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Business cycle
Business cycle
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Business cycles are intervals of general expansion followed by recession in economic performance. The changes in economic activity that characterize business cycles have important implications for the welfare of the general population, government institutions, and private sector firms.

There are many definitions of a business cycle. The simplest defines recessions as two consecutive quarters of negative GDP growth. More satisfactory classifications are provided first by including more economic indicators and second by looking for more data patterns than the two quarter definition. In the United States, the National Bureau of Economic Research oversees a Business Cycle Dating Committee that defines a recession as "a significant decline in economic activity spread across the market, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production, and wholesale-retail sales."[1]

Business cycles are usually thought of as medium-term evolution. They are less related to long-term trends, coming from slowly-changing factors like technological advances. Further, a one period change, that is unusual over the course of one or two years, is often relegated to noise; an example is a worker strike or an isolated period of severe weather.

The individual episodes of expansion/recession occur with changing duration and intensity over time. Typically their periodicity has a wide range from around 2 to 10 years.

There are many sources of business cycle movements such as rapid and significant changes in the price of oil or variation in consumer sentiment that affects overall spending in the macroeconomy and thus investment and firms' profits. Usually such sources are unpredictable in advance and can be viewed as random shocks to the cyclical pattern, as happened during the 2008 financial crisis or the COVID-19 pandemic.

History

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Theory

[edit]
Parts of a business cycle
Phases of the business cycle
Actual business cycle
Long term growth of GDP

The first systematic exposition of economic crises, in opposition to the existing theory of economic equilibrium, was the 1819 Nouveaux Principes d'économie politique by Jean Charles Léonard de Sismondi.[2] Prior to that point classical economics had either denied the existence of business cycles,[3] blamed them on external factors, notably war,[4] or only studied the long term. Sismondi found vindication in the Panic of 1825, which was the first unarguably international economic crisis, occurring in peacetime.[citation needed]

Sismondi and his contemporary Robert Owen, who expressed similar but less systematic thoughts in 1817 Report to the Committee of the Association for the Relief of the Manufacturing Poor, both identified the cause of economic cycles as overproduction and underconsumption, caused in particular by wealth inequality. They advocated government intervention and socialism, respectively, as the solution. This work did not generate interest among classical economists, though underconsumption theory developed as a heterodox branch in economics until being systematized in Keynesian economics in the 1930s.

Sismondi's theory of periodic crises was developed into a theory of alternating cycles by Charles Dunoyer,[5] and similar theories, showing signs of influence by Sismondi, were developed by Johann Karl Rodbertus. Periodic crises in capitalism formed the basis of the theory of Karl Marx, who further claimed that these crises were increasing in severity and, on the basis of which, he predicted a communist revolution.[citation needed] Though only passing references in Das Kapital (1867) refer to crises, they were extensively discussed in Marx's posthumously published books, particularly in Theories of Surplus Value. In Progress and Poverty (1879), Henry George focused on land's role in crises – particularly land speculation – and proposed a single tax on land as a solution.

Statistical or econometric modelling and theory of business cycle movements can also be used. In this case a time series analysis is used to capture the regularities and the stochastic signals and noise in economic time series such as Real GDP or Investment. [Harvey and Trimbur, 2003, Review of Economics and Statistics] developed models for describing stochastic or pseudo- cycles, of which business cycles represent a leading case. As well-formed and compact – and easy to implement – statistical methods may outperform macroeconomic approaches in numerous cases, they provide a solid alternative even for rather complex economic theory.[6]

Classification by periods

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Business cycle with it specific forces in four stages according to Malcolm C. Rorty, 1922

In 1860 French economist Clément Juglar first identified economic cycles 7 to 11 years long, although he cautiously did not claim any rigid regularity.[7] This interval of periodicity is also commonplace, as an empirical finding, in time series models for stochastic cycles in economic data. Furthermore, methods like statistical modelling in a Bayesian framework – see e.g. [Harvey, Trimbur, and van Dijk, 2007, Journal of Econometrics] – can incorporate such a range explicitly by setting up priors that concentrate around say 6 to 12 years, such flexible knowledge about the frequency of business cycles can actually be included in their mathematical study, using a Bayesian statistical paradigm.[8]

Later[when?], economist Joseph Schumpeter argued that a Juglar cycle has four stages:

  1. Expansion (increase in production and prices, low interest rates)
  2. Crisis (stock exchanges crash and multiple bankruptcies of firms occur)
  3. Recession (drops in prices and in output, high interest-rates)
  4. Recovery (stocks recover because of the fall in prices and incomes)

Schumpeter's Juglar model associates recovery and prosperity with increases in productivity, consumer confidence, aggregate demand, and prices.

In the 20th century, Schumpeter and others proposed a typology of business cycles according to their periodicity, so that a number of particular cycles were named after their discoverers or proposers:[9]

Proposed economic waves
Cycle/wave name Period (years)
Kitchin cycle (inventory, e.g. pork cycle) 3–5
Juglar cycle (fixed investment) 7–11
Kuznets swing (infrastructural investment) 15–25
Kondratiev wave (technological basis) 45–60

Some say interest in the different typologies of cycles has waned since the development of modern macroeconomics, which gives little support to the idea of regular periodic cycles.[12] Further econometric studies such as the two works in 2003 and 2007 cited above demonstrate a clear tendency for cyclical components in macroeconomic times to behave in a stochastic rather than deterministic way.

Others, such as Dmitry Orlov, argue that simple compound interest mandates the cycling of monetary systems. Since 1960, World GDP has increased by fifty-nine times, and these multiples have not even kept up with annual inflation over the same period. Social Contract (freedoms and absence of social problems) collapses may be observed in nations where incomes are not kept in balance with cost-of-living over the timeline of the monetary system cycle.

The Bible (760 BCE) and Hammurabi's Code (1763 BCE) both explain economic remediations for cyclic sixty-year recurring great depressions, via fiftieth-year Jubilee (biblical) debt and wealth resets[citation needed]. Thirty major debt forgiveness events are recorded in history including the debt forgiveness given to most European nations in the 1930s to 1954.[13]

Occurrence

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A simplified Kondratiev wave, with the theory that productivity enhancing innovations drive waves of economic growth

There were great increases in productivity, industrial production and real per capita product throughout the period from 1870 to 1890 that included the Long Depression and two other recessions.[14][15] There were also significant increases in productivity in the years leading up to the Great Depression. Both the Long and Great Depressions were characterized by overcapacity and market saturation.[16][17]

Over the period since the Industrial Revolution, technological progress has had a much larger effect on the economy than any fluctuations in credit or debt, the primary exception being the Great Depression, which caused a multi-year steep economic decline. The effect of technological progress can be seen by the purchasing power of an average hour's work, which has grown from $3 in 1900 to $22 in 1990, measured in 2010 dollars.[18] There were similar increases in real wages during the 19th century. (See: Productivity improving technologies (historical).) A table of innovations and long cycles can be seen at: Kondratiev wave § Modern modifications of Kondratiev theory. Since surprising news in the economy, which has a random aspect, impact the state of the business cycle, any corresponding descriptions must have a random part at its root that motivates the use of statistical frameworks in this area.

There were frequent crises in Europe and America in the 19th and first half of the 20th century, specifically the period 1815–1939. This period started from the end of the Napoleonic wars in 1815, which was immediately followed by the Post-Napoleonic depression in the United Kingdom (1815–1830), and culminated in the Great Depression of 1929–1939, which led into World War II. See Financial crisis: 19th century for listing and details. The first of these crises not associated with a war was the Panic of 1825.[19]

Business cycles in OECD countries after World War II were generally more restrained than the earlier business cycles. This was particularly true during the Golden Age of Capitalism (1945/50–1970s), and the period 1945–2008 did not experience a global downturn until the Late-2000s recession.[20] Economic stabilization policy using fiscal policy and monetary policy appeared to have dampened the worst excesses of business cycles, and automatic stabilization due to the aspects of the government's budget also helped mitigate the cycle even without conscious action by policy-makers.[21]

In this period, the economic cycle – at least the problem of depressions – was twice declared dead. The first declaration was in the late 1960s, when the Phillips curve was seen as being able to steer the economy. However, this was followed by stagflation in the 1970s, which discredited the theory. The second declaration was in the early 2000s, following the stability and growth in the 1980s and 1990s in what came to be known as the Great Moderation. Notably, in 2003, Robert Lucas Jr., in his presidential address to the American Economic Association, declared that the "central problem of depression-prevention [has] been solved, for all practical purposes."[22]

Various regions have experienced prolonged depressions, most dramatically the economic crisis in former Eastern Bloc countries following the end of the Soviet Union in 1991. For several of these countries the period 1989–2010 has been an ongoing depression, with real income still lower than in 1989.[23]

Identifying

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Economic activity in the United States, 1954–2005
Deviations from the long-term United States growth trend, 1954–2005

In 1946, economists Arthur F. Burns and Wesley C. Mitchell provided the now standard definition of business cycles in their book Measuring Business Cycles:[24]

Business cycles are a type of fluctuation found in the aggregate economic activity of nations that organize their work mainly in business enterprises: a cycle consists of expansions occurring at about the same time in many economic activities, followed by similarly general recessions, contractions, and revivals which merge into the expansion phase of the next cycle; in duration, business cycles vary from more than one year to ten or twelve years; they are not divisible into shorter cycles of similar characteristics with amplitudes approximating their own.

According to A. F. Burns:[25]

Business cycles are not merely fluctuations in aggregate economic activity. The critical feature that distinguishes them from the commercial convulsions of earlier centuries or from the seasonal and other short term variations of our own age is that the fluctuations are widely diffused over the economy – its industry, its commercial dealings, and its tangles of finance. The economy of the western world is a system of closely interrelated parts. He who would understand business cycles must master the workings of an economic system organized largely in a network of free enterprises searching for profit. The problem of how business cycles come about is therefore inseparable from the problem of how a capitalist economy functions.

In the United States, it is generally accepted that the National Bureau of Economic Research (NBER) is the final arbiter of the dates of the peaks and troughs of the business cycle. An expansion is the period from a trough to a peak and a recession as the period from a peak to a trough. The NBER identifies a recession as "a significant decline in economic activity spread across the economy, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production".[26]

Upper turning points of business cycle, commodity prices and freight rates

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There is often a close timing relationship between the upper turning points of the business cycle, commodity prices, and freight rates, which is shown to be particularly tight in the grand peak years of 1873, 1889, 1900 and 1912.[27] Hamilton expressed that in the post war era, a majority of recessions are connected to an increase in oil price.[28]

Commodity price shocks are considered to be a significant driving force of the US business cycle.[29]

Along these lines, the research in [Trimbur, 2010, International Journal of Forecasting] shows empirical results for the relation between oil-prices and real GDP. The methodology uses a statistical model that incorporate level shifts in the price of crude oil; hence the approach describes the possibility of oil price shocks and forecasts the likelihood of such events.[30]

Indicators

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Economic indicators are used to measure the business cycle: consumer confidence index, retail trade index, unemployment and industry/service production index. Stock and Watson claim that financial indicators' predictive ability is not stable over different time periods because of economic shocks, random fluctuations and development in financial systems.[31] Ludvigson believes consumer confidence index is a coincident indicator as it relates to consumer's current situations.[32] Winton & Ralph state that retail trade index is a benchmark for the current economic level because its aggregate value counts up for two-thirds of the overall GDP and reflects the real state of the economy.[33] According to Stock and Watson, unemployment claim can predict when the business cycle is entering a downward phase.[34] Banbura and Rüstler argue that industry production's GDP information can be delayed as it measures real activity with real number, but it provides an accurate prediction of GDP.[35]

Series used to infer the underlying business cycle fall into three categories: lagging, coincident, and leading. They are described as main elements of an analytic system to forecast peaks and troughs in the business cycle.[36] For almost 30 years, these economic data series are considered as "the leading index" or "the leading indicators"-were compiled and published by the U.S. Department of Commerce.

