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A system for dating long wave phases in economic development

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Abstract

Long wave chronologies are generally established by identifying phase periods associated with relatively higher and lower average growth rates in the world economy. However, the long recognition lag typical of the phase-growth approach prevents it from providing timely information about the present long wave phase period. In this paper, using world GDP growth rates data over the period 1871–2016, we develop a system for long wave phases dating, based on the systematic timing relationship between cyclical representations in growth rates and in levels. The proposed methodology allows an objective periodization of long waves which is much more timely than that based on the phase-growth approach. We find a striking concordance of the established long waves chronology with the dating chronologies elaborated by long wave scholars using the phase-growth approach, both in terms of the number of high- and low-growth phases of the world economy and their approximate time of occurrence. In terms of the current long wave debate, our findings suggest that the upswing phase of the current fifth long wave is still ongoing, and thus the recent financial/economic crisis only marks a flattening in the current upswing phase of the world economy.

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Notes

  1. See, for example, the popular book recently published by the Marxist economist Paul Mason (2015) on post-capitalism which has brought long wave thinking into a much wider audience than the typical specialist academic audience.

  2. Berry (1991) proposes that K-waves (short for Kondratiev waves) reflecting long-run fluctuations in economic growth are “structural” and not growth cycles. Similarly in Devezas and Corradine (2001).

  3. The term techno-economic paradigm instead of “technological paradigm” (Dosi 1982) reflects the idea that such changes do not merely involve engineering trajectories for specific products or process technologies.

  4. The most important novel feature of the MLP is that a transition of a socio-technical system stems from the interaction of events in its basic components, that is, (innovation) niches, socio-technical regimes, and landscapes (Grin et al. 2010).

  5. The transition to a new techno-economic regime is a period of structural change in which the process of transformation in the economy cannot proceed smoothly, not only because it implies massive transformation and much destruction of existing plant, but mainly because the prevailing pattern of social behavior and the existing institutional structure are shaped around the requirements and possibilities created by the previous paradigm (Perez 2002).

  6. Price series have been for a long time the only economic data available and consistently measured. Indeed, annual data on price indexes go back to late eighteenth century, allowing researchers to use the longest possible time span as well as a number of observations greater than any corresponding international dataset. By contrast, output variables have been reconstructed by economic historians relatively recently and mostly back to the mid-nineteenth century.

  7. The data source is the Maddison Project Database, version 2018 (Bolt et al. 2018). World GDP before 1950 is computed by summing real GDP across different subsets of countries (each country’s real GDP is preliminarily obtained by multiplying real GDP per capita in 2011$ by population in mid-year thousands). The country coverage before 1950 changes as follows: 1850- (Australia, Belgium, Chile, Denmark, France, Germany, Great Britain, Greece, Indonesia, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, US), 1870- (Finland, Canada, New Zealand, Japan, Brazil, Uruguay, Venezuela, Sri Lanka), 1884- (India), 1900- (Argentina, Bolivia, Colombia, Ecuador, Mexico and Per√π), 1902 (Cuba, Philippines), 1906- (Panama), 1920- (Costa Rica, Guatemala, Honduras, El Salvador, Nicaragua, Ireland, Romania, Former USSR), 1923- (Turkey). Country coverage, in terms of World GDP, goes from 62% in 1870 to 78% in 1929. After 1950 regional data for the World aggregate are used. All data are available at www.ggdc.net/maddison.

  8. For example, between the late 18th and early 20th centuries the level of wholesale prices tends to display a very large amount of variation over time around a trendless or slightly declining trend, whereas after WWII, prices start increasing as a consequence of a change in the process of price determination (van Ewijk 1982), the effect being the emergence of a strong positive trend (Gallegati et al. 2017).

  9. Alternative approaches considering a class of models where the change is gradual or happens continuously are the innovation outlier models (see Vogelsang and Perron 1998), smooth transition models (Terasvirta 1994) and Markov regime-switching models (Hamilton 1989).

  10. These filters are derived by approximating the frequency domain properties of the ideal band-pass filter. Since the exact band-pass filter is a moving average of infinite order, a finite order approximation is necessary in practical applications.

  11. The ideal band-pass filter can be better approximated with the longer moving averages. Using a larger number of leads and lags allows for a better approximation of the exact band-pass filter, but makes unusable more observations at the beginning and end of the sample.

