The scope of the present paper is to answer the question of whether industrial robots are a GPT.
To answer this question on a macro level, a panel data analysis for nine European countries was conducted to test different characteristics of a GPT according to the literature. Each of the six research questions could be linked to a specific regression model and the coefficients tend in the expected directions. Firstly, robots increase labour productivity (M1) but have no effect on the level of Division of Labour (DoL) (M3). The latter point reflects the idea that a GPT does not lead to a concentration in some industries but evenly spreads its productivity gains throughout the economy. These results have to be interpreted cautiously because the overall explanatory power of the model was very low, indicating that the output measure is not affected by any of the regressors. Still, both results show that pervasiveness is (already) caused by robots. Additionally, model M2 demonstrated that robots are not only an example of an innovative input, but that robots themselves are inherently driven by technical progress. By implementing an innovative, novel measure of technical progress (the Robot Task Intensity Index - RTII) within the operational stock of robots, the technical improving nature of robots is demonstrated (M2). This improvement effect remains stable even when Software Capital is included in the regression model. Interestingly, Software Capital could also be identified as an enhancing force increasing returns to scale from robots. The Scherer approach (M4) showed that there are returns to scale for the full specified model, i.e., using all types of capital. For the purpose of defining robots as GPTs, this finding is sufficient as, as the discussion in Section 2 indicates the existence of high returns to scale speak against technologies being considered a GPT. Nonetheless, the effect seems moderate which corresponds to the finding in the time-split regression model (M5). It was shown that productivity gains from robots slowed down for the time period between 2005 and 2015 in contrast to the period between 1995 and 2004. This particular finding is a clear indication in support of the idea that robots can already be considered a GPT. Finally, yet importantly, robots give rise to innovational complementarities as they lead to capital deepening but not to capital augmentation for the process of labour productivity creation (M6).
Overall, none of the six hypotheses developed in order to investigate specific characteristics of a GPT on a macro level could be rejected. Although the results speak in favour of robots being a GPT, several aspects have to be mentioned critically: As a macro perspective was chosen, the appearance and size of potential spillovers from robots could not explicitly be accounted for, which is a crucial part of the understanding of a GPT. Additionally, it was not possible to distinguish between the types of returns to scale (i.e., whether due to Dynamic vs. Static Externalities). Hence, in future research, the relationship between a GPT and industrial robots can be elaborated in more detail, for example by using firm-level data, or by considering trade relations among the most robotized European countries. Nevertheless, the study revealed the impact that robots have on the development of labour productivity. For policymakers, the combined enhancement of modern robot technologies as well as sophisticated software applications, which serve as complementary innovations to industrial robots, appear to be effective tools to generate growth in outcome and labour productivity among European countries and thus to counteract the productivity slowdown.
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Appendix
Table 16: Task Categories by Field of Application No. task Id. No. Task-Description 1 111 Metal casting
2 112 Plastic moulding
3 113 Stamping forging, bending
4 114 Handling operations at machine tools 5 115 Machine tending for other processes 6 116 Measurement, inspection, testing
7 117 Palletizing
8 118 Packaging, picking, placing
9 119 Material handling
10 120 Handling operations unspecified
11 161 Arc welding
12 162 Spot welding
13 163 Laser welding
14 164 other welding
15 165 Soldering
16 166 Welding unspecified
17 171 Painting and enameling
18 172 Application of adhesive, sealing material
19 179 Others dispensing/spraying
20 180 Dispensing unspecified
21 191 Laser cutting
22 192 Water jet cutting
23 193 Mechanical cutting/grinding/deburring
24 198 Other processing
25 199 Processing unspecified
26 201 Assembling
27 203 Disassembling
28 209 Assembling unspecified
29 901 Cleanroom for FPD
30 902 Cleanroom for semiconductors
31 903 Cleanroom for others
32 905 Others
33 999 Unspecified
Source: IFR (2017:41)
Table 17: Correlation Matrix Real
VA
Robots HEMPE CapCT CapIT CapSoft 𝜂 Real
VA
1
Robots 0.79 1
HEMPE 0.89 0.56 1
CapCT 0.67 0.92 0.41 1
CapIT 0.86 0.77 0.64 0.65 1
CapSoft 0.45 0.58 0.12 0.58 0.65 1
RTII 0.53 0.72 0.37 0.74 0.35 0.32 1
Source: Own calculations
Note: All Values are reported in diff logs.
Table 18: Correlation Matrix Real
VA
Robots HEMPE CapCT CapIT CapSoft 𝜂 Real
VA
1
Robots 0.87 1
HEMPE 0.56 0.76 1
CapCT 0.86 0.72 0.46 1
CapIT 0.86 0.47 0.52 0.57 1
CapSoft 0.68 0.63 0.7 0.65 0.96 1
RTII 0.6 0.68 0.56 0.39 0.29 0.155 1
Source: Own calculations
Note: Significance levels are stated in parentheses.
Bolded value indicates a potential multicollinearity issue as the variance inflation factor ( 𝑅2
1−𝑅2)
is greater than ten (Hair et al., 2014).
All Values are reported in diff logs per capita hours worked.
Table 19: Unit Root CADF Tests-Results (Intercept), α=5%
Table 20: Wooldridge's test for Serial Correlation in FE panels Tests-Results, α=5%
Source: Own calculations
Table 21: Pesaran Tests- of Cross-Sectional Dependence, α=5%
Real VA
P-Value < 2.2e-16 <2.2e-16 < 2.2e-16 <2.2e16 <2.2e-16 <2.2e-16 <2.2e-16
Independ. No No No No No No No
Source: Own calculations
Table 22: Breusch Pagan Heteroscedasticity Test-Results, α=5%
M1 M2 M3 M4 M5.1 M5.2 M6
Table 23: Hausman Test. α=5%
M1 M2 M3 M4 M5.1 M5.2 M6
P-Value 0.995 0.999 0.977 0.991 <7.9e-13 <2.2e-16 0.995
FE prior No No No No Yes Yes No
Note: If either heteroscedasticity or serial correlation is present, the variances of the FE and RE estimators are not valid and the corresponding Hausman test statistic is inappropriate (Baltagi, 2005). This was avoided by using solely stationary variables, which provided uncorrelated errors. For both M5 models only time effects were considered as otherwise the degrees of freedom were too low.
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