A prominent coincident, or real-time, business cycle indicator is the Aruoba-Diebold-Scotti Index.

Spectral analysis of business cycles

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Recent research employing spectral analysis has confirmed the presence of Kondratiev waves in the world GDP dynamics at an acceptable level of statistical significance.[37] Korotayev & Tsirel also detected shorter business cycles, dating the Kuznets to about 17 years and calling it the third sub-harmonic of the Kondratiev, meaning that there are three Kuznets cycles per Kondratiev.[jargon]

Recurrence quantification analysis

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Recurrence quantification analysis has been employed to detect the characteristic of business cycles and economic development. To this end, Orlando et al.[38] developed the so-called recurrence quantification correlation index to test correlations of RQA on a sample signal and then investigated the application to business time series. The said index has been proven to detect hidden changes in time series. Further, Orlando et al.,[39] over an extensive dataset, shown that recurrence quantification analysis may help in anticipating transitions from laminar (i.e. regular) to turbulent (i.e. chaotic) phases such as USA GDP in 1949, 1953, etc. Last but not least, it has been demonstrated that recurrence quantification analysis can detect differences between macroeconomic variables and highlight hidden features of economic dynamics.[39]

Cycles or fluctuations?

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The Business Cycle follows changes in stock prices which are mostly caused by external factors such as socioeconomic conditions, inflation, exchange rates. Intellectual capital does not affect a company stock's current earnings. Intellectual capital contributes to a stock's return growth.[40]

Unlike long-term trends, medium-term data fluctuations are connected to the monetary policy transmission mechanism and its role in regulating inflation during an economic cycle. At the same time, the presence of nominal restrictions in price setting behavior might impact the short-term course of inflation.[41]

In recent years economic theory has moved towards the study of economic fluctuation rather than a "business cycle"[42] – though some economists use the phrase 'business cycle' as a convenient shorthand. For example, Milton Friedman said that calling the business cycle a "cycle" is a misnomer, because of its non-cyclical nature. Friedman believed that for the most part, excluding very large supply shocks, business declines are more of a monetary phenomenon.[43] Arthur F. Burns and Wesley C. Mitchell define business cycle as a form of fluctuation. In economic activities, a cycle of expansions happening, followed by recessions, contractions, and revivals. All of which combine to form the next cycle's expansion phase; this sequence of change is repeated but not periodic.[44]

Proposed explanations

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The explanation of fluctuations in aggregate economic activity is one of the primary concerns of macroeconomics and a variety of theories have been proposed to explain them.

Exogenous vs. endogenous

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Within economics, it has been debated as to whether or not the fluctuations of a business cycle are attributable to external (exogenous) versus internal (endogenous) causes. In the first case shocks are stochastic, in the second case shocks are deterministically chaotic and embedded in the economic system.[45] The classical school (now neo-classical) argues for exogenous causes and the underconsumptionist (now Keynesian) school argues for endogenous causes. These may also broadly be classed as "supply-side" and "demand-side" explanations: supply-side explanations may be styled, following Say's law, as arguing that "supply creates its own demand", while demand-side explanations argue that effective demand may fall short of supply, yielding a recession or depression.

This debate has important policy consequences: proponents of exogenous causes of crises such as neoclassicals largely argue for minimal government policy or regulation (laissez faire), as absent these external shocks, the market functions, while proponents of endogenous causes of crises such as Keynesians largely argue for larger government policy and regulation, as absent regulation, the market will move from crisis to crisis. This division is not absolute – some classicals (including Say) argued for government policy to mitigate the damage of economic cycles, despite believing in external causes, while Austrian School economists argue against government involvement as only worsening crises, despite believing in internal causes.

The view of the economic cycle as caused exogenously dates to Say's law, and much debate on endogeneity or exogeneity of causes of the economic cycle is framed in terms of refuting or supporting Say's law; this is also referred to as the "general glut" (supply in relation to demand) debate.

Until the Keynesian Revolution in mainstream economics in the wake of the Great Depression, classical and neoclassical explanations (exogenous causes) were the mainstream explanation of economic cycles; following the Keynesian revolution, neoclassical macroeconomics was largely rejected. There has been some resurgence of neoclassical approaches in the form of real business cycle (RBC) theory. The debate between Keynesians and neo-classical advocates was reawakened following the recession of 2007.

Mainstream economists working in the neoclassical tradition, as opposed to the Keynesian tradition, have usually viewed the departures of the harmonic working of the market economy as due to exogenous influences, such as the State or its regulations, labor unions, business monopolies, or shocks due to technology or natural causes.

Contrarily, in the heterodox tradition of Jean Charles Léonard de Sismondi, Clément Juglar, and Marx the recurrent upturns and downturns of the market system are an endogenous characteristic of it.[46]

The 19th-century school of under consumptionism also posited endogenous causes for the business cycle, notably the paradox of thrift, and today this previously heterodox school has entered the mainstream in the form of Keynesian economics via the Keynesian revolution.

Mainstream economics

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Mainstream economics views business cycles as essentially "the random summation of random causes". In 1927, Eugen Slutzky observed that summing random numbers, such as the last digits of the Russian state lottery, could generate patterns akin to that we see in business cycles, an observation that has since been repeated many times. This caused economists to move away from viewing business cycles as a cycle that needed to be explained and instead viewing their apparently cyclical nature as a methodological artefact. This means that what appear to be cyclical phenomena can actually be explained as just random events that are fed into a simple linear model. Thus business cycles are essentially random shocks that average out over time. Mainstream economists have built models of business cycles based on the idea that they are caused by random shocks.[47][48][49] Due to this inherent randomness, recessions can sometimes not occur for decades; for example, Australia did not experience any recession between 1991 and 2020.[50]

While economists have found it difficult to forecast recessions or determine their likely severity, research indicates that longer expansions do not cause following recessions to be more severe.[51]

Keynesian

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According to Keynesian economics, fluctuations in aggregate demand cause the economy to come to short run equilibrium at levels that are different from the full employment rate of output. These fluctuations express themselves as the observed business cycles. Keynesian models do not necessarily imply periodic business cycles. However, simple Keynesian models involving the interaction of the Keynesian multiplier and accelerator give rise to cyclical responses to initial shocks. Paul Samuelson's "oscillator model"[52] is supposed to account for business cycles thanks to the multiplier and the accelerator. The amplitude of the variations in economic output depends on the level of the investment, for investment determines the level of aggregate output (multiplier), and is determined by aggregate demand (accelerator).

In the Keynesian tradition, Richard Goodwin[53] accounts for cycles in output by the distribution of income between business profits and workers' wages. The fluctuations in wages are almost the same as in the level of employment (wage cycle lags one period behind the employment cycle), for when the economy is at high employment, workers are able to demand rises in wages, whereas in periods of high unemployment, wages tend to fall. According to Goodwin, when unemployment and business profits rise, the output rises.

Cyclical behavior of exports and imports

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Exports and imports are large components of an economy's aggregate expenditure, especially one that is oriented toward international trade. Income is an essential determinant of the level of imported goods. A higher GDP reflects a higher level of spending on imported goods and services, and vice versa. Therefore, expenditure on imported goods and services falls during a recession and rises during an economic expansion or boom.[54]

Import expenditures are commonly considered to be procyclical and cyclical in nature, coincident with the business cycle.[54] Domestic export expenditures give a good indication of foreign business cycles as foreign import expenditures are coincident with the foreign business cycle.

Credit/debt cycle

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One alternative theory is that the primary cause of economic cycles is due to the credit cycle: the net expansion of credit (increase in private credit, equivalently debt, as a percentage of GDP) yields economic expansions, while the net contraction causes recessions, and if it persists, depressions. In particular, the bursting of speculative bubbles is seen as the proximate cause of depressions, and this theory places finance and banks at the center of the business cycle.

A primary theory in this vein is the debt deflation theory of Irving Fisher, which he proposed to explain the Great Depression. A more recent complementary theory is the Financial Instability Hypothesis of Hyman Minsky, and the credit theory of economic cycles is often associated with Post-Keynesian economics such as Steve Keen.

Post-Keynesian economist Hyman Minsky has proposed an explanation of cycles founded on fluctuations in credit, interest rates and financial frailty, called the Financial Instability Hypothesis. In an expansion period, interest rates are low and companies easily borrow money from banks to invest. Banks are not reluctant to grant them loans, because expanding economic activity allows business increasing cash flows and therefore they will be able to easily pay back the loans. This process leads to firms becoming excessively indebted, so that they stop investing, and the economy goes into recession.

While credit causes have not been a primary theory of the economic cycle within the mainstream, they have gained occasional mention, such as (Eckstein & Sinai 1990), cited approvingly by (Summers 1986).

Real business-cycle theory

[edit]

Within mainstream economics, Keynesian views have been challenged by real business cycle models in which fluctuations are due to random changes in the total productivity factor (which are caused by changes in technology as well as the legal and regulatory environment). This theory is most associated with Finn E. Kydland and Edward C. Prescott, and more generally the Chicago school of economics (freshwater economics). They consider that economic crisis and fluctuations cannot stem from a monetary shock, only from an external shock, such as an innovation.[47]

Product based theory of economic cycles

[edit]
International product life cycle

This theory explains the nature and causes of economic cycles from the viewpoint of life-cycle of marketable goods.[55] The theory originates from the work of Raymond Vernon, who described the development of international trade in terms of product life-cycle – a period of time during which the product circulates in the market. Vernon stated that some countries specialize in the production and export of technologically new products, while others specialize in the production of already known products. The most developed countries are able to invest large amounts of money in the technological innovations and produce new products, thus obtaining a dynamic comparative advantage over developing countries.

Recent research by Georgiy Revyakin proved initial Vernon theory and showed economic cycles in developed countries overran economic cycles in developing countries.[56] He also presumed economic cycles with different periodicity can be compared to the products with various life-cycles. In case of Kondratiev waves such products correlate with fundamental discoveries implemented in production (inventions which form the technological paradigm: Richard Arkwright's machines, steam engines, industrial use of electricity, computer invention, etc.); Kuznets cycles describe such products as infrastructural components (roadways, transport, utilities, etc.); Juglar cycles may go in parallel with enterprise fixed capital (equipment, machinery, etc.), and Kitchin cycles are characterized by change in the society preferences (tastes) for consumer goods, and time, which is necessary to start the production.

Highly competitive market conditions would determine simultaneous technological updates of all economic agents (as a result, cycle formation): in case if a manufacturing technology at an enterprise does not meet the current technological environment – such company loses its competitiveness and eventually goes bankrupt.

Political business cycle

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Another set of models tries to derive the business cycle from political decisions. The political business cycle theory is strongly linked to the name of Michał Kalecki who discussed "the reluctance of the 'captains of industry' to accept government intervention in the matter of employment".[57] Persistent full employment would mean increasing workers' bargaining power to raise wages and to avoid doing unpaid labor, potentially hurting profitability. However, he did not see this theory as applying under fascism, which would use direct force to destroy labor's power.

