Abstract
Using inventions with a high degree of recombinant novelty as proxy for radical technologies, this work provides a long-run quantitative analysis of the relationship between radical technologies and productivity growth. The empirical analysis is based on a cliometric approach and relies on Granger’s causality to test the sign and direction of causality between the flow of radical technologies and productivity levels, in the USA between 1920 and 2000. At the aggregate level, results show that radical technologies cause a temporary acceleration of productivity growth and explain a considerable part of productivity variations. At technology-field level, the analysis indicates that productivity growth is driven by a few technological fields, mainly concentrated in science based sectors and in the sectors of specialized suppliers of capital equipment. Finally, with respect to the controversial issue of the endogeneity of radical technologies, at the aggregate level we find no causal relationship running from productivity to radical technologies, suggesting that these are exogenous. However, at technology-field level, we find a few endogenous technologies. Most of these are “demand-driven” as their flow increases when productivity grows, but they have no impact on productivity. Only in one technological field, the flow of radical technologies increases when productivity decreases and, at the same time, has a positive impact on productivity. This latter case may explain why technological revolutions and the whole process of long-run economic development are partly endogenous.
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Data availability statement
The data supporting the findings of this study are not publicly available but were provided by Bocconi ICRIOS. See Coffano, Monica and Tarasconi, Gianluca, Crios—Patstat Database: Sources, Contents and Access Rules (February 1, 2014). Available at SSRN: http://ssrn.com/abstract=2404344
Notes
Without pretending to be exhaustive, see for example Añón Higón 2007; Antonelli et al. 2010; Castellacci 2010; Coad et al. 2016; Crespi and Pianta 2008; Scherer 1984; Hall and Mairesse 1995; Hasan and Tucci 2010; Verspagen 1995; Baumann and Kritikos 2016; Bogliacino and Pianta 2011; Morris 2018; Juhász et al. 2020.
See references on note 2.
The choice of the analyzed period (1920–2000) depends on the availability of data in the CRIOS dataset.
As an example, consider Patent US 4234565 with priority year 1977. The patent is assigned to three IPC codes (A23K001; C08F220; A61K009) from which three combinations can be identified: (A23K001; C08F220), (A23K001; A61K009) and (A61K009; C08F220). Since only the first combination is new (it has never been used by patents with priority year before 1977), the NR index is 1/3 = .33, that is, 1/3 of the combinations is new.
Lobo and Strumsky (2015) give the example of nanotechnology, which has been introduced in 2004 in the USPC system and has led to recodification of previous patents, thus the first nanotechnology patent was granted in 1986.
We also have computed all these variables in stocks using a perpetual inventory method (Hall et al. 2010). Econometric results were very similar to those of variables in flow and, therefore, are not here reported.
In Bergeaud et al. (2016), TFP is computed assuming the following Cobb –Douglas production function: TFP = Y/(Kα * Lβ), where α and β are the elasticities of output with respect to the inputs K and L. Assuming unitary returns to scale (α + β = 1), TFP is then calculated as a residual according to the following equation: TFP = Y/(Kα * L1− α). Three basic series have been used: GDP (Y), labor (L), and capital (K). Labor (L), is calculated by using data on total employment (N) and working time (H). The capital indicator is constructed by the perpetual inventory method (PIM). α is computed by assuming that production factors are remunerated at their marginal productivity (at least over the medium to long term). Given that labor costs (wages and related taxes and social security contributions) represent roughly two-thirds of income, we have β = 0.7 and α = 0.3.
Data have been provided in Bergeaud et al. (2016). Following this work, data have been smoothed by using the HP filter (lambda = 500).
Note that retroactive reclassification of existing patents occurs on a regular basis. See the example on note 8.
Also note that 48% of all granted patents have only one IPC code, which corroborates previous studies (Verhoeven et al. 2016). Among the remaining patents that have at least two codes, the majority, namely 46%, have only two codes and one combination.
“The methodology of vector autoregression appears useful for studying historical series on climatic, economic and demographic variables where we do not yet have a sufficient theoretical foundation for specifying and estimating structural models”, p. 295.
Non-structural VAR models are sometimes criticized for requiring to include in the model a number of variables matching the degree of freedom in order to avoid estimation problems (Johnston and Dinardo 1999), and for the lack of theory on which they rely.
A \({X}_{t}\) process is known as stationary if all its moments are invariants for any change of the origin of time. There are two types of non-stationary processes: the TS processes (Trend Stationary Processes) which present non-stationarity of the deterministic type and the DS processes (Difference Stationary Processes) for which non-stationarity is due to a random type. These processes are respectively stationarized by a deviation from the deterministic trend and with a differences filter. In this last case, the number of filters indicates the order of integration of the variable. A variable is integrated of order "\(D\)" if it is necessary to differentiate it "D" times to make it stationary. In our tests, a variable Xt which is stationarised with a difference filter is noted DXt; a variable Yt which is stationarised with a deviation from a linear trend is noted SYt. A variable Zt which needs to be stationarised with a difference filter and a deviation from a linear trend is noted SDZt.
