Research diversification and impact: the case of national nanoscience development


Newcomer nations, promoted by developmental states, have poured resources into nanotechnology development, and have dramatically increased their nanoscience research influence, as measured by research citation. Some achieved these gains by producing significantly higher impact papers rather than by simply producing more papers. Those nations gaining the most in relative strength did not build specializations in particular subfields, but instead diversified their nanotechnology research portfolios and emulated the global research mix. We show this using a panel dataset covering the nanotechnology research output of 63 countries over 12 years. The inverse relationship between research specialization and impact is robust to several ways of measuring both variables, the introduction of controls for country identity, the volume of nanoscience research output (a proxy for a country’s scientific capability) and home-country bias in citation, and various attempts to reweight and split the samples of countries and journals involved. The results are consistent with scientific advancement by newcomer nations being better accomplished through diversification than specialization.

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  1. 1.

    Suggesting that the case for diversification may well vary with a country’s stage of development, Imbs and Wazciarg (2003) show that poorer and richer countries tend to have lower levels of industrial diversity, relative to modestly rich countries. Similar forces could yield similar effects for scientific diversification.

  2. 2.

    Indeed, despite significant efforts, we have been unable to locate any studies of this relationship at the national level. At the firm level, Lee et al. (2012) find that firms with more specialized research portfolios filed more high impact patents; while Matusik and Fitza (2012) show that venture capital firms that are highly specialized and highly diversified were more successful than those who were moderately diversified in taking firms they invest in public.

  3. 3.

    In keeping with previous literature, we define the developmental state simply as a government that, motivated by desire for economic advancement, intervenes in industrial affairs (Woo-Cumings 1999). For our purposes, technological affairs are an aspect of industrial ones.

  4. 4.

    Iran could certainly be included in this list, given the dramatic rise in its nanotechnology research output, which surpassed that of Brazil by 2009. However, for most of the sample period, there are too few papers from Iran to analyze its patterns of specialization.

  5. 5.

    To the extent that research is cheaper in lower-income countries, the dollar figures provided in this section overstate the research budgets of high-relative to low-income countries. There is no index comparing the cost of scientific research across countries that would permit us to compare these budgets in terms of purchasing power.

  6. 6.

    We have re-run the analyses appearing in the next section for k = 8, and the results did not change qualitatively. The only exception is Fig. 8, which involves the Hirschman Herfindahl Index of concentration. The HHI is known to be sensitive to changes in the number of subfields across which concentration is measured.

  7. 7.

    The citation rate for a subfield in some year is the ratio between the average citation rate of papers published in that subfield that year and the average citation rate of all nanotechnology papers published that year.

  8. 8.

    Using a share-of-citations measure allows for the fact the fact that more recent publications have had less time to be cited and compete for recognition in a larger pool of publications. We emphasize that this is a measure if relative influence, increases in which only tell us that the number of citations to papers involving country c have grown more rapidly than have citations to all papers in the field.

  9. 9.

    One obvious drawback to this simple share-of-citations measure is that it gives greater weight to internationally coauthored papers (Aksnes 2006). Shares of citations and publications measures recalculated to attribute papers to countries in inverse proportion to the number of countries that authored each paper yield the same qualitative findings as the figures and table presented here, indicating that our results are not driven by an over-counting of internationally coauthored papers. Fractional attribution of internationally coauthored papers yields only two minor changes: (1) The growth over time in China’s RCI is slightly more pronounced, because the citation rates of Chinese- only papers have converged on those of papers involving authors from both China and other countries; (2) Russia’s relative RCI drops with fractional attribution, as its internationally collaborative papers are more highly cited than Russian only papers.

  10. 10.

    Some countries may tend to cite their own work, and lower-income countries are more likely to cite work in lower ranked journals (Didegah et al. 2012). There is little we can do to correct our estimates of influence and impact for this with the data available, but we will check that our estimates of the relationship between diversification and impact are plausibly robust to such problems.

  11. 11.

    We tried using a re-based impact factor (RBI—see Sect. 3.3) as a proxy for impact. The RBI normalizes citations across subfields by subfield norms and then takes the country’s publication-share-weighted average of these normalize citation across sub fields (King 2004). Rebasing has imperceptible effects on Fig. 5, indicating that results in this section are not sensitive to the weighting of citations in different subfields.

  12. 12.

    Eigenfactor scores as reported by Web of Science, 2010 Journal Rankings. The cutoff presented here for prestigious journals was provided by nanotechnologist at a top-ranked engineering department who we asked to identify the lowest ranked journal they would support their graduate students submitting papers to. Lowering the bar does not alter our results qualitatively.

  13. 13.

    Four out of the five concentration measures (C4, C8, C20 and the Gini Coefficient) studied by Van Zeebroeck et al. (2006) are not suitable when the number of subfields is small (Khramova et al. 2013).

  14. 14.

    Some countries do rank differently by CV and by Nonconformity. As noted, this is unsurprising given that coefficients of variation are very sensitive to changes in the country-mean value of RLA and RLAM.

  15. 15.

    We have chosen not to present estimates using Top5 because the measure is censored at zero. Estimates from Tobit models with country fixed effects are not consistent, due to the incidental parameters problem. We have, however, analyzed the behavior of Top5 using linear regressions with fixed effects (ignoring censoring), and the results are qualitatively identical to those using RBI and RCI, although the coefficient on the diversification measure is sometimes less statistically significant. Tobit models without country fixed effects yield negative and highly significant coefficients on the diversification measure.

  16. 16.

    Brazil provides an illustrative example. While Brazil is now developing a research mix that resembles that of the rest of the world, it had, until at least 2005, focused on nanobio and alternative energy applications, not only within nanotechnology but also across all of science and technology (Fink et al. 2012). As its nanotechnology research mix has become more concentrated, its research impact has declined—the only of our six Newcomer nations for which this is the case.


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We are grateful to Richard Appelbaum, Matthew Gebbie, Shirley Han, Barbara Harthorn, Luciano Kay, Sumita Pennathur and Galen Stocking for support and for useful discussions of our results. Rachael Drew, Quinn McCreight, Caitlin Vejby and Chris Wegemer provided invaluable research assistance. This material is based upon work supported by the National Science Foundation under Grant No. SES 0531184. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. This work was conducted under the auspices of the University of California at Santa Barbara’s Center for Nanotechnology in Society (

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Correspondence to Aashish Mehta.

Appendix: Influence decompositions using only papers appearing in good journals

Appendix: Influence decompositions using only papers appearing in good journals

Figures 11, 12 and 13; Table 5

Fig. 11

Changes in research quantity. Share of all papers appearing in high impact journals that involve an author from this country/bloc. Note Internationally collaborative papers are attributed to more than one country. a Incumbents. b Newcomers

Fig. 12

Changes in research impact. Relative citation rate of papers appearing in good journals that involve an author from this country/bloc. Note International collaborative papers are attributed to more than one country. a Incumbents. b Newcomers

Fig. 13

Changes in research influence. Share of all citations to papers appearing in good journals that involve an author from this country/bloc. Note: Internationally collaborative papers are attributed to more than one country. a Incumbents. b Newcomers

Table 5 Decomposition of scientific influence, sample limited to high quality journals

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Herron, P., Mehta, A., Cao, C. et al. Research diversification and impact: the case of national nanoscience development. Scientometrics 109, 629–659 (2016).

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  • Diversification
  • Specialization
  • Impact
  • Nanotechnology
  • Nanoscience
  • Developmental state

JEL Classification

  • O10
  • O25
  • O30