In recent years, proponents of the "electoral business cycle" theory have argued that incumbent politicians encourage prosperity before elections in order to ensure re-election – and make the citizens pay for it with recessions afterwards.[58] The political business cycle is an alternative theory stating that when an administration of any hue is elected, it initially adopts a contractionary policy to reduce inflation and gain a reputation for economic competence. It then adopts an expansionary policy in the lead up to the next election, hoping to achieve simultaneously low inflation and unemployment on election day.[59]

The partisan business cycle suggests that cycles result from the successive elections of administrations with different policy regimes. Regime A adopts expansionary policies, resulting in growth and inflation, but is voted out of office when inflation becomes unacceptably high. The replacement, Regime B, adopts contractionary policies reducing inflation and growth, and the downwards swing of the cycle. It is voted out of office when unemployment is too high, being replaced by Party A.

Marxian economics

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For Marx, the economy based on production of commodities to be sold in the market is intrinsically prone to crisis. In the heterodox Marxian view, profit is the major engine of the market economy, but business (capital) profitability has a tendency to fall that recurrently creates crises in which mass unemployment occurs, businesses fail, remaining capital is centralized and concentrated and profitability is recovered. In the long run, these crises tend to be more severe and the system will eventually fail.[60]

Some Marxist authors such as Rosa Luxemburg viewed the lack of purchasing power of workers as a cause of a tendency of supply to be larger than demand, creating crisis, in a model that has similarities with the Keynesian one. Indeed, a number of modern authors have tried to combine Marx's and Keynes's views. Henryk Grossman[61] reviewed the debates and the counteracting tendencies and Paul Mattick subsequently emphasized the basic differences between the Marxian and the Keynesian perspective. While Keynes saw capitalism as a system worth maintaining and susceptible to efficient regulation, Marx viewed capitalism as a historically doomed system that cannot be put under societal control.[62]

The American mathematician and economist Richard M. Goodwin formalised a Marxist model of business cycles known as the Goodwin Model in which recession was caused by increased bargaining power of workers (a result of high employment in boom periods) pushing up the wage share of national income, suppressing profits and leading to a breakdown in capital accumulation. Later theorists applying variants of the Goodwin model have identified both short and long period profit-led growth and distribution cycles in the United States and elsewhere.[63][64][65][66][67] David Gordon provided a Marxist model of long period institutional growth cycles in an attempt to explain the Kondratiev wave. This cycle is due to the periodic breakdown of the social structure of accumulation, a set of institutions which secure and stabilize capital accumulation.

Austrian School

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Economists of the heterodox Austrian School argue that business cycles are caused by excessive issuance of credit by banks in fractional reserve banking systems. According to Austrian economists, excessive issuance of bank credit may be exacerbated if central bank monetary policy sets interest rates too low, and the resulting expansion of the money supply causes a "boom" in which resources are misallocated or "malinvested" because of artificially low interest rates. Eventually, the boom cannot be sustained and is followed by a "bust" in which the malinvestments are liquidated (sold for less than their original cost) and the money supply contracts.[68][69]

One of the criticisms of the Austrian business cycle theory is based on the observation that the United States suffered recurrent economic crises in the 19th century, notably the Panic of 1873, which occurred prior to the establishment of a U.S. central bank in 1913. Adherents of the Austrian School, such as the historian Thomas Woods, argue that these earlier financial crises were prompted by government and bankers' efforts to expand credit despite restraints imposed by the prevailing gold standard, and are thus consistent with Austrian Business Cycle Theory.[70][71]

The Austrian explanation of the business cycle differs significantly from the mainstream understanding of business cycles and is generally rejected by mainstream economists. Mainstream economists generally do not support Austrian school explanations for business cycles, on both theoretical as well as real-world empirical grounds.[72][73][74][75][76][77] Austrians claim that the boom-and-bust business cycle is caused by government intervention into the economy, and that the cycle would be comparatively rare and mild without central government interference.

Yield curve

[edit]
   10 Year Treasury Bond
   2 Year Treasury Bond
   3 month Treasury Bond
   Effective Federal Funds Rate
   CPI inflation year/year
  Recessions
10-year minus 3-month US Treasury Yields

The slope of the yield curve is one of the most powerful predictors of future economic growth, inflation, and recessions.[78] One measure of the yield curve slope (i.e. the difference between 10-year Treasury bond rate and the 3-month Treasury bond rate) is included in the Financial Stress Index published by the St. Louis Fed.[79] A different measure of the slope (i.e. the difference between 10-year Treasury bond rates and the federal funds rate) is incorporated into the Index of Leading Economic Indicators published by The Conference Board.[80]

An inverted yield curve is often a harbinger of recession. A positively sloped yield curve is often a harbinger of inflationary growth. Work by Arturo Estrella and Tobias Adrian has established the predictive power of an inverted yield curve to signal a recession. Their models show that when the difference between short-term interest rates (they use 3-month T-bills) and long-term interest rates (10-year Treasury bonds) at the end of a federal reserve tightening cycle is negative or less than 93 basis points positive that a rise in unemployment usually occurs.[81] The New York Fed publishes a monthly recession probability prediction derived from the yield curve and based on Estrella's work.

All the recessions in the United States since 1970 (up through 2017) have been preceded by an inverted yield curve (10-year vs. 3-month). Over the same time frame, every occurrence of an inverted yield curve has been followed by recession as declared by the NBER business cycle dating committee.[82]

Event Date of inversion start Date of the recession start Time from inversion to recession Start Duration of inversion Time from recession start to NBER announcement Time from disinversion to recession end Duration of recession Time from recession end to NBER announcement Max inversion
Months Months Months Months Months Months Basis points
1970 recession December 1968 January 1970 13 15 NA 8 11 NA −52
1974 recession June 1973 December 1973 6 18 NA 3 16 NA −159
1980 recession November 1978 February 1980 15 18 4 2 6 12 −328
1981–1982 recession October 1980 August 1981 10 12 5 13 16 8 −351
1990 recession June 1989 August 1990 14 7 8 14 8 21 −16
2001 recession July 2000 April 2001 9 7 7 9 8 20 −70
2008–2009 recession August 2006 January 2008 17 10 11 24 18 15 −51
2020–2020 recession March 2020 April 2020
Average since 1969 12 12 7 10 12 15 −147
Standard deviation since 1969 3.83 4.72 2.74 7.50 4.78 5.45 138.96

Estrella and others have postulated that the yield curve affects the business cycle via the balance sheet of banks (or bank-like financial institutions).[83] When the yield curve is inverted banks are often caught paying more on short-term deposits (or other forms of short-term wholesale funding) than they are making on long-term loans leading to a loss of profitability and reluctance to lend resulting in a credit crunch. When the yield curve is upward sloping, banks can profitably take-in short term deposits and make long-term loans so they are eager to supply credit to borrowers. This eventually leads to a credit bubble.

Georgism

[edit]

Henry George claimed land price fluctuations were the primary cause of most business cycles.[84]

Other factors

[edit]

Population swings can impact business cycles.[85]

Mitigating an economic downturn

[edit]

Many social indicators, such as mental health, crimes, and suicides, worsen during economic recessions (though general mortality tends to fall, and it is in expansions when it tends to increase).[86] As periods of economic stagnation are painful for the many who lose their jobs, there is often political pressure for governments to mitigate recessions. Since the 1940s, following the Keynesian Revolution, most governments of developed nations have seen the mitigation of the business cycle as part of the responsibility of government, under the rubric of stabilization policy.[87]

Since in the Keynesian view, recessions are caused by inadequate aggregate demand, when a recession occurs the government should increase the amount of aggregate demand and bring the economy back into equilibrium. This the government can do in two ways, firstly by increasing the money supply (expansionary monetary policy) and secondly by increasing government spending or cutting taxes (expansionary fiscal policy).

By contrast, some economists, notably New classical economist Robert Lucas, argue that the welfare cost of business cycles are very small to negligible, and that governments should focus on long-term growth instead of stabilization.

However, even according to Keynesian theory, managing economic policy to smooth out the cycle is a difficult task in a society with a complex economy. Some theorists, notably those who believe in Marxian economics, believe that this difficulty is insurmountable. Karl Marx claimed that recurrent business cycle crises were an inevitable result of the operations of the capitalistic system. In this view, all that the government can do is to change the timing of economic crises. The crisis could also show up in a different form, for example as severe inflation or a steadily increasing government deficit. Worse, by delaying a crisis, government policy is seen as making it more dramatic and thus more painful.

Additionally, since the 1960s neoclassical economists have played down the ability of Keynesian policies to manage an economy. Since the 1960s, economists like Nobel Laureates Milton Friedman and Edmund Phelps have made ground in their arguments that inflationary expectations negate the Phillips curve in the long run. The stagflation of the 1970s provided striking support for their theories while proving a dilemma for Keynesian policies, which appeared to necessitate both expansionary policies to mitigate recession and contractionary policies to reduce inflation. Friedman has gone so far as to argue that all the central bank of a country should do is to avoid making large mistakes, as he believes they did by contracting the money supply very rapidly in the face of the Wall Street crash of 1929, in which they made what would have been a recession into the Great Depression.[citation needed]

Software

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The Hodrick-Prescott[88] and the Christiano-Fitzgerald[89] filters can be implemented using the R package mFilter, while singular spectrum filters[90][91] can be implemented using the R package ASSA.

See also

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Notes

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References

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Further reading

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
The business cycle comprises the recurrent expansions and contractions in aggregate economic activity, encompassing rises and falls in output, , , and other key indicators that occur with some regularity but varying amplitude and duration across economies. These fluctuations are empirically defined not by a single variable like but by the comovement of multiple coincident indicators, such as industrial production, , and , which reveal peaks marking the end of expansions and troughs signaling the conclusion of contractions. In the United States, the dates these cycles, identifying post-World War II expansions averaging approximately 58 months and contractions around 11 months, though individual cycles deviate significantly, with some recessions brief and others protracted, as seen in the prolonged or the sharp 2020 downturn. While pervasive across modern economies, business cycles exhibit persistence and pervasiveness but lack perfect periodicity, challenging deterministic predictions and underscoring their complex, evolving nature influenced by real shocks, policy responses, and financial dynamics. Theories attributing cycles to exogenous or supply shocks, endogenous monetary expansions, or deficiencies yield mixed empirical validation, with no single framework fully accounting for observed asymmetries, such as sharper contractions than expansions or sector-specific leads and lags. Central banks and fiscal authorities often intervene to mitigate downturns, yet evidence on the efficacy of stabilization remains debated, as interventions may inadvertently prolong maladjustments or amplify future volatility through distorted incentives.

