2929Copernican Journal of Finance & Accounting e-ISSN 2300-3065 p-ISSN 2300-12402024, volume 13, issue 3 Date of submission: April 26, 2024; date of acceptance: September 2, 2024. * Contact information: xiang.gao@lsus.edu, Department of Accounting and Busi- ness Law, College of Business, Louisiana State University Shreveport, Shreveport, LA, 71115, USA, phone: +13187975152; ORCID ID: https://orcid.org/0009-0002-3261-0017. ** Contact information: emmanuel.sequeira@sjsu.edu, Department of Accounting and Finance, Lucas College and Graduate School of Business, San José State Universi- ty, San Jose, CA, 95131, USA, phone: +14089243585; ORCID ID: https://orcid.org/0009- 0008-0612-3876. *** Contact information: qifeng.wu@lsus.edu, Department of Accounting and Business Law, College of Business, Louisiana State University Shreveport, Shreveport, LA, 71115, USA, phone: +131879752467; ORCID ID: https://orcid.org/0009-0008-8123-5229. Gao, X., Sequeira, E., & Wu, Q. (2024). The Impact of Female Executives on the Quality and Compa- rability of Financial Statements: Evidence from the U.S. Copernican Journal of Finance & Account- ing, 13(3), 29–45. http://dx.doi.org/10.12775/CJFA.2024.012 Xiang (gail) gao* Louisiana State University Shreveport emmanuel sequeiRa** San José State University qifeng (chaRles) wu*** Louisiana State University Shreveport the impact of female eXecutives on the quality and compaRability of financial statements: evidence fRom the u.s. Keywords: Financial Statement Comparability, gender, CFO. J E L Classification: G30, J16, M10. Abstract: In this article, we examine the impact of female CFOs on Financial Statement Comparability (FSC) in a U.S. setting. Using a sample of publicly traded U.S. firms be- Xiang (Gail) Gao, Emmanuel Sequeira, Qifeng (Charles) Wu3030 tween the years 1992 and 2023, we compare firms with female CFOs against those with male CFOs using pooled panel regressions. We also examine firms with male to female CFO changes and vice versa using difference in difference regressions. We find that fe- male CFO firms have a 1.1% higher FSC on average than male CFO firms. Further, firms that switch from having a male CFO to a female CFO experience FSC increases of 17.5%. On the contrary, firms that switch from having female CFOs to male CFOs experience FSC decreases of 7.6%. Thus, we conclude that firms with female CFOs have higher FSC than those with male CFOs. We believe that our findings are significant to regulators and legislators, and also add supporting evidence in favor of the push for increased fe- male representation in the C-suite.  Introduction Introduction Representation of women in the workplace has steadily increased in recent years (McKinsey & Company, 2022; Catalyst, 2022). This has led both academ- ics and practitioners to increasingly study the impact of gender on corporate decision-making and firm performance. While Ernst & Young and The Peterson Institute for International Economics find that firms with female leadership ex- perience a 15% increase in profitability (Kobayashi, 2021), a study by Quanto- pian finds that female CEO-led Fortune 1000 firms outperformed the S&P 500 by 226% (Wechsler, 2015). In this paper, we examine the impact of female CFOs on U.S. firms’ financial statement comparability (henceforth referred to as FSC). Existing literature in psychology, accounting and finance finds that wom- en are less risk-taking, less overconfident, adopt safer corporate policies, and are associated with improved components of financial reporting quality and improved corporate leadership during crises (Huang & Kisgen, 2013; Francis, Hasan, Wu & Yan, 2014a; Francis, Hasan, Park & Wu, 2015; Sinha, 2023). These outcomes could be partially due to women’s more ethical traits and their ten- dency to comply with rules and regulations (Beu, Buckley & Harvey, 2003; Chun, 2005; Linley, Maltby, Wood, Joseph, Harrington, Peterson, Park & Selig- man, 2007). However, while the need for FSC has been stressed by the Finan- cial Accounting Standards Board (FASB) (1980; 2010), International Account- ing Standards Board (IASB) (2010), academic literature (De Franco, Kothari & Verdi, 2011), and financial statement analysis textbooks (Revsine, Collins, Johnson, Mittelstaedt & Soffer, 2012), existing studies have not yet examined how the executive's gender impacts FSC in the United States. We thus fill this significant gap in the existing academic literature, by examining the effect of CFO gender on FSC in a U.S. setting and argue that firms with female CFOs have greater FSC. thE imPaCt of fEmalE ExECutivEs on thE quality ComParaBility… 3131 We examine the effects of female CFOs in a U.S. setting because we argue that as the title ‘Chief Financial Officer’ suggests, a firm’s CFO is primarily re- sponsible for the firm’s financial reporting choices and decisions. Existing lit- erature also provides compelling evidence that among all senior managers, the CFO has the strongest impact on their firm’s financial reporting decisions (Fran- cis et al., 2015; Jiang, Petroni & Wang, 2010). Thus, a number of studies that ex- amine the effects of executive gender on financial reporting outcomes do so by analyzing the effects of CFO gender (Barua, Davidson, Rama & Thiruvadi, 2010; Huang & Kisgen, 2013; Francis et al., 2014a; Francis et al., 2015; Gupta, Mortal, Chakrabarty, Guo & Turban, 2020). We test our hypothesis using a sample of U.S. listed firm CFOs obtained from Execucomp for the years 1992 to 2023, using pooled panel regressions and a difference in difference (DiD) research design model. We require both pre- and post-transition CFOs to be in office for at least three consecutive years, excluding the transition year. FSC is measured through the difference of total accruals as in Francis, Pinnuck and Watanabe (2014b), and with the approach employed by De Franco et al. (2011), who measure comparability as the degree to which earnings for two firms in the same industry covary over time. Our results consistently show that firms with female CFOs have higher FSC than those with male CFOs. Our pooled panel regression results show that fe- male CFOs have 1.1% higher FSC than male CFOs. Further, firms with female to male CFO switches experience decreases in FSC of 7.6%. Finally, our tests also show that firms that switch from having a male CFO to a female CFO have in- creases in FSC of 17.5%. Our results are robust to the inclusion of industry and year fixed effects. Our paper makes several contributions to existing literature. Firstly, to our knowledge, this is the first paper that investigates the effects of gender on FSC in a U.S. firm setting. Our paper differs from Wang, Zhang, Ho and Usman (2023) since we focus on CFOs of U.S. listed firms, while they examine Chinese listed firms. Chinese firms are significantly different from U.S. listed firms in a variety of ways. Lu, Shin and Zhang (2023) find that Chinese firms have an increased preference for predictive attributes of earnings that can signal sta- ble firm performance. They consider earnings smoothing and earnings man- agement as two distinct constructs and various stakeholders desire earnings smoothing. They also do not consider public disclosure as relevant in reducing cost