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Conforming to Gender Stereotypes and Entrepreneurs’ Financing Outcomes

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Weixi Liu discusses how broader gender differences in entrepreneurial confidence play out in the context of bank lending.

Image courtesy of interviewee. February 1, 2024

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The paper builds on previous research by Professors Mark Carlin and Sue Marlow, examining gender differences in financing activities and outcomes for UK small businesses, especially during the financial crisis. It challenges the notion of gender discrimination in lending, revealing that female entrepreneurs often have lower demand for finance but may receive equal or sometimes better financing outcomes than their male counterparts, particularly in adverse economic conditions. This video video phenomenon is attributed to the perception of female entrepreneurs as more risk-averse, which lenders may favor.

The current paper seeks to explore the mechanisms behind these findings, including how deviations from gender stereotypes affect financing outcomes. It focuses on the overconfidence levels of female entrepreneurs and examines how this trait influences their financing requests and outcomes. Despite women being generally risk-averse, overconfident female entrepreneurs may take on riskier financial options compared to their male counterparts.

On the demand side, the study finds that while female entrepreneurs still prefer internal finance, overconfident women exhibit a tendency towards riskier, longer-term financing options. On the supply side, lenders show a preference for moderate overconfidence; however, excessive overconfidence in female entrepreneurs can lead to lower chances of loan approval due to perceived risk-taking.

The research utilizes data from a UK SME Finance Monitor, analyzing firm characteristics and financing outcomes related to overconfidence. It concludes that there is no inherent gender discrimination in lending, but deviations from gender norms can affect the approval process disproportionately for women. This video video highlights the need for lenders to balance the use of hard and soft information in their decision-making processes to avoid misjudging borrowers based on gender stereotypes.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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This paper was originated from an earlier paper,

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by Professor Mark Carlin, which is part of the author team

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of this current paper,

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and Professor Sue Marlow from, Ingham University,

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where we look at, possible gender discrimination

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or gender differences in the financing activities, as well

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as the financing outcome for UK small businesses,

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especially, during the financial crisis.

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Um, the finding from that previous paper is that, well,

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whereas, female entrepreneurs or

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or firm owned by female owner managers, generally,

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have a lower demand for finance there by no means,

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discriminated by the banks, were discriminating, I mean,

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so they are by no means offer less finance

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than male entrepreneurs.

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Uh, in fact, actually, we found

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that under certain circumstances, especially

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during the financial crisis

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or negative economic, environment, they're

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actually more likely to get the loan they applied for.

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Therefore, we, we call them positively, discriminated.

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And our explanation for this positive discrimination is

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that, so female entrepreneurs are deemed

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as more risk averse.

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And, this,

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more conservative risk attitude was actually favored

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by banks or by lenders, especially during the,

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financial crisis.

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And therefore, while they are more likely to

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re rely on internal finance

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and less likely to turn on risky external finance,

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actually lender prefer to offer finance to those,

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more conservative female entrepreneurs.

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But, a natural extension of this previous paper is

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that term we want look slightly deeply

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into the mechanism of, this, phenomenon.

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So under what circumstances, are female more likely

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or less, less likely to get the finance they require?

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And, the question is that what happens if,

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female entrepreneurs, act in a way

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that deviate from their, average

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or from their gender stereotype?

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Um, so an angle we look at this question is

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to look at their behavior biases,

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in particular look at their overconfidence level,

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but basically, they will look at, so, the,

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miscalibration between their belief of, the success,

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the, successful financial application

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and their actual objective, success probability

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or their financial activities, governed by their firm

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and, business

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and, entrepreneurial characteristics as well

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as their financial fundamentals.

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So that's the basic idea of this paper.

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And what we found is that, yes, we finding,

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using a different data set,

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but at the same time a large scale data set on,

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small businesses.

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We find that, yes,

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female entrepreneurs are still less likely

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to rely on external finance.

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Um, but so,

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for those overconfident female entrepreneurs,

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they actually exhibit, exhibit,

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a risk shifting behavior,

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whereas they are more likely than their male counterparts

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to rely on or to apply for, long, long-term risky,

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type of finance to finance long-term and risky projects.

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And, this is the demand side finding.

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And on, on the supply side, we find that whereas,

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lenders generally prefer a little bit

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of overconfidence, but,

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but while it is, so either male

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or female, if they deviate too much from their gender norm

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or gender average, their chance

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of getting finance is significantly lower.

