Dimensions of human capital and technological diffusion

Abstract

We examine the impact of a comprehensive set of measures of human capital on recently created, direct measures of technology adoption using country-level panel data for the period 1964–2003, covering a wide range of technologies in various sectors of the economy. We consider many dimensions of human capital, using both qualitative and quantitative measures, as well as indirect measures that capture the role of “learning by doing” intrinsic to the process of technological diffusion. Our analysis, which examines the human capital and technological diffusion link more comprehensively relative to previous studies, suggests that the link is a conditional one, resting on various aspects of human capital and the nature of the technology in question. Overall, the results suggest that the type of human capital that is formed via the learning-by-doing mechanism may be the most important determinant of technological diffusion, followed by, to a substantially less degree, qualitative determinants such as cognitive skills (measured using test scores) and quantitative or other measures (such as years of schooling and life expectancy). Our conclusions are robust to the inclusion of institutional variables and other factors that determine technological diffusion.

This is a preview of subscription content, access via your institution.

Fig. 1

Notes

  1. 1.

    The Cross Country Historical Adoption of Technology (CHAT) data set captures both the extensive and intensive margins of 104 technologies from 8 sectors for a sample of more than 150 countries, over a period of 1800–2000.

  2. 2.

    Our paper complements the earlier work by Cinnirella and Streb (2017) who explore the association between different types of quantitative measures of human capital and technological innovation for Prussia in the late nineteenth century, in addition to “training-on-the-job”, an aspect which our results suggest is a very important determinant of technological diffusion. In addition, our paper relates to the literature on growth and innovations which employs measures of human capital such as educational attainment and knowledge as among key factors impacting upon the innovation process and growth of economies (Drivas et al. 2018; Dakhli and De Clercq 2004; Barro 2001). In contrast, however, we employ qualitative constructs of human capital such as cognitive skills. Furthermore, our analysis is more comprehensive in scope, looking at various direct measures of technologies over time and across countries.

  3. 3.

    This may be justifiable in the sense that the mathematics test consists of basic mathematical knowledge applied to set of analytical problems. The science test, in contrast, is more knowledge specific rather than analytical. Of course, this may be contentious and the reader may not agree with our interpretation. Our choice of the labels “specific” and “generic”, however, proves convenient as well as intuitive in the context of discussing and interpreting the results to follow. Our variable embodying generic skills is a proxy for analytical skills of a very general level.

  4. 4.

    In addition to our reasoning above, Comin et al (2008) suggest that past level of technology adoption is a strong predictor of current levels; as such a dynamic specification is appropriate. In Comin and Hobijn (2004), which to our knowledge is the only other study analyzing the impact of human capital on technology measures based on the CHAT data set, the lagged variable is not considered and the focus is on quantitative measures of human capital such as secondary school enrollment.

  5. 5.

    Comin and Hobijn (2009a, b) refer to the definition of “technology” in Merriam-Webster’s Collegiate Dictionary. It defines technology as “a manner of accomplishing a task especially using technical processes, methods, or knowledge”. Given this definition the basic idea behind technological measures in CHAT is to cover these various aspects of technology. For example, it includes the quantity of capital goods required to achieve a specific task (e.g., number of sail ships (measured in tonnage) in use in a country), amounts of times a specific task that have been completed (e.g., metric tonnes of steel produced) and the number of users of a the specific manner in which the task was accomplished (e.g., number of subscribers of cable TV in a household).

  6. 6.

    Comin and Mestieri (2013) use a different theoretical construct for intensive margin of technology in their theoretical framework.

  7. 7.

    Comin and Mestieri (2013) suggest use of population or Gross Domestic Product as scaling factors.

  8. 8.

    As indicated earlier, our sample includes a set of 50 countries and includes both developed and developing countries. Here the choice of countries to illustrate the concept of technological lags is motivated purely by the ease of graphical presentation.

  9. 9.

    We have 21 technologies in our sample, but the tables in the paper include results for 14 technologies while regressions for other technologies are presented in the online appendix. The technologies used are selected from the Comin and Hobijn (2009a, b) paper.

  10. 10.