of capital due to the prevalence of private communication channels. Final- ly, Chinese firms do not have a bias for conservative reporting. Thus, we argue Xiang (Gail) Gao, Emmanuel Sequeira, Qifeng (Charles) Wu3232 that the results found by Wang et al. (2023) are not generalizable outside of China, and through our focus on U.S. firms, we obtain more generalizable find- ings for firms listed in most developed financial economies. We also contribute to existing literature by demonstrating that female CFOs of U.S. listed firms are associated with increased levels of FSC, which is a highly desirable characteristic to enhance the usefulness of financial reporting. Last- ly, by adding to the existing literature on the impact of managerial characteris- tics on corporate outcomes and by demonstrating that female managers do en- hance components of financial reporting quality, we contribute by addressing the tension in existing literature related to the effects of executive gender on corporate finance outcomes. Literature review and hypothesis developmentLiterature review and hypothesis development Existing accounting, finance, and psychology literature describes significant differences between the behaviors and choices made by men and women. Wom- en are observed to have more ethical character traits (Linley et al., 2007; Chun, 2005) and are more trustworthy and compliant with rules and regulations (Beu et al., 2003). Women are also found to be more risk averse than men in both general and financial settings (Croson & Gneezy, 2009; Hinz, McCarthy & Turner, 1997). These risk aversion tendencies also extend to the C-suite, with observations of increased accounting conservatism (Francis et al., 2015), low- er levels of tax aggressiveness (Francis et al., 2014a), and lower likelihoods of financial misreporting (Gupta et al., 2020) by female managers. These behav- iors have significant impacts on decision making and firm performance (Huang & Kisgen, 2013; Kobayashi, 2021; Sinha, 2023). FSC is defined by FASB as the qualitative characteristic that enables users to identify and understand similarities in, and differences among, economic phe- nomena (FASB, 2010, QC 21, p. 19). While both FASB and IASB emphasize the importance of FSC in financial reporting, it is difficult for financial statement users to make comparisons across firms and interpret financial information, since firms are able to employ a variety of GAAP compliant accounting choices (Choi, Choi, Myers & Ziebart, 2019). Folsom, Perez and Wu (2024) find that man- agerial investment patterns are negatively related to FSC, a relationship driven by entrenched managers undertaking higher risk investments. thE imPaCt of fEmalE ExECutivEs on thE quality ComParaBility… 3333 Since a firm’s CFO is primarily responsible for the firm’s financial reporting choices and decisions, and since existing literature provides compelling evi- dence that the CFO has the strongest impact on a firm’s financial reporting de- cisions (Francis et al., 2015; Gupta et al., 2020; Jiang et al., 2010), we argue that female CFOs will be more likely to take measures to improve their firms’ infor- mation environments through increasing FSC, leading to better interpretations of their financial reports. H1: Female CFOs of U.S. listed firms are associated with higher FSC than male CFOs. However, this result is not trivial. Existing literature also obtains findings contrary to the perceived risk and performance related benefits of firms with female managers (Faccio, Marchica & Mura, 2016; Ahern & Dittmar, 2012; Berg- er, Kick & Schaeck, 2014). Thus, finding evidence in support of our hypothesis will help to alleviate this tension in existing literature. The research methodology and the course of the research processThe research methodology and the course of the research process This paper uses pooled panel regressions and a difference in difference (DiD) research design on a sample of U.S. listed firm CFOs between 1992 and 2023. We obtain our study’s data from a variety of sources. We obtain executive gen- der information from the Execucomp database for the years 1992 to 2023, and also collect firm accounting data and stock return information from the Com- pustat and CRSP databases, respectively. As in Francis et al. (2015), we require firms to have both pre- and post-transition CFOs to be in office for at least three consecutive years excluding the transition year. We follow De Franco et al. (2011) to define accounting comparability as the mapping between economic events and financial statements. For each firm- year, the effect of stock returns on firm earnings is estimated using the previ- ous 16 quarters of data. For each quarter, earnings is measured as the ratio of net income before extraordinary items and the market value of equity at the start of the quarter. We estimate firm i’s earnings using equation (1) below: managerial investment patterns are negatively related to FSC, a relationship driven by entrenched managers undertaking higher risk investments. Since a firm’s CFO is primarily responsible for the firm’s financial reporting choices and decisions, and since existing literature provides compelling evidence that the CFO has the strongest impact on a firm’s financial reporting decisions (Francis et al., 2015; Gupta et al., 2020; Jiang et al., 2010), we argue that female CFOs will be more likely to take measures to improve their firms’ information environments through increasing FSC, leading to better interpretations of their financial reports. H1: Female CFOs of U.S. listed firms are associated with higher FSC than male CFOs. However, this result is not trivial. Existing literature also obtains findings contrary to the perceived risk and performance related benefits of firms with female managers (Faccio, Marchica & Mura, 2016; Ahern & Dittmar, 2012; Berger, Kick & Schaeck, 2014). Thus, finding evidence in support of our hypothesis will help to alleviate this tension in existing literature. THE RESEARCH METHODOLOGY AND THE COURSE OF THE RESEARCH PROCESS This paper uses pooled panel regressions and a difference in difference (DiD) research design on a sample of U.S. listed firm CFOs between 1992 and 2023. We obtain our study’s data from a variety of sources. We obtain executive gender information from the Execucomp database for the years 1992 to 2023, and also collect firm accounting data and stock return information from the Compustat and CRSP databases respectively. As in Francis et al. (2015), we require firms to have both pre- and post-transition CFOs to be in office for at least three consecutive years excluding the transition year. We follow De Franco et al. (2011) to define accounting comparability as the mapping between economic events and financial statements. For each firm-year, the effect of stock returns on firm earnings is estimated using the previous 16 quarters of data. For each quarter, earnings is measured as the ratio of net income before extraordinary items and the market value of equity at the start of the quarter. We estimate firm i’s earnings using equation (1) below: 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸�� � �� � �� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝐸𝐸𝐸𝐸�� � ��� (1) (1) Here, Returnit