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Figure 1 – uploaded by Saleh F . A . Khatib
Figure 1 Source: Developed by the author Endogeneity represents a crucial issue in research, as it undermines the essential conditions necessary for asserting causality. As shown in Figure 1, the endogeneity can be raised due to few reasons such as omitted variables, simultaneity or measurement errors. The difficulty in predicting its bias in advance, as highlighted by Aerts et a/. (2007) and Aldamen et al. (2012), emphasizes the importance of addressing and rectifying endogeneity. Neglecting to account for it may result in biased and unreliable outcomes, posing a risk of drawing erroneous conclusions regarding the cause-and-effect relationships between the concepts under investigation. This, in turn, can lead to misleading theoretical and managerial implications. Modeling the link between the variables will be difficult if endogeneity is not appropriately handled (Abu Afifa et a/., 2022). Depending on the data available — whether it be a panel, time series, or cross-sectional — the appropriate methodologies should be selected, with instrumental variables (IV) are the most robust techniques.
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