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4.1 Determinants of the general risk question

We ran six different regressions to test how the willingness to take risk in general is

affected by gender, standard of living and parental education. It is notable that the standard of living is a proxy that refers to the subjective answer (on a scale from 1 to 5) to the survey item that states: “Study grant and the financial backing of parents will guarantee sufficient standard of living during my studies” This means that the determinant of standard of living is not an absolute value (of disposable income for example), which would measure the actual standard of living. It only measures the respondent’s believes of the hypothetical living standards during the time of studying. One could argue that a more risk-averse person will understate the standard of living with a given income level, when compared to a person who is willing to take more risk in general. The abitur determinants are binary variables that indicate whether mother/father has completed the Finnish Matriculation Examination or equivalent degree of studies. The education determinants refer to the degree of education on an eight point scale (value of 3 refers to the abitur level).

The summary of the regressions are presented in a table 4.1. As we can see the coefficients of the standard of living and parental education variable are on the contrary to the results of Dohmen et al. (2011). Results in table 4.1 suggest that there are no gender differences in the willingness of taking risks in general. This result remains consistent with all the 6 regressions. Somewhat surprisingly the parental education does not seem to have any impact on individual risk attitudes in the sample. Standard of living is the only determinant that has significant coefficient on the willingness to take risks. The coefficient is quite small but remains to be significant even after controlling for parental education. This contradicts our assumption that higher standard of living will imply higher willingness to take risk in general. In fact table 4.1 says the exact opposite: respondents who are more optimistic about their standard of living are slightly less willing to take risk in general. Strong arguments from this result should not be made as it might not be a reliable proxy of standard of living.

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Table 4.1 Primary determinants of the general risk question

(1) (2) (3) (4) (5) (6) Female 0.013 0.004 0.01 0.008 -0.002 -0.001 [0.061] [0.061] [0.062] [0.062] [0.062] [0.063] Abitur mother -0.05 -0.05 [0.076] [0.076] Abitur father 0.003 0.01 [0.066] [0.660] Education mother -0.019 -0.019 [0.019] [0.020] Education father -0.007 -0.006 [0.017] [0.017]

Standard of living (proxy) -0.081** -0.073* -0.074*

[0.027] [0.027] [0.027]

Observations 3385 3365 3294 3294 3278 3278

Dependent variable: willingness to take risk in general

Note: Coefficients in columns (1) to (6) are from OLS regressions where dependent variable is the response to the general risk question. Abitur refers to the secondary school graduate binary variable presented in table 3.1. Education is the degree of education on a scale from 0 to 7. Proxy of standard of living refers to the individual beliefs of the standard of living on a five point scale (see last row of table 3.1). Standard errors are shown in brackets. *, ** and *** denote the significance at 10, 5 and 1 percent level, respectively.

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Table 4.2 Fields of education as the determinants of the general risk question

Female

-0.049 0.849 -0.030

Humanities and arts -0.011 0.117 0.031

Social sciences and business 0.014 0.468 -0.079

Natural sciences -0.038 -1.052 0.115

Technology 0.022 -1.333 0.059

Agriculture and Forestry -0.049

Health and welfare -0.013 0.593 -0.002

Services 0.079 -0.231 0.008

Dependent variable: Field of further education

Willingness to take risk x Female Willingness

to take risk Teacher education and

educational science

Table 4.2 shows the results from probit regressions where dependent binary variable is the chosen field of further education. The differences in risk attitudes between different applicant groups are only minor. There is a slight tendency that students who apply in the field of teacher education and natural sciences are more risk averse. Applicants in a field of services are a bit more willing to take risk relative to other applicants. To examine if the responses to the general risk question differs between women and men, we made variable which is a product of binary variable ‘female’ and the value of the willingness to take risk. We can therefore examine the differences between genders to the general risk question within different fields of education. This determinant is in the fourth column of the table 4.2 above. We find no significant gender differences in the willingness to take risk within subject-of-study subgroups.

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4.2 Do students who are more willing to take risks in general apply to more selective (low admission rate) universities or polytechnics?

Table 4.3 Determinants of the admission rate of objective degree

Coef. Std. Err.

Constant 14.28 0.451 0.000

Female -4.150 0.295 0.000

Risk neutral -1.370 0.359 0.000

Risk lover -1.527 0.378 0.000

Low standard of living (proxy) -0.148 0.326 0.649

Mother abitur 0.274 0.357 0.443

Father abitur 0.015 0.309 0.960

Dependent variable: admission rate (%) of the chosen objective degree

Note: Bolded coefficients are statistically significant at the 1 percent level. All determinants are binary variables. N=2340.