  12. Wavelets, their generation and their potential usefulness are discussed in intuitive terms in Ramsey (2010, 2014). A more technical exposition with many examples of the use of wavelets in a variety of fields is provided by Percival and Walden (2000), while excellent introductions to wavelet analysis with many interesting economic and financial examples are given in Gencay et al. (2002) and Crowley (2007).

  13. Since the data need not be detrended nor are corrections for war years needed anymore, with wavelet analysis we can avoid the practice of studying history by erasing part of the history (Freeman and Louca 2001).

  14. The mother wavelet plays a role similar to sines and cosines in the Fourier decomposition. They are compressed or dilated, in the time domain, to generate cycles to fit actual data.

  15. For the DWT, where the number of observations is N = 2J, the number of coefficients at each dyadic scale is: N=N/2J + N/2J + N/2J-1 + ... + N/4 + N/2, that is, there are N/2J coefficients sJ,k, N/2J coefficients dJ,k, N/2J-1 coefficients dJ-1,k ... and N/2 coefficients d1,k.

  16. The two phases are generally called Phase A and Phase B in the long waves literature.

  17. Step cycles, first analyzed by Friedman and Schwartz (1963) in their work on money, were initially proposed with deviation cycles to identify growth cycles.

  18. The letters refer to the sequence of turning points for different classification of business cycles: α and β are peaks and troughs for growth rate cycles, B and C peaks and trough for classical cycles, A and D peaks and troughs for growth cycles.

  19. The application of the MODWT with a number of levels J = 5 with annual time series produces five wavelet details vectors D1, D2, D3, D4 and D5 which correspond to fluctuations between 2 and 4, 4–8, 8–16, 16–32 and 32–64 years, respectively. Wavelet decomposition analysis was carried out with R package waveslim from B. Whitcher.

  20. The first wave (steam engine) and the upswing of the second wave cover the first half of the nineteenth century and thus are excluded from our sample.

  21. The end of the fourth wave divides scholars’ opinions. For example, if according to Mason (2015) there is no evidence of a wave upswing beginning around 1990, Tyfield (2016) shows not only a clear global upswing through 1990s, but also a further upswing from 2000.

  22. According to the paradigm of technological innovation, there are two periods within an innovation paradigm with the diffusion of innovation preceded by a technological development period occurring during the downswing phase of a long wave.

  23. A complete list of existing long wave chronologies may be found in Bosserelle (2012).

  24. Maddison employs several macroeconomic indicators, namely the rate of growth of the volume of output, the output per head and exports, the cyclical variations in output and exports, unemployment, and the rate of change in consumer prices.

  25. That the end of the fourth wave did not expire in the 1990s is also argued by Mason (2015), although in his view the downturn of the fourth wave prolonged until 2008.

  26. Before WWI Maddison’s dating fails to detect a phase change in the early 1890s probably because his methodology aims at identifying major changes in growth momentum.

  27. Goldstein (1999, p.90) signals “as a source of potential difficulty for estimating long cycles the interaction between the impact of WWI and the rapid expansion of the 1920s”. Over the 1920s the US experienced an unprecedented period of sustained industrial and economic growth based on the implementation of standardized mass-production in industry and large-scale diffusion and use of new products such as the automobile, household appliances, and other mass-produced products (the US average annual growth rate of real GNP over the 1920–29 period equal to 4.6%). Moreover, over the same period, the relative international economic strength of the US economy increased considerably, both in terms of world industrial output and the share of the world market, at the expense of the European countries, the manufacturing industries, transport system and agricultural land of which had been greatly damaged during WWI.

  28. We thank one of the two anonymous referees for suggesting these specific robustness checks. Wavelet estimated variables are presented in the middle and bottom panels of Figure 4.

  29. Before 1950 real GDP per-capita is computed by summing real GDP across countries and dividing by population.

  30. The anticipation of the downswing phase in the first decade of the new century is consistent with the holdings of Perez (2009) and Reati and Toporowski (2009) that countries have already entered the deployment period of the ICT-based techno-economic paradigm.