    For a detailed description of these technologies please refer to Comin and Hobijn (2009a, b) or part A of the supplementary online appendix to this paper.

  11. 11.

    The measure developed in Hanushek and Woessmann (2012) is an extension of Hanushek and Kimko (2000). Details for countries and tests are present in Hanushek and Woessmann (2012). The observations for mathematics and science test scores are sporadically spread across the time period 1963–2003. This is because, firstly, the tests are not conducted every year. Secondly, the countries may not participate in every test that is conducted. Hence, we have missing observations for mathematics and science test scores in our data set. In order to extrapolate observations for these years, we use averages of the available test scores for the countries in our sample.

  12. 12.

    Details of this qualitative measure of education, from Hanushek and Woessmann (2012), are also presented in part A of the online supplementary appendix.

  13. 13.

    It is possible that there are some technologies where other countries could be leaders. However, US leads in most cases and is hence chosen for consistency, as well as comparability with the Comin et al (2008) approach to measure usage lags of technologies.

  14. 14.

    In this estimation procedure, we instrument current variables at time t by their past lags, which eliminates correlation between explanatory variables and the error term. Furthermore, in our GMM estimations the use of these past lags as instruments may (effectively) control for the possible endogeneity of human capital acquisition employed as explanatory variable for adoption and diffusion of technology. However, we do not explicitly control for endogeneity of human capital due to our assumption that it is strictly exogenous, as per the assumptions underlying the difference-GMM approach. Given that the left-hand-side variable is a microeconomic, sectoral variable while our human capital measures are macroeconomic in flavor we believe this is a reasonable assumption. However, we perform robustness checks based on various diagnostic tests and use the system GMM approach of Blundell and Bond (2000) for all cases. The results for these are presented in part B of the online appendix.

  15. 15.

    We obtain data for life expectancy for the years 1964–2003 from World Development Indicators (WDI) of the World Bank (2015). It is measured as life expectancy at birth in total years. See www.worldbank.org for details.

  16. 16.

    We thank two anonymous referees for these suggestions.

  17. 17.

    Comin et al (2008) include technologies such as electricity production, internet, personal computers, telephones, cell phones, cars, trucks, passenger and cargo planes and tractors. We consider a larger set from the updated data set in Comin and Hobijn (2009a, b).

  18. 18.

    A complete sector-wise overview of these results including a larger set of technologies is available on request. The more succinct presentation of these results in the form of tables in the paper does not affect the overall findings and interpretation of the analysis.

  19. 19.

    In the sector-wise analysis, presented in the online appendix, we have 21 technologies each in the mathematics and science panels. The coefficient of mathematics and science skills is significant in 10 and 5 intensity of usage of technologies, respectively. Hence, we could suggest that in a broad sense a labor force embodied with mathematics skills is more suitable for improving the adoption of technology relative to on embodied with science skills. However, there are obvious caveats to such an interpretation as we would expect specialized skills, more directly measured, to impact in sectors where they were relevant. We discuss this caveat in further detail at the end of this section.

  20. 20.

    While such technologies do not require mathematics skills per se, their prevalence requires human capital in the form of qualified technicians and engineers to provide maintenance and technical support service, who are typically endowed with such skills. It is in this sense that we suggest that the generic nature of mathematics skills is relevant. Following Hanushek and Kimko (2000), we interpret these measures as an indirect proxy of the quality of the labor force of an economy.

  21. 21.

    Other factors, such as property rights associated with technological transfer, may be of relevance, as suggested by Spielman and Ma (2015).

  22. 22.

    For example, World Development Indicators (WDI) do not have such indicators for health. However, some country-specific studies do provide this information from their respective national databases (Ceppa et al. 2012).

  23. 23.

    Supplementary appendix B contains descriptive evidence regarding data used for analysis. A complete sector-wise overview of these results including a larger set of technologies is available on request. The more succinct presentation of these results in the form of tables in the paper does not affect the overall findings and interpretation of the analysis.

  24. 24.

    We use a system GMM approach given the persistent nature of the lagged variable; the Sargan test in the difference-GMM approach also suggested inappropriate identifying restrictions.

