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.
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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