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What Is a Kondratiev Wave?


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    Highlights

  • Kondratiev Waves are long-term economic cycles of about 50 years, alternating between prosperity and decline, supposedly driven by technological innovation
  • Nikolai Kondratiev developed this theory by analyzing historical commodity prices, but it led to his persecution under Stalin
  • The theory is not widely accepted by economists, often seen as a result of data manipulation creating illusory patterns
  • Later economists like Joseph Schumpeter linked these waves to innovation, though consensus on their existence and timing remains elusive
Table of Contents

What Is a Kondratiev Wave?

Let me explain what a Kondratiev Wave is: it's a long-term economic cycle affecting commodity prices and other prices, thought to stem from technological innovation, leading to extended periods of prosperity followed by economic decline. This concept comes from Nikolai D. Kondratiev, an agricultural economist who observed these long-term cycles in agricultural and industrial commodity prices. He saw them as involving phases of evolution and self-correction.

You might also hear it called a 'Kondratieff Wave,' 'supercycle,' 'K-Wave,' 'surge,' or 'long wave.'

Key Takeaways

Kondratiev Waves appear as long-term patterns, roughly 50 years long, in statistically transformed economic time series data. They were first described by Nikolai Kondratiev and have been examined by other economists and financial commentators since. However, mainstream economists do not generally accept Kondratiev's theory, viewing it as a statistical illusion from his data transformations.

Understanding Kondratiev Waves

Kondratiev's work started with researching agricultural commodity prices, leading him to historical data on wheat and other crops from major European markets with long price records. He gathered nearly 150 years of data from various markets and combined them into long time series. Then, he transformed this raw price data into moving averages and rates-of-change to uncover long-term trends.

Through this process, he identified what he believed was a regular wave-like pattern in commodity prices, lasting about 50 years. He claimed to see two full cycles in his data: one from 1790-1849 and another from 1850-1896, with a third wave midway through at the time. His goal was to apply these insights to Soviet economic planning for prices and production.

But his ideas weren't well-received. Communist officials disliked them because they implied capitalist economies weren't doomed to collapse but just went through ups and downs. Kondratiev supported Lenin's New Economic Policy, which brought back some market elements after early central planning failures, but this clashed with Stalin's regime. As a result, he was imprisoned for 8 years near Moscow, then retried, sentenced to another 10 years, and ultimately executed by the NKVD at Kommunarka in Moscow.

Later Applications of Kondratiev’s Theory

Some economists later showed interest in Kondratiev's theory, though it's more popular among non-economists and investors than trained economists. Proponents often disagree on the waves' timing, direction, and causes. Financial forecasters have tried incorporating Kondratiev Waves into their predictive models.

In his book Economic Cycles, Joseph Schumpeter discussed wave-like patterns of various lengths, including Kondratiev Waves alongside shorter ones, to explain historical economic trends. He pointed to technological innovation as the main driver of these long waves.

Do Kondratiev Waves Really Exist?

Economists generally do not accept the existence of Kondratiev Waves. The arbitrary and conflicting views on their timing and nature mean even supporters can't agree on what they are or where we stand in the cycle. With only a few full waves in the available data, drawing firm conclusions is difficult.

Additionally, the Slutsky-Yule Effect—a mathematical property of random time series—shows that transformations like moving averages and rates-of-change, which Kondratiev used, can create fake wave patterns in random data. This suggests his findings are likely artifacts of data processing, lacking real explanatory or predictive value, especially since moving averages are inherently backward-looking.

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