is the raw stock return for quarter t. The estimated coeffi- cients αi and βi are firm i’s accounting system or function that maps firm i’s economic events into its financial statements. For firm j from the same three- Xiang (Gail) Gao, Emmanuel Sequeira, Qifeng (Charles) Wu3434 digit SIC industry as firm i, and with the same fiscal year-end month as firm i, the accounting system is proxied as Here, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� is the raw stock return for quarter t. The estimated coefficients 𝛼𝛼𝑖𝑖 and 𝛽𝛽𝑖𝑖 are firm i’s accounting system or function that maps firm i’s economic events into its financial statements. For firm j from the same three-digit SIC industry as firm i, and with the same fiscal year-end month as firm i, the accounting system is proxied as 𝛼𝛼𝑗𝑗^ and 𝛽𝛽𝑗𝑗 ^ . To measure the closeness of the functions between firms i and j, we then use the economic event (𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�) to compute the estimated earnings difference from each firm-pair’s accounting system parameters (𝛼𝛼𝑖𝑖^ , 𝛽𝛽𝑖𝑖 ^ or 𝛼𝛼𝑗𝑗^ , 𝛽𝛽𝑗𝑗 ^ ), respectively. Then, we apply firm i’s and firm j’s estimated accounting functions to firm i’s economic events, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅��: 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (2) 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (3) Here, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the accounting function of i and return of firm i in quarter t. Also, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the firm j’s accounting function and return of firm i in quarter t. The pairwise comparability score between firm i’s and firm j’s accounting systems (𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅) is calculated as a negative one (-1) times the average of all pairwise comparability scores between firm i and firm j, that is, the absolute differences between the predicted earnings using firm i’s and firm j’s accounting functions, in 16 consecutive quarters: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶��� � � � ��∑ �𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���������� (4) Higher values are indicative of increased accounting comparability since 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅 in the equation is nonpositive. In our calculations and tables, we multiple this measure by -1, for ease of readability, to present our COMP measures as positive numbers. We then define a dummy variable for CFO gender, FCFO, that takes the value 1 if Execucomp’s Female CFO value is 1, and 0 otherwise. As in Francis et al. (2014a), Francis et al. (2014b), and Francis et al. (2015), we control for profitability (ROA), net cash flows (Cash), total accruals (TA), leverage ratio (LEV), market- to-book ratio (MB), the probability of a loss (Loss), and research and development expenses for each firm (R&D). All variable descriptions can be found in the appendix. and Here, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� is the raw stock return for quarter t. The estimated coefficients 𝛼𝛼𝑖𝑖 and 𝛽𝛽𝑖𝑖 are firm i’s accounting system or function that maps firm i’s economic events into its financial statements. For firm j from the same three-digit SIC industry as firm i, and with the same fiscal year-end month as firm i, the accounting system is proxied as 𝛼𝛼𝑗𝑗^ and 𝛽𝛽𝑗𝑗 ^ . To measure the closeness of the functions between firms i and j, we then use the economic event (𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�) to compute the estimated earnings difference from each firm-pair’s accounting system parameters (𝛼𝛼𝑖𝑖^ , 𝛽𝛽𝑖𝑖 ^ or 𝛼𝛼𝑗𝑗^ , 𝛽𝛽𝑗𝑗 ^ ), respectively. Then, we apply firm i’s and firm j’s estimated accounting functions to firm i’s economic events, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅��: 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (2) 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (3) Here, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the accounting function of i and return of firm i in quarter t. Also, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the firm j’s accounting function and return of firm i in quarter t. The pairwise comparability score between firm i’s and firm j’s accounting systems (𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅) is calculated as a negative one (-1) times the average of all pairwise comparability scores between firm i and firm j, that is, the absolute differences between the predicted earnings using firm i’s and firm j’s accounting functions, in 16 consecutive quarters: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶��� � � � ��∑ �𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���������� (4) Higher values are indicative of increased accounting comparability since 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅 in the equation is nonpositive. In our calculations and tables, we multiple this measure by -1, for ease of readability, to present our COMP measures as positive numbers. We then define a dummy variable for CFO gender, FCFO, that takes the value 1 if Execucomp’s Female CFO value is 1, and 0 otherwise. As in Francis et al. (2014a), Francis et al. (2014b), and Francis et al. (2015), we control for profitability (ROA), net cash flows (Cash), total accruals (TA), leverage ratio (LEV), market- to-book ratio (MB), the probability of a loss (Loss), and research and development expenses for each firm (R&D). All variable descriptions can be found in the appendix.  . To measure the closeness of the functions between firms i and j, we then use the economic event (Returni ) to compute the estimated earnings difference from each firm-pair’s accounting system parameters ( Here, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� is the raw stock return for quarter t. The estimated coefficients 𝛼𝛼𝑖𝑖 and 𝛽𝛽𝑖𝑖 are firm i’s accounting system or function that maps firm i’s economic events into its financial statements. For firm j from the same three-digit SIC industry as firm i, and with the same fiscal year-end month as firm i, the accounting system is proxied as 𝛼𝛼𝑗𝑗^ and 𝛽𝛽𝑗𝑗 ^ . To measure the closeness of the functions between firms i and j, we then use the economic event (𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�) to compute the estimated earnings difference from each firm-pair’s accounting system parameters (𝛼𝛼𝑖𝑖^ , 𝛽𝛽𝑖𝑖 ^ or 𝛼𝛼𝑗𝑗^ , 𝛽𝛽𝑗𝑗 ^ ), respectively. Then, we apply firm i’s and firm j’s estimated accounting functions to firm i’s economic events, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅��: 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (2) 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (3) Here, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the accounting function of i and return of firm i in quarter t. Also, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the firm j’s accounting function and return of firm i in quarter t. The pairwise comparability score between firm i’s and firm j’s accounting systems (𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅) is calculated as a negative one (-1) times the average of all pairwise comparability scores between firm i and firm j, that is, the absolute