At the aggregate level ERS tests indicate that TFP, Rec_flow, T1rec_flow are non-stationary because of a unit root (DS processes); New_ipc is non-stationary because of a linear trend (TS process) and Pat_flow is non-stationary because of the two (Mixed process). Thus, stationary variables are now respectively noted DTPF, DRECFLOW, DT1RECFLOW, SNBNEWPIC, and SDPATFLOW.
Here, we test the existence of causal links between D(TFP), D(recflow), D(T1recflow), SD(patflow) and S(nbnewipc). See note 16.
There is a causal relationship from Rec_flow to TFP with a significance level of 10%.
As for the aggregate analysis, all the intermediary tests performed are presented in the supplementary materials. ERS tests are presented in SM, part 5; cointegration tests are in SM, part 6; VAR Models estimations are in SM, part 7.
For example, the technological field 3 (Telecommunications) is highly concentrated in the NACE sector 26 “Manufacture of computer, electronic and optical products”, which originates 68% of all Telecommunications patents. The remaining sectors related to this technological field have a share between 0 and 5%. Similarly, for the technological field 19 (Basic materials chemistry), patents originate at 45% from NACE sector 20 “Manufacture of chemicals and chemical products”, the remaining sectors have a share between 0 and 5%.
They comprise motor vehicles, mineral oil refining, coke and nuclear fuel, rubber and plastics, basic metals, and financial services related to information technology.
We thank one anonymous referee for this important comment.
References
Clark J, Freeman C, Soete L (1981) Long waves, inventions, and innovations. Futures 13:308–322. https://doi.org/10.1016/0016-3287(81)90146-4
Diebolt C (2016) Cliometrica after 10 years: definition and principles of cliometric research. Cliometrica 10:1–4. https://doi.org/10.1007/s11698-015-0136-z
Gruber M, Harhoff D, Hoisl K (2013) Knowledge recombination across technological boundaries: scientists vs. engineers. Manage Sci 59:837–851. https://doi.org/10.1287/mnsc.1120.1572
Harberger AC (1998) A vision of the growth process. Am Econ Rev 88:1–32
Aghion P, Howitt P, Brant-Collett M, García-Peñalosa C (1998) Endogenous growth theory. MIT Press
Añón Higón D (2007) The impact of R&D spillovers on UK manufacturing TFP: a dynamic panel approach. Res Policy 36:964–979. https://doi.org/10.1016/j.respol.2007.03.006
Antonelli C, Krafft J, Quatraro F (2010) Recombinant knowledge and growth: the case of ICTs. Struct Chang Econ Dyn 21:50–69. https://doi.org/10.1016/j.strueco.2009.12.001
Arthur WB (2009) The nature of technology: what it is and how it evolves. Penguin
Arthur WB (2007) The structure of invention. Res Policy 36:274–287. https://doi.org/10.1016/j.respol.2006.11.005
Arts S, Veugelers R (2015) Technology familiarity, recombinant novelty, and breakthrough invention. Ind Corp Chang 24:1215–1246. https://doi.org/10.1093/icc/dtu029
Bakker G, Crafts N, Woltjer P (2015) A Vision of the Growth Process in a Technologically Progressive Economy: the United States, 1899–1941. https://doi.org/10.13140/RG.2.1.1145.2248
Baumann J, Kritikos AS (2016) The link between R&D, innovation and productivity: are micro firms different? Res Policy 45:1263–1274. https://doi.org/10.1016/j.respol.2016.03.008
Bergeaud A, Cette G, Lecat R (2016) Productivity trends in advanced countries between 1890 and 2012. Rev Income Wealth 62:420–444. https://doi.org/10.1111/roiw.12185
Bogliacino F, Pianta M (2016) The Pavitt Taxonomy, revisited: patterns of innovation in manufacturing and services. Econ Polit 33:153–180. https://doi.org/10.1007/s40888-016-0035-1
Bogliacino F, Pianta M (2011) Engines of growth. Innovation and productivity in industry groups. Struct Chang Econ Dyn 22:41–53. https://doi.org/10.1016/j.strueco.2010.11.002
Box GEP, Jenkins GM (1976) Time Series Analysis, Forecasting and Control. Holden Day, San Francisco
Bresnahan TF, Trajtenberg M (1995) General purpose technologies “Engines of growth”? J Econom 65:83–108
Bruneau C (1996) Analyse econometrique de la Causalite: Un Bilan de la Litterature. Papers.
