Abstract
This article makes a contribution to the study of the determinants of political knowledge from a comparative perspective. Along with the usual suspects explaining knowledge at the individual level (that is, individual differences in motivation, ability and exposure to political news in the media), this article analyses the extent to which socio-economic, political and communicational contexts affect what people know about politics. More importantly, the article analyses whether information-rich contexts contribute towards reducing inequalities in knowledge. The results are obtained via two-level hierarchical linear models using the 2009 European Election Studies, Voter Study and confirm that citizens’ levels of political knowledge are driven by the context. They also demonstrate that information-rich environments crucially narrow knowledge inequalities between high- and low-status citizens. These findings thus suggest that socio-economic policies have the capacity to alter the balance between the information-rich and the information-poor.
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Notes
There are other variants of the potential impact of the institutional design on what citizens know about politics such us: the type of democratic system (that is, the contrast between presidential, semi-presidential and parliamentary systems), the degree of competitiveness of elections and the existence of compulsory voting (Gordon and Segura, 1997); but as explained later in the article, none of them appear to show a significant effect on what people know about politics in Europe.
I use the EES, European Parliament Election Study 2009, Voter Study, Second Pre-Release, 23 June 2011. Data collection was started on the first working day following the 2009 European Parliament elections (from 4 to 7 June 2009). Intended sample size was 1000 successful interviews with each EU member state. Data collection was done by CATI phone interview. More detailed information about the EES 2009 Voter study can be found in www.piredeu.eu.
It is true that using post-electoral surveys can be problematic for measuring political knowledge. Moreover, there is a risk of overestimating levels of knowledge because during electoral campaigns voters receive the highest degree of political information of the whole legislature. Acquiring information at those times is less costly than in the middle of a mandate. Nevertheless, for the purposes of this article, the potential overestimation of knowledge is likely to affect all countries equally and therefore comparisons of the levels of political knowledge across countries is not seen as problematic.
I am aware that there is a debate in the specialised literature about the difficulties in constructing valid indexes of factual political knowledge (see, for instance, Mondak, 1999). Consequently, I tried alternative measures of political knowledge, for example, by counting the number of ‘incorrect’ and ‘DK’ answers. However, these alternative indexes almost seem to work identically to the conventional index that simply counts the number of correct answers. As a result, I have preferred to use this conventional index, as its format makes it directly comparable with previous studies on other democracies. The index has a value of Cronbach's α=0.625.
Alternative indicators used by previous studies (Gordon and Segura, 1997) such as the degree of proportionality of the national electoral system or the existence of compulsory voting did not turn out to be statistically significant according to the previous tests I performed. Results are available on request from the author.
I have tried alternative measures such as: (i) Tax revenue of social security funds as a percentage of total taxation, and as a percentage of GDP; (ii) Government expenditure on Education as a Percentage of GDP (2007); (iii) Government expenditure on Health as a Percentage of GDP (2007); (iv) The Gini Index (I used an alternative data set for this variable): (Democracy Cross-national Data, Release 3.0 Spring 2009 from www.pippanorris.com/). All of them turned out to be statistically significant with the expected direction: that is, the higher the size of the state, the higher the value of knowledge. This provides additional evidence in favour of the robustness of the results presented here.
Hallim and Mancini deduce this typology from a careful comparison of four main media system dimensions: the degree of state intervention in the media system, the degree of autonomy of media from partisan dictates, the historical development of media markets and the degree of professionalism in journalism (note that the discussion of these four dimensions is beyond the scope of this study; for a full discussion, see Hallin and Mancini, 2004).
Recall that this is a rating (not a ranking score) variable.
The specific variables used are as follows: General exposure to the Media (‘In a typical week, how many days do you follow the news?’ From 0 to 7 days, then recoded from 0 to 1); Exposure to the main channels of each country TV news (from 0 to 7, then recoded from 0 to 1); Exposure to the main newspapers’ news in each country (from 0 to 7, then re-coded from 0 to 1); Education (from 0 to 6, recoded from 0 to 1); Subjective income level: (specified with four dummy variables coming originally from the question: ‘Taking everything into account, at about what level is your family's standard of living?’); Male (1 for male, 0 for female); Age (in years, recoded from 0 to 1); Political Interest (1 for those who declare to be very and quite interested in politics, 0 for those who are not interested in politics); Voted (1 for those who voted in past election, 0 for those who did not vote); Ideology (1 for those declaring a position on the ideological scale , 0 for those who did not).
Additional indicators of the convenience to adopt a multilevel model estimation are the three R2. More specifically, R2 between increases from 0.41 in Equation 2 to 0.58 in Equation 3. Both the R2 within and the R2 overall also increases, but to a lesser extent. I also performed a Likelihood Ratio Test of the null hypothesis that no multilevel estimation was needed. The result is that we can reject this hypothesis, with a χ2 value=2368.92 (P=0.0000) for the null model (Equation 1 in Table 2) and with a χ2 value=1646.75 (P=0.0000) for the model including only the independent variables measured at the individual level (Equation 2 in Table 2).
Fitted values of political knowledge in Figure 2 are calculated from Equation 3 in Table 2, and with all predictors (except the one of interest in each case: number of effective parties, government expenditure on social protection and Press Freedom) set to their typical values (that is, means for quantitative variables and proportions for categorical variables).
The predicted value of knowledge for a citizen living in a country with two parties is equal to 3.35, whereas this number increases up to 4.15 for a citizen living in a country with five parties (the given difference then is: 4.15−3.35=0.80).
A recent article by Lesson (2008) also show the significant influence of Media Freedom on what people declare that they know about politics.
Graphs in Figure 3 were created following Brambor et al (2006).
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Acknowledgements
This work was supported by the Spanish Ministry of Science and Innovation (grant number: CSO2008-03819/SOCI). This work was presented at the Research Seminar of IPP (CSIC, Madrid) and at the Research Seminar of the UAB research group: Democracy, Elections and Citizenship. I wish to thank the participants for their useful and generous comments and I also wish to thank Agustì Bosch for his generous help in collecting the data for the number of effective parties.
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Fraile, M. Do information-rich contexts reduce knowledge inequalities? The contextual determinants of political knowledge in Europe. Acta Polit 48, 119–143 (2013). https://doi.org/10.1057/ap.2012.34
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DOI: https://doi.org/10.1057/ap.2012.34