differences between the predicted earnings using firm i’s and firm j’s accounting functions, in 16 consecutive quarters: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶��� � � � ��∑ �𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���������� (4) Higher values are indicative of increased accounting comparability since 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅 in the equation is nonpositive. In our calculations and tables, we multiple this measure by -1, for ease of readability, to present our COMP measures as positive numbers. We then define a dummy variable for CFO gender, FCFO, that takes the value 1 if Execucomp’s Female CFO value is 1, and 0 otherwise. As in Francis et al. (2014a), Francis et al. (2014b), and Francis et al. (2015), we control for profitability (ROA), net cash flows (Cash), total accruals (TA), leverage ratio (LEV), market- to-book ratio (MB), the probability of a loss (Loss), and research and development expenses for each firm (R&D). All variable descriptions can be found in the appendix. , Here, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� is the raw stock return for quarter t. The estimated coefficients 𝛼𝛼𝑖𝑖 and 𝛽𝛽𝑖𝑖 are firm i’s accounting system or function that maps firm i’s economic events into its financial statements. For firm j from the same three-digit SIC industry as firm i, and with the same fiscal year-end month as firm i, the accounting system is proxied as 𝛼𝛼𝑗𝑗^ and 𝛽𝛽𝑗𝑗 ^ . To measure the closeness of the functions between firms i and j, we then use the economic event (𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�) to compute the estimated earnings difference from each firm-pair’s accounting system parameters (𝛼𝛼𝑖𝑖^ , 𝛽𝛽𝑖𝑖 ^ or 𝛼𝛼𝑗𝑗^ , 𝛽𝛽𝑗𝑗 ^ ), respectively. Then, we apply firm i’s and firm j’s estimated accounting functions to firm i’s economic events, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅��: 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (2) 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (3) Here, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the accounting function of i and return of firm i in quarter t. Also, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the firm j’s accounting function and return of firm i in quarter t. The pairwise comparability score between firm i’s and firm j’s accounting systems (𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅) is calculated as a negative one (-1) times the average of all pairwise comparability scores between firm i and firm j, that is, the absolute differences between the predicted earnings using firm i’s and firm j’s accounting functions, in 16 consecutive quarters: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶��� � � � ��∑ �𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���������� (4) Higher values are indicative of increased accounting comparability since 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅 in the equation is nonpositive. In our calculations and tables, we multiple this measure by -1, for ease of readability, to present our COMP measures as positive numbers. We then define a dummy variable for CFO gender, FCFO, that takes the value 1 if Execucomp’s Female CFO value is 1, and 0 otherwise. As in Francis et al. (2014a), Francis et al. (2014b), and Francis et al. (2015), we control for profitability (ROA), net cash flows (Cash), total accruals (TA), leverage ratio (LEV), market- to-book ratio (MB), the probability of a loss (Loss), and research and development expenses for each firm (R&D). All variable descriptions can be found in the appendix. or Here, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� is the raw stock return for quarter t. The estimated coefficients 𝛼𝛼𝑖𝑖 and 𝛽𝛽𝑖𝑖 are firm i’s accounting system or function that maps firm i’s economic events into its financial statements. For firm j from the same three-digit SIC industry as firm i, and with the same fiscal year-end month as firm i, the accounting system is proxied as 𝛼𝛼𝑗𝑗^ and 𝛽𝛽𝑗𝑗 ^ . To measure the closeness of the functions between firms i and j, we then use the economic event (𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�) to compute the estimated earnings difference from each firm-pair’s accounting system parameters (𝛼𝛼𝑖𝑖^ , 𝛽𝛽𝑖𝑖 ^ or 𝛼𝛼𝑗𝑗^ , 𝛽𝛽𝑗𝑗 ^ ), respectively. Then, we apply firm i’s and firm j’s estimated accounting functions to firm i’s economic events, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅��: 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (2) 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (3) Here, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the accounting function of i and return of firm i in quarter t. Also, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the firm j’s accounting function and return of firm i in quarter t. The pairwise comparability score between firm i’s and firm j’s accounting systems (𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅) is calculated as a negative one (-1) times the average of all pairwise comparability scores between firm i and firm j, that is, the absolute differences between the predicted earnings using firm i’s and firm j’s accounting functions, in 16 consecutive quarters: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶��� � � � ��∑ �𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���������� (4) Higher values are indicative of increased accounting comparability since 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅 in the equation is nonpositive. In our calculations and tables, we multiple this measure by -1, for ease of readability, to present our COMP measures as positive numbers. We then define a dummy variable for CFO gender, FCFO, that takes the value 1 if Execucomp’s Female CFO value is 1, and 0 otherwise. As in Francis et al. (2014a), Francis et al. (2014b), and Francis et al. (2015), we control for profitability (ROA), net cash flows (Cash), total accruals (TA), leverage ratio (LEV), market- to-book ratio (MB), the probability of a loss (Loss), and research and development expenses for each firm (R&D). All variable descriptions can be found in the appendix. , Here, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� is the raw stock return for quarter t. The estimated coefficients 𝛼𝛼𝑖𝑖 and 𝛽𝛽𝑖𝑖 are firm i’s accounting system or function that maps firm i’s economic events into its financial statements. For firm j from the same three-digit SIC industry as firm i, and with the same fiscal year-end month as firm i, the accounting system is proxied as 𝛼𝛼𝑗𝑗^ and 𝛽𝛽𝑗𝑗 ^ . To measure the closeness of the functions between firms i and j, we then use the economic event (𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�) to compute the estimated earnings difference from each firm-pair’s accounting system parameters (𝛼𝛼𝑖𝑖^ , 𝛽𝛽𝑖𝑖 ^ or 𝛼𝛼𝑗𝑗^ , 𝛽𝛽𝑗𝑗 ^ ), respectively. Then, we apply firm i’s and firm j’s estimated accounting functions to firm i’s economic events, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅��: 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (2) 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (3) Here, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the accounting function of i and return of firm i in