Caiani A, Godin A, Lucarelli S (2014) Innovation and finance: a stock flow consistent analysis of great surges of development. J Evol Econ 24:421–448. https://doi.org/10.1007/s00191-014-0346-8
Carlaw KI, Lipsey RG (2006) GPT-driven, endogenous growth. Econ J 116:155–174. https://doi.org/10.1111/j.1468-0297.2006.01051.x
Castellacci F (2010) Structural change and the growth of industrial sectors: empirical test of a GPT model. Rev Income Wealth 56:449–482. https://doi.org/10.1111/j.1475-4991.2010.00380.x
Coad A, Segarra A, Teruel M (2016) Innovation and firm growth: does firm age play a role? Res Policy 45:387–400. https://doi.org/10.1016/j.respol.2015.10.015
Coffano M, Tarasconi G (2014) CRIOS - Patstat Database: Sources, Contents and Access Rules (February)
Conrad AH, Meyer JR (1958) The economics of slavery in the Ante Bellum South. J Polit Econ 66:95–130. https://doi.org/10.1086/258020
Crafts NFR (1995) Exogenous or endogenous growth? The industrial revolution reconsidered. J Econ Hist 55:745–772
Crespi F, Pianta M (2008) Demand and innovation in productivity growth. Int Rev Appl Econ 22:655–672. https://doi.org/10.1080/02692170802407429
Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74:427–431. https://doi.org/10.1080/01621459.1979.10482531
Diebolt C, Hagemann H (2019) Mixing history of economic thought with cliometrics: room for debates on economic growth. Eur J Hist Econ Thought 26:654–658. https://doi.org/10.1080/09672567.2019.1647636
Dopfer K, Foster J, Potts J (2004) Micro–meso–macro. J Evol Econ 2004(14):263–279. https://doi.org/10.1007/s00191-004-0193-0
Dosi G, Fagiolo G, Roventini A (2010) Schumpeter meeting Keynes: A policy-friendly model of endogenous growth and business cycles. J Econ Dyn Control 34:1748–1767. https://doi.org/10.1016/J.JEDC.2010.06.018
Eckstein Z, Schultz TP, Wolpin KI (1984) Short-run fluctuations in fertility and mortality in pre-industrial Sweden. Eur Econ Rev 26:295–317. https://doi.org/10.1016/0014-2921(84)90093-X
Elliott G, Rothenberg TJ, Stock JH (1996) Efficient tests for an autoregressive unit root. Econometrica 64:813. https://doi.org/10.2307/2171846
Engle R, Granger C (Eds) (1991) Long-run economic relationships: readings in cointegration. Oxford University Press
Engle RF, Granger CWJ (1987) Co-integration and error correction: representation, estimation, and testing. Econometrica 55:251. https://doi.org/10.2307/1913236
Epicoco M (2021) Technological revolutions and economic development: endogenous and exogenous fluctuations. J Knowl Econ 12:1437–1461. https://doi.org/10.1007/s13132-020-00671-z
Fleming L (2001) Recombinant uncertainty in technological search. Manage Sci 47:117–132. https://doi.org/10.1287/mnsc.47.1.117.10671
Fogel RW (1964) Railroads and American Economic Growth: Essays in Econometric History. Johns Hopkins Press, Baltimore
Freeman C, Perez C (1988) Structural crises of adjustment, business cycles and investment behaviour. In: Dosi G et al (eds) Technical change and economic theory. Francis Pinter, London, pp 38–66
Granger CWJ (1980) Testing for causality: a personal viewpoint. J Econ Dyn Control 2:329–352. https://doi.org/10.1016/0165-1889(80)90069-X
Granger CWJ (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424. https://doi.org/10.2307/1912791
Hall BH, Mairesse J (1995) Exploring the relationship between R&D and productivity in French manufacturing firms. J Econ 65:263–293. https://doi.org/10.1016/0304-4076(94)01604-X
Hall BH, Mairesse J, Mohnen P (2010) Chapter 24 - Measuring the returns to R&D. In: Hall BH, Rosenberg, N.B.T.-H. of the E. of I. (Eds.) Handbook of the Economics of Innovation, Volume 2. North-Holland, pp 1033–1082. https://doi.org/10.1016/S0169-7218(10)02008-3
