Product market competition and target ratcheting

ABSTRACT

This study examines the effect of product market competition (PMC) on target ratcheting by conducting an OLS (ordinary lease squares) regression model that utilizes both the revision of targets and deviation of targets to measure target ratcheting. Using executive EPS targets from S&P 1500 firms from 2012 to 2020, I find that higher PMC, as measured by TNIC similarity scores, leads to less pronounced target ratcheting and asymmetric target ratcheting, indicating that PMC, rather than past performance, provides more informational value about managerial efforts. Ultimately, this study contributes to the literature by identifying PMC as a determinant of target ratcheting.

KEYWORDS:

• Product market competition
• target ratcheting
• target difficulty
• market fluidity

JEL CLASSIFICATION:

• L13
• M12
• M41; G40

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ Asymmetric target ratcheting, where upward adjustments are more significant than downward adjustments, is common when past performance holds greater informational value (Aranda, Arellano, and Davila 2014; Bol and Lill 2015; S. Kim and Shin 2017).

² The reason this study specifically focuses on the sample period from 2012 to 2020 is that the product market competition data obtained from the Hoberg and Phillips data library is only available until 2021. Furthermore, since the measure of target ratcheting requires a future measure t + 1, the year 2021 is excluded from the sample. Therefore, the sample period spans from 2012 to 2020.

³ EPS, a commonly used metric in executive annual bonus plans, offers consistency. Annual bonuses, unlike long-term incentives, see yearly adjustments based on actual performance, leading to increased variability. Additionally, EPS performance targets and actual EPS data are manually collected from proxy statements (DEF 14A) accessible on the EDGAR website.

⁴ For more detailed descriptions of TNIC similarity scores by Hoberg and Phillips (2016), please refer to the Hoberg and Phillips data library at https://hobergphillips.tuck.dartmouth.edu/.

⁵ The higher similarity scores, the more intense the PMC the firm is experiencing (Arouri, El Ghoul, and Gomes 2021).

⁶ Please refer to Appendix A1 for detailed descriptions of the variables.

⁷ Even after incorporating firm-fixed effects and clustering standard errors at the firm level, the main results remain consistent. Furthermore, when employing alternative proxies such as TNIC_HHI (market concentration) and FLUIDITY (market fluidity), the main findings persist (Hoberg and Phillips 2016; Hoberg, Phillips, and Prabhala 2014).

⁸ When considering the model of target ratcheting, there may be some endogeneity issues. However, this endogeneity would not invalidate the findings, as many studies on target ratcheting have consistently used the same empirical model (Bol and Lill 2015; Bouwens and Kroos 2011; Leone and Rock 2002).

⁹ Each firm is paired with an appropriate industry-size peer portfolio, determined by 2-digit SIC industrial codes. Furthermore, Control variables, LNMV_i,t, BTM_i,t, LEV_i,t, ROA_i,t and OCF_i,t, are included in the model (Abernethy, Kuang, and Qin 2015; Carter, Ittner, and Zechman 2009).