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This is especially true for women, whereas they, they are

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actually awarded for being, mort,

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moderately overconfidence,

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but when women exhibit extreme overconfidence,

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so they're more likely to be punished, by the lenders

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for being overconfidence

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or being risk-taking than, applicants who,

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who, who are led by male owners.

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So that's our main findings here.

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But of course, so we have to define

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what overconfidence here is.

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So overconfidence is basically defined as, basically,

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in, in two respects, either as an overestimation of,

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the two expected, return of any activity

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or underestimation of, the risk of,

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an activity or, basically underestimation

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of the probability, probability of failure.

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So basically, so we use overconfidence as

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a measure of risk attitude

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by the entrepreneurs in our study.

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And, we, in this study, we want to examine, rather,

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so basically, whether an abnormal risk attitude,

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attitude will affect the, behavior

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and outcome of financing activities, by,

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entrepreneurs of different genders,

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a wide variety of literature.

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And, in those, behavior finance, such

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as Ian Robinson's, general, I guess,

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financial economics paper,

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and also on gender studies, for example, the, mar

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on gender finance,

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or, for small businesses in entrepreneurs,

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entrepreneurship theory and practice.

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Um, basically, so our argument, can be twofold.

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First about over, as I I said over confidence is a measure

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of miscalibration of the true unexpected

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outcome of, an activity.

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So, so if the two are not aligned to each other,

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so we say that there is a bias, on, on, on,

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on behavior perspective, perspective, in terms

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of, financing activities, in terms of,

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general corporate finance, the over confidence is not all

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bad because we, we normally think that,

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if someone's overconfident, it means that, he

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or she may be, taking excess risks,

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we'll jeopardize the, the whole outcome

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of any activities.

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But this is not the, whole story

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because, from a financier or from a lender perspective,

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or from an investor's per perspective, they actually want

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to promote, or we want to encourage a moderate level

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of overconfidence

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because overconfidence means that,

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the people are willing to, exert costly effort

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and effort taking, as we know, is the key

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to especially the key to entrepreneur success.

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Um, so this is part of the story.

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And the second part of the story is that,

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on gender finance, so there are lots of paper

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on gender stereotypes, in general.

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So, in terms of finance, female entrepreneurs,

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or female are risk avert.

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They don't like risk.

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And, so they, their behavior is more conservative,

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whereas, men are more, well also labeled as,

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risk takers.

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They like to take risk.

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And, so of course there are other,

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literature basically, from social row series,

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about, the exact me mechanism

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of risk preferences by, by, by different genders.

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For example, women tends to be, well exhibit,

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fairness or equality, they're more likely to cooperate so

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that, that they're the more negotiable,

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but which all this means that, so women are more likely,

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or, or more willing to relapse the search

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for their own goals for the general or general good.

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So therefore, so we argue from the demand side,

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so women are natural risk avert so that they are less likely

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to rely on, risky external finance

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and will rely on internal finance,

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which is costless on the supply side.

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Yes, in general, as I argued, so

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landers prefer a little bit over competence to, to,

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to promote an effort taking,

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but once you are too over competence,

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or if you deviate too much from the optimal level

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of over competence, so landers would not like it.

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So, yes, this is particular true for women.

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So if you compare men

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and women, for female entrepreneurs,

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they are naturally less likely to be overconfident.

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So that, so landers would like to,

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if they can observe their level of over confidence,

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landers would like to promote, a little bit

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of overconfidence by female entrepreneurs

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and reward them more for being slightly more overconfident.

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Whereas for men, since they are overconfident

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or they're more overconfident in the first place, the reward

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of being, over confidence is,

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you can see lower than female,

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but at the same time, so there's reward,

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but at the same time, there's punishment

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for being too over confidence.

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And that's what we argue.

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That's the if things female by, construction,

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they are deemed to be, less risk taking

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or more risk averse if they exhibit too much risk taking,

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tendencies,

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or in our case, if they exhibit a very high

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overconfidence level.

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So, of course, so banks will,

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or lenders will, will think that, they, they,

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they did become, they become too deviant from their gender

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norm that though they will exhibit,

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very high risk taking activities, which will jeopardize

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or endanger the whole project or the whole investment.