quarter t. Also, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the firm j’s accounting function and return of firm i in quarter t. The pairwise comparability score between firm i’s and firm j’s accounting systems (𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅) is calculated as a negative one (-1) times the average of all pairwise comparability scores between firm i and firm j, that is, the absolute differences between the predicted earnings using firm i’s and firm j’s accounting functions, in 16 consecutive quarters: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶��� � � � ��∑ �𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���������� (4) Higher values are indicative of increased accounting comparability since 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅 in the equation is nonpositive. In our calculations and tables, we multiple this measure by -1, for ease of readability, to present our COMP measures as positive numbers. We then define a dummy variable for CFO gender, FCFO, that takes the value 1 if Execucomp’s Female CFO value is 1, and 0 otherwise. As in Francis et al. (2014a), Francis et al. (2014b), and Francis et al. (2015), we control for profitability (ROA), net cash flows (Cash), total accruals (TA), leverage ratio (LEV), market- to-book ratio (MB), the probability of a loss (Loss), and research and development expenses for each firm (R&D). All variable descriptions can be found in the appendix. ), respectively. Then, we apply firm i’s and firm j’s estimated accounting functions to firm i’s economic events, Returnit: Here, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� is the raw stock return for quarter t. The estimated coefficients 𝛼𝛼𝑖𝑖 and 𝛽𝛽𝑖𝑖 are firm i’s accounting system or function that maps firm i’s economic events into its financial statements. For firm j from the same three-digit SIC industry as firm i, and with the same fiscal year-end month as firm i, the accounting system is proxied as 𝛼𝛼𝑗𝑗^ and 𝛽𝛽𝑗𝑗 ^ . To measure the closeness of the functions between firms i and j, we then use the economic event (𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�) to compute the estimated earnings difference from each firm-pair’s accounting system parameters (𝛼𝛼𝑖𝑖^ , 𝛽𝛽𝑖𝑖 ^ or 𝛼𝛼𝑗𝑗^ , 𝛽𝛽𝑗𝑗 ^ ), respectively. Then, we apply firm i’s and firm j’s estimated accounting functions to firm i’s economic events, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅��: 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (2) 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (3) Here, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the accounting function of i and return of firm i in quarter t. Also, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the firm j’s accounting function and return of firm i in quarter t. The pairwise comparability score between firm i’s and firm j’s accounting systems (𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅) is calculated as a negative one (-1) times the average of all pairwise comparability scores between firm i and firm j, that is, the absolute differences between the predicted earnings using firm i’s and firm j’s accounting functions, in 16 consecutive quarters: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶��� � � � ��∑ �𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���������� (4) Higher values are indicative of increased accounting comparability since 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅 in the equation is nonpositive. In our calculations and tables, we multiple this measure by -1, for ease of readability, to present our COMP measures as positive numbers. We then define a dummy variable for CFO gender, FCFO, that takes the value 1 if Execucomp’s Female CFO value is 1, and 0 otherwise. As in Francis et al. (2014a), Francis et al. (2014b), and Francis et al. (2015), we control for profitability (ROA), net cash flows (Cash), total accruals (TA), leverage ratio (LEV), market- to-book ratio (MB), the probability of a loss (Loss), and research and development expenses for each firm (R&D). All variable descriptions can be found in the appendix. (2) Here, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� is the raw stock return for quarter t. The estimated coefficients 𝛼𝛼𝑖𝑖 and 𝛽𝛽𝑖𝑖 are firm i’s accounting system or function that maps firm i’s economic events into its financial statements. For firm j from the same three-digit SIC industry as firm i, and with the same fiscal year-end month as firm i, the accounting system is proxied as 𝛼𝛼𝑗𝑗^ and 𝛽𝛽𝑗𝑗 ^ . To measure the closeness of the functions between firms i and j, we then use the economic event (𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�) to compute the estimated earnings difference from each firm-pair’s accounting system parameters (𝛼𝛼𝑖𝑖^ , 𝛽𝛽𝑖𝑖 ^ or 𝛼𝛼𝑗𝑗^ , 𝛽𝛽𝑗𝑗 ^ ), respectively. Then, we apply firm i’s and firm j’s estimated accounting functions to firm i’s economic events, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅��: 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (2) 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (3) Here, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the accounting function of i and return of firm i in quarter t. Also, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the firm j’s accounting function and return of firm i in quarter t. The pairwise comparability score between firm i’s and firm j’s accounting systems (𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅) is calculated as a negative one (-1) times the average of all pairwise comparability scores between firm i and firm j, that is, the absolute differences between the predicted earnings using firm i’s and firm j’s accounting functions, in 16 consecutive quarters: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶��� � � � ��∑ �𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���������� (4) Higher values are indicative of increased accounting comparability since 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅 in the equation is nonpositive. In our calculations and tables, we multiple this measure by -1, for ease of readability, to present our COMP measures as positive numbers. We then define a dummy variable for CFO gender, FCFO, that takes the value 1 if Execucomp’s Female CFO value is 1, and 0 otherwise. As in Francis et al. (2014a), Francis et al. (2014b), and Francis et al. (2015), we control for profitability (ROA), net cash flows (Cash), total accruals (TA), leverage ratio (LEV), market- to-book ratio (MB), the probability of a loss (Loss), and research and development expenses for each firm (R&D). All variable descriptions can be found in the appendix. (3) Here, E(Earnings)iit refers to the predicted earnings of firm i, given the account- ing function of i and return of firm i in quarter t. Also, E(Earnings)ijt refers to the predicted earnings of firm i, given the firm j’s accounting function and return of firm i in quarter t. The pairwise comparability score between firm i’s and firm j’s accounting systems (COMPijt) is calculated as a negative one (-1) times the av- erage of all pairwise comparability scores between firm i and firm j, that is, the absolute differences between the predicted earnings