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How much does intelligence predict lifetime income? A Meta-Analysis
Název práce v češtině: Nakolik vypovídá inteligence o celoživotních příjmech? Meta-analýza
Název v anglickém jazyce: How much does intelligence predict lifetime income? A Meta-Analysis
Klíčová slova: inteligence a příjem, výnosy schopností, metaanalýza, publikační selektivita, Bayesovské průměrování modelů
Klíčová slova anglicky: intelligence and income, returns to ability, meta-analysis, publication bias, Bayesian model averaging
Akademický rok vypsání: 2019/2020
Typ práce: bakalářská práce
Jazyk práce: angličtina
Ústav: Institut ekonomických studií (23-IES)
Vedoucí / školitel: doc. PhDr. Zuzana Havránková, Ph.D.
Řešitel: skrytý - zadáno vedoucím/školitelem
Datum přihlášení: 22.09.2021
Datum zadání: 22.09.2021
Datum a čas obhajoby: 11.09.2023 09:00
Místo konání obhajoby: Opletalova, O206, místnost. č. 206
Datum odevzdání elektronické podoby:01.08.2023
Datum proběhlé obhajoby: 11.09.2023
Oponenti: Mgr. Ing. Kseniya Bortnikova
 
 
 
Seznam odborné literatury
Almlund, A., Duckworth, A. L., Heckman, J. & T. Kautz (2011): “Personality Psychology and Economics.” In Handbook of the Economics of Education (eds. Hanushek, E., Machin, S. & L.Woessmann), volume 4, pp. 1-181. Elsevier.
Andrews, I. & M. Kasy (2019): “Identification of and Correction for Publication Bias.” American Economic Review 109(8): pp. 2766–2794.
Barrett, G. V. & R. L. Depinet (1991): “A reconsideration of testing for competence rather than for intelligence.” American Psychologist 46: pp. 1012−1024.
Bom, P. R. D. & H. Rachinger (2019): “A Kinked Meta-Regression Model for Publication Bias Correction.” Research Synthesis Methods 10(4): pp. 497-514.
Bowles, S., Gintis, H. & M. Osborne (2001): “The Determinants of Earnings: A Behavioral Approach.” Journal of Economic Literature 39 (4): pp. 1137-1176.
Egger, M., Smith, G. D., Schneider, M. & C. Minder (1997): “Bias in meta-analysis detected by a simple, graphical test.” British Medical Journal 315(7109): pp. 629–634.
Elliott, G., Kudrin, N., & K. Wuthrich (2021): “Detecting p-hacking.” Econometrica (forthcoming).
Flynn, J. R. (2007). “What is intelligence? Beyond the Flynn effect.” Cambridge: Cambridge University Press.
Furukawa, C. (2019): “Publication Bias under Aggregation Frictions: Theory, Evidence, and a New Correction Method.” Unpublished paper, MIT.
George, E. I. (2010): “Dilution priors: Compensating for model space redundancy.” In IMS Collections Borrowing Strength: Theory Powering Applications. A Festschrift for Lawrence D. Brown, volume 6, pp. 158-165. Institute of Mathematical Statistics.
Gottfredson, L. S. (1997): “Why g matters: The complexity of everyday life.” Intelligence 24, pp. 79−132.
Havranek, T. (2015): “Measuring Intertemporal Substitution: The Importance of Method Choices and Selective Reporting.” Journal of the European Economic Association 13(6), pp. 1180-1204.
Hedges, L. V. (1992): “Modeling publication selection effects in meta-analysis.” Statistical Science 7(2): pp. 246–255.
Ioannidis, J. P., Stanley, T. D. & H. Doucouliagos (2017): “The Power of Bias in Economics Research.” Economic Journal 127(605): pp. 236–265.
Jensen, A. R. (1980): “Bias in Mental Testing.” New York: The Free Press.
Jensen, A. R. (1998): “The g factor: The science of mental ability.” Westport, CT: Praeger.
Judge, T. A., Higgins, C. A., Thoresen, C. J., & M. R. Barrick (1999): “The Big Five personality traits, general mental ability, and career success across the life span.” Personnel Psychology 52: pp. 621–651.
Legg, S. & M. Hutter (2007): “A collection of definitions of intelligence.” Technical Report. Retrieved from http://arxiv.org/pdf/0706.3639.pdf on July 15, 2021.
Ng, T. W. H., Eby, L. T., Sorensen, K. L. & D. C. Feldman (2005): “Predictors of objective and subjective career success: A meta-analysis.” Personnel Psychology 58: pp. 367-408.
Sánchez-Álvarez, N., Berrios Martos, M. P. & N. Extremera (2020): “A Meta-Analysis of the Relationship Between Emotional Intelligence and Academic Performance in Secondary Education: A Multi-Stream Comparison.” Frontiers in Psychology 11: art. 1517.
Schmidt, F. L., & J. Hunter (2004): “General Mental Ability in the World of Work: Occupational Attainment and Job Performance.” Journal of Personality and Social Psychology 86(1): pp. 162–173.
Stanley, T. D. (2005): “Beyond Publication Bias.” Journal of Economic Surveys 19(3): pp. 309–345.
Stanley, T. D., S. B. Jarrell, & H. Doucouliagos (2010): “Could It Be Better to Discard 90% of the Data? A Statistical Paradox.” The American Statistician 64: pp. 70–77.
Strenze, T. (2007): “Intelligence and socioeconomic success: A meta-analytic review of longitudinal research.” Intelligence 35(5): pp. 401-426.
Strenze, T. (2015): “Intelligence and socioeconomic success: A study of correlations, causes and consequences.” Dissertation thesis (supervisor Henn Käärik). Institute of Social Studies, University of Tartu, Estonia. 119 pages.
Předběžná náplň práce v anglickém jazyce
Research question and motivation

For decades, researchers were analyzing the relationship between intelligence and its causal effects. As a result, they have found that the scores of intelligence tests attract some desirable outcomes, and on the other hand, repel several undesirable outcomes. Since organizations are also concerned about the career of their employees due to the potential contribution to organizational success (Judge et al., 1999), one of the most discussed and relevant positive outcomes is socioeconomic success (or career success).