References

  • Allianz Global Investors (2010) Analysis and trends: the sixth Kondratieff-long waves of prosperity. Allianz Global Investors, Frankfurt

    Google Scholar 

  • Anas J, Ferrara L (2004) Detecting cyclical. Turning Points: The ABCD Approach and Two Probabilistic Indicators. J Bus Cycle Meas Anal 12(2):193–225

    Article  Google Scholar 

  • Anas J, Billio M, Ferrara L, Mazzi GL (2008) A system for dating and detecting turning points in the euro area. Manch Sch 76(5):549–577

    Article  Google Scholar 

  • Baxter M (1994) Real exchange rates and real interest differentials. Have We Missed the Business-Cycle Relationship? J Mon Econ 33:5 37

    Google Scholar 

  • Baxter M, King R (1999) Measuring business Cycles: approximate band-pass Filters for economic time series. Rev Econ Stat 81:575–593

    Article  Google Scholar 

  • Becker R, Enders W, Lee J (2006) A stationary test with an unknown number of smooth breaks. J Time Ser Anal 27:381–409

    Article  Google Scholar 

  • Bernard L, Gevorkyan A, Palley T, Semmler W (2014) Time scales and mechanisms of economic cycles: a review of theories of long waves. Review of Keynesian Economics 2(1):87–107

    Article  Google Scholar 

  • Berry BJL (1991) Introduction in long wave rhythms in economic development and political behavior. John Hopkins University Press, Baltimore

    Google Scholar 

  • Bolt J, Inklaar R, de Jong H, van Zanden JL (2018) Rebasing, Maddison: new income comparisons and the shape of long-run economic development. Maddison Project Working Paper n:10

  • E Bosserelle (2012) La croissance economique dans le long terme: S. Kuznets versus N.D. Kondratiev - Actualité d'une controverse apparue dans l’entre-deux-guerres. Economies et Societes, Cahiers de l'ISMEA, serie Histoire economique quantitative, AF, 45 1655‑1688

  • Chase-Dunn C, Grimes P (1995) World-systems analysis. Annu Rev Sociol 21:387 417

    Article  Google Scholar 

  • Christiano LJ, Fitzgerald TJ (2003) The band pass filter. Intern Econ Rev 44(2):435–465

    Article  Google Scholar 

  • Crowley P (2007) A guide to wavelets for economists. J Econ Surv 21:207–267

    Article  Google Scholar 

  • Daubechies I (1992) Ten lectures on wavelets, CBSM-NSF regional conference series in applied mathematics, vol 61. SIAM, Philadelphia

    Google Scholar 

  • Devezas TC, Corradine JT (2001) The biological determinants of long-wave behaviour in socioeconomic growth and development. Technol. Forecast. Soc. Change 68(1):57

    Article  Google Scholar 

  • Devezas TC (2010) Crises, depressions, and expansions: global analysis and secular trends. Technol Forecast Soc Change 77:739 761

    Google Scholar 

  • Dosi G (1982) Technological paradigms and technological trajectories. Res Policy 11:147 162

    Article  Google Scholar 

  • van Duijn JJ (1983) The long wave in economic life. Allen and Unwin, Boston, MA

    Google Scholar 

  • Enders W, Lee J (2009) A unit root test using a Fourier series to approximate smooth breaks. Oxf Bull Econ Stat

  • Erten B, Ocampo JA (2013) Super cycles of commodity prices since the mid nineteenth century. World Devel 44:14 30

    Article  Google Scholar 

  • Everts M, Filters B-P (2006) Munich Personal RePec Archive Paper no 2049

  • van Ewijk C (1982) A spectral analysis of the Kondratieff cycle. Kyklos 35(3):468 499

    Google Scholar 

  • Freeman C (1983) Long waves in the world economy. Frances Pinter, London

    Google Scholar 

  • Freeman C (2009) Techno-economic paradigms. Essays in honour of Carlota Perez. In: Drechsler W, Kattel R, Reinert ES (eds) Schumpeter’s business Cycles and techno-economic paradigms. Anthem Press, London

    Google Scholar 

  • Freeman C, Perez C (1988) In: Dosi G, Freeman C, Nelson R, Silverberg G, Soete L (eds) Technical Change and Economic TheoryStructural crises of adjustment: business Cycles and investment behaviour. Pinter Publisher, London

    Google Scholar 

  • Freeman C, Louca F (2001) As time Goes by: from the industrial revolutions to the information revolution. Oxford University Press, Oxford

    Google Scholar 

  • Friedman M, Schwartz AJ (1963) A monetary history of the United States. NBER Publications. Princeton University Press, Princeton, pp 1867–1960