References

  1. Acemoglu D, Zilibotti F (2001) Productivity differences. Q J Econ 116(2):563–606

    Google Scholar 

  2. Acemoglu D, Johnson S, Robinson JA (2005) Institutions as a fundamental cause of long-run growth. Handbook Econ Growth 1:385–472

    Google Scholar 

  3. Ainsworth M, Over M (1994) AIDS and African development. World Bank Res Obs 9(2):203–240

    Google Scholar 

  4. Aitken BJ, Harrison AE (1999) Do domestic firms benefit from direct foreign investment? Evidence from Venezuela. Am Econ Rev 89(3):605–618

    Google Scholar 

  5. Arellano M, Bond S (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud 58(2):277–297

    Google Scholar 

  6. Barro RJ (1998) Human capital and growth in cross-country regressions. Harvard University, Cambridge

    Google Scholar 

  7. Barro RJ (2001) Human capital and growth. Am Econ Rev 91(2):12–17

    Google Scholar 

  8. Barro RJ (2013) Health and economic growth. Ann Econ Finance 14(2):329–366

    Google Scholar 

  9. Barro RJ, Lee JW (2010) A new data set of educational attainment in the world, 1950–2010. J Dev Econ 104:184–198

    Google Scholar 

  10. Barro RJ, Lee JW (2013) A new data set of educational attainment in the world, 1950–2010. J Dev Econ 104:184–198

    Google Scholar 

  11. Barro RJ, Sala-I-Martin X (1995) Economic growth theory. Mac Graw-Hill, New York

    Google Scholar 

  12. Basu S, Weil DN (1998) Appropriate technology and growth. Q J Econ 113(4):1025–1054

    Google Scholar 

  13. Benhabib J, Spiegel MM (1994) The role of human capital in economic development evidence from aggregate cross-country data. J Monet Econ 34(2):143–173

    Google Scholar 

  14. Blundell R, Bond S (2000) GMM estimation with persistent panel data: an application to production functions. Econom Rev 19(3):321–340

    Google Scholar 

  15. Branstetter L (2006) Is foreign direct investment a channel of knowledge spillovers? Evidence from Japan’s FDI in the United States. J Int Econ 68(2):325–344

    Google Scholar 

  16. Ceppa DP, Kosinski AS, Berry MF, Tong BC, Harpole DH, Mitchell JD, Onaitis MW (2012) Thoracoscopic lobectomy has increasing benefit in patients with poor pulmonary function: a Society of Thoracic Surgeons Database analysis. Ann Surg 256(3):487

    Google Scholar 

  17. Cinnirella F, Streb J (2017) The role of human capital and innovation in economic development: evidence from post-Malthusian Prussia. J Econ Growth 22(2):193–227

    Google Scholar 

  18. Comin D, Hobijn B (2004) Cross-country technology adoption: making the theories face the facts. J Monet Econ 51(1):39–83

    Google Scholar 

  19. Comin DA, Hobijn B (2009a) The CHAT dataset. National Bureau of Economic Research, Cambridge

    Google Scholar 

  20. Comin D, Hobijn B (2009b) Lobbies and technology diffusion. Rev Econ Stat 91(2):229–244

    Google Scholar 

  21. Comin D, Mestieri M (2013) Technology diffusion: measurement, causes and consequences. NBER Working Paper. 19052

  22. Comin D, Hobijn B, Rovito E (2008) Technology usage lags. J Econ Growth 13(4):237–256

    Google Scholar 

  23. Conley T, Udry C (2001) Social learning through networks: the adoption of new agricultural technologies in Ghana. Am J Agric Econ 83(3):668–673

    Google Scholar 

  24. Conley TG, Udry CR (2010) Learning about a new technology: pineapple in Ghana. Am Econ Rev 100(1):35–69

    Google Scholar 

  25. Dakhli M, De Clercq D (2004) Human capital, social capital, and innovation: a multi-country study. Entrepreneurship Reg Dev 16(2):107–128

    Google Scholar 

  26. Drivas K, Economidou C, Tsionas EG (2018) Production of output and ideas: efficiency and growth patterns in the United States. Reg Stud 52(1):105–118

