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Effect of Linguistic Heterogeneity on Technology Transfer: An Economic Study of FIFA Football Rankings

Abstract

This paper used Fédération Internationale de Football Association (FIFA) world ranking points data to examine how linguistic heterogeneity has an impact on technology transfer from the most developed countries. The major findings were that the learning effect from the most developed countries on team performance is larger for developing countries than for developed ones and that linguistic heterogeneity has a detrimental effect on technology transfer for developed, but not developing countries. The results presented here are interpreted to imply that the importance of common and proper comprehension of team strategy among members, which improves team performance but is hampered by linguistic heterogeneity, depends on the stage of development.

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Notes

  1. 1.

    See FIFA HP (http://www.fifa.com/aboutfifa/federation/index.html).

  2. 2.

    In this paper, the degree of development is measured by the FIFA World Ranking, instead of per capital GDP, since in developing countries in the field of football this is relevant to the results of football match, rather GDP.

  3. 3.

    an increase in competitive balance is also observed within Major League Baseball (e.g., Schmidt 2001; Schmidt and Berri 2005).

  4. 4.

    Greece is a European country but is not generally regarded as a most developed one in football.

  5. 5.

    It must be noted that striking result of World Cup 2002 held in Japan-Korea was significantly the result of a home advantage (Torgler 2004). Nevertheless, Euro2004 and 2008 were held in Portugal and Austria-Switzerland, respectively, leading me to assume that a home for Greece and Turkey..

  6. 6.

    Italy’s Serie A, England’s Premiership, Germany’s Bundesliga, and Spain’s Primera Division. Although Wilson and Ying (2003) added France’s Le Championnat to these other leagues, the records of teams belonging to Le Chamionnat are inferior to those from the other leagues in the UEFA Champions League that determines the champion club among European professional leagues. Therefore in this study I omitted France from the group of the most developed football countries.

  7. 7.

    In Major League Baseball, the expansion of teams in the league led to an increased competitive balance (Schmidt, 2002). The members of FIFA increased from 167 in1993 to 208 in 2008. Therefore, the effects of expansion on competitive balance appearing in international football are in line with those seen in the MLB.

  8. 8.

    There are alternative indexes for competitive balance, such as the Gini coefficient (Schmidt 2002).

  9. 9.

    Information spillover and social learning from others is weaker in a heterogeneous population (Munshi 2004).

  10. 10.

    In August 1993, the FIFA introduced a ranking system for senior national teams. Current rankings are calculated by performances in the current year. The method of calculation of world ranking points changed at the beginning of 1999. From that point, current rankings are calculated by performances over the last 8 years. Further, from 2005, the basic calculation criteria changed again, with rankings calculated by performances over the last 4 years. Hence, rankings after 1999 suffer from serial correlations that result in estimation bias. Thus this paper focuses on rankings prior to 1999. We focus on the period 1993 to 1998. Variables such as population and real GDP used for the estimation were collected from the Penn world table (http://pwt.econ.upenn.edu/php_site/pwt61_form.php).

  11. 11.

    ln YFIFA and WCAPER are available at http://www.fifa.com/en/mens/statistics/rank/procedures/0,2540,3,00.html. lnGDP and lnPOP are collected from Penn & World Table (http://pwt.econ.upenn.edu/php_site/pwt_index.php). NOFFLAG and ETHFRA are used in Collier and Gunning (1999) and Taylor and Hudson (1972), respectively. Data sets for NOFFLAG and ETHFRA are available from the World Bank HP(http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/EXTPROGRAMS/EXTMACROECO/0,,contentMDK:20392406~menuPK:836389~pagePK:64168182~piPK:64168060~theSitePK:477872,00.html). OPEN and UNEMP are collected from the World Bank (2006).

  12. 12.

    I use the index as below as a proxy for the level of local technology, which is also used by Yamamura(2008a). Total ranking points in the locality minus own raking are calculated and then divided by the number of FIFA members in the locality minus 1.

  13. 13.

    A ethno-linguistic fractionalization score is used in Taylor and Hudson (1972).

  14. 14.

    In 1998, 140 of 205 FIFA members had no experience of an appearance in the World Cup.

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Correspondence to Eiji Yamamura.

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Yamamura, E. Effect of Linguistic Heterogeneity on Technology Transfer: An Economic Study of FIFA Football Rankings. Atl Econ J 40, 85–99 (2012). https://doi.org/10.1007/s11293-011-9295-x

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Keywords

  • FIFA raking
  • Technology transfer
  • Linguistic heterogeneity

JEL

  • L83
  • O19
  • Z13