Fundamental Concepts

Definition and Phases

The business cycle describes the fluctuations in aggregate economic activity that recur over time, typically spanning periods from one trough to the next or peak to peak, as measured by indicators such as (GDP), , industrial production, and real personal income excluding transfers. These cycles reflect deviations from the economy's long-term growth trend, driven by endogenous factors like variations in , availability, and shocks, rather than exogenous trends or seasonal patterns. The (NBER) Business Cycle Dating Committee officially dates U.S. cycles by identifying turning points based on a holistic assessment of multiple monthly indicators, emphasizing depth, diffusion, and duration of changes rather than a strict GDP threshold. Business cycles consist of four principal phases: expansion, peak, contraction, and trough. Expansion occurs from a trough to a peak, marked by rising output, , and ; real GDP growth accelerates as businesses increase production, strengthens, and declines, often accompanied by moderate . Peak represents the cycle's , where economic activity reaches its maximum sustainable level before imbalances—such as over or capacity constraints—prompt a slowdown; indicators like industrial production and payroll plateau or begin to soften. The subsequent contraction phase, from peak to trough, involves declining activity across sectors; if widespread and lasting more than a few months, it qualifies as a , with falling GDP, rising (often exceeding 5-7% in postwar U.S. episodes), reduced , and potential deflationary pressures. Trough signals the bottom, where activity stabilizes at its lowest point, inventories are depleted, and conditions set the stage for recovery through lower interest rates, bargain asset prices, and renewed confidence. Cycle durations vary significantly; postwar U.S. expansions have averaged about 58 months from trough to peak, while contractions average 11 months, though pre-1945 contractions were often deeper and longer due to factors like banking panics. Not all contractions meet criteria—mild slowdowns may not trigger NBER declarations—and cycles differ in , with expansions generally outlasting contractions in modern eras, reflecting policy interventions and structural changes. This phased structure underscores the cyclical nature of economies, where booms sow seeds of busts through mechanisms like excess leverage, but recoveries demonstrate resilience absent permanent decline.

Stylized Facts and Empirical Regularities

Business cycles exhibit several empirical regularities, or stylized facts, derived from statistical analysis of detrended macroeconomic , such as those filtered using the Hodrick-Prescott method to isolate cyclical components from long-term trends. These patterns, observed primarily in U.S. data since , highlight the co-movements, volatilities, and asymmetries across cycles, providing benchmarks for theoretical models. Expansions typically outlast contractions, reflecting in cycle phases. In the U.S., the average expansion duration reached 49.9 months, compared to 10.7 months for contractions, a shift from prewar averages of 25.3 months and 20.5 months, respectively; this pattern holds across NBER-dated cycles, with contractions rarely exceeding 18 months since 1945. Cycles recur without fixed periodicity, varying in amplitude and timing due to differing shocks, though postwar output volatility has declined, with standard deviations of detrended GDP falling by about half from the to the . Key comovements underscore procyclicality in aggregate activity:
  • Output and components: Real GDP, consumption, and fluctuate procyclically with output, but displays higher volatility (standard deviation roughly twice that of output), while consumption is smoother (about half the volatility of output).
  • Labor market: and hours worked are procyclical and volatile, comparable to output; is strongly countercyclical, rising sharply in contractions.
  • Production and trade: Industrial production and durable goods output lead the cycle and amplify fluctuations; net exports tend to be acyclical or weakly countercyclical.
VariableCyclicalityRelative Volatility (vs. Output)
Consumption (nondurables/services)ProcyclicalLower (~0.5)
(fixed capital)ProcyclicalHigher (~2.0)
ProcyclicalSimilar (~1.0)
Unemployment rateCountercyclicalHigh amplitude in downturns
Persistence is evident in high autocorrelation of output deviations, with first-order correlations around 0.9 for quarterly detrended GDP, implying gradual recoveries rather than abrupt reversals. Lead-lag relations show and changes preceding output peaks, while consumption lags slightly, consistent with accelerator effects in demand-driven fluctuations. These regularities, robust across economies with minor variations (e.g., higher volatility in emerging markets), challenge models ignoring financial leverage or sectoral rigidities, as credit and asset prices increasingly cohere with real cycles in recent decades.

Historical Overview

Pre-20th Century Observations

Economic fluctuations, manifesting as booms followed by busts, were observed sporadically in pre-industrial eras through events like the South Sea Bubble crisis of 1720 in Britain, where speculative fervor in joint-stock companies led to a market and widespread bankruptcies. Similar speculative episodes, such as the Mississippi Company bubble in during the same year, highlighted risks of overextended credit but were not yet conceptualized as recurrent cycles tied to broader production and trade rhythms. These incidents were typically attributed to moral failings or isolated rather than systemic patterns in economic activity. The advent of the in the late 18th century intensified observations of periodic disruptions, particularly in Britain and the , where expanding and amplified vulnerabilities to gluts and contractions. In the U.S., the marked an early major downturn, triggered by postwar deflation, falling agricultural prices, and the Second Bank of the United States tightening after speculative land booms, resulting in widespread bank failures, spikes, and a contraction lasting until 1821. This was followed by the , precipitated by speculative real estate bubbles, bank suspensions, and a collapse in exports, leading to a depression with exceeding 30% in urban areas and over 600 bank failures by 1842. The involved railroad overinvestment and grain price drops, causing 5,000 business failures and a sharp GDP decline, while the stemmed from railroad speculation and European financial strains, ushering in a six-year depression with U.S. reaching 14% and thousands of firm insolvencies. In , analogous crises included Britain's 1825 banking panic from Latin American loan defaults and the 1866 Overend-Gurney failure, which exposed interconnected banking risks. Swiss Jean Charles Léonard de , in his 1819 work Nouveaux principes d'économie politique, provided one of the earliest systematic critiques of recurrent gluts under industrial , arguing that relative to —driven by unequal —inevitably led to crises, challenging of markets and advocating limits on expansion. Sismondi's analysis highlighted causal links between technological advances, wage suppression, and periodic surges, framing crises as inherent to unbalanced growth rather than mere aberrations. French physician and Clément Juglar advanced this in 1862 with Des crises commerciales et de leur retour périodique, empirically documenting cycles of commercial crises every 7 to 11 years in , Britain, and the U.S., attributing them to excessive bank credit expansion during prosperity phases, followed by , inventory buildup, and forced liquidations upon credit reversal. Juglar's statistical review of banking data and failure rates from 1802 onward established periodicity as a verifiable pattern, influencing later cycle theories by emphasizing monetary factors over exogenous shocks. These observations underscored empirical regularities, such as crises clustering after credit-fueled booms and propagating via linkages, though pre-20th century analysts lacked comprehensive , relying instead on proxies like banknotes in circulation, import volumes, and bankruptcy records. British economist , in his 1848 , acknowledged recurrent commercial crises every decade but attributed them primarily to speculative excesses rather than inherent systemic instability, reflecting a classical emphasis on real factors like harvest failures alongside monetary ones. , in Capital (1867), viewed cycles as manifestations of capitalism's contradictions—overaccumulation and falling profit rates—leading to absolute , though his predictions of terminal breakdown diverged from observed recoveries. Overall, 19th-century evidence from recurrent panics demonstrated business cycles as endogenous to credit-dependent industrial economies, with durations averaging 5-10 years and depths varying by sector exposure, laying groundwork for formalized post-1900.

20th Century Cycles and Theories

The (NBER) identifies multiple contractions in U.S. economic activity during the 20th century, with peaks and troughs marking cycle turning points based on indicators like industrial production, , and real GDP. The featured severe downturns, including the 1920-1921 following and the , which began with a peak in August 1929 and reached a trough in March 1933, entailing a 30% decline in real GDP, a 25% rate, and widespread failures numbering over 9,000 by 1933. A secondary occurred from May 1937 to June 1938 amid policy shifts reducing fiscal stimulus. Post-World War II expansions proved longer and milder than prewar cycles, with the NBER recording contractions in 1948-1949, 1953-1954, 1957-1958, 1960-1961, and 1969-1970, averaging under a year in duration compared to pre-1945 averages exceeding 18 months. This era's growth stemmed from pent-up consumer demand after wartime , reconversion of production to , and GDP expansion averaging 3.8% annually from 1946 to 1973. The 1970s introduced , with recessions from to March 1975 and January to 1980, driven by oil price shocks quadrupling crude costs in 1973-1974, yielding simultaneous peaks above 12% and nearing 9%. Theoretical advancements paralleled these cycles, emphasizing empirical measurement and causal mechanisms. and formalized cycle identification in their 1946 work Measuring Business Cycles, defining cycles via comovements in aggregates like output and employment rather than mere trends. proposed long waves of 40-60 years in the , attributing them to technological innovations and capital investment clusters, with 20th-century phases including an upswing from the late 1890s steam-powered industrialization to a 1920s-1930s downswing. John Maynard Keynes's 1936 General Theory attributed depressions to deficient from sticky wages, pessimistic expectations, and liquidity traps, advocating countercyclical fiscal deficits to stabilize cycles, influencing policies that halved unemployment from 25% in 1933 to 14% by 1937 despite incomplete recovery. In contrast, Austrian economists and developed the in the 1920s-1930s, positing that central bank-induced credit expansions lower interest rates below natural levels, fostering malinvestments in longer production processes that collapse into busts, as evidenced by the 1920s U.S. boom fueled by easing. The 1970s stagflation undermined Keynesian dominance, as demand stimulus exacerbated inflation without resolving supply constraints from energy shocks, prompting Milton Friedman's monetarist critique that unstable growth, not alone, drove cycles, with U.S. M2 fluctuations correlating to output volatility. Joseph Schumpeter's 1939 theory integrated , where entrepreneurial innovations propel expansions but obsolescence triggers recessions, aligning with post-1945 booms tied to consumer durables and . These frameworks highlighted endogenous factors like monetary distortions and innovation over exogenous shocks alone, though debates persist on their empirical fit given data limitations in early measurements.

Post-1945 Developments and Modern Cycles

The period following marked a shift in the frequency and severity of U.S. business cycles, with the (NBER) identifying 13 recessions from 1945 to 2020, averaging about 10 months in duration—shorter than the 18-month average for 1919–1945. The initial postwar recession (October 1945–October 1946) stemmed from rapid , slashing federal spending from 41.9% of GDP in 1945 to 8.9% by 1948 and releasing 12 million veterans into the labor market, yet output surged as resources shifted from wartime production to consumer goods like automobiles and housing. This facilitated a robust expansion through the late and , driven by gains from , , and pent-up consumer demand, with real GDP growth averaging 3.8% annually from 1947 to 1960. Subsequent cycles in the 1950s and 1960s featured mild contractions, such as those from August 1957–April 1958 (caused by tight monetary policy amid the Eisenhower administration's fiscal restraint) and December 1969–November 1970 (linked to Vietnam War inflation pressures), reflecting improved stabilization through countercyclical fiscal and monetary tools influenced by Keynesian frameworks. However, the 1973–1975 recession, lasting 16 months with unemployment peaking at 9%, introduced stagflation—simultaneous high inflation (12.3% in 1974) and stagnation—triggered by the 1973 oil embargo quadrupling prices and the collapse of the Bretton Woods system in 1971, which unleashed floating exchange rates and commodity volatility; empirical evidence attributes this to expansionary policies exacerbating supply shocks rather than demand deficiencies alone. The late 1970s and early 1980s saw back-to-back recessions (January–July 1980 and July 1981–November 1982), with the latter's 10.8% unemployment peak resulting from Federal Reserve Chairman Paul Volcker's aggressive interest rate hikes to 20% in 1981, successfully curbing inflation from 13.5% in 1980 to 3.2% by 1983 but at the cost of industrial contraction. From the mid-1980s to 2007, the "Great Moderation" prevailed, characterized by halved volatility in quarterly GDP growth (standard deviation falling from 2.8% pre-1984 to 1.4% thereafter) and fewer severe downturns, as expansions lengthened to an average of 57 months versus 38 months prior. Attributions include enhanced monetary policy credibility under rules like the Taylor rule, which targeted inflation and output gaps more effectively; structural shifts such as improved inventory management via just-in-time systems; and favorable shocks like stable oil prices until 2000, though econometric decompositions suggest policy improvements accounted for up to 40% of the decline in variance, outweighing luck-based explanations. This era ended with the 2007–2009 Great Recession (December 2007–June 2009, 18 months), the deepest since the Depression with a 4.3% GDP drop and 10% unemployment, precipitated by the subprime mortgage collapse and leveraged financial institutions amplifying housing bubble deflation, underscoring endogenous credit cycles over exogenous shocks. Modern cycles since 2000 exhibit heightened financial sector influence, with the 2001 (March–November, mild at 0.3% GDP contraction) tied to the dot-com equity bust and 9/11 disruptions, followed by a credit-fueled expansion reliant on low federal funds rates (1% in ). The post-2009 recovery, the longest on record at 128 months until February 2020, featured subdued growth (averaging 2.3% annually) sustained by balance sheet expansion from $0.9 trillion in 2008 to $4.5 trillion by 2014—and fiscal stimuli, though growth stagnated at 1.1% yearly versus 2.1% pre-2000, partly due to regulatory burdens and demographic aging. The 2020 (February–April, a record 2-month span with 19.2% annualized GDP plunge) arose from halting activity, but rapid fiscal outlays ($5.9 trillion in and subsequent packages) and monetary accommodation enabled a V-shaped rebound, with falling from 14.8% in April 2020 to 3.5% by 2023. By , the cycle has shown resilience amid 2022–2023 inflation (peaking at 9.1%), addressed via rate hikes to 5.25–5.5%, averting through normalization and , though debates persist on whether prolongs imbalances like asset bubbles.
Recession PeriodDuration (Months)Key Triggers
Nov 1948–Oct 194911Postwar adjustment, inventory drawdown
Jul 1953–May 195410 end, Fed tightening
Aug 1957–Apr 19588Tight credit, auto sector slump
Apr 1960–Feb 196110Monetary restraint
Dec 1969–Nov 197011Fiscal tightening,
Nov 1973–Mar 197516Oil shocks,
Jan–Jul 19806Oil shock II, credit controls
Jul 1981–Nov 198216Volcker
Jul 1990–Mar 19918, savings & loan crisis
Mar–Nov 20018Dot-com bust, 9/11
Dec 2007–Jun 200918
Feb–Apr 20202 shutdowns

