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A tale of two technological capabilities: economic growth revisited from a technological capability transition perspective

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Abstract

This study revisits economic growth from the perspective of technological capability types and the transition related to growth slowdowns in middle-income countries. The objectives of this study are twofold. First, we reveal the development pattern of national technological capabilities in the global market by means of two capability indices. Second, we investigate the heterogeneous contribution of these indices to economic growth by income level. To this end, we first propose an analytical framework that evaluates two types of technological capabilities, that is, implementation capability and design capability, developed by different knowledge types and learning modes. Using the calculated indices with a sample of 42 countries from 1996 to 2016, we conduct econometric analysis via Granger causality testing between the two capability indices, a dynamic panel regression of the global connections on the capability development, and a panel quantile regression on income per capita. Our results show that: (1) the sequential pattern of national technological capability development from the implementation-based to the design-based; (2) a positive influence of higher global connections on the capability development; and (3) an increasing contribution of design capability towards economic growth but a decreasing contribution of implementation capability when approaching the higher quantiles of the income level. Finally, our study’s implications concern the process of sustained economic growth and the fundamental cause of the middle-income trap—specifically, the need for capability transition.

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Notes

  1. For reference, we summarized the well-known indices of national technological capability and innovation performances in Table 9, based on data from international organizations and collective agencies.

  2. Here, “building” is explained in a similar context to the “knowledge building” literature (Scardamalia and Bereiter 2010).

  3. We performed the Levin–Lin–Chu (LLC) test for the null hypothesis of a unit-root (or non-stationarity) in each index (Levin et al. 2002), and the result is shown in Table 12. In the case of the variable IC, the bias-adjusted test statistics is − 2.51 (or − 2.13 when removing the cross-sectional mean), rejecting the null hypothesis at the 5% significance level. Similarly, the test statistics from the case of DC is − 2.11 (or − 3.86 when removing the cross-sectional mean), rejecting the null hypothesis at the 5% significance level. This result, therefore, implies that Granger causality between IC and DC could only be interpreted as a short-run causality under Eq. 3. For reference, it should be noted that the null hypothesis for both IC and DC could not be rejected at the 1% or lower significance levels. In this case, one might consider an alternative approach to the Granger causality test between non-stationary (or non-stationary but cointegrated) variables.

  4. In detail, we set GMM type instruments of log_GDPpci,t and standard instruments of ICi,t and DCi,t for the differenced equation. As a result of the Arellano-Bond test for no autocorrelation hypothesis in first-differenced errors, we confirmed that our model is free from misspecification.

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Acknowledgements

This work has supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2017R1A2B4009376).

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  1. Jung-In Yeon

    Present address: Sustainable Growth Initiative (SGI), The Korea Chamber of Commerce and Industry, 39, Sejongdaero, Jung-gu, Seoul, 04513, Korea

Authors and Affiliations

  1. Technology Management, Economics and Policy Program, Seoul National University, Bldg. 37, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Korea

    Jung-In Yeon & Jeong-Dong Lee

  2. Department of International Trade, Duksung Women’s University, Chamirisa Bldg. 33, Samyang-ro 144-gil, Dobong-gu, Seoul, 91369, Korea

    Chulwoo Baek

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Appendix

Appendix

See Table 9, 10, 11 and 12.

Table 9 Review of other indices to measure national technological capability and performances in literature
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Table 10 Description of the technological capability index and corresponding indicators
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Table 11 Summary statistics of the data after the treatments, that is, imputation and normalization
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Table 12 Levin–Lin–Chu unit-root test result for IC and DC
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Yeon, JI., Lee, JD. & Baek, C. A tale of two technological capabilities: economic growth revisited from a technological capability transition perspective. J Technol Transf 46, 574–605 (2021). https://doi.org/10.1007/s10961-020-09809-2

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