Hall BH, Ziedonis RH (2001) The patent paradox revisited: an empirical study of patenting in the U.S. semiconductor industry, 1979–1995. RAND J Econ 32:101–128
Hasan I, Tucci CL (2010) The innovation–economic growth nexus: global evidence. Res Policy 39:1264–1276. https://doi.org/10.1016/J.RESPOL.2010.07.005
Helpman E, Trajtenberg M (1994) A time to sow and a time to reap: growth based on general purpose technologies. Cambridge, MA. https://doi.org/10.3386/w4854
Hendry DF, Mizon GE (1993) Evaluating dynamic models by encompassing the VAR. In: Phillips PCB (ed) Models, methods, and applications of econometrics. Basil Blackwell, Oxford, pp 272–300
Johansen S (1988) Statistical analysis of cointegration vectors. J Econ Dyn Control 12:231–254. https://doi.org/10.1016/0165-1889(88)90041-3
Johnston J, Dinardo J (1999) Méthodes économétriques. Economica, Paris
Juhász R, Squicciarini M, Voigtländer N (2020) Technology Adoption and Productivity Growth: Evidence from Industrialization in France. "NBER Working Paper Series", N° 27503. https://doi.org/10.1016/0165-1889(88)90041-3
Keijl S, Gilsing VA, Knoben J, Duysters G (2016) The two faces of inventions: the relationship between recombination and impact in pharmaceutical biotechnology. Res Policy 45:1061–1074. https://doi.org/10.1016/j.respol.2016.02.008
Kleinknecht A, van der Panne G (2008) Technology and long waves in economic growth. In: Davis JB, Dolfsma W (eds) The Elgar Companion to Social Economics. Edward Elgar, Cheltenham, pp 597–606
Kuersteiner GM (2010) Granger-Sims causality. In: Macroeconometrics and time series analysis. Palgrave Macmillan UK, London, pp 119–134. https://doi.org/10.1057/9780230280830_14
Kuznets S (1930) Secular movements in production and prices: their nature and their Bearing Upon Cyclical Fluctuations. University of Michigan
Lorentz A, Ciarli T, Savona M, Valente M (2016) The effect of demand-driven structural transformations on growth and technological change. J Evol Econ 26:219–246. https://doi.org/10.1007/s00191-015-0409-5
Markard J, Raven R, Truffer B (2012) Sustainability transitions: an emerging field of research and its prospects. Res Policy 41:955–967. https://doi.org/10.1016/J.RESPOL.2012.02.013
Mensch G (1979) Stalemate in technology: innovations overcome the depression. Ballinger Pub. Co.
Mokyr J (1993) The British industrial revolution: an economic perspective. Westview Press
Monfort A, Rabemananjara R (1990) From a var model to a structural model, with an application to the wage–price spiral. J Appl Econom 5:203–227. https://doi.org/10.1002/jae.3950050302
Morris DM (2018) Innovation and productivity among heterogeneous firms. Res Policy 47:1918–1932. https://doi.org/10.1016/j.respol.2018.07.003
Nelson RR, Winter SG (1982) An evolutionary theory of economic change. Cambridge University Press
Neuhäusler P, Frietsch R, Kroll H (2019) Probabilistic concordance schemes for the re-assignment of patents to economic sectors and scientific publications to technology fields, Discussion Papers “Innovation Systems and Policy Analysis.” Fraunhofer Institute for Systems and Innovation Research (ISI)
Newbold P, Granger CWJ (1974) Experience with forecasting univariate time series and the combination of forecasts. J R Stat Soc Ser A 137:131. https://doi.org/10.2307/2344546
Pavitt K (1984) Sectoral patterns of technical change: Towards a taxonomy and a theory. Res Policy 13:343–373
Perez C (2010) Technological revolutions and techno-economic paradigms. Cambridge J Econ 34:185–202. https://doi.org/10.1093/cje/bep051
Perez C (2002) Technological revolutions and financial capital : the dynamics of bubbles and golden ages. E. Elgar Pub.