Identification and Measurement

Key Economic Indicators

The National Bureau of Economic Research (NBER) Business Cycle Dating Committee identifies turning points in the U.S. business cycle by evaluating a range of monthly economic indicators, emphasizing depth, diffusion, and duration of changes rather than any single metric. Primary coincident indicators include nonfarm payroll employment from the establishment survey, which tracks job creation across sectors and typically rises during expansions and falls in contractions; household survey employment, capturing self-employment and smaller firms; real personal income excluding transfer payments, reflecting wage and salary growth adjusted for inflation; real personal consumption expenditures, measuring household spending on goods and services; real wholesale-retail sales adjusted for price changes, indicating demand for consumer and business goods; and industrial production, gauging output in manufacturing, mining, and utilities. These indicators provide contemporaneous evidence of economic activity, with declines signaling recessions when widespread and sustained, as seen in the 2007-2009 downturn where nonfarm payrolls dropped by over 8 million jobs from peak to trough. Leading indicators, compiled by , anticipate cycle turns by 6-12 months and include components like average weekly hours in manufacturing, which shorten before downturns due to cost-cutting; new orders for consumer and capital goods, signaling future production; initial claims, spiking with layoffs; manufacturers' new orders for nondefense capital goods excluding aircraft; building permits for residential , forecasting housing activity; stock prices (), often peaking before ; the interest rate spread between 10-year Treasury bonds and , where inversions have preceded every U.S. since ; and average consumer expectations for business conditions. The composite leading index, averaging these, declined notably before the 2020 contraction, reaching a low of 99.3 in April 2020 from a pre-pandemic peak. Lagging indicators confirm cycle phases after they occur, such as the average duration of , which extends beyond recessions; labor cost per unit of output, rising with pressures in recoveries; outstanding commercial and industrial loans, increasing post-trough; and the for services, reflecting persistent inflation trends. The Conference Board's coincident economic index, comprising payroll employment, less transfers, and sales, and industrial production, moves in tandem with GDP and helps validate NBER dates; for instance, it fell 5.1% from November 2007 to June 2009.
Indicator TypeExamplesRole in Cycle Measurement
LeadingNew orders, yield spread, stock pricesPredict turns; e.g., inverted yield curve preceded 8 of last 8 recessions.
CoincidentEmployment, industrial production, real salesTrack current activity; align with GDP depth.
LaggingUnemployment duration, CPI servicesConfirm phases; lag by 6+ months.
While (GDP) serves as a quarterly benchmark for overall output—contracting in two consecutive quarters as a rule-of-thumb signal—NBER prioritizes monthly for precision, avoiding over-reliance on GDP due to revisions and procyclical components like adjustments. Internationally, bodies like the use similar metrics, including GDP, employment, and trade volumes, adapted to national availability.

Dating and Classification Methods

The (NBER) Business Cycle Dating Committee determines the official chronology of U.S. business cycles by identifying the months of peaks (preceding contractions) and troughs (preceding expansions) in overall economic activity. This process relies on monthly coincident indicators such as real GDP, total nonfarm payroll employment, household employment, , industrial production, and , with quarterly data incorporated when monthly figures are unavailable. The committee applies qualitative judgment to assess turning points, prioritizing changes that are deep (substantial amplitude), diffuse (widespread across indicators and sectors), and prolonged (typically exceeding a few months), without fixed quantitative thresholds. Recessions are classified as the intervals from peak to trough, defined as significant declines in activity that are pervasive across the economy and persist beyond brief interruptions, distinct from the informal rule of two consecutive quarters of negative real GDP growth, which the NBER explicitly rejects as insufficiently comprehensive. Expansions span from trough to peak, while full cycles encompass complete peak-to-peak or trough-to-trough periods; these classifications inform empirical analysis but do not dictate policy triggers. Internationally, dedicated committees adapt similar approaches to local data; the French Business Cycle Dating Committee, for instance, uses a blend of quantitative filters and expert review of indicators like GDP, industrial production, and to date turning points, identifying four recessions in since 1970. The CEPR-EABCN Euro Area Business Cycle Dating Committee establishes reference chronologies for the euro area by aggregating national series into synthetic measures of output, , and orders, applying consensus judgment to confirm phases amid data revisions. In research settings, algorithmic methods supplement -based ; the Bry-Boschan procedure, developed to approximate NBER-style chronologies, detects turning points in filtered by locating local maxima and minima, then imposing economic constraints such as minimum phase durations (e.g., five months for peaks-to-troughs) to eliminate short-lived fluctuations. This non-parametric technique is widely applied to univariate aggregates like GDP or multivariate indices, enabling reproducible classifications across datasets, though it may overlook subtle effects captured in committee reviews.

Advanced Analytical Techniques

Advanced analytical techniques in business cycle measurement extend beyond simple dating algorithms by employing econometric filters and spectral methods to decompose into trend, cyclical, and irregular components, enabling precise estimation of cycle amplitudes, durations, and co-movements. These methods, rooted in time series analysis, address the challenge of isolating medium-term fluctuations (typically 6 to 32 quarters) from long-term growth and high-frequency noise, assuming economic variables exhibit quasi-periodic behavior around a trend. Pioneered in the late , such techniques facilitate empirical testing of cycle properties but are subject to assumptions about bands and data-generating processes, potentially introducing artifacts if misspecified. The Hodrick-Prescott (HP) filter, formalized by Hodrick and Prescott in their 1997 analysis of postwar U.S. cycles, minimizes a loss function balancing fit to the observed series and smoothness of the estimated trend, with the smoothing parameter λ set to 1600 for quarterly data to target business cycle horizons. This two-sided filter yields a cyclical component as the deviation from the trend, widely applied to GDP and other aggregates to quantify volatility; for instance, it reveals U.S. output cycle standard deviations of about 1.5-2% in post-1945 expansions. However, critics highlight its tendency to generate spurious dynamics, such as excessive smoothness in trends leading to overstated cycle persistence, and end-point bias in real-time applications where future data revisions alter estimates. Band-pass filters offer a frequency-domain alternative, explicitly targeting user-specified cycle lengths without assuming a specific trend form. The Baxter-King filter (1999) approximates an ideal band-pass by applying moving averages to discard low- and high-frequency components, ideal for symmetric two-sided filtering of historical data; applied to U.S. industrial production, it extracts cycles with periods of 8-20 quarters, showing reduced phase shifts compared to HP. For real-time analysis, the Christiano-Fitzgerald filter (2003) employs asymmetric weights to mitigate end-point issues, preserving more recent observations while attenuating noise; empirical tests on macroeconomic series demonstrate its superiority in cycle turning points, with mean squared errors 10-20% lower than HP in out-of-sample evaluations. These filters assume stationarity in the and can distort if true cycles deviate from the imposed bandwidth, as evidenced by simulations where misspecification amplifies irregular variance. Spectral analysis decomposes series into frequency components via Fourier transforms, identifying dominant cycle periodicities without prior trend specification. In business cycle contexts, it computes power spectra to reveal peaks at 2-8 year horizons in variables like , as in Adelman (1965) decompositions of U.S. indicators showing spectral mass concentrated at 4-5 years pre-1960. Cross-spectral methods further quantify lead-lag relations and coherence across series, finding, for example, industrial production leading consumption by 1-2 quarters with coherence above 0.7 at cycle frequencies. Multivariate extensions, such as dynamic factor models incorporating spectral densities, enhance robustness by aggregating indicators, though they require assumptions of linearity and Gaussianity that may overlook nonlinear regime shifts observed in historical data. Vector autoregression (VAR) models provide a dynamic framework for cycle , estimating impulse responses and variance contributions from shocks while capturing inter-variable . Structural VARs impose economic restrictions (e.g., long-run neutrality) to identify supply versus disturbances, revealing that technology shocks account for 50-70% of U.S. output variance at business cycle frequencies in postwar samples. Historical decompositions in VARs attribute fluctuations, such as the 2008-2009 downturn, to shocks amplifying initial declines by 20-30% within two years. Limitations include sensitivity to lag selection and identification schemes, with Bayesian variants mitigating via priors calibrated to micro-founded models. These techniques, while powerful for , rely on stable parameters, potentially understating structural breaks evident in pre- and post-1980s cycles.

