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Gender Differences in Risk Taking: A Meta-Analysis
Název práce v češtině: Gender Differences in Risk Taking: A Meta-Analysis
Název v anglickém jazyce: Gender Differences in Risk Taking: A Meta-Analysis
Klíčová slova: genderové rozdíly, podstupování rizika, metaanalýza, publikační selektivita
Klíčová slova anglicky: gender differences, risk taking, meta-analysis, publication bias
Akademický rok vypsání: 2023/2024
Typ práce: bakalářská práce
Jazyk práce: angličtina
Ústav: Institut ekonomických studií (23-IES)
Vedoucí / školitel: Mgr. Martina Lušková
Řešitel: skrytý - zadáno vedoucím/školitelem
Datum přihlášení: 24.06.2024
Datum zadání: 24.06.2024
Seznam odborné literatury
Byrnes, J. P., Miller, D. C., & Schafer, W. D. (1999). Gender differences in risk taking: A meta-analysis. Psychological Bulletin, 125(3), 367–383.
Eckel, C.C. and Grossman, P.J. (2008). Forecasting risk attitudes: An experimental study using actual and forecast gamble choices. Journal of Economic Behavior & Organization, 68(1), pp.1–17.
Charness, G. and Gneezy, U. (2012). Strong Evidence for Gender Differences in Risk Taking. Journal of Economic Behavior & Organization, 83(1), pp.50–58.
Dwyer, P.D., Gilkeson, J.H. and List, J.A. (2002). Gender differences in revealed risk taking: evidence from mutual fund investors. Economics Letters, [online] 76(2), pp.151–158.
Croson, R., & Gneezy, U. (2009) Gender Differences in Preferences. Journal of Economic Literature, 47 (2): 448-74.
Ioannidis, J.P.A., Stanley, T.D. & Doucouliagos, H. (2017). The Power of Bias in Economics Research. The Economic
Journal, 127(605), pp. F236–F265.
Andrews, I. & Kasy, M. (2019). Identification of and Correction for Publication Bias. American Economic Review, 109 (8): 2766-94.
Egger, M., Smith, G.D., Schneider, M. and Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test.
BMJ, 315(7109), pp.629–634.
Furukawa, C. (2019). Publication Bias under Aggregation Frictions: Theory, Evidence, and a New Correction Method. MIT Working Paper.
Bom, P. R., & Rachinger, H. (2019). A kinked meta‐regression model for publication bias correction. Research synthesis methods, 10(4), 497-514.
van Aert, R., & van Assen, M. (2018). Correcting for publication bias in a meta-analysis with the P-uniform* method. MetaArXiv Preprints.
Zuzana Irsova & Pedro R. D. Bom & Tomas Havranek & Heiko Rachinger, (2023). Spurious Precision in Meta-Analysis, Working Papers IES 2023/05, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Feb 2023.
Steel, Mark F J. (2020). Model Averaging and Its Use in Economics. Journal of Economic Literature, 58 (3): 644-719.
Předběžná náplň práce v anglickém jazyce
Research question and motivation

Gender differences are for researchers ongoing and tempting topic to study. Especially in these days, when comparing differences between men and women in all different kinds of fields are probably discussed more than ever.
In my bachelor’s thesis I am going to study the gender differences in risk taking. Risk taking is defined as an activity with a significant probability of loss or harm in order to achieve a goal. Tendencies to taking risks can significantly influence career choices, investment strategies, health related behaviors, but also ordinary daily life.

Several studies have already explored the gender differences in risk taking, many of them with an outcome that men generally tend to take more risks than women in various activities (Eckel & Grossman, 2008, Croson & Gneezy, 2009, Byrnes, Miller and Schafer, 1999). For example, study by Eckel & Grossman (2008) claimed that men are willing to take higher risks related to gambling, the same results but related to financial risks were found out by Charness & Gneezy (2012) and Dwyer, Gilkenson & List (2002) agreed with this outcome for taking risks in investment decisions.

A meta-analysis dealing with this topic can provide not only comprehensive overview of various studies and a clearer understanding of how substantial these differences are, but it can also help to identify potential trends and patterns which might not be apparent from individual studies alone. It can also reveal in which contexts are the differences more or less significant.


Contribution

Understanding the different behavior between men and women in risk taking can provide impact across various fields. It can provide insights into psychological and social factors that influence decision-making processes and thus has a contribution to the field of behavioral economics and psychology. For example, policymakers can use the evidence from a meta-analysis to design various gender-difference policies in finance, healthcare, or education.

With my bachelor’s thesis I want to follow up on already existing meta-analysis conducted by Byrnes, Miller and Schafer (1999). The main aim of my work is to update this meta-analysis, include more recent studies and expend it with new techniques for testing publication bias and heterogeneity.


Methodology

Collection of the data is the key for conducting a meta-analysis. Thus, the first step is to design a search query and with the help of Google Scholar search for relevant studies concerning the research question. I will also include a previous meta-analysis on this topic by Byrnes, Miller and Schafer (1999).
The important step is to carefully go through all the founded relevant literature and decide for each study, whether it should be included in the meta-analysis or not. The decision will mainly depend on collected estimates of the effect I am interested in that are directly comparable, the corresponding standard errors, and variables that reflect the most important ways in which studies differ. After finalizing the list of included studies, it is crucial to decide which variables to collect. Then collecting of all the c and cleaning them will be the next step. Once the database is set, I will perform the examination of publication bias and heterogeneity.

To examine a publication bias, I will use linear techniques, nonlinear techniques, and also techniques that allow for endogeneity in standard errors. More specifically, as for the linear techniques, I will use a funnel plot (Egger, Smith, Schneider & Minder, 1997) and for the nonlinear techniques, I will employ several methods explained by Ioannidis et. al (2017), Andrews and Kasy (2019), Furukawa (2019), Bom & Rachinger (2019), then I will also use the p-uniform* method (Aert & Assen, 2019) and Instrumental Variable Estimator (MAIVE). In the second part of the meta-analysis, to account for model uncertainty, I will use Bayesian (BMA) and frequentist model averaging. Additionally, I will report robustness checks with different priors or wights for BMA.

Outline
1) Introduction
2) Theoretical Framework and Literature Review
3) Data
- Data collection
- Data overview
4) Publication bias
- Funnel plot
- Linear methods
- Nonlinear methods
- Methods allowing for endogeneity
5) Heterogeneity
- Variables
- Model Averaging
6) Conclusion
 
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