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The politicians’ wage gap: insights from German members of parliament


Using a unique dataset of German members of parliament (MPs), this paper analyzes the politicians’ wage gap (PWG). After controlling for observable characteristics as well as accounting for election probabilities and campaigning costs, we find a positive income premium for MPs which is statistically and economically significant. Our results are consistent with the citizen candidate model, with a PWG of 35%–65% when comparing MPs to citizens occupying executive positions. However, it shrinks to zero when restricting the control group to top level executives.

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  1. The German case is of special interest as the reputation of politicians in Germany seems to be lower than the reputation of most other occupations and has been decreasing for many years (Allensbacher Archiv 2008). In addition, trust in German politicians is rather low compared to several other European countries (European Social Survey 2007).

  2. Previous research has only examined income differentials between public and private sector employees. See Ehrenberg and Schwarz (1987), Bender (1998) and Gregory and Borland (1999) for overviews. Although most studies concentrate on US data, similar results are obtained for other countries (e.g., Pederson et al. 1990; Hartog and Oosterbeek 1993; Melly 2005; Gorodnichenko and Peter 2007).

  3. See Besley (2004), Mattozzi and Merlo (2008), Gersbach (2009), Braendle and Stutzer (2010) and De Paola and Scoppa (2011). For empirical applications, see Ferraz and Finan (2009), Gagliarducci et al. (2010) and Gagliarducci and Nannicini (2012).

  4. We also estimate the conditional wage gap, defined as \(\mathit{PWG}^{\mathit{cond}}(\widetilde{X})=\frac{W^{\mathit{MP}}(\widetilde{X})}{\vphantom{\int^{M}}W^{\mathit{cit}}(\widetilde{X})}-1\). It neglects election probabilities and campaigning costs and is nested in (3). This wage gap is observed by the electorate and thus relevant for the perception of the political elite’s pay.

  5.  As this choice may induce distortions, we experiment with several alternatives—including the categories’ lower bounds. The results do not change qualitatively. In terms of quantitative effects, note that the chosen upper bound level is a conservative assumption (Becker et al. 2009). Hence, if the estimated effects are biased, they will be underestimated. We check the information on outside earnings with other data sources, such as newspaper reports and MPs’ personal statements.

  6.  Office-related allowances mainly cover expenses at the constituency level (about 3,700 euros per month), staff costs (more than 14,000 euros) and travel costs. Party salaries can be quite substantial. For example, a vice-chairman of the Social Democratic Party (SPD) receives 3,451 euros per month.

  7. Exceptions are the three districts in the East of Berlin where the Left Party’s candidates received the majority of first votes several times. In 2005 a candidate running for the Green Party was successful in another Berlin district for the first time.

  8. The predicted probabilities for the major parties’ candidates are displayed in Fig. 1 in the Appendix. Note that there are two distinct curves with similar shapes for both major parties. The one more to the left (with fewer observations) represents Eastern German districts where the Left Party receives a much larger share of votes than in the West and the probability of winning the relative majority is greater for a given vote share.

  9. See Fig. 2 in the Appendix. In some cases (especially for Christian and Social Democrats) predicted probabilities are rather low even for highly ranked candidates. This is due to the fact that in some federal states one of the major parties regularly wins almost every district (first vote) and hence the respective party does not send any list candidate to parliament.

  10. There are only very few MPs who provide information on individual campaigning costs (see, e.g., Martin Dörmann reporting personal expenses of 10,000 euros,, 10-19-2011). Moreover, neither party headquarters nor parliamentary groups were willing or able to provide detailed information upon request.

  11. Regression outputs are available from the authors upon request.

  12. In this section we focus on the results when applying the unconditional income concepts for MPs following definition (3). We also estimate the conditional wage gap. As expected PWG estimates shift upwards (see Table 9 in the Appendix).

  13. Note that \(\widehat{\beta_{1}}\) can be interpreted only in percentage terms for small values. From (5) it follows that ln(Y|P=1)−ln(Y|P=0)=β 1 and thus \(\frac{Y|P=1-Y|P=0}{Y|P=0} =\exp(\beta_{1})-1\).

  14. Note that survey respondents in the SOEP report their party preferences. Hence, we are able to use information on party affiliation not only for MPs but also for the citizens in our sample.

  15. This might help to explain why Kotakorpi and Poutvaara (2011) find that generous remuneration for public office has stronger effects for female than for male candidates in Finland. Similarly, running the earnings regression on the MP sample yields an insignificant gender dummy estimate.

  16. Especially the Social Democrats as well as the Left Party traditionally are supported by blue-collar workers, with close ties to trade unions. The right-wing parties in Germany are historically more business-friendly, which might explain why they receive around 70% of total party donations (Bundestag 2011a). These patterns can be expected to have an effect on MPs’ earnings after retiring from politics (Eggers and Hainmueller 2009; Querubin and Snyder 2009).

  17. Previous research has shown that the Big Five are stable over time (Cobb-Clark and Schurer 2011); hence we can use the panel structure of the data and link the personality information from 2005 to our 2006 data.

  18.  Based on the findings by (Caprara et al. 2003), we also assign politicians an above average level of extraversion as a robustness check. The results do not change as extraversion does not significantly affect earnings.

  19. In that sense, matching is comparable to non-parametric regression methods such as kernel estimation, since it allows identification without explicit assumptions regarding the (potentially non-linear) functional form of the association between dependent and independent variables.

  20. Rosenbaum and Rubin (1983) show that propensity score matching ensures independence of treatment from the potential outcome, which is one of the two identifying assumptions of the matching estimator—the other one being the common support assumption.

  21. Note that the interpretation of the coefficients of the propensity score estimation is not economically relevant. Neither is the purpose of the propensity score estimation to predict the selection into treatment, but to balance the covariates. For completeness, estimation results of the probit estimations are presented in Table 10 in the Appendix.

  22. As was done for OLS, we also provide matching estimators for the PWG based on the conditional income of the politicians. Ignoring campaigning costs and the election probabilities raises the politicians’ earnings and, thus, the PWG. Table 9 in the Appendix shows that the \(\widehat{\mathit{ATT}}\) varies between zero and 0.5 depending on the income definition and the sample used.

  23. Another estimation technique to account for unobserved heterogeneity is the application of a fixed-effects regression (see Diermeier et al. 2005 for an application to US Congress members). However, this would require a panel dataset of MPs, and we only have data for one legislative period. Moreover, there is no variation in the dummy variable identifying MPs.

  24. There is evidence collected from the MPs’ websites that their working times vary between 50 and 70 hours a week. We find similar values for the executive samples.


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The authors would like to thank Johannes Becker, Marco Caliendo, Benny Geys, Dan Hamermesh, Steffen Künn, Andreas Lichter, Ross McRae, Andrew Oswald, Panu Poutvaara, Johannes Rincke and Martin Sprenger as well as the editor William F. Shughart II and three anonymous referees for helpful comments and suggestions. Andreas Peichl is grateful for financial support from the Deutsche Forschungsgemeinschaft (DFG: PE 1675/1).

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Correspondence to Sebastian Siegloch.



Fig. 1
figure 1

Election probabilities: Electoral districts

Fig. 2
figure 2

Election probabilities: Party lists

Table 8 Characteristics of MPs by party affiliation
Table 9 OLS and matching—conditional wage gap
Table 10 Propensity score estimation

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Peichl, A., Pestel, N. & Siegloch, S. The politicians’ wage gap: insights from German members of parliament. Public Choice 156, 653–676 (2013).

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  • Politicians’ wage gap
  • Citizen candidate model
  • Remuneration from public office
  • Outside earnings