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Skill transferability and the stability of transition pathways- A learning-based explanation for patterns of diffusion

Abstract

Understanding and governing technology transitions is essential to cope with major challenges of the 21st century such as climate change or digitization. In this paper, a learning-based approach is developed to explain the dynamics of different transition pathways. Technological know-how is necessary to make effective use of technical innovations embodied in capital. Firms and employees accumulate technology specific knowledge when working with specific machinery. Radical innovation differs by technology type and pre-existing knowledge may be imperfectly transferable across types. This paper addresses the implications of cross-technology transferability of skills for firm-level technology adoption and its consequences for the direction of macro-level technological change. A microeconomically founded model of technological learning is introduced. The model is based on empirical and theoretical insights from the innovation literature. In a simulation study using the macro-economic ABM Eurace@unibi-eco and applied to the context of green 2 technology diffusion, it is shown that a high transferability of knowledge has ambiguous effects. It accelerates the diffusion process initially but comes at the cost of long-term technological stability and specialization. For firms, it is easy to adopt new technology, but also easy to switch back to the incumbent type. Technological instability can be macroeconomically costly.

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Notes

  1. The technical paradigm is more narrowly defined and represents the mindset of engineers and their way of defining a technological problem and its solution.

  2. Note that this is a technology race between competing technologies in the presence of increasing returns à la Arthur (1989). It should not be confused with a patent race where multiple innovators compete for first attaining a patent for a given technological problem (e.g. Doraszelski 2003).

  3. In another paper, it is shown that this framework can be generalized to a green entrant technology that is favorable because consumers have a higher willingness to pay for green products or lower production costs of green machinery (Hötte 2020b). Other models in climate and environmental economics frame the problem of green technology diffusion as an externality problem with unequal social and private costs. This study is aimed at improving the understanding of learning when a new technology suffers from lower maturity. Climate research has sufficiently shown that the problem of climate economics is not the search for optimal abatement levels trading off costs and benefits of mitigation. Rather, it is important to understand how the transition can be accelerated (cf. Steffen et al. 2018; IPCC 2018). Moreover, the trade-off of mitigation costs and benefits is extremely sensitive to the assumptions of technological change (Löschel 2002). This underlines the relevance of improving the understanding of change. However, it is possible to extend the model and to incorporate climate-induced damage functions and to frame the analysis as an optimal policy problem that can be numerically approached.

  4. This example has actually a historical counterpart, when delivery services for milk, bread or postal services used electric vehicles in the 60-70s (Høyer 2008). A similar example refers to the diffusion of organic farming, which was mainly driven by consumer preference, but lacking experiences in farming practices, regulatory compliance procedures and marketing were reasons for re-conversion to conventional farming (Flaten et al. 2010).

  5. Further information about its computation and relation to other convergence measures can be found in the Supplementary Material SM.II.

  6. The average level of green technology use in T does not necessarily coincide with the transition frequency. The average share of green (conventional) technology use may range well below 100% in the subset of green (conventional) regimes. The average \({\nu ^{c}_{t}}\) accounts for {34.16%, 29.80%, 64.20%} for χdist = 0, 0.5, 1.

  7. It should be noted that the pricing mechanism for capital goods is important for the convergence. In the model, capital prices are adaptively adjusted in response to changes in the relative demand. The frontier is only a stabilizing mechanism if relative technological progress is faster than the relative increase in nominal prices. This is an assumption but alternative configurations are possible in which the economy does not converge because the leading technology becomes too expensive. This might be the case if, for example, resources to produce capital goods are scarce and type-specific. In other words, this mechanism is sensitive to the price elasticity of capital goods supply. In these simulations, the price responsiveness is sufficiently moderate that convergence is possible even when spillovers are perfect.

  8. A technical explanation of this experiment is available in the A4.3 and a short discussion of the results can be found in the SM.III.2.

  9. Further information about its computation is available in the SM.II.

  10. More detail about the IV approach, alternative model specifications and results can be found in SM.II and Hötte (2019d).

  11. Technical detail can found in SM.II.

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Acknowledgments

I want to thank two anonymous reviewers for their valuable feedback and careful revisions that helped me to improve this work significantly. Moreover, gratitude is also owed to Herbert Dawid, Philipp Harting and Antoine Mandel who made this work possible. The author thankfully acknowledges financial support by the German National Academic Foundation, the Deutsch-französische Hochschule and the Bielefeld Graduate School of Economics and Management. This work uses a modified version of the Eurace@unibi model, developed by Herbert Dawid, Simon Gemkow, Philipp Harting, Sander van der Hoog and Michael Neugart, as an extension of the research within the EU 6th Framework Project Eurace.

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Correspondence to Kerstin Hötte.