using firm i’s and firm j’s accounting functions, in 16 consecutive quarters: Here, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� is the raw stock return for quarter t. The estimated coefficients 𝛼𝛼𝑖𝑖 and 𝛽𝛽𝑖𝑖 are firm i’s accounting system or function that maps firm i’s economic events into its financial statements. For firm j from the same three-digit SIC industry as firm i, and with the same fiscal year-end month as firm i, the accounting system is proxied as 𝛼𝛼𝑗𝑗^ and 𝛽𝛽𝑗𝑗 ^ . To measure the closeness of the functions between firms i and j, we then use the economic event (𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�) to compute the estimated earnings difference from each firm-pair’s accounting system parameters (𝛼𝛼𝑖𝑖^ , 𝛽𝛽𝑖𝑖 ^ or 𝛼𝛼𝑗𝑗^ , 𝛽𝛽𝑗𝑗 ^ ), respectively. Then, we apply firm i’s and firm j’s estimated accounting functions to firm i’s economic events, 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅��: 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (2) 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝛼𝛼^� � 𝛽𝛽^� ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅�� (3) Here, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the accounting function of i and return of firm i in quarter t. Also, 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� refers to the predicted earnings of firm i, given the firm j’s accounting function and return of firm i in quarter t. The pairwise comparability score between firm i’s and firm j’s accounting systems (𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅) is calculated as a negative one (-1) times the average of all pairwise comparability scores between firm i and firm j, that is, the absolute differences between the predicted earnings using firm i’s and firm j’s accounting functions, in 16 consecutive quarters: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶��� � � � ��∑ �𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���� � 𝐸𝐸�𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑖𝑖𝑅𝑅𝐸𝐸𝐸𝐸���������� (4) Higher values are indicative of increased accounting comparability since 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑗𝑗𝑅𝑅 in the equation is nonpositive. In our calculations and tables, we multiple this measure by -1, for ease of readability, to present our COMP measures as positive numbers. We then define a dummy variable for CFO gender, FCFO, that takes the value 1 if Execucomp’s Female CFO value is 1, and 0 otherwise. As in Francis et al. (2014a), Francis et al. (2014b), and Francis et al. (2015), we control for profitability (ROA), net cash flows (Cash), total accruals (TA), leverage ratio (LEV), market- to-book ratio (MB), the probability of a loss (Loss), and research and development expenses for each firm (R&D). All variable descriptions can be found in the appendix. (4) Higher values are indicative of increased accounting comparability since COMPijt in the equation is nonpositive. In our calculations and tables, we multi- ply this measure by -1, for ease of readability, to present our COMP measures as positive numbers. We then define a dummy variable for CFO gender, FCFO, that takes the value 1 if Execucomp’s Female CFO value is 1, and 0 otherwise. As in Francis et al. (2014a), Francis et al. (2014b), and Francis et al. (2015), we control for profitability (ROA), net cash flows (Cash), total accruals (TA), lev- erage ratio (LEV), market-to-book ratio (MB), the probability of a loss (Loss), and research and development expenses for each firm (R&D). All variable de- scriptions can be found in the appendix. We then run a pooled panel regression of the comparability measure on the various determinants of comparability listed above and include our Female CFO dummy variable (FCFO) as one of the independent variables in the regres- sion. The model is estimated following Francis et al. (2015) as: thE imPaCt of fEmalE ExECutivEs on thE quality ComParaBility… 3535 We then run a pooled panel regression of the comparability measure on the various determinants of comparability listed above and include our Female CFO dummy variable (FCFO) as one of the independent variables in the regression. The model is estimated following Francis et al. (2015) as: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶�� � 𝛽𝛽� � 𝛽𝛽� ∗ 𝑃𝑃𝐶𝐶𝑃𝑃𝐶𝐶 � 𝛽𝛽� ∗ 𝑅𝑅𝐶𝐶𝑅𝑅�� � 𝛽𝛽� ∗ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶�� � 𝛽𝛽� ∗ 𝑇𝑇𝑅𝑅�� � 𝛽𝛽� ∗ 𝐿𝐿𝐿𝐿𝐿𝐿�� � 𝛽𝛽� ∗ 𝐶𝐶𝑀𝑀�� � 𝛽𝛽� ∗ 𝐿𝐿𝐿𝐿𝐶𝐶𝐶𝐶�� � 𝛽𝛽� ∗ 𝑅𝑅𝑅𝑅�� � 𝛽𝛽�� ∗ 𝑆𝑆𝑆𝑆𝑆𝑆𝐿𝐿�� � ��� (5) If our hypothesis is supported, we expect 𝛽𝛽1 to be positive and significantly associated with 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶��. As Huang and Kisgen (2013) and Francis et al. (2015) describe, female executives are not randomly assigned to firms, leading to endogeneity concerns. In addition, unobservable contemporaneous changes at the same time as CFO changes could also affect the firm’s FSC. To alleviate these concerns, we also use a difference in differences (DiD) framework as in Huang and Kisgen (2013) and Francis et al. (2015). We test our findings on a set of firms that transition due to turnover from male to female CFOs and compare the results against a set of control firms that continue to have male CFOs after the turnover transition. The model is represented as: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶�� � 𝛽𝛽� � 𝛽𝛽� ∗ 𝐶𝐶𝐿𝐿𝐶𝐶𝑃𝑃�� � 𝛽𝛽� ∗ 𝐶𝐶𝐿𝐿𝐶𝐶𝑃𝑃 ∗ 𝑃𝑃𝐶𝐶𝑃𝑃𝐶𝐶_𝑆𝑆𝑆𝑆𝑆𝑆𝑇𝑇𝐶𝐶𝑆𝑆�� � 𝛽𝛽� ∗ 𝑅𝑅𝐶𝐶𝑅𝑅�� � 𝛽𝛽� ∗ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶�� � 𝛽𝛽� ∗ 𝑇𝑇𝑅𝑅�� � 𝛽𝛽� ∗ 𝐿𝐿𝐿𝐿𝐿𝐿�� � 𝛽𝛽� ∗ 𝐶𝐶𝑀𝑀�� � 𝛽𝛽� ∗ 𝐿𝐿𝐿𝐿𝐶𝐶𝐶𝐶�� � 𝛽𝛽� ∗ 𝑅𝑅𝑅𝑅�� � 𝛽𝛽�� ∗ 𝑆𝑆𝑆𝑆𝑆𝑆𝐿𝐿�� � ��� (6) In equation (6), 'Post' is an indicator set to 1 after a CFO transition and 0 before. 'FCFO_SWITCH' marks male to female CFO transitions as 1, and 0 otherwise. A positive, significant 𝛽𝛽2suggests such transitions improve accounting comparability. RESULTS Table 1 presents summary statistics for our variables. Of the 24,495 CFO firm-year observations, 9% are female. The average financial statement comparability (COMP) is 65.27, with a median of 66.67. Sample firms have an average ROA of 1%, leverage ratio of 21%, log of total assets size of 7.72, market to book ratio of 3.19, and 17% report losses. The average cash holdings level is 17%. Table 1. Summary statistics Variable N Mean Std. Dev. Min. Median Max. FCFO 24,495 0.09 0.32 0.00 0.00 1.00 We then run a pooled panel regression of the comparability measure on the various determinants of comparability listed above and include our Female CFO dummy variable (FCFO) as one of the independent variables in the regression. The model is estimated following Francis et al. (2015) as: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶�� � 𝛽𝛽� � 𝛽𝛽� ∗ 𝑃𝑃𝐶𝐶𝑃𝑃𝐶𝐶 � 𝛽𝛽� ∗ 𝑅𝑅𝐶𝐶𝑅𝑅�� � 𝛽𝛽� ∗ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶�� � 𝛽𝛽� ∗ 𝑇𝑇𝑅𝑅�� � 𝛽𝛽� ∗ 𝐿𝐿𝐿𝐿𝐿𝐿�� � 𝛽𝛽� ∗ 𝐶𝐶𝑀𝑀�� � 𝛽𝛽� ∗ 𝐿𝐿𝐿𝐿𝐶𝐶𝐶𝐶�� � 𝛽𝛽� ∗ 𝑅𝑅𝑅𝑅�� � 𝛽𝛽�� ∗ 𝑆𝑆𝑆𝑆𝑆𝑆𝐿𝐿�� � ��� (5) If our hypothesis is supported, we expect 𝛽𝛽1 to be positive and significantly associated with 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶��. As Huang and Kisgen (2013) and Francis et al. (2015) describe, female executives are not randomly assigned to firms, leading to endogeneity concerns. In addition, unobservable contemporaneous changes at the same time as CFO changes could also affect the firm’s FSC. To alleviate these concerns, we also use a difference in differences (DiD) framework as in Huang and Kisgen (2013) and Francis et al. (2015). We test our findings on a set of firms that transition due to turnover from male to female CFOs and compare the results against a set of control firms that continue to have male CFOs after the turnover transition. The model is represented as: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶�� � 𝛽𝛽� � 𝛽𝛽� ∗ 𝐶𝐶𝐿𝐿𝐶𝐶𝑃𝑃�� � 𝛽𝛽� ∗ 𝐶𝐶𝐿𝐿𝐶𝐶𝑃𝑃 ∗ 𝑃𝑃𝐶𝐶𝑃𝑃𝐶𝐶_𝑆𝑆𝑆𝑆𝑆𝑆𝑇𝑇𝐶𝐶𝑆𝑆�� � 𝛽𝛽� ∗ 𝑅𝑅𝐶𝐶𝑅𝑅�� � 𝛽𝛽� ∗ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶�� � 𝛽𝛽� ∗ 𝑇𝑇𝑅𝑅�� � 𝛽𝛽� ∗ 𝐿𝐿𝐿𝐿𝐿𝐿�� � 𝛽𝛽� ∗ 𝐶𝐶𝑀𝑀�� � 𝛽𝛽� ∗ 𝐿𝐿𝐿𝐿𝐶𝐶𝐶𝐶�� � 𝛽𝛽� ∗ 𝑅𝑅𝑅𝑅�� � 𝛽𝛽�� ∗ 𝑆𝑆𝑆𝑆𝑆𝑆𝐿𝐿�� � ��� (6) In equation (6), 'Post' is an indicator set to 1 after a CFO transition and 0 before. 