Rip A, Kemp R (1998) Technological change. In: Rayner S, Malone EL (eds) Human choice and climate change. Battelle Press, Columbus, pp 327–399
Salanié B (1999) Guide pratique des séries non-stationnaires. Économie Prévision 137:119–141. https://doi.org/10.3406/ecop.1999.5953
Saviotti PP, Pyka A (2013) The co-evolution of innovation, demand and growth. Econ Innov New Technol 22:461–482. https://doi.org/10.1080/10438599.2013.768492
Saviotti PP, Pyka A (2004) Economic development by the creation of new sectors. J Evol Econ 14:1–35. https://doi.org/10.1007/s00191-003-0179-3
Schaefer A, Schiess D, Wehrli R (2014) Long-term growth driven by a sequence of general purpose technologies. Econ Model 37:23–31. https://doi.org/10.1016/J.ECONMOD.2013.10.014
Scherer FM (1984) Using linked patent data and R&D data to measure technology flows. In: Griliches Z (Ed.) R&D, patents and productivity. University of Chicago Press
Schmoch U (2008) Concept of a technology classification for country comparisons. Final Report to the World Intellectual Property Organisation (WIPO)
Schot J, Kanger L (2018) Deep transitions: emergence, acceleration, stabilization and directionality. Res Policy 47:1045–1059. https://doi.org/10.1016/J.RESPOL.2018.03.009
Schumpeter JA (1939) Business cycles: a theoretical, historical, and statistical analysis of the capitalist process. McGrow-Hill
Silverberg G, Lehnert D (1993) Long waves and ‘evolutionary chaos’ in a simple Schumpeterian model of embodied technical change. Struct Chang Econ Dyn 4:9–37. https://doi.org/10.1016/0954-349X(93)90003-3
Sims CA (1980) Macroeconomics and reality. Econometrica 48:1. https://doi.org/10.2307/1912017
Strumsky D, Lobo J (2015) Identifying the sources of technological novelty in the process of invention. Res Policy 44:1445–1461. https://doi.org/10.1016/j.respol.2015.05.008
Strumsky D, Lobo J, van der Leeuw S (2012) Using patent technology codes to study technological change. Econ Innov New Technol 21:267–286. https://doi.org/10.1080/10438599.2011.578709
Timmer MP, Inklaar R, O’Mahony M, Ark B van (2011) Productivity and economic growth in Europe: A comparative industry perspective. International Productivity Monitor 21:3–23
Verhoeven D, Bakker J, Veugelers R (2016) Measuring technological novelty with patent-based indicators. Res Policy 45:707–723. https://doi.org/10.1016/j.respol.2015.11.010
Youn H, Strumsky D, Bettencourt LMA, Lobo J (2015) Invention as a combinatorial process: evidence from US patents. J R Soc Interface 12. https://doi.org/10.1098/rsif.2015.0272
Verspagen B (1995) R&D and productivity: a broad cross-section cross-country look. J Product Anal 6:117–135. https://doi.org/10.1007/BF01073407
Acknowledgements
The authors would like to thank in particular Claude Diebolt, for his inspiring doctoral courses, for our stimulating discussions, and for his comments on this manuscript. We also would like to thank two anonymous referees, and in particular one of them, for his/her attentive analysis of the manuscript and extremely helpful suggestions. Finally, we would like to thank André Lorentz, Julien Pénin, as well as the participants at CSI-CHPE seminar (BETA, University of Strasbourg), at the 2021 AFSE conference (Lille), at the 2021 ISS Conference (Rome), and the 2021 EAEPE Conference (Naples). The responsibility for eventual errors remains exclusively ours.
Funding
