No place like home: geography and culture in the dissemination of economic research articles


This paper examines the existence of distance and border effects in the dissemination of knowledge in economics research using a state-of-the-art gravity model for domestic and international citations between 1970 and 2016 for the top 20 source countries. We extend the model with two novel indicators, English proficiency and bilateral internet ties-two key forces in the dissemination of research and knowledge more generally. Our results show that (i) citations decrease with distance, (ii) citations exhibit a significant home bias greater than 1.68, i.e. a more than 50% higher propensity to cite domestic articles, (iii) home bias as well as geographic and cultural distance measures remain significant and at persistent levels over time, (iv) bilaterally high levels of English proficiency are insignificant for citations beyond the measure of general language similarity, (v) countries with closer internet ties have higher shares of bilateral citations, and (vi) geographic proximity is insignificant for citations to econometric articles while cultural linkages are significant.

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Fig. 1
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Source: WoS and Scopus

Fig. 3
Fig. 4

Source: Own calculations based on WoS data

Fig. 5
Fig. 6


  1. 1.

    More generally, Jaffe et al. (1993) provides evidence for strong geographic concentration of patent citations which is further confirmed by Breschi and Lissoni (2009) who links this concentration of knowledge back to the lack of mobility of inventors who are likely to remain in their co-inventor network.

  2. 2.

    The twenty leading countries are Australia, Belgium, Canada, China, Czech Republic, Denmark, France, Germany, India, Israel, Italy, Japan, Netherlands, Republic of Korea, Romania, Spain, Sweden, Switzerland, United Kingdom, and USA.

  3. 3.

    Articles with authors in multiple countries are attributed to each country.

  4. 4.

    We exclude citing countries with less than 100 total citing articles from our analysis.

  5. 5.

    To understand why, imagine picking a random economics article written in 2016, it is reasonable to assume that the article cites at least one article from the United States. By just counting citing articles for the United States overall, we would end up simply counting all 2017 economics articles. However, a new article might not reference American articles from each of the last 10 years and is very unlikely to reference 47 American articles covering each year from 1970 to 2016. Technically, we only observe an article’s first citation to a given year-country which underestimates countries and years that have relatively high citation averages.

  6. 6.

    The average number of references given per paper almost doubled over the last 20 years from 25 to 40 [see Kuld and O’Hagan (2018)].

  7. 7.

  8. 8.

    To this end Chung (2011) uses Yahoo’s search function and LexiURL Searcher, a social science web analysis tool developed by Thelwall (2009). At the time, Yahoo had indexed about 47 billion websites. For more detailed information on obtaining the measure of bilateral hyperlinks, please refer to Chung (2011).

  9. 9.

    For the United States, usually the sum of the domains .edu, .us, .mil and .gov has been used Barnett et al. (2001) in the literature. In previous studies [e.g. Barnett and Sung (2005)], the .com domain had either been disregarded or completely attributed to the United States.

  10. 10.

    For comparison, Gloetzl and Aigner (2017) count articles in 441 economics journals from 1980 to 2014. In this sample, North American researchers alone authored half of the world’s economics articles indexed by WoS between 1980 and 2014 which then received 75% of total citations. 98.4% of the economics articles from the top ten countries are written in English (WoS classification). The next two languages are French with 0.8% and German with 0.3% of the total indexed article output of these countries (WoS).

  11. 11.

    To obtain positive counts, we show the exponential of log demeaned by citing and cited country (thereby using the geometric mean).

  12. 12.

    In addition, incentives to cite articles within local networks create opportunity costs for citations to unconnected researchers.

  13. 13.

    \(P_i=\left( \displaystyle \sum _{j=1}^{N} (a_{ij}p_{ij})^{1-\sigma } \right) ^\frac{1}{1-\sigma }\).

  14. 14.

    The results are unchanged for Poisson with clustered errors, but other methods are less robust with respect to the aggregation level.

  15. 15.

    \(Asinh(x)=\ln (x+\sqrt{x^2+1})\). For \(x \ge 2, asinh(x)\approx \ln (x) + \ln (2)\), but \(asinh(0)=0\). Also suggested for citation counts by Card and DellaVigna (2020), for instance.

  16. 16.

    However, both, OLS and negative binomial regressions, have stronger identification assumptions and are not consistent with the theoretical multilateral resistance model. As an indication, they overestimate the actual sum of citing articles considerably. For instance, the total flows involving the United States are overestimated by 20% (OLS) and 31% (negative binomial) in the standard specification. While smaller countries are underestimated, the ratios of total estimated flows to actual flows are 1.15 (OLS) and 1.22 (NegBin2) as opposed to 1 using Poisson.

  17. 17.

    The estimation tables underlying the results for the figures presented are available upon request.

  18. 18.

    Additionally we ran a specification excluding the US (results not presented but available upon request). If we do so, this value rises to 16% but can be as big as 90% for larger language differences.

  19. 19.

    The lower effect estimated for geographic distance reflects the change in the sample size and year. Table 6, for instance, shows that the omission of the home variable alone does not affect the same distance estimate significantly in the baseline sample.

  20. 20.

    These articles are classified by WoS as economics as well as mathematics or statistics and probability. The data are collected for articles written in the top ten leading countries between 2004 and 2008.


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Correspondence to Christiane Hellmanzik.

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Hellmanzik, C., Kuld, L. No place like home: geography and culture in the dissemination of economic research articles. Empir Econ (2020).

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  • Gravity
  • Border effect
  • Economics
  • Citations
  • Knowledge dissemination

JEL Classification

  • A14
  • F16
  • O34