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Therefore, so the outcome of financing activities

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for very overconfident, female entrepreneurs tends

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to be, less favorable than male entrepreneurs.

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That's our whole, main argument here.

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First about the data, the data we use is a,

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a very large scale, representative, database for,

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UK SMEs.

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It's called, SM e Finance Monitor.

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It tees a, quarterly conducted, a survey by,

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John and Bradstreet,

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but also, so, commissioned

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by the government in the UK government to basically

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to examine or to gauge the financing activities,

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by small businesses in the uk.

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And, so we use, six waves of data,

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between 2011

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and two, two, so basically six quarters worth of data.

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And each way we have about five

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observations in total.

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We have, firm level observations.

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Of course, not all of them are useful, is

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because that, so, based on this data, we,

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the data asks a question that allows us

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to measure the overconfidence level,

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because previously we know that is basically defined

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as the miscalibration or difference between the actual

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and the perceived.

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You can see, probability of, of success or, or, or,

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or riskiness of any activities.

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Um, in this, in our data sets,

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there is a survey question asked

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for all financing applicants.

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So pay attention here.

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So it is based on, condition on, on, on, on a firm

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or on, on, on a business applying for finance.

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So this question does not apply for any firm

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that does not apply for finance.

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Um, so that will reduce our sample size significantly.

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Um, so for those apply for finance,

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there's a question asked on that about,

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before, so pre-application, how confident are you,

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in getting the finance?

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So from this questions, we can, master the,

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the exam expectations about their, their,

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their expectation about, the, the, the odds of success

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for their financing activities.

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And, of course, on other side, side,

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we can measure statistically the objective,

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probability of, alone being approved

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based on business and owner characteristics,

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and based on some, kind

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of financial accounting fundamentals.

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So the between the two, so the

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perceived probability of approval

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and the actual objective of estimated

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or predicted probability of approval, so is used

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to as our measure of over confidence, of course,

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by definition, a large positive number

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because it is a, a probability.

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So in extreme, a value of one means

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that the, the son

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or the, the entrepreneur is extremely overconfident,

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basically means that, he thinks, or he

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or she, she thinks that she, they will,

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get the finance, for sure.

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Whereas, the actual odds of getting finance is zero.

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Of course, a minus one means that,

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one is extremely underconfident,

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so they're not confident at all,

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and they totally miss the probability of their,

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of getting finance.

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So the method we, we, we use is basically,

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a single linear regression to look at, the probability.

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So the, effect of overconfidence,

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not only the linear relationship,

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but also the quadr relationship,

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because I argue, I said just now that,

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so over confidence is not, at,

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at all bad, not all bad.

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So there's a trade off between, the levels

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of over confidence.

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So that we met, we, we examine the non-linear

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or quad relationship between overco levels

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and the a, the, probability of demand for finance

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and, the, amount of, demand.

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And b on the supply side, the probability of,

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their finance application being approved in general

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on, on the demand side.

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Um, so we still find that, in general, women demand,

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a lower amount of finance.

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This is, in line with their risk preferences

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because they are more risk averse and, and, so

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therefore they are less likely to rely on,

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risky external finance.

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Uh, but on the other hand, once a, a,

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a female entrepreneur becomes, very overconfidence,

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so it doesn't mean necessarily mean that they will apply

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for more finance or take on more risk, in this sense.

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So what they exhibit is what we call risk shifting.

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So rather than rely on short term,

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smaller amount of finance,

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they generally rely on longer term finance, not necessarily,

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by a large amount,

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because we know that for small businesses, the loan amount

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is dually determined by the size

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and, the age of the businesses.

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But here we mainly argue that they will rely on more risky

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type of finance to find long term risky projects.

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So this is, the demand findings.

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And on the supply side, generally speaking,

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we find this,

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a long linear inverted u shape relationship between,

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over confidence and the, probability

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of a loan being approved.

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Whereas, so there is an optimal level of over confidence,

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and once so either, below

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or above this optimal level of, over confidence,

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we see a lower probability of approval.

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But, most importantly, we argue that if, if,

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a general entrepreneur becomes overly,

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over confidence, so banks will observe this

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and think it,

17:03.405 --> 17:06.485
and treat it as a sign of excess risk taking, and

17:06.485 --> 17:09.525
therefore they will, they are less likely to,

17:09.575 --> 17:11.885
among the finance to these applicant.