This study has been funded by the Lorraine Region and the European Regional Development Fund – ERDF (project CPER ARIANE “DYN-TECH”).
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Appendices
Appendix 1. List of technological fields
Table
Appendix 2. Causality test for aggregate models
Pairwise Granger Causality Tests: 1920–2000 | Obs | F-Statistic | Prob | Lag | Sign |
|---|---|---|---|---|---|
Null Hypothesis: | |||||
DTFP does not Granger Cause DRECFLOW | 75 | 0.88795 | 0.49451 | 5 | + |
DRECFLOW does not Granger Cause DTFP | 2.03684 | 0.08522 | |||
DTFP does not Granger Cause DT1RECFLOW | 75 | 1.05102 | 0.39584 | 5 | + |
DT1RECFLOW does not Granger Cause DTFP | 2.88883 | 0.02054* | |||
DTFP does not Granger Cause SDPATFLOW | 79 | 1.62032 | 0.20693 | 1 | |
SDPATFLOW does not Granger Cause DTFP | 0.12464 | 0.72503 | |||
DTFP does not Granger Cause SNEWIPC | 76 | 0.37247 | 0.82747 | 4 | |
SNEWIPC does not Granger Cause DTFP | 1.33790 | 0.26504 |
Appendix 3. Variance decomposition of TFP
3.1 VAR Model with TFP and T1RECFLOW
Variance Decomposition of DTFP: | |||
|---|---|---|---|
Period | S.E | DT1RECFLOW | DTFP |
1 | 0.157148 | 0.556998 | 99.44300 |
2 | 0.165163 | 0.506200 | 99.49380 |
3 | 0.173102 | 8.767873 | 91.23213 |
4 | 0.173943 | 8.913487 | 91.08651 |
5 | 0.183706 | 9.349787 | 90.65021 |
6 | 0.217845 | 35.51668 | 64.48332 |
7 | 0.223046 | 38.37395 | 61.62605 |
8 | 0.238325 | 44.85286 | 55.14714 |
9 | 0.240548 | 44.02831 | 55.97169 |
10 | 0.244055 | 45.00751 | 54.99249 |
15 | 0.261907 | 51.62544 | 48.37456 |
20 | 0.284367 | 58.54662 | 41.45338 |
Appendix 4. Causality test at the sectoral level
Pairwise Granger Causality Tests: 1900–2000 | Obs | F-Statistic | Prob | Sign | Lag |
|---|---|---|---|---|---|
Null Hypothesis: | |||||
DIPC1 does not Granger Cause DTFPHW | 74 | 4.02570 | 0.00115** | + | 7 |
DTFPHW does not Granger Cause DIPC1 | 1.03979 | 0.41365 | |||
DIPC2 does not Granger Cause DTFPHW | 80 | 0.75679 | 0.38704 | 1 | |
DTFPHW does not Granger Cause DIPC2 | 0.03463 | 0.85287 | |||
DIPC3 does not Granger Cause DTFPHW | 75 | 2.45599 | 0.03399* | + | 6 |
DTFPHW does not Granger Cause DIPC3 | 0.64038 | 0.69747 | |||
DDIPC4 does not Granger Cause DTFPHW | 78 | 0.34614 | 0.70857 | 2 | |
DTFPHW does not Granger Cause DDIPC4 | 1.21960 | 0.30130 | |||
DIPC5 does not Granger Cause DTFPHW | 80 | 0.03207 | 0.85834 | 1 | |
DTFPHW does not Granger Cause DIPC5 | 3.39034 | 0.06943 | |||
SDIPC6 does not Granger Cause DTFPHW | 80 | 0.54062 | 0.46441 | 1 | |
DTFPHW does not Granger Cause SDIPC6 | 0.30277 | 0.58375 | |||
SIPC7 does not Granger Cause DTFPHW | 76 | 0.52304 | 0.75796 | 5 | |
DTFPHW does not Granger Cause SIPC7 | 0.51831 | 0.76149 | |||
SDIPC8 does not Granger Cause DTFPHW | 80 | 0.01074 | 0.91773 | 1 | |
DTFPHW does not Granger Cause SDIPC8 | 0.50721 | 0.47850 | |||
DIPC9 does not Granger Cause DTFPHW | 80 | 1.26569 | 0.26407 | 1 | |
DTFPHW does not Granger Cause DIPC9 | 0.02517 | 0.87435 | |||
DIPC10 does not Granger Cause DTFPHW | 80 | 0.77628 | 0.38102 | 1 | |
DTFPHW does not Granger Cause DIPC10 | 1.09347 | 0.29897 | |||
DIPC11 does not Granger Cause DTFPHW | 80 | 0.05182 | 0.82053 | 1 | |
DTFPHW does not Granger Cause DIPC11 | 0.00070 | 0.97899 | |||
DIPC12 does not Granger Cause DTFPHW | 80 | 2.58322 | 0.11209 | 1 | |
DTFPHW does not Granger Cause DIPC12 | 2.76415 | 0.10046 | |||
