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Government popularity and the economy: first evidence from German microdata

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Abstract

This is one of the first studies to estimate a popularity function at the microlevel. Using German microlevel data for the years 1991, 1992, 1998, and 2008, we show that a positive assessment of the economy significantly improves government popularity, while negative evaluations decrease satisfaction with the government. Voters take the (current and expected) national and personal economic situation into account. We find no evidence for a grievance asymmetry, i.e., voters not only punish the government for a bad economy but also reward them in good times. Finally, we show that popularity functions are only very crude proxies for vote functions, with the latter being mostly driven by party identification.

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Notes

  1. Vote and popularity (VP) functions have been estimated for many countries and time periods, and it is virtually impossible to discuss the extensive body of research in an article like this due to space constraints. Excellent overviews are given by Lewis-Beck and Paldam (2000), Lewis-Beck and Stegmaier (2000, 2007), and Paldam (2008). Berlemann and Enkelmann (2012) survey and discuss the large field of popularity functions for the United States.

  2. Hesli and Bashkirova (2001) study public support for Russian presidents between 1991 and 1999. However, there is no such study for any Western country.

  3. In a related study, Norpoth and Gschwend (2010) use government popularity to predict election outcomes in Germany.

  4. According to a survey by Paldam (2008), questions Q2 through Q4 represent unsettled (“controversial”) issues in the field. In the German context, evidence is even less clear.

  5. Among others, Downs’ theory has been further developed by Davis et al. (1970) and Kirchgässner (1986). In these models—and also in Downs (1957, ch. 8)—voters maximize utility by minimizing the distance between their own position and the (estimated) party position in the politico-economic space. From these models, an aggregate popularity function can be derived (see, e.g.,Kirchgässner 1986; Neck and Karbuz 1997). For our purpose, however, this approach is less helpful since we focus on the voter’s evaluation of the economy as such, i.e., our measure of performance is a valence issue and not a position issue (see Stokes 1963). Our argumentation, thus, follows the basic ideas of Downs (1957, ch. 3).

  6. After the German reunification an additional, unscheduled survey was conducted in 1991.

  7. The study employs the cumulative GGSS/ALLBUS dataset “ALLBUS/GGSS 1980–2008, study number 4570” (GESIS 2010), which is also available in English language (study number 4572).

  8. Alternatively, it is possible to create a dummy variable that is one if the respondent falls into the two or four highest answer categories. However, the construction of our dummy variable is (linguistically) intuitive as the three top answer categories include the word “satisfied” while the others include the word “dissatisfied” in their answer. In addition, we have run an ordered logit regression that supports our intuitive division. A good economy increases the likelihood of choosing a positive category, and vice versa. As the results are qualitatively identical, we decided to present the results for the dummy variable.

  9. The questions read as follows:

    Current national economy: “How would you generally rate the current economic situation in Germany?”

    Future national economy: “What do you think will the economic situation in Germany will be like in one year?”

    Current personal economy: “How would you generally rate your own current financial situation?”

    Future personal economy: “What will your own financial situation be like in one year?”

  10. This approach is in line with, e.g., Hesli and Bashkirova (2001). The estimation of OLS or ordered logit models with the categorial popularity variable as the dependent variable as well as linear probability models with the dichotomous variable as a dependent variable leave the qualitative results unaffected.

  11. We also estimated a model in which the categorial economic variables were re-coded to variables with three outcomes (good, neutral, or bad). The results are virtually identical to the ones presented in the text.

  12. Results for all variables including the control variables can be found in Tables 9 and 10 in the Appendix.

  13. Regarding the goodness-of-fit, the model performs quite well. We present the most common McFadden’s \(R^2\) which is, however, severely downward biased. According to McFadden (1979, p. 307), a value above 0.20 already indicates an excellent fit. The share of correct predictions is an alternative and more intuitive measure. Assuming that a respondent is satisfied with the government if the predicted probability is above 50 %, we are able to predict about 70 % of all answers correctly.

  14. The coefficients in Table 5 describe the effect (in percentage points) of a change from neutral to the respective category. For example, changing from a neutral position to very bad regarding the current national economy decreases the probability of expressing approval by 19 % points.

  15. Comparing the effect of a bad/very bad assessment of the present national economy versus a good/very good economy, we find weak statistical evidence for a grievance asymmetry. In a model in which all economic variables are re-coded to three-category variables, we find an asymmetry with respect to the present and future national economies in the entire sample. The effect of a bad assessment is significantly larger than the corresponding effect of a positive assessment.

  16. Paldam (1991, p. 9), for example, states that “[t]he two functions are so alike that I shall speak of VP-functions.”

  17. On the microlevel no popularity function estimates exist. At the macrolevel, results are hardly comparable since elections are comparatively rare events that allow for only simple statistics, while popularity functions are sometimes based on more than 50 years of monthly data.

  18. On the other hand, it should be noted that most vote functions rely on answers regarding vote intention. In this case, the last two points are very weak.

  19. We also estimated vote functions for each single year (see Table 11 in the Appendix). Economic variables are often not statistically significant and the coefficients are small in size. In all cases, vote intention is mainly explained by past voting behavior which is highly significant in all years.

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Acknowledgments

I would like to thank Michael Berlemann, Markus Leibrecht, Christian Pfeifer, participants at the Scottish Economic Society 2012 Annual Conference and two anonymous reviewers for helpful comments and discussions.

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  1. Leuphana University, Scharnhorststrasse 1, 21335 , Lüneburg, Germany

    Sören Enkelmann

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Correspondence to Sören Enkelmann.

Appendix: Detailed results

Appendix: Detailed results

See Tables 9, 10 and 11.

Table 9 Popularity function (including control variables)
Full size table
Table 10 Popularity function with categorial variables
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Table 11 Vote function, single years
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Enkelmann, S. Government popularity and the economy: first evidence from German microdata. Empir Econ 46, 999–1017 (2014). https://doi.org/10.1007/s00181-013-0707-4

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