17:12.265 --> 17:16.405
But on the other hand, if we compare the relationship,

17:16.465 --> 17:21.005
if we compare this, inverted U shape, between female

17:21.385 --> 17:24.525
and male entrepreneurs will see that

17:25.305 --> 17:28.245
the U shape will more, will be more steep

17:28.905 --> 17:32.165
for female entrepreneurs, which means that so

17:32.695 --> 17:37.285
banks will put a higher weight for deviation,

17:37.355 --> 17:39.685
from gender stereotypes

17:40.025 --> 17:42.685
for female entrepreneurs than male entrepreneurs.

17:43.105 --> 17:46.645
Um, particularly, or specifically what we find that term.

17:46.785 --> 17:51.005
So for moderate level of overconfidence, we see

17:51.005 --> 17:54.405
that the probability of approval is higher

17:54.545 --> 17:57.485
for female entrepreneurs than for male entrepreneurs,

17:57.855 --> 17:58.885
which means that

17:59.115 --> 18:03.695
because female are naturally more risk averse, so

18:03.695 --> 18:06.615
that banks have more tendency or, or

18:06.635 --> 18:10.535
or more likely to, use this,

18:10.535 --> 18:13.895
landing mechanism to encourage a slightly

18:14.035 --> 18:17.815
or moderate level of overconfidence than male entrepreneurs.

18:17.885 --> 18:19.775
Whereas on the other hand,

18:20.035 --> 18:24.375
if a female entrepreneur becomes overly overconfident,

18:25.495 --> 18:27.715
so they will be punished

18:28.135 --> 18:32.475
by a large extent than male, entrepreneurs, the same,

18:32.575 --> 18:35.195
argument applies to the, are the end of the story,

18:35.285 --> 18:36.675
which is under confidence.

18:37.255 --> 18:41.035
So if, a FEMA is overly under confidence,

18:41.655 --> 18:44.115
so which they, which means that they're, they're not likely

18:44.115 --> 18:45.795
to take any risk at all.

18:46.375 --> 18:49.235
Um, equally, of course, on the one hand,

18:49.235 --> 18:51.715
they will not apply for external finance,

18:51.855 --> 18:54.195
but if they do apply for external finance,

18:54.425 --> 18:57.275
they're also likely to be rejected as well.

18:57.895 --> 19:01.795
The conclusion is that, so whereas, we know, so

19:02.905 --> 19:06.315
find, because of the technology, advancement,

19:06.795 --> 19:08.915
recently banks are more likely to use this,

19:08.915 --> 19:13.075
complex computer-based algorithms to, look at the,

19:13.075 --> 19:15.355
quality of, borrowers.

19:16.055 --> 19:18.555
Uh, at the same time, they also use some,

19:18.815 --> 19:23.115
so screening devices, such as, behavioral measures

19:23.215 --> 19:26.875
to look at the, risk preferences of entrepreneurs or,

19:26.895 --> 19:31.075
or the borrowers in order to determine their, their, so,

19:31.135 --> 19:32.795
or in order to incorporate

19:32.795 --> 19:36.235
that term soft information into their hard information in

19:36.235 --> 19:37.715
making their long decisions.

19:38.175 --> 19:41.555
And, our conclusion is that basically in general,

19:41.665 --> 19:44.235
there's no gender discrimination for male

19:44.335 --> 19:45.835
and female entrepreneurs.

19:46.455 --> 19:49.195
Um, so once banks

19:49.295 --> 19:52.715
or lender start to look at software, information,

19:52.825 --> 19:55.395
they will start to discriminate,

19:55.535 --> 19:59.635
or they will start to punish, any borrowers

19:59.815 --> 20:03.075
who deviate too much from their gender average.

20:03.775 --> 20:05.475
So this is our main finding.

20:05.615 --> 20:09.835
But, of course the in, in the implication is that,

20:09.895 --> 20:12.915
so there is a fine balance between the extent

20:12.915 --> 20:16.755
to which the banks use such, sought, information.

