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International organisations and policy diffusion: the global norm of lifelong learning

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Abstract

This article analyses the role of international organisations in global policy diffusion, drawing on the example of lifelong learning, a currently widely appreciated concept in education policy. I explain this success based on a sociological institutionalist framework, arguing that lifelong learning has become a global norm in education policy. For this purpose, I conduct a quantitative study of 99 countries from 1996 to 2004, showing how the idea of lifelong learning has been disseminated by international organisations and how states have reacted to this development. I first outline the theoretical framework, highlighting in particular the crucial role of international organisations. In a further step, I present the data and methods. In the third part, I analyse the activities of several international organisations on lifelong learning. In the fourth step, I show how lifelong learning has spread, distinguishing the idea of lifelong learning and reforms linked to it. Fifth, as the quantitative analysis shows, international organisations like the Organization for Economic Cooperation and Development and the European Union can explain a large part of dissemination when it comes to the idea of lifelong learning, but reforms are more dependent on national preconditions like the wealth of a country. In the conclusions, I sum up the article's main findings and outline further research areas linked to global diffusion processes.

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Notes

  1. Other important causes for lifelong learning diffusion would be herding effects or the emulation of countries in their neighbours’ policies (Berry and Berry 2007). From this point of view, countries in a particular region would be more likely to adopt lifelong learning policies than others. Subsequent analyses will also account for this factor, but given the idea of a global policy of lifelong learning, I do not further test this assumption as a single hypothesis.

  2. Temporarily restricted data should not lead to the fact that only long-running diffusion processes are examined. Given that the focus of diffusion studies is on the process, and that globalisation and internationalisation processes are likely to result in more and different diffusion patterns (compare Jahn 2006: 406–7), a short time period of available data represents a methodological challenge that needs to be tackled.

  3. Since data were derived from UNESCO sources, it was not possible to establish variation with regard to UNESCO contacts. Therefore the effects of this organisation are not assessed quantitatively. Detailed calculations and further details of the methodology can be found in Jakobi (2009a, 2006).

  4. Categories are ‘Low Income’, ‘Lower Middle Income’, ‘Upper Middle Income’ and ‘High Income’, based on a World Bank classification.

  5. The metric values have been recalculated as categories of ‘service economy’, following a principle of approximately the same size on any category in the data set. This classification results in a ‘Low Service Economy’ represented by a share of services of up to 48 per cent of the GDP, a ‘Medium Service Economy’ with 49–62 per cent and a ‘High Service Economy’ with a share of more than 62 per cent.

  6. According to these categories the geographical dispersion of the data is as follows: Sub-Saharan Africa (Botswana, Ethiopia, Ghana, Kenya, Lesotho, Malawi, Mauritius, Namibia, Nigeria, Seychelles, Sierra Leone, South Africa, Sudan, Swaziland, Tanzania, Uganda, Zambia, Zimbabwe), Asia and Oceania (Afghanistan, Australia, Bangladesh, Bhutan, Cambodia, China, North Korea, India, Indonesia, Iran, Japan, Malaysia, Maldives, Myanmar, Nepal, New Zealand, Pakistan, Philippines, South Korea, Sri Lanka, Thailand), Central Europe and former USSR (Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Kyrgyzstan, Latvia, Poland, Republic of Moldova, Romania, Russian Federation, Serbia and Montenegro, Slovakia, Slovenia, The Former Yugoslavian Republic of Macedonia, Ukraine), Latin America and Caribbean (Argentina, Barbados, Belize, Brazil, Costa Rica, Guyana, Jamaica, Mexico, Peru, Suriname, Trinidad and Tobago), Middle East and North Africa (Bahrain, Cyprus, Egypt, Iraq, Israel, Jordan, Kuwait, Libya, Malta, Oman, Qatar, Saudi Arabia, Syria, Turkey, United Arab Emirates, Yemen), North America (Canada, United States of America), Western Europe (Austria, Belgium, Denmark, Finland, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Sweden, United Kingdom).

  7. So far, case-based evidence from related research suggests that lifelong learning ideas and reforms are adopted by democracies as well as non-democracies, for example in Myanmar, China and Zimbabwe (Jakobi 2009a).

  8. Table 2 represents such an approach, assessing the spread independently for different points in time.

  9. Other variables (e.g. GNI per capita) have been recalculated to a mean value for these 8 years; special attention has been paid to whether large changes have occurred that could influence results.

  10. See Jahn (2006: 410–11, 415) for a discussion of these variables. See Holzinger et al. (2008) for assessing the effects of transnational communication with a long-term and more detailed, but geographically restricted database.

  11. Assessing UNESCO influence is thus not possible, since no variance would exist on the side of the independent variable: All countries in the data set have close contact to UNESCO, since this contact is where the original data come from.

  12. Logistic regression analysis is based on odds and iterations. Therefore, its R2 measures are not strictly comparable to the R2 of a linear regression analysis.

  13. Cox-and-Snell-R2 is per definition less than 1 (Backhaus et al. 2003: 441). The estimations of variance based on Nagelkerke-R2 presented are thus slightly higher than results based on McFadden-R2 would be. However, literature tends to use McFadden-R2 (Andreß et al. 1997: 287; Hoffmann 2004: 38).

  14. A value <1 represents a negative influence of the variable, and is marked with a ‘−’ in the tables.

  15. For example, models that compare the impact of the service sector and that of the GNI are only calculated with cases that have values for both. In such a case, the number of cases analysed is reduced to 84.

  16. It should be noted that lifelong learning is only one part of the overall education policy discourse in these organisations. Therefore, it seems reasonable to speak of an organisation influencing a national lifelong learning agenda, but not of an organisation selecting members or partner countries due to their lifelong learning policies and vice versa. The underlying data set is thus not biased due to membership- or policy-selection. There is, however, some bias in the data set concerning countries in Latin America: Countries there tend to submit their reports in the Spanish language, which renders their overall number in the data set rather low (see endnote 6). These countries partly have contact with the OECD through membership or participation in the programmes (Mexico, Argentina, Brazil, Jamaica, Peru). Despite the small number, there thus exists some variance on the independent variable ‘IO contact’. Similar arguments apply to Africa, where the number of countries is somewhat higher (but formerly French colonies are underrepresented in the data set, given that they often hand in reports in French.)

  17. I also tested the model that was significant with respect to the diffusion of the idea of lifelong learning, but this did not show significant values. This and further calculations are available upon request.

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Acknowledgements

This research has been funded by the German Research Foundation (DFG). I thank the reviewers and editors, as well as participants of the ECPR Joint Session 2009 on education policy for very helpful comments on an earlier version of this article.

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Jakobi, A. International organisations and policy diffusion: the global norm of lifelong learning. J Int Relat Dev 15, 31–64 (2012). https://doi.org/10.1057/jird.2010.20

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