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Theory matters for identifying a causal role for genetic factors in socioeconomic outcomes.

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Title: Theory matters for identifying a causal role for genetic factors in socioeconomic outcomes.
Authors: Durlauf, Steven N. (AUTHOR), Rustichini, Aldo (AUTHOR)
Source: Behavioral & Brain Sciences. 2023, Vol. 46, p1-57. 57p.
Subjects: Socioeconomic factors, Educational outcomes
Abstract: Any empirical claim about the role of genes in socioeconomic outcomes involves successfully addressing the identification problem. This commentary argues that socioeconomic outcomes such as education are sufficiently complex, involving so many mechanisms, that understanding the role genes requires the use of formal theoretical structures. [ABSTRACT FROM AUTHOR]
Copyright of Behavioral & Brain Sciences is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Theory matters for identifying a causal role for genetic factors in socioeconomic outcomes.
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  Data: Any empirical claim about the role of genes in socioeconomic outcomes involves successfully addressing the identification problem. This commentary argues that socioeconomic outcomes such as education are sufficiently complex, involving so many mechanisms, that understanding the role genes requires the use of formal theoretical structures. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Behavioral & Brain Sciences is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1017/S0140525X22002084
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