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Social ties and concern for global warming


Recent research focusing on social factors affecting risk perceptions has suggested that social networks might help to explain why differences of opinion about climate change persist across segments of the lay public despite the scientific consensus. Even though concern for global warming in itself might seem irrelevant for most social ties, we show that it is significant enough to be reflected in the structure of social networks. To do this, we programmed a Facebook application that collected survey data on concerns and network data on friendships. We found that respondents tend to have friends with similar concerns as their own, the unconcerned respondents have fewer friends, and any two respondents who disagreed about the seriousness of global warming were less than half as likely to be friends. The results indicate that the structure of the social network may hinder changes in opinions, explaining why opinions persist despite the scientific consensus. The results suggest that the communication of climate science could be improved by strategies that aim to overcome these network effects.

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  1. 1.

    The employment question was adapted from the World Value Survey (WVS 2009) allowing the respondent to choose one from a list of options.

  2. 2.

    The question asking the respondent to choose a position on a left–right scale was adapted from the World Value Survey (WVS 2009), to enable comparison.

  3. 3.

    The question was adapted from the World Value Survey (WVS 2009), to enable comparison with a representative sample of the Finnish population.

  4. 4.

    We found a small but significant negative correlation between the respondent’s distance from the friends’ average and the number of friends (ρ = −0.053, t = −3.72, degrees of freedom 4825, p-value <0.001). This means that people who deviate from the average opinion of their friends tend to have fewer friends. The friends’ average was estimated by the average among friends who participated in the survey.

  5. 5.

    We tested each model for possible non-linearity with respect to concern for global warming with nested models, but none resulted in a significantly better fit according to F-tests.

  6. 6.

    This can be calculated by multiplying the regression coefficient for concern with the distance between ends of the scale: 3×17.232≈52.

  7. 7.

    Note that the local clustering coefficient needs to be estimated, as we observed a tie between the respondent’s two friends only if at least one of them had participated (see Appendix).

  8. 8.

    We added a separate dummy variable for both being men or both being women.

  9. 9.

    The marginal effects were calculated for two people of the same age and opposite sex.

  10. 10.

    Full regression tables are included in the Appendix.


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This research was conducted while working at the University of Helsinki and made possible by the funding from the Graduate School fellowship of the Finnish Doctoral Programme in Economics. The Facebook application and survey was designed and carried out together with Panu Poutvaara. The author also wishes to thank Jan-Erik Lönnqvist and Markku Verkasalo for help with the survey design and Pekka Ilmakunnas, Heikki Kauppi, Anssi Kohonen, Otto Kässi, Jaakko Nelimarkka, Aino Silvo, Essi Eerola and Markku Ollikainen for valuable comments on the paper. The raffle prices were funded by Ifo Institute and Koneen Säätiö.

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Correspondence to Juha V. A. Itkonen.

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Appendix A: Local clustering coefficient

We did not observe the local clustering coefficient directly, but we could estimate it for those who have friends in the sample. Consider the full undirected Facebook network (N, G), where N is the set of nodes and \(G \subset \{(i,j) \ \colon i,j\in N,\ i\neq j \}\) is the set of friendships.

Let N i = {jN :(i, j) ∈ G} be node i’s set of friends and d i the number of friends. The number of pairs of friends is p i = d i (d i −1)/2. Let c i = |{(j, k) ∈ G :j, kN i }|/2 be the number of friendships between the members of N i . Now the true local clustering coefficient is

$$t_{i}= \frac{c_{i}}{p_{i}}.$$

Let \(\tilde {N} \subset N\) be the set of nodes we sampled. Let \(\tilde {G} = \{(i,j) \in G \ \colon i\in \tilde {N} \text { or } j\in \tilde {N} \} \subset G\) be the set of friendships we sampled. Now, for each \(i\in \tilde {N}\) we observed N i .

Let \(\tilde {d_{i}}= |N_{i} \cap \tilde {N} |\) be the number of i’s friends in the sample. We observed all pairs except the ones where both nodes were outside of the sample. The number of pairs we observed was then the number of all pairs less the unobserved pairs, which we denote by \(\tilde {p}_{i} = d_{i}(d_{i}-1)/2 - (d_{i}-\tilde {d}_{i})(d_{i}-\tilde {d}_{i}-1)/2= d_{i}\tilde {d}_{i} - (\tilde {d}_{i}^{2} +\tilde {d}_{i})/2.\)

Let \( \tilde {c}_{i} = | \{ (j,k)\in G\ \colon j,k\in N_{i} \text { and } (j \in \tilde {N} \text { or } k \in \tilde {N}) \} |/2\) be the number of friendships between the members of N i that we observed.

The estimated clustering coefficient is then

$$\tilde{t}_{i}= \frac{\tilde{c_{i}}}{\tilde{p_{i}}}. $$

This is what we could observe and use for our analysis.

Appendix B: Full regression tables

Here we report in full the regression tables of the main text. Tables 4 and 5 present all coefficient estimates of Tables 2 and 3, respectively.

Table 4 Regression analysis of number of friends and local clustering coefficient
Table 5 Logistic regression on the probability of friendship

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Itkonen, J.V.A. Social ties and concern for global warming. Climatic Change 132, 173–192 (2015).

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  • Climate change
  • Social networks
  • Science communication