To be able to examine the relationship between intelligence and success, it is necessary to specify their meanings and the way of measuring them. Usually, socioeconomic success is measured by the educational level, occupational prestige, and income of an individual in adulthood. And when it comes to the nature and definition of intelligence, it is a bit more complicated as scientists from different fields and of different persuasion have given various definitions to intelligence (see Legg & Hutter, 2007). In Strenze (2015), a description of what is generally meant by intelligence was determined by Gottfredson (for a detailed explanation, see Gottfredson, 1997: 13), even though some other prominent scientists would probably not approve this choice (e.g., Flynn, 2007).

Despite some sources, such as popular press and textbooks, state that intelligence has no impact on any important real-life outcomes (Barrett & Depinet, 1991), other papers leave no room for doubt that people with higher IQ tests are better educated, hold more prestigious occupations, and earn higher incomes than people with lower scores (Gottfredson, 1997; Jensen, 1980, 1998; Schmidt & Hunter, 2004). To unite the diverse study results of the effect of intelligence on economic success, Strenze (2007) conducted a meta-analytic review of longitudinal research. Based on 85 data sets (135 samples), he has shown that intelligence is a powerful determinant of economic success, however, its predictive power is not convincingly better than parental socioeconomic status (SES) or school grades. He has also concluded that the results depend on the age of the participant in the sample, whereas there is little evidence of any historical changes in the relationship between intelligence and success.

I want to build on and extend the previous work Strenze (2007), and my goal is to answer additional questions on this topic. First of all, I will update his dataset with new studies and additional variables that will be captured along the way. Since the author does not account for publication selection, the second issue will be an investigation of bias that occurs in the published academic researches and stems from the differences in the design of studies. The third matter I will elaborate on is a thorough discussion of the heterogeneity behind the estimates.

Contribution

In my thesis, I want to dig deeper than the previous meta-analysis of Strenze (2007). First, I intend to enlarge the sample with more recent surveys and gather other possible independent variables that could drive the estimated effect. Second, by using modern meta-analysis techniques commonly used in economics, including linear and also non-linear tests, I would like to explore and correct for publication bias. Next, I plan to apply novel methods, Bayes and Frequentist model averaging, to handle model uncertainty and to investigate the heterogeneity in the literature. Lastly, with the previous outcomes, I would try to construct a synthetic study to estimate the best-practice effect that is corrected for the detected biases.

Methodology

Since the meta-analysis of Strenze was conducted in 2007, 14 years ago, I will update the work of Strenze with the most recent data. In order to create a dataset from primary studies, I will collect the data with the help of Google Scholar. Furthermore, in the meta-analysis of Strenze (2007), only 49 data sets from overall 85 data sets provided information on the relationship between intelligence and socioeconomic success, thus I will reconsider some of the studies that were and were not included in the previous meta-analysis – each one of them will be examined, whether they are appropriate for my research. After that, I will have to standardize the effect so they will be comparable.

Next, it is important to determine the presence of publication bias, which occurs when results of published studies are systematically different from results of unpublished studies (see Ioannidis et al., 2007). To do so, I will perform the common techniques used in economics, including the graphical test called the funnel plot (Egger et al., 1997) and the more rigorous alternative, the funnel asymmetry test by Stanley (2005). The latter is testing whether a linear relationship between the effect and its standard error is present, which is a problem due to the assumption of the linearity of such relationship and exogeneity of the standard error. Along with these tests, I will also apply some newly developed non-linear tests, including the Top 10 method by Stanley et al. (2010), the weighted average of adequately powered by Ioannidis et al. (2017), the selection model by Andrews & Kasy (2019), the stem-based method by Furukawa (2019), and the endogenous-kink model by Bom & Rachinger (2020). As my research also includes psychology studies, I will include the non-linear classical model of Hedges (1992) or R package weight that are typically used in the field of psychology. Moreover, I would also like to apply p-uniform and p-curve into my analysis as these tests are analogical to the previous two.

The last problem, I would like to address is the potential heterogeneity which refers to the variation in study outcomes between studies. For that, we use methods that are dealing with model uncertainty (uncertainty due to imperfections and idealizations made in physical model formulations which come from a large number of explanatory variables), including model averaging. The most common approach is Bayesian model averaging (BMA) for my baseline model. In addition to the BMA model, I will use the Frequentist model averaging as a robustness check.

Outline

1. Introduction
• Motivation, contribution, and main findings
2. Introduction of the relationship between intelligence and success
• Definitions
• Estimating the effect and its significance
• Existing surveys of the effect
3. Data collection
• Selection criteria and final data set
• Interpretation of summary statistics
4. Publication bias
• Its importance
• Testing publication bias (funnel plot, linear and non-linear rigorous tests, …)
• Interpretation of the results
5. Heterogeneity
• Identification of major sources of heterogeneity
• Testing heterogeneity (BMA, Frequentist MA, …)
• Interpretation of the results
6. The best-practice estimate
7. Conclusion
 
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