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Intangible assets and the efficiency of manufacturing firms in the age of digitalisation: the Russian case

Yulia Turovets
Yulia Turovets
   | 07 may 2021
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Publicado en línea: 07 may 2021
Páginas: 7 - 26
Recibido: 10 nov 2020
Aceptado: 28 feb 2021
DOI: https://doi.org/10.2478/emj-2021-0001
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© 2021 Yulia Turovets, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1

Framework of the study in the context of productivity analysisSource: elaborated by the author based on Kumbhakar and Fuss, 2000; Coelli et al., 2003; Corrado, Hulten and Sichel, 2005; Borras and Edquist, 2013.
Framework of the study in the context of productivity analysisSource: elaborated by the author based on Kumbhakar and Fuss, 2000; Coelli et al., 2003; Corrado, Hulten and Sichel, 2005; Borras and Edquist, 2013.

Fig. 2

Distribution of technical efficiency for the full sample with inefficiency determinants
Distribution of technical efficiency for the full sample with inefficiency determinants

Fig. 3

Distribution of technical efficiency for the firms from high-tech sectors with inefficiency determinants
Distribution of technical efficiency for the firms from high-tech sectors with inefficiency determinants

Fig. 4

Distribution of technical efficiency for the firms from low-tech sectors with inefficiency determinants
Distribution of technical efficiency for the firms from low-tech sectors with inefficiency determinants

Fig. 5

Average technical efficiency dynamic in 2009–2018 for the full sample modelNote: TE — technical efficiency for the full sample, TE high-tech — technical efficiency for the firms of the high-tech sectors, TE low-tech — technical efficiency for the firms of the low-tech sectors.
Average technical efficiency dynamic in 2009–2018 for the full sample modelNote: TE — technical efficiency for the full sample, TE high-tech — technical efficiency for the firms of the high-tech sectors, TE low-tech — technical efficiency for the firms of the low-tech sectors.

Estimations of time-invariant (TI) and time-variant (TVD) models by the two time periods and the groups of firms

Model 1 TI-model without trend and inefficiency determinants for high-tech firmsModel 2 TI-model without trend and inefficiency determinants for low-tech firmsModel 3 TVD-model for high-tech firmsModel 4 TVD-model for high-tech firms after 2014Model 5 TVD-model for low-tech firmsModel 6 TVD-model for low-tech firms after 2014
Production frontier (dependent variable ln_y)
Number of observations1,4721,44314727701443764
ln_fa_real0.107*** (0.014)0.073*** (0.007)0.064*** (.018)0.076*** (0.010)0.047*** (0.016)0.033** (0.014)
ln_assets0.339*** (0.016)0.430*** (0.019)0.294*** (0.027)0.462*** (0.029)0.42*** (0.028)0.311*** (0.030)
ln_l0.552*** (0.023)0.483*** (0.024)0.642*** (0.038)0.450*** (0.038)0.558*** (0.032)0.522*** (0.033)
ln_ita_real0.010*** (0.004)0.014*** (0.003)0.014*** (0.005)0.014*** (0.005)0.004 (0.005)0.005 (0.006)
t0.071 (0.045)0.039*** (0.008)0.234*** (0.031)0.016 (0.014)
const4.542*** (0.202)4.729*** (0.186)6.148 (0.651)4.281*** (0.284)2.920*** (0.318)6.878*** (0.336)
eta−0.047** (0.019)−0.042*** (0.012)−0.240*** (0.022)0.001 (0.012)
−1.373*** (0.067)−2.219 (0.102)
−0.136** (0.070)0.116 (0.056)

Strategies and policy tools to support digitalisation according to the technology intensity of firms

High-tech firmsLow-tech firms
Develop an intangible technology asset<italic>Effects</italic>