20:17.145 --> 20:21.995
Whereas, more realistic

20:22.015 --> 20:25.435
or more reasonable use of soft information will help banks

20:25.535 --> 20:30.205
to, to better judge the quality of,

20:30.385 --> 20:34.805
the borrowers, but overly rely on the soft information,

20:35.345 --> 20:40.285
will mean that, so sometimes, banks will tend

20:40.285 --> 20:44.965
to mistakenly reject those, so better quality,

20:45.185 --> 20:46.765
borrowers basically

20:46.815 --> 20:50.005
Based on their gender, characteristics

20:50.105 --> 20:51.125
or stereotypes.

20:51.665 --> 20:54.845
So, which means, we argue that there is a balance

20:54.985 --> 20:57.845
or is a trade off between, so the use

20:57.865 --> 21:00.885
of far hard information such as, fundamentals

21:00.885 --> 21:02.885
and the use of, behavioral measures.

00:00 - This paper was originated from an earlier paper, by Professor Mark Carlin, which is part of the author team of this current paper, and Professor Sue Marlow from, Ingham University, where we look at, possible gender discrimination or gender differences in the financing activities, as well

00:19 - as the financing outcome for UK small businesses, especially, during the financial crisis. the finding from that previous paper is that, well, whereas, female entrepreneurs or or firm owned by female owner managers, generally, have a lower demand for finance there by no means, discriminated by the banks, were discriminating, I mean, so they are by no means offer less finance than male entrepreneurs. in fact, actually, we found

00:51 - that under certain circumstances, especially during the financial crisis or negative economic, environment, they're actually more likely to get the loan they applied for. Therefore, we, we call them positively, discriminated. And our explanation for this positive discrimination is that, so female entrepreneurs are deemed as more risk averse. And, this, more conservative risk attitude was actually favored

01:22 - by banks or by lenders, especially during the, financial crisis. And therefore, while they are more likely to re rely on internal finance and less likely to turn on risky external finance, actually lender prefer to offer finance to those, more conservative female entrepreneurs. But, a natural extension of this previous paper is that term we want look slightly deeply into the mechanism of, this, phenomenon.

01:56 - So under what circumstances, are female more likely or less, less likely to get the finance they require? And, the question is that what happens if, female entrepreneurs, act in a way that deviate from their, average or from their gender stereotype? so an angle we look at this question is to look at their behavior biases, in particular look at their overconfidence level, but basically, they will look at, so, the,

02:32 - miscalibration between their belief of, the success, the, successful financial application and their actual objective, success probability or their financial activities, governed by their firm and, business and, entrepreneurial characteristics as well as their financial fundamentals. So that's the basic idea of this paper. And what we found is that, yes, we finding, using a different data set,

03:04 - but at the same time a large scale data set on, small businesses. We find that, yes, female entrepreneurs are still less likely to rely on external finance. but so, for those overconfident female entrepreneurs, they actually exhibit, exhibit, a risk shifting behavior, whereas they are more likely than their male counterparts

03:31 - to rely on or to apply for, long, long-term risky, type of finance to finance long-term and risky projects. And, this is the demand side finding. And on, on the supply side, we find that whereas, lenders generally prefer a little bit of overconfidence, but, but while it is, so either male or female, if they deviate too much from their gender norm or gender average, their chance of getting finance is significantly lower.

04:08 - This is especially true for women, whereas they, they are actually awarded for being, mort, moderately overconfidence, but when women exhibit extreme overconfidence, so they're more likely to be punished, by the lenders for being overconfidence or being risk-taking than, applicants who, who, who are led by male owners. So that's our main findings here. But of course, so we have to define

04:43 - what overconfidence here is. So overconfidence is basically defined as, basically, in, in two respects, either as an overestimation of, the two expected, return of any activity or underestimation of, the risk of, an activity or, basically underestimation of the probability, probability of failure. So basically, so we use overconfidence as a measure of risk attitude by the entrepreneurs in our study.