Theoretical Explanations

Exogenous Shock Theories

Exogenous shock theories attribute business cycle fluctuations primarily to unpredictable external disturbances that impinge on an otherwise stable , initiating deviations from equilibrium that propagate through adjustment dynamics until stability is restored. These shocks are modeled as orthogonal innovations—random events uncorrelated with prior economic variables—encompassing supply disruptions, geopolitical events, or natural calamities rather than endogenous instabilities like expansions or feedbacks. Proponents argue that such impulses explain the irregular timing and of cycles, as internal mechanisms alone would predict more uniform patterns. Supply-side shocks, particularly in energy markets, exemplify this framework. The 1973 OPEC oil embargo, imposed in October 1973 following the , quadrupled global oil prices from approximately $3 to $12 per barrel by early 1974, disrupting production costs and consumer spending across oil-importing nations. In the United States, this contributed to the , during which real GDP contracted by 3.2% from peak to trough, industrial production fell 15%, and inflation surged to double digits amid —a combination challenging demand-driven explanations. Similar dynamics appeared in the 1979 Iranian Revolution oil shock, which halved global supply and exacerbated the 1980–1982 downturn, with U.S. peaking at 10.8% in late 1982. More recently, the 2022 triggered commodity price spikes, with exceeding $120 per barrel in March 2022, amplifying inflationary pressures and slowing global growth to 3.5% in 2022 from 6.0% in 2021. Demand-side or uncertainty shocks from pandemics and disasters also feature prominently. The outbreak in early 2020 acted as a and shock, halting global trade and services; U.S. real GDP plunged 31.2% annualized in Q2 2020, the sharpest quarterly drop on record, with spiking to 14.8% in April 2020 before partial recovery. Natural disasters, such as the in , reduced output by an estimated 0.5% of GDP in 2011 through disruptions in . Geopolitical conflicts, including World War II's resource reallocations, have historically compressed cycles by imposing sudden fiscal and trade shifts. Empirical analysis often employs structural (SVAR) models to isolate shock contributions, decomposing output variance into exogenous components; for instance, shocks account for up to 20% of U.S. probability in some estimates. However, identification relies on restrictive assumptions, such as long-run neutrality restrictions, and suggests shocks explain only a of cycle variance—typically 10–30% for non-productivity disturbances—leaving room for amplification via endogenous channels like inventory adjustments or financial frictions. Critics contend that truly exogenous shocks are rare and irregularly timed, failing to account for the persistence and comovement of cycles, as random impulses alone yield implausibly volatile or acyclical responses without internal propagation; tests rejecting dominant shock-driven cycles highlight this limitation.

Endogenous Credit and Monetary Theories

Endogenous and monetary theories attribute business cycles to internal dynamics of financial intermediation, where banks and other institutions generate and in response to economic conditions, leading to self-reinforcing expansions and contractions without requiring external shocks. In these frameworks, creation is not merely passive but actively shapes and , often amplifying fluctuations through feedback loops involving borrower confidence, asset prices, and servicing. Unlike exogenous theories, which emphasize impulses like or errors, endogenous approaches highlight how stability itself sows seeds of via escalating leverage and mismatches between short-term financing and long-term projects. The (ABCT), originating with Ludwig von Mises's analysis of money and circulation and systematized by in , exemplifies endogenous monetary distortion. Central banks' expansion of below rate—defined as the rate equilibrating savings and —artificially lowers borrowing costs, spurring malinvestments in time-intensive capital goods like durable and . This creates a temporary boom, with resource allocation skewed toward unsustainable higher-order production stages, but rising rates and depleted savings eventually force liquidation, yielding recessionary corrections. Empirical applications include the U.S. housing boom preceding the 2008 crisis, where rate cuts from 5.25% in 2006 to near-zero fueled mortgage securitization and overbuilding, culminating in defaults exceeding $1 trillion by 2009. Critics, including mainstream macroeconomists, argue ABCT underemphasizes demand deficiencies, but proponents cite historical correlations between growth and subsequent contractions, such as M2 expansion of 15% annually in the early preceding the downturn. Hyman Minsky's financial instability hypothesis (FIH), developed in works from the 1950s through the 1980s, posits that capitalist economies endogenously traverse from hedge financing—where cash flows cover debt principal and interest—to speculative and Ponzi units reliant on refinancing or capital gains, as prolonged prosperity erodes margins of safety. This progression, driven by euphoric expectations and competitive lending pressures, builds systemic vulnerability until a trigger like interest rate hikes or profit squeezes prompts mass deleveraging. Minsky's 1975 analysis linked this to post-World War II U.S. cycles, noting how the 1966 and 1969-1970 credit crunches followed speculative surges in corporate debt. The hypothesis gained validation in the 2007-2008 global financial crisis, with subprime mortgage Ponzi-like structures collapsing amid $14 trillion in global credit market losses, underscoring FIH's emphasis on private debt dynamics over public spending. Post-Keynesian endogenous money theories, advanced by scholars like Basil Moore and in the , view the money supply as demand-determined, with commercial banks creating deposits via loans rather than awaiting reserves. This accommodates volatile driven by "animal spirits," generating cycles through accelerator effects where rising output boosts credit demand, inflating asset bubbles until debt burdens exceed incomes. Models simulate fluctuations with money multipliers varying endogenously, as seen in U.S. data where () growth preceded GDP peaks by quarters in the and expansions. Such theories critique exogenous money assumptions in mainstream models for ignoring bank discretion, evidenced by the post-1979 U.S. shift to non-borrowed reserves, which correlated with altered nominal cycle behaviors.

Real Business Cycle and Supply-Side Models

The real business cycle (RBC) theory, pioneered by Finn E. Kydland and in their 1982 paper "Time to Build and Aggregate Fluctuations," attributes business cycle fluctuations primarily to real shocks, particularly exogenous changes in (TFP), rather than monetary or demand disturbances. In this framework, economic expansions and contractions represent optimal equilibrium responses by rational, forward-looking agents to these shocks, with cycles emerging from intertemporal substitution in labor and capital decisions under flexible prices and complete markets. The model extends the neoclassical growth framework by incorporating variable labor supply and time-to-build investment delays, generating comovements in output, , and that align with observed U.S. postwar data, such as high output volatility and procyclical labor input. RBC models employ (DSGE) methods, calibrated using historical moments like standard deviations of GDP (around 1.8% quarterly for U.S. data) and correlations between consumption and (typically 0.8-0.9), to simulate cycles without invoking market frictions or policy errors. Kydland and Prescott's approach demonstrated that TFP shocks alone could explain approximately 70% of U.S. output variability, challenging Keynesian and monetarist views by emphasizing supply-side efficiency over . This method, rather than traditional , prioritizes matching empirical regularities like the of to consumption (about three times higher) derived from microeconomic on agent . Supply-side models extend RBC principles by incorporating policy-induced shocks to , such as variations in rates, regulations, or prices, which alter incentives for production and . For instance, adverse supply shocks like the 1973 oil embargo raised production costs, contributing to with simultaneous output declines and acceleration, consistent with reduced TFP growth from 2.8% annually pre-1973 to negative rates in affected sectors. These models predict asymmetric cycle dynamics, where positive supply enhancements (e.g., technological diffusion or ) yield sustained expansions, while negative shocks propagate through lags, explaining events like the 1990s productivity boom tied to IT adoption. Empirical tests, including vector autoregressions on U.S. from 1947-2000, show supply shocks accounting for 50-60% of cycle variance in non-oil sectors, underscoring their role over factors in long-run trend deviations. Kydland and Prescott received the 2004 in for advancing RBC theory's methodological contributions, including time inconsistency in and quantitative business cycle analysis, which validated supply-driven explanations against stylized facts like countercyclical hours variability. While critics question the magnitude of measured TFP shocks (often 1-2% quarterly volatility) as implausibly large for driving recessions, proponents cite Solow residuals adjusted for utilization, confirming real shocks' dominance in VAR decompositions across economies from 1960-2010. Supply-side variants further integrate institutional factors, such as labor market rigidities varying by regime, to reconcile RBC predictions with from episodes like the U.S. recovery following tax reforms that boosted marginal incentives and TFP by 1.5% annually.

Demand-Side and Keynesian Explanations

Demand-side explanations of business cycles attribute economic fluctuations primarily to variations in , which comprises household consumption, business investment, , and net exports, rather than supply-side factors. These theories argue that short-run deviations from arise when falls short of the economy's potential output, leading to and reduced production, while excess demand fuels inflationary booms. formalized this perspective in his 1936 work The General Theory of Employment, Interest, and Money, positing that economies can equilibrate below due to insufficient spending, challenging classical assumptions of automatic . A core mechanism in Keynesian theory is the volatility of , driven by fluctuating expectations of future profitability, termed "animal spirits" by Keynes—waves of optimism or pessimism that cause businesses to over- or under-invest independently of current interest rates or fundamentals. These expectation-driven shifts in the propagate through the economy via the investment multiplier, where an initial decline in reduces incomes, prompting further cuts in consumption and amplifying the downturn; conversely, rising sparks expansions. Banking systems exacerbate cycles by expanding during optimistic phases to finance speculative booms and contracting amid pessimism, as seen in Keynes's evolving views from overinvestment theories in 1913 to liquidity traps in . Keynesian models incorporate and rigidities, preventing rapid adjustments that would restore equilibrium, thus allowing deficiencies to persist and generate cyclical . The illustrates this: individual attempts to save more during uncertainty reduce , worsening recessions despite higher overall savings rates. Empirical estimates of the —the ratio of output change to change—support demand-side effects in recessions, ranging from 1.5 to 2.0, higher than in expansions (around 0.5), indicating that demand stimuli can counteract slumps under certain conditions like traps or zero lower bounds. New Keynesian extensions, developed since the 1980s, integrate such as and staggered price-setting to explain why shocks dominate short-run fluctuations, with evidence from vector autoregressions identifying disturbances as contributors to U.S. output variability post-World War II. However, these explanations face challenges from data showing that pure failures rarely initiate cycles without preceding expansions or supply disruptions, and multiplier estimates often fall below unity in normal times, suggesting limited potency outside severe downturns. Academic consensus, while leaning toward -side roles due to institutional influences in , acknowledges that real business cycle models better fit some data without invoking persistent shortfalls.

Political and Institutional Factors

The political business cycle theory, originally formalized by in 1975, asserts that elected governments pursue expansionary fiscal and monetary policies in the lead-up to elections to stimulate growth and reduce , thereby enhancing reelection prospects, followed by contractionary measures to curb resulting . This opportunistic model implies predictable electoral timing in economic booms and busts, with pre-election output growth averaging 0.5-1% higher in some historical samples from countries. Empirical tests, however, yield mixed results; while fiscal expansions—such as increased transfers and public spending—show some alignment with election calendars in the United States from 1948 to 1984, evidence for manipulation is weaker, particularly after the gained greater independence in the 1980s. Studies across democracies often fail to detect robust cycles in or , attributing this to rational voter expectations and institutional constraints rather than systematic exploitation. Partisan business cycle theory, advanced by in the 1980s, posits that ideological differences between ruling parties generate asymmetric fluctuations: left-leaning governments favor policies boosting employment at the cost of higher , while right-leaning ones prioritize , potentially accepting higher short-term . Rational expectations variants predict temporary output surprises post-election based on partisan shifts, with U.S. data from 1948 to 2008 showing Democratic administrations correlating with 1-2% higher GDP growth in early terms but elevated , contrasted by Republican-led periods with subdued growth yet lower price pressures. Cross-national evidence from 18 economies supports partisan effects on output volatility until the 1990s, though and delegated monetary authority have diminished their magnitude, as central banks insulated from electoral pressures stabilize cycles. Critics note that endogeneity—where economic conditions influence party success—complicates , and recent analyses find partisan signals more evident in fiscal composition than aggregate fluctuations. Institutional frameworks, encompassing , rights enforcement, and regulatory predictability, modulate business cycle amplitude and persistence by shaping incentives and shock absorption. Empirical panel analyses of 45 countries from 1960 to 2000 reveal that stronger institutions—measured by indices like the World Bank's governance indicators—reduce output volatility by 20-30% through enhanced credit access and reduced expropriation risks, with cultural factors like trust amplifying synchronization to global cycles. In , structural reforms improving labor market flexibility and fiscal rules, such as the Eurozone's enacted in 1997, have shortened cycle durations, evidenced by fewer and milder recessions post-2000 compared to pre-reform eras. Conversely, weak institutions in emerging markets exacerbate cycles via corruption-driven misallocation, as seen in Latin American debt crises of the where institutional fragility prolonged contractions by impeding adjustment. Government , often peaking around elections, regime changes, or fiscal cliffs, amplifies fluctuations by elevating risk premia and curbing durable goods . The Economic Uncertainty Index, constructed from coverage of policy disputes since 1985, correlates with U.S. recessions, where a one-standard-deviation rise (roughly doubling from baseline) depresses private by 1.5-3% and raises by 0.2-0.5 percentage points within quarters. models confirm bidirectional , with uncertainty shocks accounting for up to 20% of variance in output during events like the 2011 U.S. debt ceiling crisis or referendum in 2016. This channel underscores how institutional designs promoting policy continuity, such as independent central banks or balanced-budget rules, mitigate endogenous volatility beyond partisan or opportunistic motives.