    Google Scholar 

  27. Foster AD, Rosenzweig MR (1995) Learning by doing and learning from others: human capital and technical change in agriculture. J Polit Econ 103(6):1176–1209

    Google Scholar 

  28. Hanushek EA, Kimko DD (2000) Schooling, labor-force quality, and the growth of nations. Am Econ Rev 90(5):1184–1208

    Google Scholar 

  29. Hanushek EA, Woessmann L (2012) Do better schools lead to more growth? Cognitive skills, economic outcomes, and causation. J Econ Growth 17(4):267–321

    Google Scholar 

  30. Herzer D (2011) The long-run relationship between outward foreign direct investment and total factor productivity: evidence for developing countries. J Dev Stud 47(5):767–785

    Google Scholar 

  31. Hulten CR (2000) Measuring innovation in the New Economy. Unpublished paper, University of Maryland

  32. Jamison DT, Lau LJ, Wang J (1998) Health’s contribution to economic growth, 1965–90. Health, health policy and economic outcomes, 61–80

  33. Jovanovic B, Nyarko Y (1996) Learning by doing and the choice of technology. Econometrica 64(6):1299–1310

    Google Scholar 

  34. Krueger AB, Lindahl M (2001) Education for growth: why and for whom? J Econ Lit 39(4):1101–1136

    Google Scholar 

  35. Lahiri R, Ding J, Chinzara Z (2018) Technology adoption, adaptation and growth. Econ Model 70:469–483

    Google Scholar 

  36. Li X, Liu X, Parker D (2001) Foreign direct investment and productivity spillovers in the Chinese manufacturing sector. Econ Syst 25(4):305–321

    Google Scholar 

  37. Lipsey RG, Carlaw KI (2004) Total factor productivity and the measurement of technological change. Can J Econ/Revue canadienned’ économique 37(4):1118–1150

    Google Scholar 

  38. Madsen JB (2014) Human capital and the world technology frontier. Rev Econ Stat 96(4):676–692

    Google Scholar 

  39. Messinis G, Ahmed AD (2013) Cognitive skills, innovation and technology diffusion. Econ Model 30:565–578

    Google Scholar 

  40. Meyer KE, Sinani E (2009) When and where does foreign direct investment generate positive spillovers? A meta-analysis. J Int Bus Stud 40(7):1075–1094

    Google Scholar 

  41. Nelson RR, Phelps ES (1966) Investment in humans, technological diffusion, and economic growth. Am Econ Rev 56(1/2):69–75

    Google Scholar 

  42. Parente SL, Prescott EC (1994) Barriers to technology adoption and development. J Polit Econ 102(2):298–321

    Google Scholar 

  43. Sinani E, Meyer KE (2004) Spillovers of technology transfer from FDI: the case of Estonia. J Comp Econ 32(3):445–466

    Google Scholar 

  44. Spielman DJ, Ma X (2015) Private sector incentives and the diffusion of agricultural technology: evidence from developing countries. J Dev Stud 52(5):696–717

    Google Scholar 

  45. Sun S (2011) Foreign direct investment and technology spillovers in China’s manufacturing sector. Chin Econ 44(2):25–42

    Google Scholar 

  46. Vandenbussche J, Aghion P, Meghir C (2006) Growth, distance to frontier and composition of human capital. J Econ Growth 11(2):97–127

    Google Scholar 

Download references

Acknowledgements

We would like to thank Vincent Hoang, Janice How, Sandy Suardi, Shrabani Saha, Peter Siminski and participants at various seminars and conferences for thoughtful discussions and comments. We take responsibility for any errors.

Funding

This study is not funded by any grant.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Radhika Lahiri.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 83 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Asif, Z., Lahiri, R. Dimensions of human capital and technological diffusion. Empir Econ 60, 941–967 (2021). https://doi.org/10.1007/s00181-019-01777-3

Download citation

Keywords

  • Cognitive skills
  • Economic growth
  • Educational achievements
  • Educational attainments
  • Human capital
  • Technology

JEL Classification

  • I2
  • O1
  • O14
  • O13