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Appendix A: Simulation results

Appendix A: Simulation results

A.1 Settings

Table 3 Simulation settings

A.2 Baseline

Here, only some general features of the simulated time series data are shown. Information about the empirical validation is available in SM.I. More detail on this baseline simulations is available in Hötte (2019b). A longer discussion of a similar simulation is provided in Hötte (2020a). A difference to the simulations in Hötte (2020a) is given by lower diffusion barriers of the green technology and another specification of the learning function. The simulation model, the simulated data and a selection of results of descriptive statistics is available in a separate data publication (Hötte 2019d).

In Fig. 7, the time series are dis-aggregated into green, conventional and so-called switching regimes. A simulation run is classified as switching regime if the diffusion process is very volatile, i.e. when \({\nu ^{c}_{t}}\) heavily fluctuates between low and high levels or when \({\nu ^{c}_{t}}\) has not converged to more than 90% or less than 10% in T (cf. Hötte 2020a). This is associated with uncertainty about the final technological state. The time series data illustrate that “technological uncertainty” is costly in terms of aggregate (log) output (Fig. 7j). It is associated with wasted resources because R&D and learning time are invested in a technology that becomes obsolete in the long run. This leads to a delayed technological specialization compared to the green or conventional regimes with a more clear-cut technological path selection. Figure 7d and e show that this is associated with a delayed divergence in relative knowledge stocks which is a reason and a result of uncertainty.

Fig. 7
figure 7

Time series of macroeconomic and technological indicators. Different line shapes indicate different regime types (: transition, : conv, : switch)

Figure 7g shows the evolution of relative prices for capital goods and Fig. 7h shows this price normalized by the relative productivity. Figure 7f illustrates the price for material resource inputs normalized by real wages. The price evolves such that it accounts for roughly 9.5% of wage costs for an average firm during the whole time horizon.

Figure 7c illustrates an alternative environmental performance measure, called eco-efficiency, that measures the environmental impact (here amount of natural resource inputs) per unit of produced output. The eco-efficiency also improves when the productivity performance in the lock-in regime improved by technical progress which is a relative decoupling of production from environmental damages. Principally, there can be a trade-off between the specialization in the conventional technology and the switch to green technology if the success of the transition is uncertain. However, modeling this trade-off is very sensitive to the modeling assumptions regarding the environmental impact, initial conditions about the available technology options and productivity improvements in both sectors. In this study, the focus is on replacement dynamics in a theoretical way which allows to be agnostic about assumptions that may critically affect such a trade-off analysis.

A two-sided Wilcoxon test indicates that the differences between green and conventional regimes a (cf. Fig. 7) are significant (see SM.I.3).

A.3 The ease of learning

The pace of relative technological learning is also dependent on the technological difficulty χint. If a technology is very easy to learn, i.e. χint = 0, the learning progress is independent of the time invested in learning which is proxied by \(\nu ^{c}_{i,t}\). If χint is high, the progress is sensitive to \(\nu ^{c}_{i,t}\), i.e. learning is more effective if employees work only with one technology type. In an experiment that is longer discussed in Hötte (2019c), it had been shown that χint is only of minor importance in the presence of cross-technology spillovers.

The impact of the difficulty on the learning speed is most critical in times when firms are transitioning to alternative technology. During a phase of technology change, a trade-off in the allocation of the learning time exists. This trade-off is more pronounced when a technology is difficult to learn. A technology that is easier to learn is associated with lower technology switching costs. This may have an ambiguous effect on green technology diffusion. It is easier to switch to green technology, but it is also easier to switch back if the difficulty is symmetric. Whether increasing returns to learning stabilize an ongoing diffusion process, depends on the extent to which the green technology is adopted in the first years.

The adoption in the early phase is facilitated by cross-technology spillovers reflected in a lower distance χdist. If the transferability is sufficiently low, increasing returns to learning contribute to the stabilization of the technological regimes.

A.4 Interactions between spillovers and the ease of learning

In the Monte-Carlo experiment in Section 4.3, the learning parameters are drawn at random, i.e. χdist ∈ [0, 1] and χint ∈ [0, 2]. Diffusion barriers at the day of market entry are fixed at a level of 3% (βA = βb = .03) as before. The transition probability accounts for 64%.

In Table 4, means and standard deviation of the initialization are summarized as aggregate and dis-aggregated by regime subsets. The p-value in the last column indicates whether the difference in means between the two regime types is significant. The average mean of the distance χdist is significantly lower in the subset of green regimes. The difference in the χint is only weakly significant at a 10% level. Some general descriptive information of these simulations is provided in SM.III.2.

Table 4 Initialization of learning parameters

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Hötte, K. Skill transferability and the stability of transition pathways- A learning-based explanation for patterns of diffusion. J Evol Econ 31, 959–993 (2021). https://doi.org/10.1007/s00191-020-00710-7

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Keywords

  • Technological transition
  • Technology diffusion
  • Technological knowledge
  • Knowledge spillover
  • Learning
  • Absorptive capacity
  • Agent-based model

JEL Classification

  • O11
  • O33
  • D21
  • Q55
  • C63