Dataset

The Federation of German Consumer Organisations is the umbrella organisation of consumer centers. Member organisations include the consumer centers in the 16 federal states and 25 other associations dealing with consumer policy. The consumer centers exist in all German federal states and deal with all kinds of consumer affairs. They are non-profit organisations whose work is supported by federal state funding, municipal, and district support for the indi-vidual advice centers and by project funding from the national government.

Our basic dataset is provided by the consumer center Baden-Wuerttemberg e. V. In the finan-cial sector, the consumer center Baden-Wuerttemberg offers general insurance advice,

con-struction loan advice, and a general counseling interview dealing with financial investments and old-age provisions. We use written protocols of these interviews to generate the basic dataset. For a detailed description of the dataset and the role of the consumer centers in Ger-many, see Section 4.3. At the end of the interview, the household receives the protocol, which includes a recommendation of an investment strategy. The proposal specifies the asset types, e.g. call money or federal savings bonds, investment amounts, and further notes if necessary.

In addition, the households are provided with the latest test results relating to the recommend-ed asset types from (for example) “Finanztest” or other journals. After the interview, the households are left on their own concerning the responsibility of implementing the strategy.

This procedure is different from a typical counseling interview at a bank. During the inter-view, the bank normally offers its products directly and thus advice might coincide with a purchasing decision. The clear separation of advice at the consumer center and the later pur-chase of the assets somewhere else afford us a unique opportunity. We use the recommended strategy by the consumer center to set up a personalized questionnaire.

Because of data privacy reasons, we could not match the basic original dataset to the res-ponses of the questionnaire and we thus also have to elicit personal data within the question-naire. It is organized as follows: In Part A of the questionnaire, we are interested in the gener-al reasons for the interview and for the choice of the consumer center. Second, we elicit the satisfaction with the interview and the advisor with the help of several items. Moreover, we ask households about the (perceived) benefits of the interview. For comparison, we also ask if the household had a second interview with another advisor since the one with the consumer center. For the satisfaction and benefit questions, we use a 7-point Likert scale with appropri-ate endpoints in each case (see Appendix A).³¹ In Part B, we collect data about gender, age, education, income, financial knowledge, risk tolerance³²

31 Amongst others Weng (2004), Preston and Colman (2000), and Cicchetti et al. (1985) show that reliability, validity, and discriminating power increases up to 7-point scales. After that additional effects can hardly be ob-served. In addition, too many scales might overburden subjects (see Viswanathan et al. (2004)).

, and investor type. We also ask for overconfidence, i.e. we include one question for “better-than-average” and one for “miscali-bration” (for the different forms of overconfidence see e.g. Svenson (1981), Lichtenstein et al.

(1982), or Glaser et al. (2005)).

32 We use a single self-assessment question to elicit the risk attitude. Amongst others Nosic and Weber (2010), Kapteyn and Teppa (2002), and Weber and Hsee (1998) show that intuitive subjective risk measures are better able to explain risk taking behavior (e.g. portfolio choices) than objective measures such as historical volatilities or more sophisticated measures such as lottery questions.

Part C includes the recommended investment strategy. We ask whether the households totally, partly, or not at all followed the advice provided by the consumer center. Moreover, we ask for the reason(s) not to follow the advice and offer room for suggestions on how to improve the advice offer of the consumer center. See Appendix A for the complete questionnaire.

From the consumer center we received protocols from 824 interviews, which took place from 2006 to 2008. Address data was available for 779 households. Of these 779 households, 212 returned the questionnaire to the consumer center, yielding a return rate of 27.21 percent. This rate is surprisingly high as we did not pay the participants but only included a prepaid envelope and information about the financial crisis as a little “thank-you” for filling in and returning the questionnaire.

5.3.2 Descriptive Statistics

Table 5.1 shows descriptive statistics of our dataset. Out dataset consists of 212 household observations; to be more precise, we have interview notes from 94 women, 47 men, 70 couples, and one observation with missing gender. The mean age in our sample is 46.62 years (median 45 years). The age of a couple is determined as the average of both partners. If we count every individual (regardless of marital status) the mean age is 46.72 (median 45). This composition is not unusual for an advised sample. All households in our sample are advised because questionnaires were sent only to those who previously had a counseling interview at the consumer center. Bluethgen et al. (2008) study a dataset with advised and non-advised individuals from a large German retail bank. They find that advised clients tend to be older, wealthier, more risk averse, and more likely to be female.

Income (gross income per year) is divided into 5 categories, with 1: less than 25,000 Euro, 2:

between 25,000 and 50,000 Euro, 3: between 50,000 and 75,000 Euro, 4: between 75,000 and 100,000 Euro, and 5: more than 100,000 Euro. The mean income category for single women is 2.09 and for men it is 2.52. Naturally, couples have a higher household income with a mean of 3.15.

