137 4.3 Impact of interview’s topic

CZAJA/TILLMANN/WALTER Occupational self-selection among bankers and financial regulators

IV-137

CZAJA/TILLMANN/WALTER Occupational self-selection among bankers and financial regulators

IV-138

Applying the STM algorithm to our interview data, we include the group membership of an interviewee, i.e. banker or regulator as well as the year of an interview as topical prevalence covariates. Put differently, when deriving interview topics we incorporate the fact that bankers and regulators have different thematic focuses and that these focuses change over time. Moreover, in addition to single words we also included bigrams and trigrams in the topic generating process, i.e. coherent combinations of two or three words.

Stop words without a deeper meaning were excluded.¹²

After estimating topic proportions for each interview, we create 30 variables capturing topic proportions for each interview and include those variables (except one to avoid perfect multicollinearity) in our baseline model described in section 4.1. Table IV-4 reports the regression results.

Table IV-4: Regression results with inclusion of interviews’ topic proportions

This table presents a rerun of our main regression extended by the 29 variables indicating the proportions of the 30 identified topic within an interview as derived from structural topic modelling (we exclude one variable to avoid perfect multicollinearity). Our main explanatory variable is 𝑅𝑒𝑔𝑢𝑙𝑎𝑡𝑜𝑟_𝑗 that denotes an indicator variable equaling one if the interviewee is a regulator and zero otherwise. Control variables are the same as presented in Table IV-3. Please see Table IV-A.1 in Appendix IV-A for detailed description of all variables. Double-clustered standard errors for interviewee and year are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10%

levels, respectively.

Selfishness Cognitive complexity Dishonesty Overconfidence

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES SelfRef Narc CogScore FRE CogPro DScore SAB Clout

Regulator -1.024* -4.170*** 1.645** -4.955*** 1.381** 0.172 -0.894** -6.353**

(-1.669) (-3.238) (2.019) (-2.714) (2.479) (0.313) (-2.143) (-2.084)

Observations 493 493 493 493 493 493 493 493

Adjusted R² 0.403 0.191 0.303 0.399 0.438 0.224 0.318 0.429

Further Controls YES YES YES YES YES YES YES YES

Topic Proportions YES YES YES YES YES YES YES YES

Year FE YES YES YES YES YES YES YES YES

Interviewer FE YES YES YES YES YES YES YES YES

As can be seen from Table IV-4, incorporating the topic proportions allows us to significantly increase the explanatory power of our regression models explaining up to 43.8% (CogPro) of variation in our dependent variables. Estimated coefficients, however,

12 Based on the model diagnostics reported in Figure IV-D.1 and Figure IV-D.2 in Appendix IV-D, we set the number of topics to 30. The ten most prevalent derived topics are presented as word clouds in Figure D.3 in Appendix IV-D, while the distribution of top ten topics (by prevalence) by interviewee group is illustrated in Figure IV-D.4.

CZAJA/TILLMANN/WALTER Occupational self-selection among bankers and financial regulators

IV-139

remain qualitatively the same. In addition, we find a statistically significant effect according the narcissism proxy variable amounting to -4.170, which corresponds to 1.020 standard deviations, indicating excessive selfishness among bankers compared to regulators.

5. Discussion and concluding remarks

Analyses of bankers’ and regulators’ language use in a uniform interview setting indicate that their linguistic styles differ significantly suggesting different psychological characteristics among them. Even after controlling for a battery of extant determinants of the characteristics of linguistic markers as well as interviews’ topic proportions, bankers display a higher selfishness amounting to 0.481 (1.020) as measured by the standardized score for SelfRef (Narc). Thus, we can confirm our first hypothesis. Analogously, we document bankers being more prone to overconfidence by reporting a 0.414 and 0.546 higher standardized score for SAB and Clout, respectively. Therefore, again, we can confirm our hypothesis. On the other hand, regulators are associated with a higher cognitive complexity, the value of which is 0.490, 0.736 and 0.587 higher than that of bankers, depending on the standardized measure applied. This finding contradicts our hypothesis of smarter bankers. In fact, our finding support early exploratory research by Posner and Schmidt (1982) as well as empirical evidence provided by Crewson (1995) documenting that public administrators are more capable than business administrators in terms of arithmetic reasoning, mathematical knowledge and verbal expression. In the same vein, Lyons et al. (2006) show that public administrators are doing more intellectually stimulating work, i.e. performing ability challenging projects and tasks. On the contrary, especially in the corporate context, emotional intelligence, which we cannot measure here, plays a special role (Côté et al., 2010; Sadri, 2012). With regard to dishonesty, we can neither reject nor confirm our hypothesis. Although Cohn et al. (2014) document strong evidence of dishonesty in the banking industry, measuring dishonesty by means of linguistic measures show no significant differences between bankers and regulators. Our results, thus, support the main result of Rahwan et al. (2019) who find no evidence for dishonesty in the banking industry by replicating the analysis of Cohn et al.

(2014) with participants from different populations, i.e. bankers from different-sized banks from the Asia Pacific region, Middle East and Europe. It follows that bankers’

psychological characteristics might vary across jurisdictions.

CZAJA/TILLMANN/WALTER Occupational self-selection among bankers and financial regulators

IV-140

The empirical strategy used in this study to assess group differences between bankers and regulators is itself novel, enabling us to systematically investigate actors’ psychological characteristics. The advantage of this empirical strategy arises mainly from the fact that classical personality tests are not an option in this context. Nevertheless, our procedure is subject to possible limitations.

First, unfortunately, our final sample of regulators consists of only 58 interviews. One way to address this issue would be to include speeches, debates and other interview formats in the investigation. However, this would lead to a possible speech source effect as documented for example in Slatcher et al. (2007), i.e. differences in linguistic measures due to varying communication partners, networks and locations.

Second, ideally, we would have analyzed personality data of economics and business administrations students who subsequently decided to work either in the financial industry or as financial regulators. This approach would allow us to isolate the relationship between psychological characteristics and occupational self-selection.

Unfortunately, however, this respective data is lacking. Nevertheless, in the absence of such data, our approach allows us to gain new insights into the psychological characteristics of bankers and regulators, which would not be possible without this approach.

Based on the strong evidence for the relationship between psychological characteristics and job performance including problematic practices (e.g., Barrick et al., 2002; Cohn et al., 2014; O’Boyle et al., 2011) as well as against the background that financial regulators frequently make subjective decisions about considered banks (Rosen, 2003), it might be expedient to alter the incentives to work in the considered occupations. Here, it must be policy makers’ objective to reduce fraudulent practices that cause economic damages as well as a lack of stability and reputation of the financial system (e.g., Abrantes-Metz et al., 2012; Barth et al., 2012; Cohn et al., 2014). At the same time, a discussion on a further restriction of the revolving door between regulation and industry is indispensable.¹³ However, to make informed decisions and especially against the background that our

13 According to Lucca et al. (2014), the evidence on the revolving door is consistent with the view that the flow of workers between regulators and banks predominantly contributes to an exchange of information, not a „quid-pro-quo“.

CZAJA/TILLMANN/WALTER Occupational self-selection among bankers and financial regulators

IV-141

investigation can only provide a glimpse into the psychological characteristics of bankers and regulators, future studies on the current topic are required.

CZAJA/TILLMANN/WALTER Occupational self-selection among bankers and financial regulators

IV-142