05:20 - And, we, in this study, we want to examine, rather, so basically, whether an abnormal risk attitude, attitude will affect the, behavior and outcome of financing activities, by, entrepreneurs of different genders, a wide variety of literature. And, in those, behavior finance, such as Ian Robinson's, general, I guess, financial economics paper, and also on gender studies, for example, the, mar

05:57 - on gender finance, or, for small businesses in entrepreneurs, entrepreneurship theory and practice. basically, so our argument, can be twofold. First about over, as I I said over confidence is a measure of miscalibration of the true unexpected outcome of, an activity. So, so if the two are not aligned to each other, so we say that there is a bias, on, on, on, on behavior perspective, perspective, in terms

06:34 - of, financing activities, in terms of, general corporate finance, the over confidence is not all bad because we, we normally think that, if someone's overconfident, it means that, he or she may be, taking excess risks, we'll jeopardize the, the whole outcome of any activities. But this is not the, whole story because, from a financier or from a lender perspective, or from an investor's per perspective, they actually want

07:08 - to promote, or we want to encourage a moderate level of overconfidence because overconfidence means that, the people are willing to, exert costly effort and effort taking, as we know, is the key to especially the key to entrepreneur success. so this is part of the story. And the second part of the story is that, on gender finance, so there are lots of paper on gender stereotypes, in general.

07:39 - So, in terms of finance, female entrepreneurs, or female are risk avert. They don't like risk. And, so they, their behavior is more conservative, whereas, men are more, well also labeled as, risk takers. They like to take risk. And, so of course there are other, literature basically, from social row series, about, the exact me mechanism

08:13 - of risk preferences by, by, by different genders. For example, women tends to be, well exhibit, fairness or equality, they're more likely to cooperate so that, that they're the more negotiable, but which all this means that, so women are more likely, or, or more willing to relapse the search for their own goals for the general or general good. So therefore, so we argue from the demand side, so women are natural risk avert so that they are less likely to rely on, risky external finance

08:54 - and will rely on internal finance, which is costless on the supply side. Yes, in general, as I argued, so landers prefer a little bit over competence to, to, to promote an effort taking, but once you are too over competence, or if you deviate too much from the optimal level of over competence, so landers would not like it. So, yes, this is particular true for women. So if you compare men

09:23 - and women, for female entrepreneurs, they are naturally less likely to be overconfident. So that, so landers would like to, if they can observe their level of over confidence, landers would like to promote, a little bit of overconfidence by female entrepreneurs and reward them more for being slightly more overconfident. Whereas for men, since they are overconfident or they're more overconfident in the first place, the reward of being, over confidence is,

09:57 - you can see lower than female, but at the same time, so there's reward, but at the same time, there's punishment for being too over confidence. And that's what we argue. That's the if things female by, construction, they are deemed to be, less risk taking or more risk averse if they exhibit too much risk taking, tendencies, or in our case, if they exhibit a very high

10:28 - overconfidence level. So, of course, so banks will, or lenders will, will think that, they, they, they did become, they become too deviant from their gender norm that though they will exhibit, very high risk taking activities, which will jeopardize or endanger the whole project or the whole investment. Therefore, so the outcome of financing activities for very overconfident, female entrepreneurs tends to be, less favorable than male entrepreneurs.

11:02 - That's our whole, main argument here. First about the data, the data we use is a, a very large scale, representative, database for, UK SMEs. It's called, SM e Finance Monitor. It tees a, quarterly conducted, a survey by, John and Bradstreet, but also, so, commissioned by the government in the UK government to basically to examine or to gauge the financing activities,

11:32 - by small businesses in the uk. And, so we use, six waves of data, between 2011 and two, two, so basically six quarters worth of data. And each way we have about five observations in total. We have, firm level observations. Of course, not all of them are useful, is because that, so, based on this data, we, the data asks a question that allows us

12:06 - to measure the overconfidence level, because previously we know that is basically defined as the miscalibration or difference between the actual and the perceived. You can see, probability of, of success or, or, or, or riskiness of any activities. in this, in our data sets, there is a survey question asked for all financing applicants. So pay attention here.

12:35 - So it is based on, condition on, on, on, on a firm or on, on, on a business applying for finance. So this question does not apply for any firm that does not apply for finance. so that will reduce our sample size significantly. so for those apply for finance, there's a question asked on that about, before, so pre-application, how confident are you, in getting the finance? So from this questions, we can, master the,

13:05 - the exam expectations about their, their, their expectation about, the, the, the odds of success for their financing activities. And, of course, on other side, side, we can measure statistically the objective, probability of, alone being approved based on business and owner characteristics, and based on some, kind of financial accounting fundamentals. So the between the two, so the

13:37 - perceived probability of approval and the actual objective of estimated or predicted probability of approval, so is used to as our measure of over confidence, of course, by definition, a large positive number because it is a, a probability. So in extreme, a value of one means that the, the son or the, the entrepreneur is extremely overconfident, basically means that, he thinks, or he

14:13 - or she, she thinks that she, they will, get the finance, for sure. Whereas, the actual odds of getting finance is zero. Of course, a minus one means that, one is extremely underconfident, so they're not confident at all, and they totally miss the probability of their, of getting finance. So the method we, we, we use is basically, a single linear regression to look at, the probability.