    Google Scholar 

  • Gallant AR (1981) On the bias in flexible functional forms and an essentially unbiased form. the flexible Fourier form J Econom 15:211 245

    Google Scholar 

  • Gallegati M, Gallegati M, Ramsey JB, Semmler W (2017) Long waves in prices: new evidence from wavelet analysis. Cliometrica 11(1):127 151

    Article  Google Scholar 

  • Marco Gallegati D (2018) Delli Gatti, long waves in history: a new global financial instability index. J Econ Dynam Control, 91 190:205

    Google Scholar 

  • Geels FW (2002) Technological transitions as evolutionary reconfiguration processes: a multilevel perspective and a case-study. Res Policy 31(8):1257–1274

    Article  Google Scholar 

  • Geels FW, Kemp R, Dudley G, Lyons G (eds) (2012) Automobility in transition? A Socio-Technical Analysis of Sustainable Transport. Routledge, New York

    Google Scholar 

  • Gencay R, Selcuk F, Whitcher B (2002) An introduction to wavelets and other filtering methods in finance and Economics. San Diego Academic Press, San Diego

    Google Scholar 

  • Goldstein JS (1988) Long Cycles: prosperity and war in the modern age. Yale University Press, New Haven

    Google Scholar 

  • Goldstein JP (1999) The existence, endogeneity and synchronization of long waves: structural time series model estimates. Rev Radic Polit Econ 31:61 101

    Article  Google Scholar 

  • Gordon DM (1978) Up and down the long roller coaster? In: Economics P (ed) Union for Radical. U.S. Capitalism in Crisis, URPE, New York

    Google Scholar 

  • Gore C (2010) The global recession of 2009 in a long-term development perspective. J Intern Dev 22(6):714–738

    Article  Google Scholar 

  • Grin J, Rotmans J, Schot J (2010) Transitions to sustainable development: new directions in the study of long term transformative change. Routledge, New York

  • Hamilton JD (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econom. 57:357 384

    Article  Google Scholar 

  • Harding D, Pagan A (2016) The econometric analysis of recurrent events in macroeconomics and finance. Princeton University Press, Princeton

    Book  Google Scholar 

  • Heap A (2005) China - the engine of a commodities super cycle. Citigroup Smith Barney, New York City

    Google Scholar 

  • Jerret D, Cuddington JT (2008) Broadening the statistical search for metal Price super Cycles to steel and related metals. Res Policy 33:188 195

    Google Scholar 

  • Kleinknecht A (1981) Innovation, accumulation, and crisis: waves in economic development. Review (Fernand Braudel Center) IV 687 711

  • Kohler J (2012) A comparison of the neo-Schumpeterian theory of Kondratiev waves and the multi-level perspective on transitions. Environ Innov Soc Transit 3:1–15

    Article  Google Scholar 

  • Kondratiev ND (1935) The long waves in economic life. Rev Econ Stat 17(6):105–115

    Article  Google Scholar 

  • Korotayev AV, Tsirel SV (2010) A spectral analysis of world GDP dynamics: Kondratieff waves, Kuznets swings, Juglar and Kitchin Cycles in global economic development, and the 2008-2009 economic crisis. Struct Dyn 4(1):3–57

    Google Scholar 

  • Kriedel N (2009) Long waves of economic development and the diffusion of general-purpose technologies: the case of railway networks. Economies et Societes, serie histoire economique quantitative. AF, 40 877:900

    Google Scholar 

  • Kuczynski T (1978) Spectral analysis and cluster analysis as mathematical methods for the periodization of historical processes. Kondratieff Cycles - appearance or reality? In: Proceedings of the seventh international economic history congress, vol 2. International Economic History Congress, Edinburgh, pp 79–86

    Google Scholar 

  • Kuczynski T (1982) Leads and lags in an escalation model of capitalist development: Kondratieff Cycles reconsidered, proceedings of the eighth international economic history congress. Vol. 3. International economic history congress. Budapest

  • Lewis WA (1978) Growth and fluctuations 1870–1913. Allen and Unwin, MA

    Google Scholar 

  • Maddison A (1991) Dynamic forces in capitalist development. Oxford University Press, Oxford

    Google Scholar 

  • Maddison A (2003) The world economy: historical statistics. OECD, Paris

    Book  Google Scholar 

  • Maddison A (2007) Fluctuations in the momentum of growth within the capitalist epoch. Cliometrica 1:145 175