'FCFO_SWITCH' marks male to female CFO transitions as 1, and 0 otherwise. A positive, significant 𝛽𝛽2suggests such transitions improve accounting comparability. RESULTS Table 1 presents summary statistics for our variables. Of the 24,495 CFO firm-year observations, 9% are female. The average financial statement comparability (COMP) is 65.27, with a median of 66.67. Sample firms have an average ROA of 1%, leverage ratio of 21%, log of total assets size of 7.72, market to book ratio of 3.19, and 17% report losses. The average cash holdings level is 17%. Table 1. Summary statistics Variable N Mean Std. Dev. Min. Median Max. FCFO 24,495 0.09 0.32 0.00 0.00 1.00 (5) If our hypothesis is supported, we expect βi to be positive and significantly as- sociated with COMPit. As Huang and Kisgen (2013) and Francis et al. (2015) describe, female ex- ecutives are not randomly assigned to firms, leading to endogeneity concerns. In addition, unobservable contemporaneous changes at the same time as CFO changes could also affect the firm’s FSC. To alleviate these concerns, we also use a difference in differences (DiD) framework as in Huang and Kisgen (2013) and Francis et al. (2015). We test our findings on a set of firms that transition due to turnover from male to female CFOs and compare the results against a set of control firms that continue to have male CFOs after the turnover transition. The model is represented as: We then run a pooled panel regression of the comparability measure on the various determinants of comparability listed above and include our Female CFO dummy variable (FCFO) as one of the independent variables in the regression. The model is estimated following Francis et al. (2015) as: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶�� � 𝛽𝛽� � 𝛽𝛽� ∗ 𝑃𝑃𝐶𝐶𝑃𝑃𝐶𝐶 � 𝛽𝛽� ∗ 𝑅𝑅𝐶𝐶𝑅𝑅�� � 𝛽𝛽� ∗ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶�� � 𝛽𝛽� ∗ 𝑇𝑇𝑅𝑅�� � 𝛽𝛽� ∗ 𝐿𝐿𝐿𝐿𝐿𝐿�� � 𝛽𝛽� ∗ 𝐶𝐶𝑀𝑀�� � 𝛽𝛽� ∗ 𝐿𝐿𝐿𝐿𝐶𝐶𝐶𝐶�� � 𝛽𝛽� ∗ 𝑅𝑅𝑅𝑅�� � 𝛽𝛽�� ∗ 𝑆𝑆𝑆𝑆𝑆𝑆𝐿𝐿�� � ��� (5) If our hypothesis is supported, we expect 𝛽𝛽1 to be positive and significantly associated with 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶��. As Huang and Kisgen (2013) and Francis et al. (2015) describe, female executives are not randomly assigned to firms, leading to endogeneity concerns. In addition, unobservable contemporaneous changes at the same time as CFO changes could also affect the firm’s FSC. To alleviate these concerns, we also use a difference in differences (DiD) framework as in Huang and Kisgen (2013) and Francis et al. (2015). We test our findings on a set of firms that transition due to turnover from male to female CFOs and compare the results against a set of control firms that continue to have male CFOs after the turnover transition. The model is represented as: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶�� � 𝛽𝛽� � 𝛽𝛽� ∗ 𝐶𝐶𝐿𝐿𝐶𝐶𝑃𝑃�� � 𝛽𝛽� ∗ 𝐶𝐶𝐿𝐿𝐶𝐶𝑃𝑃 ∗ 𝑃𝑃𝐶𝐶𝑃𝑃𝐶𝐶_𝑆𝑆𝑆𝑆𝑆𝑆𝑇𝑇𝐶𝐶𝑆𝑆�� � 𝛽𝛽� ∗ 𝑅𝑅𝐶𝐶𝑅𝑅�� � 𝛽𝛽� ∗ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶�� � 𝛽𝛽� ∗ 𝑇𝑇𝑅𝑅�� � 𝛽𝛽� ∗ 𝐿𝐿𝐿𝐿𝐿𝐿�� � 𝛽𝛽� ∗ 𝐶𝐶𝑀𝑀�� � 𝛽𝛽� ∗ 𝐿𝐿𝐿𝐿𝐶𝐶𝐶𝐶�� � 𝛽𝛽� ∗ 𝑅𝑅𝑅𝑅�� � 𝛽𝛽�� ∗ 𝑆𝑆𝑆𝑆𝑆𝑆𝐿𝐿�� � ��� (6) In equation (6), 'Post' is an indicator set to 1 after a CFO transition and 0 before. 'FCFO_SWITCH' marks male to female CFO transitions as 1, and 0 otherwise. A positive, significant 𝛽𝛽2suggests such transitions improve accounting comparability. RESULTS Table 1 presents summary statistics for our variables. Of the 24,495 CFO firm-year observations, 9% are female. The average financial statement comparability (COMP) is 65.27, with a median of 66.67. Sample firms have an average ROA of 1%, leverage ratio of 21%, log of total assets size of 7.72, market to book ratio of 3.19, and 17% report losses. The average cash holdings level is 17%. Table 1. Summary statistics Variable N Mean Std. Dev. Min. Median Max. FCFO 24,495 0.09 0.32 0.00 0.00 1.00 We then run a pooled panel regression of the comparability measure on the various determinants of comparability listed above and include our Female CFO dummy variable (FCFO) as one of the independent variables in the regression. The model is estimated following Francis et al. (2015) as: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶�� � 𝛽𝛽� � 𝛽𝛽� ∗ 𝑃𝑃𝐶𝐶𝑃𝑃𝐶𝐶 � 𝛽𝛽� ∗ 𝑅𝑅𝐶𝐶𝑅𝑅�� � 𝛽𝛽� ∗ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶�� � 𝛽𝛽� ∗ 𝑇𝑇𝑅𝑅�� � 𝛽𝛽� ∗ 𝐿𝐿𝐿𝐿𝐿𝐿�� � 𝛽𝛽� ∗ 𝐶𝐶𝑀𝑀�� � 𝛽𝛽� ∗ 𝐿𝐿𝐿𝐿𝐶𝐶𝐶𝐶�� � 𝛽𝛽� ∗ 𝑅𝑅𝑅𝑅�� � 𝛽𝛽�� ∗ 𝑆𝑆𝑆𝑆𝑆𝑆𝐿𝐿�� � ��� (5) If our hypothesis is supported, we expect 𝛽𝛽1 to be positive and significantly associated with 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶��. As Huang and Kisgen (2013) and Francis et al. (2015) describe, female executives are not randomly assigned to firms, leading to endogeneity concerns. In addition, unobservable contemporaneous changes at the same time as CFO changes could also affect the firm’s FSC. To alleviate these concerns, we also use a difference in differences (DiD) framework as in Huang and Kisgen (2013) and Francis et al. (2015). We test our findings on a set of firms that transition due to turnover from male to female CFOs and compare the results against a set of control firms that continue to have male CFOs after the turnover transition. The model is represented as: 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶�� � 𝛽𝛽� � 𝛽𝛽� ∗ 𝐶𝐶𝐿𝐿𝐶𝐶𝑃𝑃�� � 𝛽𝛽� ∗ 𝐶𝐶𝐿𝐿𝐶𝐶𝑃𝑃 ∗ 𝑃𝑃𝐶𝐶𝑃𝑃𝐶𝐶_𝑆𝑆𝑆𝑆𝑆𝑆𝑇𝑇𝐶𝐶𝑆𝑆�� � 𝛽𝛽� ∗ 𝑅𝑅𝐶𝐶𝑅𝑅�� � 𝛽𝛽� ∗ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶�� � 𝛽𝛽� ∗ 𝑇𝑇𝑅𝑅�� � 𝛽𝛽� ∗ 𝐿𝐿𝐿𝐿𝐿𝐿�� � 𝛽𝛽� ∗ 𝐶𝐶𝑀𝑀�� � 𝛽𝛽� ∗ 𝐿𝐿𝐿𝐿𝐶𝐶𝐶𝐶�� � 𝛽𝛽� ∗ 𝑅𝑅𝑅𝑅�� � 𝛽𝛽�� ∗ 𝑆𝑆𝑆𝑆𝑆𝑆𝐿𝐿�� � ��� (6) In equation (6), 'Post' is an indicator set to 1 after a CFO transition and 0 before. 