We ran regression for the admission rate of the selected further education with six determinants: gender, the categorical risk variable, standard of living and parental education. All determinants are binary variables. Risk neutral and risk lover coefficients refer to the effect on admission rate relative to risk-averse observations. Low standard of living is described by the survey item that self-asses the student’s standard of living. Low standard of living gets value 1 if student’s response disagreed (responses 1 and 2 on a scale from 1 to 5) with the claim “Study grant and the financial backing of parents will guarantee sufficient standard of living during my studies”. Parental education is determined simply by whether parents have completed the Finnish matriculation examination or equivalent. Table 4.4 above summarizes the results. On average, men apply to less prestigious

universities or polytechnics than women. The selected further education chosen by women has 4.15 percentage point lower admission rate, on average, relative to men. This is strong evidence against the argument that women are shying away from competition. This finding is in line with Jurajda and Munich (2011), Örs et al. (2013) and Pekkarinen (2014) whom

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did not find any reliable evidence that women are shying away from applying to

competitive further education. We have to note, however, that we cannot control the ability of the applicants. It might be that women's dominant performance in the matriculation examination should actually make women to apply more competitive objective degrees relative to men. Also, results may vary when examining the sample which actually enters to the entrance examination. For example Pekkarinen (2014) found that women do apply to less demanding universities than men but this gender difference disappeared when looking the sample of applicants who finally showed up in the examinations.

The willingness to take risk in general has a statistically significant relation to the admission rate of further degree. The general risk question was categorized into three groups (see figure 3.4) and used as a regressor for model in table 4.3. Note that the coefficients for risk neutral and risk lover refer to the effect on relative to risk averse. In other words, students defined as risk neutral, according to the response to the general risk question, applied to demanding further education. On average, the admission rate for risk neutral applicants was 1.4 percentage points lower than for risk-averse students. For risk lover subgroup the coefficient is even larger compared to the risk-averse students but there is no substantial difference relative to risk neutral students. These findings suggest that risk attitude has some impact on the willingness to compete which is usually not found in the lab experiments in the literature. For example, Almås et al. (2014) did not find any

substantial relation between risk taking and competitiveness. It seems that the general risk question gives more reliable and significant results than risk variables formulated by lottery choices (Buser, Niederle and Vesterlund, 2013, 29).

As we see from figure 3.1 and from table 3.2 the gender distribution varies heavily between fields of education. For example the share of women applicants to teacher education and educational science is almost 85 percent. As the average admission rate for teacher education studies is by far the lowest, the gender distribution will lead to a large gender difference in average admission rate between men and women. Also, women (men) who apply to highly ‘male’ (‘female’) dominated further education are likely to possess strong specific skills for certain field of education. (Jurajda and Münich, 2008). To minimize the effect of such selection of specific unobservables, which may misrepresent the regression,

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we repeat the model from table 4.3 for gender balanced subgroup. The field of education is included in the regression if the share of women applicants is over 25 percent but below 75 percent. With this condition teacher education and educational science, technology and, health and welfare are excluded from the regression.

Table 4.4 Determinants of the admission rate of objective degree in gender balanced fields of education Coef. Std. Err. Constant 13.91 0.648 0.000 Female -1.839 0.412 0.000 Risk neutral -1.615 0.513 0.002 Risk lover -2.238 0.530 0.000

Low standard of living (proxy) -0.581 0.458 0.205

Mother abitur 0.226 0.507 0.655

Father abitur -0.211 0.435 0.628

Dependent variable: admission rate (%) of the chosen objective degree

Note: Bolded coefficients are statistically significant at the 1 percent level. Risk neutral coefficient is statistically significant at the 5 percent level. All determinants are binary variables. This regression includes only applicants from gender balanced field of

educations. Furthermore, the field of education is included in the regression if the share of women applicants is over 25 but below 75 percent. With this condition teacher education and educational science, technology and, health and welfare are excluded from the regression. N=1373.

Table 4.4 summarizes the results from the regression, where dependent variable is the admission rate of further education for gender balanced subset. Gender coefficient is much smaller than in the regression for the whole sample. Women still apply, on average, more prestigious study programs but the difference to men is smaller, just below two percentage points. This result is statistically significant at the 1 percent level. The coefficients of risk attitudes seem to have even stronger impact in the gender balanced regression. The

admission rate of further education of risk neutral students is 1.6 percentage points and risk lovers 2.2 percentage points lower compared to the risk-averse applicants. This suggest that

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risk attitudes do play a role in the choice of further education and that students who are willing to take more risk in general, will choose more prestigious study programs.

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