approach can be applied much more broadly to any temporary countercyclical policy program on the national level. Federal policy programs of job creation through public works or public service employment have been repeatedly called countercyclical without in fact being so. Most such programs came into effect much too late to counter the cyclical Early and confirming signals of business cycle peaks and troughs are produced sequentially on a current basis by a system of monitoring smoothed rates of change in the composite indexes of leading and coincident indicators. Evidence is offered that the system would have identified each of the peaks and troughs of U.S. business cycles since 1949 without undue delays and false alarms. Countercyclical policies activated and deactivated by such signals would have desirable timing properties.

Stock Market Behavior around Business Cycle Peaks

(1980). Stock Market Behavior around Business Cycle Peaks. Financial Analysts Journal: Vol. 36, No. 4, pp. 55-57.

The Relationship of Monetary Decelerations to Business Cycle Peaks: Another Look at the Evidence

The Relationship of Monetary Decelerations to Business Cycle Peaks: Another Look at the Evidence

The Female-Male Differential in Unemployment Rates

THIS study attempts to explain why women have had a higher unemployment rate than men in the United States throughout the postwar period. It can be seen in Figure 1 that the female unemployment rate has exceeded the male rate since 1948 and that this differential between the two rates has been greatest at business cycle peaks. The three factors most likely to be responsible for the relatively high un

What is a Recession?

In the course of its studies of business cycles, the National Bureau of Economic Research has identified successive periods of business expansion and contraction in the United States and several other countries. Business cycle peak dates mark the end of a period of expansion and the beginning of a period of contraction; trough dates mark the end of a period of contraction and the beginning of a period of expansion. For the United States the chronology goes back to 1854 on a monthly and quarterly basis, and to 1834 on an annual basis. Between 1854 and 1961 (the last trough date) some 26 peaks and 27 troughs have been recognized. These dates identify 26 expansions and 26 contractions. The 10 contractions since 1920 are recorded in Table 1.

Cyclical Behavior of National Advertising

ALTHOUGH annual data on national advertising expenditures do not -L~L suggest much cyclical variability, examination of quarterly data shows that advertising is quite responsive to swings in general business activity. For the postwar period, in fact, conformity between business cycles and cycles in advertising is very close. There is a general tendency for peaks in national advertising to follow business-cycle peaks by several months, although the amount of this lag has shortened in recent years. On the other hand, troughs in national advertising at cycles seem to have preceded troughs in business in earlier periods, but this response of national advertising to changes in general business activity has slowed somewhat in the most recent period and is now about the same at cycle troughs as at cycle peaks. The typical length of expansions and contractions in advertising is about the same as that for gross national product. The expansions in advertising have been perceptibly larger than those in gross national product and in industrial production, except for 1958-60 when advertising increased only about as much as industrial production. Contractions in advertising typically exceed those in gross national product but are more modest than are those in industrial production. Outdoor advertising and radio spot, whose rates of growth are the smallest of the media under study, have cyclical characteristics most typical of mature, slow-growing industries. On the other hand, network and spot television are characterized by cyclical behavior of the sort associated with rapidly growing industries. The three print media (business papers, magazines, and newspapers), whose rates of growth fall between the other two groups, also have cyclical characteristics that fall between the slow-growing and the fast-growing media.