    Article  Google Scholar 

  • Mason P (2015) PostCapitalism: A Guide to our Future. Allen Lane, UK

    Google Scholar 

  • Metz R (1992) A re-examination of long waves in aggregate production series. In: Kleinknecht A (ed) New findings in long waves research. St. Martin’s Printing, New York

    Google Scholar 

  • Mintz I (1969) Dating Postwar Business Cycles: Methods and their application to Western Germany: 1950-1967. In: Occasional Paper, vol 107. NBER, New York

    Google Scholar 

  • Mintz I (1972) Dating American growth Cycles. In: Zarnowitz V (ed) The business cycle today. NBER, New York

    Google Scholar 

  • Percival DB, Walden AT (2000) Wavelet methods for time series analysis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Perez C (2002) Technological revolutions and financial capital: the dynamics of bubbles and Golden ages. Edward Elgar, UK, Cheltenham

    Book  Google Scholar 

  • Perez C (2007) Finance and technical change: a long-term view. In: Hanusch H, Pyka A (eds) The Elgar companion to neo-Schumpeterian Economics. Edward Elgar, Cheltenham

    Google Scholar 

  • Perez C (2009) The double bubble at the turn of the century: technological roots and structural implications. Camb J Econ 33(4):779–805

    Article  Google Scholar 

  • Perez C (2010) Technological revolutions and techno-economic paradigms. Camb J Econ 34:185 202

    Article  Google Scholar 

  • Proietti T (2011) Trend estimation. In: Lovric M (ed) International encyclopedia of statistical science, 1st edn. Springer, Berlin

    Google Scholar 

  • Ramsey JB (2010) Wavelets. In: Durlauf SN, Blume LE (eds) The new Palgrave dictionary of Economics. Palgrave Macmillan, Basingstoke

    Google Scholar 

  • Ramsey JB (2014) Functional representation, approximation, bases and wavelets. In: Gallegati M, Semmler W (eds) Wavelet applications in Economics and finance. Springer-Verlag, Heidelberg

    Google Scholar 

  • Ramsey JB, Zhang Z (1996) The application of waveform dictionaries to stock market index data. In: Kravtsov YA, Kadtke J (eds) Predictability of complex dynamical systems. Springer-Verlag, Berlin

    Google Scholar 

  • Reati A, Toporowski J (2009) An economic policy for the fifth long wave. PSL quart. Rev, 62 143:186

    Google Scholar 

  • Rosenberg N, Frischtak CR (1983) Long waves and economic growth: a critical appraisal. Amer Econ Rev 73:146–151

    Google Scholar 

  • Schot J, Kanger L (2018) Deep transitions: emergence, acceleration, stabilization and directionality. Res Policy 47(6):1045 1059

    Article  Google Scholar 

  • Schot J (2016) Confronting the second deep transition through the historical imagination. Technol Cult 57(2):445–456

    Article  Google Scholar 

  • Schumpeter JA (1939) Business Cycles. McGraw-Hill, New York, NY

    Google Scholar 

  • Standard Chartered, The super-cycle report. London: global research standard Chartered, 2010

    Google Scholar 

  • Swilling M (2013) Economic Crisis, Long Waves and the sustainability transition: an African perspective. Environ. Innov. Soc. Transit. 6:96–115

    Article  Google Scholar 

  • Terasvirta T (1994) Specification, estimation, and evaluation of smooth transition autoregressive models. J Amer Stat Ass 89:208 218

    Google Scholar 

  • Tyfield D (2016) On Paul Mason’s “post-capitalism”: an extended review. Mimeo

  • Tylecote A (1991) The long wave in the world economy. Routledge, London

    Google Scholar 

  • Vogelsang TJ, Perron P (1998) Additional tests for a unit root allowing for a break in the trend function at an unknown time. Intern. Econ. Rev. 39:1073 1100

    Article  Google Scholar 

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Acknowledgements

I’d like to thank two anonymous referees that with their comments contributed to greatly improve the paper. All errors and responsibilities are, of course, mine.

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  1. Department of Economics and Social Sciences, Faculty of Economics “G. Fuà”, Università Politecnica delle Marche, Piazzale Martelli 8, 60121, Ancona, Italy

    Marco Gallegati

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Gallegati, M. A system for dating long wave phases in economic development. J Evol Econ 29, 803–822 (2019). https://doi.org/10.1007/s00191-019-00622-1

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