Policy Interventions

Monetary Policy Responses

Central banks employ to counteract business cycle fluctuations, primarily by adjusting short-term interest rates to influence borrowing, investment, and . During economic expansions, policymakers typically raise rates to curb inflationary pressures and prevent overheating, while in recessions, they lower rates to reduce borrowing costs and encourage spending. This countercyclical approach relies on transmission mechanisms where cheaper credit stimulates durable goods purchases and expansion, though effects manifest with lags of 6-18 months. In response to recessions, the U.S. has historically implemented aggressive rate cuts; for instance, during the 2001 downturn, it executed 11 reductions, lowering the to 1.75% to support recovery amid the dot-com bust. Similarly, in the starting December 2007, the Fed slashed rates from 4.5% to 2% by late 2008, reaching the by December, which limited further conventional easing. Empirical studies indicate these actions mitigate downturn severity by easing financial conditions and bolstering nominal output, though policy proves less potent in recessions than expansions due to impaired credit channels and . When rates approach zero, central banks resort to unconventional tools like (QE), involving large-scale asset purchases to inject liquidity and depress longer-term yields. The Fed's QE programs post-2008, which expanded its from under $1 trillion to over $4 trillion by 2014, lowered Treasury yields by an estimated 50-100 basis points, fostering spending and averting . During the 2020 pandemic recession, QE resumed rapidly, with purchases exceeding $3 trillion in months, stabilizing markets but raising concerns over asset and future tightening challenges. Evidence suggests QE supports employment and GDP without proportionally inflating prices in the short term, yet its net impact on real activity remains debated, with some analyses attributing 0.5-1% boosts to output growth. Critiques highlight monetary policy's limitations, including diminished efficacy in liquidity traps where rates cannot go significantly negative, and risks of moral hazard from prolonged accommodation distorting resource allocation. Historical precedents, such as the Fed's initial tightening in the early 1930s exacerbating the , underscore the perils of procyclical errors, informing modern rules like the for systematic responses. Overall, while easing has empirically shortened U.S. recessions by 1-2 quarters on average since 1950, sustained low rates correlate with financial imbalances, prompting debates on normalization strategies.

Fiscal Policy Measures

Fiscal policy measures encompass government adjustments to spending and taxation aimed at mitigating business cycle fluctuations, typically through countercyclical actions that expand deficits during downturns to boost and contract them during expansions to curb . These measures operate via automatic stabilizers and discretionary interventions, with the former providing built-in responses without legislative action, such as progressive income taxes that reduce disposable income less severely in recessions and unemployment insurance that increases payouts as joblessness rises. Empirical analyses indicate automatic stabilizers dampen output volatility by 10-30% across countries, though their aggregate impact on U.S. business cycles remains modest due to limited effects on overall consumption and multipliers. Discretionary fiscal policy involves deliberate legislative changes, such as tax cuts or spending surges during recessions to stimulate , contrasted with spending restraint or tax hikes in booms. In the U.S., the 2008 Economic Stimulus Act provided $152 billion in tax rebates to households, aiming to offset the emerging , while the 2009 American Recovery and Reinvestment Act (ARRA) allocated $840 billion for spending and tax relief, including $288 billion in tax cuts and $507 billion in direct outlays. The 2020 and subsequent packages totaled over $5 trillion in relief, featuring direct payments of up to $1,200 per adult, enhanced , and business loans, which supported household liquidity amid . Estimates of fiscal multipliers—the output increase per dollar of spending—vary, with NBER studies showing values of 1.5 to 2 during recessions versus 0.5 in expansions, attributed to idle resources and monetary accommodation at the . However, local multipliers from defense spending shocks reach 1.7-2, while broader government consumption yields smaller effects, often below 1 in normal times due to leakages into imports or savings. from the ARRA suggests it shortened by up to 1.5 quarters but at high cost per job created, around 200,000200,000-500,000, raising questions about efficiency. Critiques highlight implementation lags, where policy enactment trails economic needs by months, and crowding out, whereby deficit-financed spending elevates interest rates, reducing private investment by 20-50 cents per dollar of public outlay in full-employment scenarios. posits households anticipate future tax hikes to service debt, saving much of stimulus rather than spending it; empirical tests during 2008-2020 rebates confirm 20-50% were saved or used to pay debt, muting demand effects. Accumulating public debt from repeated stimuli, as seen in U.S. debt-to-GDP rising from 64% in 2007 to 100% by 2020, risks long-term crowding out via higher future taxes or , with studies indicating procyclical biases in emerging markets amplify volatility rather than stabilize it. Despite short-term boosts, sustained discretionary correlates with larger deficits without proportional growth gains, underscoring debates over rules-based alternatives like balanced-budget mandates.

Stabilization Efforts and Their Critiques

Stabilization efforts in business cycles primarily involve countercyclical monetary and fiscal policies aimed at dampening expansions and contractions to achieve steadier growth, price stability, and employment levels. Central banks, such as the Federal Reserve, lower interest rates and expand money supply during downturns to encourage borrowing and investment, while raising rates in booms to curb inflation. Fiscal authorities implement automatic stabilizers like progressive income taxes and unemployment benefits, which inherently increase deficits in recessions by reducing revenues and boosting outlays, and discretionary measures such as stimulus spending or tax cuts. The U.S. Employment Act of 1946 institutionalized these efforts by mandating federal promotion of maximum employment and stable prices, influencing subsequent policy frameworks worldwide. Empirical assessments indicate partial success in reducing cycle volatility, particularly through automatic stabilizers that smooth consumption shocks by 61 to 73 percent in scenarios, though discretionary actions face challenges from recognition, decision, and implementation lags often leading to mistimed interventions. Post-World War II policies correlated with the , a period of subdued fluctuations from the to 2007, attributed partly to improved monetary rules like . However, fiscal multipliers for frequently fall below 1.0, suggesting limited output boosts per dollar spent, with tax cuts showing greater countercyclical efficacy in empirical time-series analyses. Critiques highlight distortions from interfering with market corrections, as articulated in , where artificial credit expansion fuels unsustainable booms, and stabilization prevents liquidation of malinvestments, prolonging adjustments and fostering by encouraging risky behavior under bailout expectations. include elevated public debt accumulation, as countercyclical deficits rarely reverse in expansions due to political pressures, and potential amplification of financial imbalances, evidenced in post-2008 quantitative easing contributing to asset bubbles without proportionally restoring productive investment. critiques further argue that predictable policies lose effectiveness, as agents anticipate and offset them, while empirical studies question net welfare gains given cycle costs resembling asymmetric "mini-disasters." Mainstream sources, often from intervention-favoring institutions, may understate these risks due to institutional biases toward policy activism.

Empirical Evidence and Validation

Testing Theoretical Predictions

Testing theoretical predictions of business cycle models primarily involves econometric methods such as to historical moments, impulse response comparisons via structural vector autoregressions (SVARs), and out-of-sample evaluations. exercises, pioneered in real business cycle (RBC) research, set model parameters to replicate key statistics like the relative volatilities of output, consumption, and , as well as their cross-correlations with GDP. For instance, Kydland and Prescott's RBC framework matched U.S. business cycle facts by attributing 70-90% of output fluctuations to persistent shocks, validated through simulations aligning model-generated variances with empirical from 1955-1977. However, formal statistical tests, such as the Watson test for moment restrictions, indicate that standard RBC models fail to adequately capture labor market dynamics, including the high volatility of hours worked relative to , often requiring modifications like indivisible labor to improve fit. Keynesian and New Keynesian predictions, emphasizing demand shocks and nominal rigidities, are tested through estimates of fiscal and monetary multipliers derived from SVARs and approaches. Empirical studies using U.S. from 1939-2008 find multipliers averaging 1.0-1.5 during recessions, supporting predictions of countercyclical amplifying output when private demand falters, though these effects diminish near . In contrast, classical and RBC models predict near-zero or negative multipliers due to and crowding out, a prediction refuted by evidence from the 2008-2009 stimulus where consumption rose with transfers. Monetarist predictions, linking cycles to volatility, face challenges from analyses showing monetary shocks explain less than 20% of U.S. output variance post-1960, undermining quantity theory claims. Dynamic stochastic general equilibrium (DSGE) models, integrating RBC and New Keynesian elements, undergo validation by comparing simulated impulse responses to those identified in SVARs under imperfect information assumptions. Recent assessments reveal that while DSGE frameworks can replicate unconditional moments, they often underperform in conditional forecasting, such as response asymmetries to positive versus negative shocks observed in the , where demand-side frictions better explained the depth and persistence of the downturn than pure supply shocks. Identification challenges persist across tests, with Bayesian methods favoring models that incorporate financial frictions over pure RBC variants, as technology shocks alone fail to Granger-cause key comovements like investment-consumption correlations in cross-country panels from 1970-2010. Endogenous credit theories gain partial support from event studies linking credit booms to subsequent busts, with deviations from natural interest rates preceding 8 of 10 major U.S. recessions since 1920, though remains debated due to omitted variables. Overall, no fully dominates empirical , with hybrid models showing superior fit but highlighting the limitations of parsimonious frameworks in capturing multifaceted cycle drivers.

Evidence from Historical Crises

The initiated a severe contraction in the United States, triggered by the collapse of & Company, a major financier of railroad expansion, leading to bank runs, the suspension of specie payments by numerous banks, and the failure of 18,000 businesses between and 1875. This event marked the start of the , characterized by , reduced , and peaking at 8.25% in 1878, with real GDP growth averaging below 2% annually through the 1870s. Empirical analysis attributes the downturn to overinvestment in railroads during a prior credit-fueled boom, followed by monetary contraction as banks liquidated assets, illustrating a classic cycle of expansion, peak, and liquidation without central bank intervention to sustain credit. The depression of 1920-1921 provides contrasting evidence of rapid recovery following acute contraction, with U.S. industrial production declining 32.5%, wholesale prices falling 15%, and unemployment reaching 11.7% amid post-World War I demobilization and monetary tightening by the Federal Reserve. Unlike later episodes, the trough lasted only 18 months, with unemployment dropping to 6.7% by 1922 and full employment restored by 1923 through wage and price deflation, federal spending cuts exceeding 50%, and private sector restructuring without fiscal stimulus or monetary easing. This outcome supports theories emphasizing liquidation of malinvestments over demand management, as output rebounded to pre-crisis levels by mid-1922, contrasting with narratives favoring interventionist policies. The Great Depression (1929-1933) represented an extreme contraction, with U.S. GDP contracting 30%, industrial production falling 47%, and unemployment surging to 25% by 1933, accompanied by international output co-movement where synchronized downturns across 17 countries averaged GDP declines of 15-20%. NBER data dates the peak in August 1929 and trough in March 1933, highlighting banking panics (over 9,000 failures) and Federal Reserve inaction on money supply, which contracted 30%, as amplifying factors beyond initial stock market losses. Recovery began post-1933 with banking reforms and partial gold standard abandonment, but initial New Deal policies, including wage rigidities and tariffs, are critiqued in empirical studies for prolonging the trough compared to quicker liquidations in prior cycles. These crises reveal patterned features in NBER-chronicled cycles, including average contraction durations of 17-21 months pre-1945, deeper output drops during panics (e.g., 10-15% GDP losses), and recoveries tied to credit normalization rather than exogenous shocks alone, challenging purely exogenous real business cycle models while aligning with endogenous monetary and credit dynamics. Cross-country evidence from the Great Depression shows trade and monetary linkages exacerbating synchronization, with gold standard adherence correlating to sharper declines in adherent nations. Mainstream academic accounts often emphasize demand deficiencies, yet historical data underscore supply-side frictions and policy distortions, with sources like Federal Reserve histories noting recurring panic cycles from 1857 to 1929 driven by fractional banking vulnerabilities.

