It has long been acknowledged that human capital is one of the key drivers of modern economic growth. Less clear, however, is which dimension of human capital matters most. From an historical perspective, Mokyr (2005) argues that beyond ‘intellectuals’, technicians, and engineers were the key to diffusion of knowledge, leading to technological progress. Indeed, in his classic article on endogenous growth, Romer (1990) emphasises the role of engineers. Who are these engineers and other technically trained workers – which we call ‘techies’ – and what sets them apart from other workers? As stated succinctly by Tambe and Hitt (2014), “the technical know-how required to implement new IT innovations is primarily embodied within the IT workforce”, i.e., IT techies. However, techies perform a much broader range of technology-related tasks beyond IT, as highlighted by the economic history literature (Kelly et al. 2014, Ben Zeev et al. 2017, Hanlon 2022, Maloney and Valencia Caicedo 2017), and as we document below. Yet, relatively little is known about the role of techies in driving firm-level productivity, especially across different tasks and broad sectors of the economy.
Previous work on firm-level productivity has emphasized research and development (R&D) expenditures, learning by exporting, and management (Doraszelski and Jaumandreu 2013, De Loecker 2013, Bloom et al. 2017, respectively). In a recent paper (Harrigan et al. 2023) we analyse a novel source of firm-level productivity: the techies.
We show that techies raise firm-level productivity. This positive effect extends beyond techies who do R&D: techies who work with information and communication technology (ICT) and other technical tasks equally affect firm-level productivity growth. We also show that these effects are important not only in manufacturing but also in non-manufacturing industries.
The power of techies in raising firm-level productivity
We provide a comprehensive description of techies based on detailed administrative and survey datasets from the French national statistical institute, INSEE. We identify techie workers by using French occupational classifications. Techie jobs are distinguished from other occupations because they relate to the installation, management, maintenance, and support of ICT, product and process design, longer-term R&D activities, and other technology-related tasks. INSEE’s documentation highlights the difference between techies and general management, or production occupations.
Fact 1: Techies across industries
Techies account for 18.3% of the private sector's wage bill in France, and work in all industries. Manufacturing industries employ the majority of techies, but a substantial share (over a third) are found in non-manufacturing firms. This highlights the importance of considering the non-manufacturing sector when studying the impact of techies on productivity. Among techies, the "Other" category holds the largest share of the techie wage bill, indicating the diverse tasks of techies beyond R&D and ICT. While 87% of R&D techies are employed in the manufacturing sector, most ICT techies are employed in non-manufacturing firms. Therefore, focusing solely on R&D in manufacturing (as most studies do) risks providing an incomplete picture of the overall influence of techies across industries.
Fact 2: Techies have more STEM education and training than other occupations
Deming and Noray (2018) argue that “STEM jobs are the leading edge of technology diffusion in the labor market”. Indeed, we find that techies possess a higher level of STEM education and training than workers in other occupations. Approximately 63% of techies have a STEM degree and/or training. In contrast, workers in non-techie occupations have much lower levels of STEM education, in particular general management. This indicates the specialised technical knowledge and skills that techies possess, emphasizing their unique technology-related role.
Fact 3: Most R&D spending is on wages and occurs ‘in-house’
Most R&D spending is conducted within the firm and is spent on wages. Firms allocate 70% of their R&D budget to pay the salaries of their R&D personnel, on average. This underscores the importance of the human capital component within the R&D process. The vast majority do not outsource any R&D services. In-house R&D give firms direct control over the development and implementation of innovations. Therefore, fostering a skilled techie workforce within the company is crucial for performing R&D.
Fact 4: Techies are positively associated with the diffusion of ICT skills within firms
Techies are associated with the diffusion of ICT skills within firms: there is a strong positive association between the employment of techies and the likelihood of firms offering ICT training. This effect is mainly driven by ICT techies rather than R&D and other techies.
Fact 5: Techies are positively associated with patenting and innovation
Techies, in particular R&D techies, are positively correlated with firms' patent filings and product and process innovation. R&D and ICT techies are associated with product innovation, while R&D and Other techies are associated with process innovation. This suggests distinct roles for each type in enhancing productivity in firms.
Techies’ ability to adopt, manage, and diffuse technology within firms sets them apart as key contributors to productivity. Previous research has extensively studied the impact of R&D expenditure on firm, industry, and national outcomes. However, our analysis goes beyond R&D techies and explores the influence of all techies, including those engaged in ICT and other technical tasks. Remarkably, we find that firms that employ techies experience significantly higher future productivity compared to those without techies.
Unleashing the productivity potential
To estimate firm-level total factor productivity and identify the causal effects of techies on productivity we apply state of the art methodology in structural production function estimation. In particular, we use two recent structural production function estimators: one developed by Grieco et al. (2016) and the other by Gandhi et al. (2020). We construct a firm-level unbalanced panel using administrative data, encompassing manufacturing and non-manufacturing firms in 2011-2019. This panel provides information on capital, labour, expenditure on materials, revenue, and an indicator for exporting. We estimate structural models focusing on firm-level total factor productivity and the causal effect of techies on productivity.