14:39 - So the, effect of overconfidence, not only the linear relationship, but also the quadr relationship, because I argue, I said just now that, so over confidence is not, at, at all bad, not all bad. So there's a trade off between, the levels of over confidence. So that we met, we, we examine the non-linear or quad relationship between overco levels

15:02 - and the a, the, probability of demand for finance and, the, amount of, demand. And b on the supply side, the probability of, their finance application being approved in general on, on the demand side. so we still find that, in general, women demand, a lower amount of finance. This is, in line with their risk preferences because they are more risk averse and, and, so therefore they are less likely to rely on,

15:33 - risky external finance. but on the other hand, once a, a, a female entrepreneur becomes, very overconfidence, so it doesn't mean necessarily mean that they will apply for more finance or take on more risk, in this sense. So what they exhibit is what we call risk shifting. So rather than rely on short term, smaller amount of finance, they generally rely on longer term finance, not necessarily, by a large amount,

16:05 - because we know that for small businesses, the loan amount is dually determined by the size and, the age of the businesses. But here we mainly argue that they will rely on more risky type of finance to find long term risky projects. So this is, the demand findings. And on the supply side, generally speaking, we find this, a long linear inverted u shape relationship between, over confidence and the, probability

16:34 - of a loan being approved. Whereas, so there is an optimal level of over confidence, and once so either, below or above this optimal level of, over confidence, we see a lower probability of approval. But, most importantly, we argue that if, if, a general entrepreneur becomes overly, over confidence, so banks will observe this and think it, and treat it as a sign of excess risk taking, and

17:06 - therefore they will, they are less likely to, among the finance to these applicant. But on the other hand, if we compare the relationship, if we compare this, inverted U shape, between female and male entrepreneurs will see that the U shape will more, will be more steep for female entrepreneurs, which means that so banks will put a higher weight for deviation, from gender stereotypes for female entrepreneurs than male entrepreneurs.

17:43 - particularly, or specifically what we find that term. So for moderate level of overconfidence, we see that the probability of approval is higher for female entrepreneurs than for male entrepreneurs, which means that because female are naturally more risk averse, so that banks have more tendency or, or or more likely to, use this, landing mechanism to encourage a slightly or moderate level of overconfidence than male entrepreneurs.

18:17 - Whereas on the other hand, if a female entrepreneur becomes overly overconfident, so they will be punished by a large extent than male, entrepreneurs, the same, argument applies to the, are the end of the story, which is under confidence. So if, a FEMA is overly under confidence, so which they, which means that they're, they're not likely to take any risk at all. equally, of course, on the one hand,

18:49 - they will not apply for external finance, but if they do apply for external finance, they're also likely to be rejected as well. The conclusion is that, so whereas, we know, so find, because of the technology, advancement, recently banks are more likely to use this, complex computer-based algorithms to, look at the, quality of, borrowers. at the same time, they also use some, so screening devices, such as, behavioral measures

19:23 - to look at the, risk preferences of entrepreneurs or, or the borrowers in order to determine their, their, so, or in order to incorporate that term soft information into their hard information in making their long decisions. And, our conclusion is that basically in general, there's no gender discrimination for male and female entrepreneurs. so once banks or lender start to look at software, information,

19:52 - they will start to discriminate, or they will start to punish, any borrowers who deviate too much from their gender average. So this is our main finding. But, of course the in, in the implication is that, so there is a fine balance between the extent to which the banks use such, sought, information. Whereas, more realistic or more reasonable use of soft information will help banks to, to better judge the quality of,

20:30 - the borrowers, but overly rely on the soft information, will mean that, so sometimes, banks will tend to mistakenly reject those, so better quality, borrowers basically Based on their gender, characteristics or stereotypes. So, which means, we argue that there is a balance or is a trade off between, so the use of far hard information such as, fundamentals and the use of, behavioral measures.

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