Insights from Recent Cycles (2008 and 2020)

The 2008-2009 recession, spanning December 2007 to June 2009 according to the (NBER), resulted in a cumulative decline of approximately 4.1% in U.S. real GDP, with the sharpest quarterly drop of 8.9% annualized in Q4 2008. This downturn originated from an endogenous buildup of imbalances, including excessive household leverage—reaching levels equivalent to over 100% of GDP in —and a inflated by low interest rates and lax lending standards post-2001. The collapse manifested through subprime defaults, leading to failures of major institutions like on September 15, 2008, and a subsequent freeze that amplified real economic contraction via financial accelerator effects, where falling asset values eroded capital and lending capacity. peaked at 10% in October 2009, underscoring how interconnected financial markets can propagate sector-specific shocks into broad cycles, consistent with theories emphasizing cycles over pure real shocks. In contrast, the 2020 recession, the shortest on record from to April 2020 per NBER chronology, featured an unprecedented annualized GDP contraction of 31.4% in U.S. Q2 2020, driven by an exogenous pandemic shock and widespread lockdowns that halted production and mobility. surged to 14.8% in April 2020, with rates dropping 37% for low-wage workers in the trough month, reflecting acute supply-chain disruptions and enforced demand suppression rather than prior imbalances. Recovery was asymmetric: high-wage sectors rebounded swiftly in a V-shaped pattern, while low-wage and contact-intensive industries faced persistent losses, with the COVID shock accounting for over 95% of variance through 2021. This cycle highlighted supply-side vulnerabilities in modern economies, where policy-induced closures exacerbated output gaps beyond voluntary behavioral changes, challenging models that attribute recessions solely to deficient . Comparative analysis reveals that both crises amplified initial triggers through mechanisms like uncertainty and leverage, but differed in origins: 2008 from endogenous malinvestments in credit-fueled assets, versus 2020's external imperative. Empirical patterns support integrated business cycle frameworks incorporating financial frictions, as credit contractions in 2008 mirrored historical precedents in deepening output losses, while 2020's rapid fiscal-monetary expansions—totaling over $5 trillion in U.S. stimulus—facilitated quick stabilization but fueled subsequent inflationary pressures from supply bottlenecks rather than overheating demand alone. These events empirically validate critiques of overreliance on stabilization policies, as post-2008 prolonged low rates and asset distortions, potentially sowing seeds for future imbalances, whereas 2020 interventions underscored trade-offs between short-term mitigation and long-term costs like distorted relative prices and in crisis lending. Overall, the cycles affirm that business fluctuations arise from real resource misallocations and policy distortions, with mainstream academic sources often underemphasizing the latter due to institutional incentives favoring interventionist narratives.

Debates and Controversies

Causes: Market vs. Interventionist Origins

The debate over the origins of business cycles centers on whether fluctuations arise primarily from inherent dynamics of decentralized market processes or from distortions introduced by government interventions, particularly monetary and fiscal policies. Proponents of market-origins theories argue that cycles reflect efficient responses to real economic shocks, such as variations in productivity or resource availability, without requiring market failures or policy errors. In contrast, interventionist-origins perspectives, notably from the Austrian school, posit that manipulations of and interest rates generate artificial booms followed by inevitable corrections, as these policies misallocate resources away from sustainable uses. Market-based explanations, exemplified by real business cycle (RBC) theory, attribute fluctuations to exogenous real shocks—primarily technology or productivity changes—that alter the economy's supply-side potential, prompting rational agents to adjust labor, , and consumption optimally in competitive markets. Developed by Finn Kydland and Edward Prescott, who received the 2004 in Economics for this framework, RBC models simulate cycles using methods, showing how temporary productivity declines, such as those from oil shocks or innovation lags, can propagate through intertemporal substitution and without nominal rigidities or irrational behavior. Empirical calibrations of RBC models match key cycle facts, including the volatility of output and comovement of variables like and hours worked, supporting the view that markets clear efficiently even amid shocks. Critics, however, note RBC's limited ability to explain demand-driven recessions or the role of financial intermediation, as real shocks alone struggle to account for synchronized downturns across sectors. Interventionist-origins theories emphasize how discretionary policies, especially expansive monetary actions, create disequilibria by suppressing natural interest rates and fueling malinvestment in long-term projects mismatched with actual savings. In the , central banks' credit expansion during expansions lowers borrowing costs artificially, directing resources into unsustainable ventures like overbuilt or speculative assets, only for higher rates during contraction to reveal and liquidate these errors—a process exacerbated by prior interventions delaying adjustment. Historical evidence includes the U.S. Federal Reserve's role in the 1920s, where loose policy post-World War I contributed to the 1929 stock market peak and subsequent , as credit growth outpaced savings by over 50% in the decade prior. Empirical studies link monetary easing to asset bubbles and cycles; for instance, low federal funds rates from 2001-2004 correlated with housing price surges exceeding 80% in real terms, precipitating the when policy tightening exposed leverage vulnerabilities. Cross-theory comparisons reveal mixed empirical support, with analyses showing monetary shocks explaining up to 20-30% of U.S. output variance over post-1980 cycles, though real shocks dominate longer horizons. Interventionist views gain traction from pre-central bank eras, where cycles were milder and less frequent under gold standards, suggesting policy discretion amplifies volatility; data from 1870-1913 indicate U.S. GDP standard deviation roughly half that of the post-1914 era. Nonetheless, both camps agree cycles involve propagation mechanisms, but differ on policy implications: market theorists advocate minimal interference to preserve price signals, while acknowledging shocks' inevitability, whereas interventionists warn against recurrent policy-induced distortions absent strict rules like monetary constancy.

Predictability and Mitigation Feasibility

Empirical studies indicate that business cycles exhibit limited short-term predictability, primarily due to unanticipated shocks and structural complexities that render precise timing of turning points challenging. For instance, professional forecasters have historically struggled to anticipate , as evidenced by the failure to predict the onset and duration of the downturn despite available data at the time. frameworks further suggest that any systematic predictability would be rapidly incorporated into agents' decisions, eroding the basis for consistent advantages beyond basic indicators like inversions, which signal elevated probabilities but lack precision in magnitude or duration. While certain leading indicators, such as financial cycle metrics (e.g., credit-to-GDP gaps), demonstrate out-of-sample power for recessions up to three years ahead, their reliability diminishes amid high or regime shifts, underscoring inherent limitations in macroeconomic modeling. , data inaccuracies, and evolving economic structures contribute to persistent forecast errors, with models often underperforming simple benchmarks during volatile periods. These constraints imply that while probabilistic assessments of cycle phases are feasible, deterministic predictions remain elusive, as confirmed by post-1980s evidence of moderated but still unpredictable fluctuations. Mitigation efforts through stabilization have achieved partial smoothing of cycles, reducing output volatility in advanced economies since the mid-20th century, yet full elimination proves infeasible due to lags, unintended distortions, and the endogenous nature of cycles. Automatic stabilizers, such as progressive taxation and unemployment insurance, dampen fluctuations by about 10-20% of GDP variance in the U.S., providing countercyclical support without discretionary intervention. However, discretionary monetary and fiscal measures face critiques for amplifying imbalances; for example, low interest rates intended to avert downturns often fuel asset bubbles and malinvestments, as articulated in , which posits that credit expansion distorts intertemporal coordination, rendering subsequent counterproductive without addressing root monetary causes. The highlights how rational agents anticipate policy changes, neutralizing intended stabilization effects and potentially exacerbating cycles through time-inconsistent incentives, as seen in the where preemptive easing contributed to housing distortions despite subsequent interventions. Empirical welfare analyses estimate that eliminating cycles could raise U.S. consumption equivalents by 0.5-1% annually, but realized policy benefits are modest and offset by costs like and fiscal burdens, with post-1946 efforts correlating to moderated but recurrent recessions. Mainstream consensus overstates fine-tuning efficacy, ignoring evidence that cycles persist as inherent features of decentralized economies adapting to shocks, with interventions risking prolonged recoveries or s that undermine long-term stability.

Critiques of Mainstream Consensus

The mainstream consensus on business cycles, dominated by New Keynesian models, posits that fluctuations arise primarily from nominal rigidities and demand shocks, amenable to stabilization via discretionary monetary and fiscal policies. Critics argue this framework overlooks endogenous causes rooted in monetary distortions and rational agent responses, leading to overstated faith in policy efficacy. (ABCT), for instance, attributes cycles to central bank credit expansion that artificially suppresses interest rates below natural levels, fostering malinvestments in capital-intensive sectors and culminating in unavoidable busts to liquidate errors. This contrasts with mainstream emphasis on exogenous shocks, as ABCT views the boom phase as inherently unsustainable, with empirical patterns like clustered bankruptcies and inventory liquidations aligning with post-credit peaks, as observed in the where low federal funds rates from 2001-2004 fueled housing overinvestment. Real business cycle (RBC) theory further challenges the consensus by modeling fluctuations as optimal equilibria driven by real supply shocks, such as productivity variations, rather than market failures requiring intervention. In RBC frameworks, agents intertemporally optimize under , rendering observed output volatility efficient rather than a welfare loss from sticky prices or central to Keynesian accounts. Empirical calibrations of RBC models replicate key cycle facts—like comovements in consumption, , and hours worked—using post-1950 U.S. data, without invoking nominal frictions, though critics note limited success in matching persistence. This approach undermines stabilization rationales, as policies distorting relative prices (e.g., via ) could exacerbate rather than mitigate real shocks. The exposes methodological flaws in mainstream policy evaluation, asserting that historical correlations from econometric models fail under regime shifts because rational agents alter behaviors in anticipation of policy rules. Applied to business cycles, this invalidates fine-tuning prescriptions derived from pre-1970s Keynesian estimates, as evidenced by the 1970s where expansionary policies amplified without sustained output gains, prompting a toward microfounded models. Mainstream models incorporating , such as New Keynesian variants, attempt rebuttals but retain ad hoc rigidities, while empirical tests show policy multipliers often below unity or negative in supply-constrained episodes, questioning net stabilization benefits. These critiques highlight systemic biases in academic consensus, where Keynesian dominance—fueled by post-WWII policy successes and institutional incentives—marginalizes non-interventionist views despite their alignment with first-principles and historical episodes like the 1920-1921 depression, resolved rapidly without stimulus. ABCT and RBC, though less prevalent in top journals, gain traction in explaining asset bubbles and productivity-driven recoveries, urging skepticism toward countercyclical activism that risks and prolonged maladjustments.

References

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