Our econometric strategy relies on two main assumptions. First, we assume that techies have a lagged impact on Hicks-neutral productivity. Second, we assume that techies influence output solely through their effect on future productivity and not through any immediate contribution to factor services affecting current output. This approach is analogous to economists' understanding of current investment spending and R&D expenditure, which primarily influences future outcomes rather than immediate output. By employing a flexible specification of the firm's productivity process, we make causal statements about the effects of R&D, ICT, and other techies' employment on firm-level productivity.
We find that firms employing techies experience significantly higher future productivity compared to those without techies. The presence of techies causes a 4–5% increase in productivity a year later, with a long-term effect exceeding 48% in non-manufacturing industries and over 57% in manufacturing firms. These estimates are driven both by extensive margins (firms that employ techies versus those that don’t) and by intensive margins (within firms that employ techies).
We find a positive effect of R&D techies in manufacturing, which is consistent with findings in Doraszelski and Jaumandreu (2013). In addition, we find that the positive impact of techies on productivity extends beyond R&D, with ICT and other techies also positively influencing productivity in both manufacturing and non-manufacturing sectors. These findings on the cross-sector importance of ICT techies corroborate those in Dhyne et al. (2020), but with a more structural approach and a more comprehensive view on technology adoption. Interestingly, R&D techies do not significantly contribute to productivity in non-manufacturing firms. Furthermore, both engineers and relatively less qualified technicians have a positive effect on firm productivity in both sectors, with engineers exhibiting a larger influence than technicians.
In conclusion, techies play a crucial role in raising firm-level productivity. Their diverse skill sets, encompassing R&D, ICT, and other technical tasks, significantly enhance productivity across industries. In ongoing work (Harrigan et al. 2018) we document that techies have, through their impact on biased technological change, a significant impact on relative demand for skill.
Policymakers should recognise the importance of nurturing a skilled techie workforce and promoting technology adoption within firms. By harnessing the power of techies, societies can unlock their full productivity potential. Importantly, this may require balancing investment in basic science and innovation with a sufficiently broad technologically-trained workforce that can implement innovations and technology in the firms that employ them.
This dovetails with the conclusion of Jacques Bughin in his 2019 Vox column that Europe’s gap in AI technology diffusion is not driven by lack of innovation (quite the contrary, much of AI innovation is based in Europe); diffusion of AI technologies lags in Europe due to a shortage of appropriately-trained workers and lack of investment – areas in which the public sector can play a role.
References
Ben Zeev, N, J Mokyr and K Van Der Beek (2017), “Flexible supply of apprenticeship in the British industrial revolution”, The Journal of Economic History 77(1): 208-250.
Bloom, N, R Sadun and J Van Reenen (2017), “Management as a Technology?”, NBER Working Paper No. 22327.
Bughin, J (2019), “How to develop enough European AI startups”, VoxEU.org, 26 February.
Deming, D J and K L Noray (2018), “STEM careers and technological change”, NBER Working Paper No. 25065.
Dhyne, E, J Konings, J Van den Bosch and S Vanormelingen (2020), “The return on information technology: Who benefits most?”, Information Systems Research 32(1): 194-211.
Doraszelski, U and J Jaumandreu (2013), “R&D and productivity: Estimating endogenous productivity”, Review of Economic Studies 80(4): 1338-1383.
Grieco, P L, S Li and H Zhang (2016), “Production function estimation with unobserved input price dispersion”, International Economic Review 57(2): 665-690.
Hanlon, W W (2022), “The rise of the engineer: Inventing the professional inventor during the Industrial Revolution”, NBER Working Paper No. 29751.
Harrigan, J, A Reshef and F Toubal (2018), “Techies, trade, and skill-biased productivity", CEPR Discussion Paper 15815 (also see the Vox Column here).
Harrigan, J, A Reshef and F Toubal (2023), “Techies and firm level productivity”, CEPR Discussion Paper No. 18183.
Kelly, M, J Mokyr and C Ó Gráda (2023), “The mechanics of the Industrial Revolution”, Journal of Political Economy 131(1): 59-94.
Maloney, W F and F Valencia Caicedo (2017), “Engineering growth: Innovative capacity and development in the Americas”, CESifo Working Paper No. 6339.
Mokyr, J (2005), “The intellectual origins of modern economic growth”, The Journal of Economic History 65(2): 285-351.
Romer, P M (1990), “Endogenous technological change”, Journal of Political Economy 98(5, Part 2): S71-S102.
Tambe, P and L M Hitt (2014), “Job hopping, information technology spillovers, and productivity growth”, Management Science 60(2): 338-355.