DIPC13 does not Granger Cause DTFPHW | 80 | 0.91104 | 0.34283 | 1 | |
DTFPHW does not Granger Cause DIPC13 | 0.18398 | 0.66917 | |||
DIPC14 does not Granger Cause DTFPHW | 77 | 0.43878 | 0.78014 | 4 | |
DTFPHW does not Granger Cause DIPC14 | 2.29456 | 0.06811 | |||
DIPC15 does not Granger Cause DTFPHW | 80 | 0.07642 | 0.78295 | 1 | |
DTFPHW does not Granger Cause DIPC15 | 0.67149 | 0.41506 | |||
DIPC16 does not Granger Cause DTFPHW | 80 | 0.21792 | 0.64195 | 1 | |
DTFPHW does not Granger Cause DIPC16 | 0.81446 | 0.36962 | |||
DIPC17 does not Granger Cause DTFPHW | 80 | 0.54610 | 0.46216 | 1 | |
DTFPHW does not Granger Cause DIPC17 | 0.00518 | 0.94284 | |||
DIPC18 does not Granger Cause DTFPHW | 75 | 1.67997 | 0.14093 | 6 | |
DTFPHW does not Granger Cause DIPC18 | 1.24985 | 0.29374 | |||
DIPC19 does not Granger Cause DTFPHW | 73 | 2.27223 | 0.03510* | + | 8 |
DTFPHW does not Granger Cause DIPC19 | 1.33192 | 0.24712 | |||
DIPC20 does not Granger Cause DTFPHW | 80 | 0.02581 | 0.87277 | 1 | |
DTFPHW does not Granger Cause DIPC20 | 0.02276 | 0.88048 | |||
DIPC21 does not Granger Cause DTFPHW | 70 | 0.65594 | 0.77144 | 11 | |
DTFPHW does not Granger Cause DIPC21 | 4.15382 | 0.00027** | + | ||
SDIPC22 does not Granger Cause DTFPHW | 80 | 0.59819 | 0.44164 | 1 | |
DTFPHW does not Granger Cause SDIPC22 | 0.01382 | 0.90672 | |||
DIPC23 does not Granger Cause DTFPHW | 80 | 0.66027 | 0.41897 | 1 | |
DTFPHW does not Granger Cause DIPC23 | 0.35726 | 0.55179 | |||
DIPC24 does not Granger Cause DTFPHW | 80 | 0.21637 | 0.64313 | 1 | |
DTFPHW does not Granger Cause DIPC24 | 1.46068 | 0.23052 | |||
DIPC25 does not Granger Cause DTFPHW | 68 | 2.21687 | 0.02647* | + | 13 |
DTFPHW does not Granger Cause DIPC25 | 2.15471 | 0.03103 | - | ||
DIPC26 does not Granger Cause DTFPHW | 80 | 0.19113 | 0.66320 | 1 | |
DTFPHW does not Granger Cause DIPC26 | 2.92610 | 0.09118 | |||
DIPC27 does not Granger Cause DTFPHW | 79 | 2.81863 | 0.06611 | 2 | |
DTFPHW does not Granger Cause DIPC27 | 9.17459 | 0.00028** | + | ||
DIPC28 does not Granger Cause DTFPHW | 75 | 3.57520 | 0.00417** | + | 6 |
DTFPHW does not Granger Cause DIPC28 | 1.53708 | 0.18108 | |||
DDIPC29 does not Granger Cause DTFPHW | 72 | 1.35098 | 0.23873 | 8 | |
DTFPHW does not Granger Cause DDIPC29 | 1.02444 | 0.42905 | |||
SIPC30 does not Granger Cause DTFPHW | 80 | 2.58781 | 0.11178 | 1 | |
DTFPHW does not Granger Cause SIPC30 | 0.09678 | 0.75657 | |||
DIPC31 does not Granger Cause DTFPHW | 76 | 2.13277 | 0.07252 | 5 | |
DTFPHW does not Granger Cause DIPC31 | 2.84369 | 0.02203* | + | ||
DIPC32 does not Granger Cause DTFPHW | 80 | 0.08396 | 0.77277 | 1 | |
DTFPHW does not Granger Cause DIPC32 | 0.00739 | 0.93171 | |||
IPC33 does not Granger Cause DTFPHW | 80 | 0.60861 | 0.43770 | 1 | |
DTFPHW does not Granger Cause IPC33 | 0.72028 | 0.39868 | |||
SIPC34 does not Granger Cause DTFPHW | 80 | 0.07242 | 0.78857 | 1 | |
DTFPHW does not Granger Cause SIPC34 | 0.04325 | 0.83581 | |||
DIPC35 does not Granger Cause DTFPHW | 80 | 0.31524 | 0.57611 | 1 | |
DTFPHW does not Granger Cause DIPC35 | 0.07792 | 0.78088 |
Appendix 5. Variance decomposition of TFP: Global VAR Model with TFP and the 5 leading technological fields
Variance Decomposition of DTFPHW: | |||||||
|---|---|---|---|---|---|---|---|
Period | S.E | IPC1 | IPC3 | IPC19 | IPC25 | IPC28 | TFP |
1 | 0.16 | 0.00 | 2.36 | 1.53 | 0.57 | 3.63 | 91.91 |