Frontier shift ++

Efficiency change −

<italic>Policy tools</italic>

Grants

Venture capital

Tax incentives

Preferential loans

Standardisation

Testbeds

Regulatory sandbox

<italic>Effects</italic>

Frontier shift +

Efficiency change +

<italic>Policy tools</italic>

Grants

Tax incentives

Preferential loans

Standardisation

Testbeds

Acquire an intangible technology asset<italic>Effects</italic>

Frontier shift −

Efficiency improvement ++

<italic>Policy tools</italic>

Tax incentives

Preferential loans

Technology transfer centres

Guidelines and information platforms

<italic>Effects</italic>

Frontier shift +

Efficiency improvement ++

<italic>Policy tools</italic>

Tax incentives

Preferential loans

Technology transfer centres

Guidelines and information platforms

Panel Estimation of the Stochastic Production Frontier for the full sample of firms

Model 1 (stochastic frontier without inefficiency determinants)Model 2 (stochastic frontier with inefficiency determinants)Model 3 (stochastic frontier with inefficiency determinants before 2014)Model 4 (stochastic frontier with inefficiency determinants after 2014)
1. Production frontier (dependent variable ln_y)
Number of observations2,9152,9151,3811,534
ln_fa_real0.106*** (0.007)0.106*** (0.007)0.124*** (0.011)0.096*** (0.009)
ln_assets0.347*** (0.013)0.354*** (0.013)0.325*** (0.019)0.381*** (0.018)
ln_l0.561*** (0.016)0.557*** (0.017)0.507*** (0.026)0.591*** (0.023)
ln_ita_real0.019*** (0.006)−0.011* (0.007)−0.018 (0.011)−0.023 (0.017)
t0.016** (0.006)0.017* (0.009)0.008 (0.031)0.082*** (0.024)
ita_t−0.003*** (0.001)−0.003*** (0.001)−0.004 (0.004)−0.001 (0.003)
const4.732*** (0.136)4.753*** (0.135)5.294*** (0.201)3.758*** (0.242)
2. Inefficiency equation (dependent variable)
ln_ita_real_t−0.108*** (0.011)−0.178*** (0.021)−0.065*** (0.013)
t−0.002*** (0.016)−0.177*** (0.066)0.235*** (0.051)
const0.072 (0.048)0.362 (0.084)0.806*** (0.137)−1.583*** (0.403)
3. Stochastic noise (dependent variable)
const−1.495*** (0.058)−1.424 (0.014)−1.336 (0.074)−1.447 (0.094)

Panel Estimation of the Stochastic Production Frontier for sub-samples of firms by R&D expenditures and R&D intensity

Model 5 (stochastic frontier for the firms of high-tech sectors)Model 6 (stochastic frontier for the firms of low-tech sectors)Model 7 (stochastic frontier for the firms with R&D expenditures)Model 8 (stochastic frontier for the firms without R&D expenditures)
Production frontier (dependent variable ln_y)
Number of observations1,4721,4437342,181
ln_fa_real0.106*** (0.017)0.112*** (0.007)0.109*** (0.013)0.076*** (0.007)
ln_assets0.426*** (0.028)0.332*** (0.015)0.350*** (0.016)0.397*** (0.019)
ln_l0.599*** (0.034)0.552*** (0.02)0.531*** (0.023)0.522*** (0.025)
ln_ita_real−0.120*** (0.026)−0.021*** (0.009)−0.019** (0.009)0.008 (0.008)
t0.004 (0.048)−0.015 (0.009)0.026* (0.013)0.021** (0.009)
ita_t0.003 (0.004)0.001 (0.001)−0.0001 (0.001)−0.004*** (0.001)
const4.021*** (0.377)5.082*** (0.149)4.838*** (0.190)4.922*** (0.17)
Inefficiency equation (dependent variable)
ln_ita_real_t−0.262*** (0.046)−0.158*** (0.018)−0.125*** (0.016)−0.102*** (0.012)
t0.296*** (0.057)−0.112*** (0.023)−0.008 (0.023)−0.036*** (0.021)
const−0.030 (0.495)0.668*** (0.096)0.373*** (0.119)0.445*** (0.10)
Stochastic noise (dependent variable)
const−1.568 (0.111)−1.315 (0.017)−1.336 (0.065)−2.082 (0.018)
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