Table 5.1: Descriptive statistics

The table reports descriptive statistics of our dataset. We report age, income class (scale from 1 (lowest) to 5 (highest), with 1: less than 25,000 Euro, 2: between 25,000 and 50,000 Euro, 3: between 50,000 and 75,000 Euro, 4: between 75,000 and 100,000 Euro, and 5: more than 100,000 Euro), education class (scale from 1 (lowest) to 6 (highest), with 1: still in school, 2: Hauptschul-graduation (Lower secondary education), 3: Realschul-graduation (Intermediate secondary education), 4: Abitur (University qualifica-tion exam), 5: University degree, and 6: Doctorate degree), financial knowledge (scale from 1 (very low) to 7 (very high)), and financial risk tolerance (scale from 1 (very low) to 7 (very high)) for single women, single men, couples, and for all households together.

Obs. Mean Median Std.Dev. Min. Max.

Risk tolerance 94 2.28 2 1.12 1 5

Men

Age 46 44.07 43 13.58 21 75

Income class 43 2.51 2 1.10 1 5

Education 47 4.64 5 1.07 1 6

Fin. knowledge 47 4.88 5 1.06 1 7

Risk tolerance 47 3.36 3 1.58 1 7

Couples

Age 68 47.03 42.75 13.50 26 75

Income class 65 3.15 3 1.08 1 5

Education 69 4.45 5 1.02 2 6

Fin. knowledge 70 4.41 5 0.91 2 6

Risk tolerance 70 2.73 2 1.52 1 6

Other

Education 210 4.42 5 1.01 1 6

Fin. knowledge 212 4.37 5 1.14 1 7

Risk tolerance 212 2.66 2 1.43 1 7

Education is divided into 6 categories, with 1: still in school, 2: Hauptschul-graduation (lower secondary education), 3: Realschul-graduation (intermediate secondary education), 4: Abitur (university qualification exam), 5: University degree, and 6: Doctorate degree. Our partici-pants are highly educated; the median household holds a university degree. Women state a mean financial knowledge of 4.08 (on a scale from 1 (very bad) to 7 (very good)), men of 4.88. Couples lie in between with a mean of 4.41. These relatively high means for education

and knowledge can again be explained by the fact that our sample is advised. Van Rooij et al.

(2007) find that financial literacy has an influence on the preferred source of financial advice:

people with low financial literacy are more likely to consult family and friends in contrast to those with high financial literacy who are more likely to read newspapers, magazines, books, and rely on financial advisors. A two-hour interview at the consumer center costs 140 Euro; in two experiments Godek and Murray (2008) analyze the willingness to pay for advice and find that people who process information rationally (in contrast to processing information expe-rientially) are willing to pay substantially more for advice.

In line with the literature (e.g. see Dohmen et al. (2010a), Weber et al. (2002), or for a meta-analysis Byrnes et al. (1999)) women state a lower risk tolerance (mean of 2.28 on a scale from 1 (very low) to 7 (very high)) than men (mean of 3.36); couples again lie in between (mean of 2.73).

As stated before, we could not match the questionnaire data with the original dataset. Never-theless, the observations vary in the recommended asset types, amounts, and notes, which we included from the original dataset, thereby enabling us to identify 207 of the 212 returned questionnaires. We do not use this information to match the dataset but for a small check if our dataset might be biased. We create a dummy variable in the original dataset that takes the value one if the household subsequently participates in the questionnaire analysis.

Table 5.2: Comparison of groups

The table compares descriptive statistics of the original dataset for two groups, those who returned the questionnaire (Questionnaire +) and those who did not (Questionnaire -). Age is measured in years, monthly income and expenditures in Euro. Risk1 (risk2) is the maximum loss in percent of the investment amount that could be tolerated over (at the end of) the investment period. Financial knowledge and expe-rience are measured on a scale from 1 (very low) to 5 (very high). The last column states z-statistics of the Wilcoxon rank-sum (Mann-Whitney) test.

Questionnaire - Questionnaire + Wilcoxon rank-sum test z-value

About 27 percent of the women returned the questionnaire, compared to about 24 percent of the men and couples. When comparing these figures to our questionnaire data (94 women, 47 men, 70 couples) we conclude that there must be some women who were part of a couple in the original dataset but responded as single women. There are several possible explanations for this; perhaps the couple has since separated or the women simply filled in the question-naire on her own. Overall, the proportions of returned questionquestion-naires are similar for women, men, and couples and the proportions of the whole sample compared to the subsamples are not significantly different either.

In addition, we find no differences in age, monthly income, expenses, or risk tolerance be-tween the households that participated and those that did not (see Table 5.2). However, we find that questionnaire participants on average have a higher knowledge of financial products (mean of 2.15 compared to 2.06, significant at the 10 percent level) and also more experience with financial products (mean of 2.12 compared to 1.99, significant at the 1 percent level).³³ The respondents overall seem representative for the whole dataset, with the possible excep-tion of a small bias regarding financial knowledge and experience. We already menexcep-tioned that our sample is highly educated as the median household holds a university degree (see Table 5.1) and we will have a more detailed look at the influence of financial knowledge later on in the analysis.

5.4 Results and Discussion