2 | 0.16 | 0.82 | 2.19 | 1.44 | 3.74 | 5.59 | 86.21 |
3 | 0.17 | 1.62 | 2.62 | 2.27 | 3.66 | 6.39 | 83.44 |
4 | 0.17 | 1.67 | 4.00 | 2.29 | 7.48 | 6.05 | 78.51 |
5 | 0.18 | 1.50 | 3.62 | 4.76 | 6.97 | 10.58 | 72.57 |
6 | 0.19 | 9.38 | 4.25 | 4.94 | 7.60 | 9.62 | 64.22 |
7 | 0.22 | 22.96 | 5.42 | 7.94 | 6.02 | 8.17 | 49.49 |
8 | 0.23 | 26.09 | 5.00 | 7.72 | 5.59 | 7.86 | 47.73 |
9 | 0.24 | 26.33 | 4.75 | 9.38 | 6.10 | 7.58 | 45.86 |
10 | 0.24 | 26.53 | 4.76 | 9.31 | 6.08 | 7.52 | 45.81 |
Appendix 6. Variance decomposition of IPC21, IPC27 and IPC31
VAR Model with TFP and IPC21 | VAR Model with TFP and IPC27 | VAR Model with TFP and IPC31 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
Period | S.E | IPC21 | TFP | Period | S.E | IPC27 | TFP | Period | S.E | IPC31 | TFP |
1 | 4.09 | 100.00 | 0.00 | 1 | 7.92 | 100.00 | 0.00 | 1 | 12.52 | 100.00 | 0.00 |
2 | 4.57 | 99.71 | 0.29 | 2 | 8.66 | 97.42 | 2.58 | 2 | 12.69 | 99.29 | 0.71 |
3 | 4.61 | 99.35 | 0.65 | 3 | 9.18 | 87.01 | 12.99 | 3 | 13.31 | 95.22 | 4.78 |
4 | 4.86 | 98.94 | 1.06 | 4 | 9.18 | 87.00 | 13.00 | 4 | 13.77 | 89.12 | 10.88 |
5 | 4.95 | 96.34 | 3.66 | 5 | 9.21 | 87.07 | 12.93 | 5 | 13.99 | 89.18 | 10.82 |
6 | 4.97 | 95.97 | 4.03 | 6 | 9.21 | 87.06 | 12.94 | 6 | 14.27 | 89.37 | 10.63 |
7 | 5.30 | 84.84 | 15.16 | 7 | 9.22 | 86.99 | 13.01 | 7 | 14.29 | 89.23 | 10.77 |
8 | 5.48 | 84.93 | 15.07 | 8 | 9.22 | 86.99 | 13.01 | 8 | 14.56 | 89.20 | 10.80 |
9 | 5.52 | 85.15 | 14.85 | 9 | 9.22 | 86.99 | 13.01 | 9 | 14.58 | 89.14 | 10.86 |
10 | 5.66 | 83.77 | 16.23 | 10 | 9.22 | 86.99 | 13.01 | 10 | 14.68 | 89.08 | 10.92 |
11 | 5.67 | 83.77 | 16.23 | 11 | 9.22 | 86.99 | 13.01 | 11 | 14.72 | 88.62 | 11.38 |
12 | 6.04 | 76.15 | 23.85 | 12 | 9.22 | 86.99 | 13.01 | 12 | 14.74 | 88.47 | 11.53 |
13 | 6.06 | 76.19 | 23.81 | 13 | 9.22 | 86.99 | 13.01 | 13 | 14.76 | 88.50 | 11.50 |
14 | 6.09 | 75.80 | 24.20 | 14 | 9.22 | 86.99 | 13.01 | 14 | 14.76 | 88.48 | 11.52 |
15 | 6.17 | 74.07 | 25.93 | 15 | 9.22 | 86.99 | 13.01 | 15 | 14.80 | 88.44 | 11.56 |
16 | 6.19 | 73.63 | 26.37 | 16 | 9.22 | 86.99 | 13.01 | 16 | 14.81 | 88.38 | 11.62 |
17 | 6.20 | 73.59 | 26.41 | 17 | 9.22 | 86.99 | 13.01 | 17 | 14.81 | 88.38 | 11.62 |
18 | 6.24 | 73.50 | 26.50 | 18 | 9.22 | 86.99 | 13.01 | 18 | 14.81 | 88.33 | 11.67 |
19 | 6.32 | 74.02 | 25.98 | 19 | 9.22 | 86.99 | 13.01 | 19 | 14.82 | 88.30 | 11.70 |
20 | 6.32 | 73.98 | 26.02 | 20 | 9.22 | 86.99 | 13.01 | 20 | 14.82 | 88.30 | 11.70 |
Appendix 7. Variance decomposition of IPC25 (VAR Model with TFP and IP25)
Variance Decomposition of IPC25: | |||
|---|---|---|---|
Period | S.E | IPC25 | TFP |
1 | 9.26 | 100.00 | 0.00 |
2 | 9.31 | 99.02 | 0.98 |
3 | 9.72 | 91.07 | 8.93 |
4 | 10.01 | 86.88 | 13.12 |
5 | 10.02 | 86.78 | 13.22 |
10 | 11.15 | 76.04 | 23.96 |
15 | 12.45 | 67.74 | 32.26 |
20 | 12.6 | 67.21 | 32.79 |
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Epicoco, M., Jaoul-Grammare, M. & Plunket, A. Radical technologies, recombinant novelty and productivity growth: a cliometric approach. J Evol Econ 32, 673–711 (2022). https://doi.org/10.1007/s00191-022-00768-5
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DOI: https://doi.org/10.1007/s00191-022-00768-5
Keywords
- Radical technologies
- Recombinant novelty
- Productivity growth
- Cliometrics
- Granger’s causality
- Technological revolutions
- Long-run economic development