'FCFO_SWITCH' marks male to female CFO transitions as 1, and 0 otherwise. A positive, significant 𝛽𝛽2suggests such transitions improve accounting comparability. RESULTS Table 1 presents summary statistics for our variables. Of the 24,495 CFO firm-year observations, 9% are female. The average financial statement comparability (COMP) is 65.27, with a median of 66.67. Sample firms have an average ROA of 1%, leverage ratio of 21%, log of total assets size of 7.72, market to book ratio of 3.19, and 17% report losses. The average cash holdings level is 17%. Table 1. Summary statistics Variable N Mean Std. Dev. Min. Median Max. FCFO 24,495 0.09 0.32 0.00 0.00 1.00 (6) In equation (6), ‘Post’ is an indicator set to 1 after a CFO transition and 0 be- fore. ‘FCFO_SWITCH’ marks male to female CFO transitions as 1, and 0 other- wise. A positive, significant β2 suggests such transitions improve accounting comparability. ResultsResults Table 1 presents summary statistics for our variables. Of the 24,495 CFO firm- year observations, 9% are female. The average financial statement comparabil- ity (COMP) is 65.27, with a median of 66.67. Sample firms have an average ROA of 1%, leverage ratio of 21%, log of total assets size of 7.72, market to book ratio of 3.19, and 17% report losses. The average cash holdings level is 17%. Xiang (Gail) Gao, Emmanuel Sequeira, Qifeng (Charles) Wu3636 Table 1. Summary statistics Variable N Mean Std. Dev. Min. Median Max. FCFO 24,495 0.09 0.32 0.00 0.00 1.00 COMP 24,495 65.27 29.51 0.00 66.67 100.00 ROA 24,495 0.01 0.03 -0.92 0.02 6.63 LEV 24,495 0.21 0.17 0.00 0.20 2.37 SIZE 24,495 7.72 1.63 2.02 7.62 13.08 TA 24,495 -0.05 0.07 -1.27 -0.04 6.00 MB 24,495 3.19 3.65 -32.57 2.41 563.34 LOSS 24,495 0.17 0.37 0.00 0.00 1.00 CASH 24,495 0.17 0.17 0.00 0.10 0.90 R&D 24,495 0.01 0.02 0.00 0.00 0.59 N o t e : This table presents summary statistics of the variables used in our study. Definitions of each variable can be found in the Appendix. S o u r c e : calculations done by authors, using data from Compustat, CRSP, and Execucomp. Untablated results from Pearson correlations provide initial supportive evi- dence of female CFOs’ impact on FSC: a positive link between FCFO and COMP. Further, among our control variables, ROA, LEV, SIZE, and LOSS are negatively correlated with COMP while CASH and R&D are positively associated. Results across tables 2 to 4 validate our hypothesis by demonstrating the positive link between female CFOs and FSC in pooled panel and difference in difference regression settings. While table 2 presents results from the pooled panel regression tests, tables 3 and 4 present results from firms that transition from female CFOs to male CFOs and vice versa, respectively. Table 2 demonstrates that female CFOs are associated with higher levels of FSC when examined in a pooled panel regression setting. Column [1] pre- sents results without the inclusion of year fixed effects, while Column [2] pre- sents results with industry and year fixed effects included. Both columns show a significant positive relationship between female CFOs and accounting compa- rability. Firms with female CFOs have 1.1% higher FSC than those with males CFOs. This result is statistically significant at the 10% significance level. Thus, thE imPaCt of fEmalE ExECutivEs on thE quality ComParaBility… 3737 the pooled regression analysis results provide primary evidence supporting our hypothesis that female CFOs are associated with higher FSC. This result is also consistent with prior studies that find that female CFOs are associated with higher financial reporting quality. Table 2. FSC for firms with female CFOs Dependent variable: COMP Coeff. (t-stat) Coeff. (t-stat) [1] [2] FCFO 0.010* 0.011* (1.80) (1.92) ROA 1.294*** 1.325*** (14.88) (15.49) LEV -0.172*** -0.200 (-13.92) (-16.15) SIZE 0.074*** 0.077*** (18.57) (19.52) TA -0.333*** -0.325*** (-11.86) (-11.73) MB 0.007*** 0.005*** (12.93) (10.43) LOSS -0.093*** -0.095 (-14.15) (-14.73) CASH -0.037** -0.014 (-2.5) (-0.98) R&D 1.152*** 1.055*** (7.48) (6.95) Industry fixed effects Yes Yes Year fixed effects No Yes Xiang (Gail) Gao, Emmanuel Sequeira, Qifeng (Charles) Wu3838 Dependent variable: COMP Coeff. (t-stat) Coeff. (t-stat) N 24,495 24,495 Adjusted R2 0.29 0.32 N o t e : This table presents multivariate regression results for the effects of female CFOs on finan- cial statement comparability. Variable descriptions can be found in the Appendix. ***, **, and * in- dicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. S o u r c e : tests are developed by authors, using data from Compustat, CRSP, and Execucomp. Table 3 presents results for tests examining differences in FSC for firms that ex- perience female to male CFO turnover transitions. If we argue that female CFOs are associated with increased accounting comparability, then having a firm switch from a female to male CFO should result in a decrease in accounting comparability. Column [1] presents results without year fixed effects included, while Column [2] includes industry and year fixed effects. The regression re- sults support our hypothesis. We find that when including only industry fixed effects, firms switching from female to male CFOs experience decreases in FSC of 6.8%. Further, when including both industry and year fixed effects, firms with a female to male CFO transition experience a 7.6% decrease in FSC. Both results are significant at the 1% level. Table 3. FSC for firms that switch from female CFOs to male CFOs Dependent variable: COMP Coeff. (t-stat) Coeff. (t-stat) [1] [2] Post -0.068*** -0.076*** (-5.33) (-5.25) ROA 1.828*** 1.831*** (7.86) (8.16) Table 2. FSC… thE imPaCt of fEmalE ExECutivEs on thE quality ComParaBility… 3939 Dependent variable: COMP Coeff. (t-stat) Coeff. (t-stat) LEV 0.058 0.052 (1.44) (1.27) SIZE 0.115*** 0.121*** (10.53) (11.37) TA -0.500*** -0.460*** (-6.78) (-6.49) MB 0.007*** 0.007*** (5.17) (5.06) LOSS -0.039* -0.046** (-1.74) (-2.11) CASH -0.240*** -0.205*** (-4.78) (-4.15) R&D -0.709 -0.632 (-1.51) (-1.41) Industry fixed effects Yes Yes Year fixed effects No Yes N 1,582 1,582 Adjusted R2 0.65 0.69 N o t e : This table presents changes in financial statement comparability for firms that switch from having female CFOs to male CFOs. Variable descriptions can be found in the Appendix. ***, **, and * indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. S o u r c e : tests are developed by authors, using data from Compustat, CRSP, and Execucomp. Finally, table 4 presents results from the difference in difference (DiD) model specification described in Huang and Kisgen (2013) and Francis et al. (2015). In this test setting, we focus on the interaction term Post × FCFO_SWITCH. We expect a significantly positive coefficient for the interaction term when firms have male to female CFO changes, relative to those firms that continue to have Table 3. FSC… Xiang (Gail) Gao, Emmanuel Sequeira, Qifeng (Charles) Wu4040 male CFOs. Column [1] presents results without the inclusion of year fixed ef- fects, while Column [2] presents results with industry and year fixed effects in- cluded. We find that when including only industry fixed effects, male to female CFO transition firms experience significant increases in FSC of 18%. Further, when including both industry and year fixed effects, male to female CFO tran- sition firms experience significant increases in FSC of 17.5%. These results are significant at the 5% level. Table 4. FSC for firms that switch from male CFOs to female CFOs Dependent variable: COMP Coeff. (t-stat) Coeff. (t-stat) [1] [2] Post -0.026*** -0.033*** (-7.34) (-8.61) Post × FCFO_SWITCH 0.180** 0.175** (1.90) (1.89) ROA 0.921*** 0.926*** (11.66) (11.98) LEV -0.123*** -0.157*** (-10.31) (-13.26) SIZE 0.019*** 0.022*** (15.78) (17.85) TA -0.215*** -0.198*** (-8.31) (-7.82) MB 0.005*** 0.003*** (9.46) (6.34) LOSS -0.092*** -0.094*** (-15.43) (-16.00) CASH -0.096*** -0.063*** (-7.00) (-4.65) thE imPaCt of fEmalE ExECutivEs on thE quality ComParaBility… 4141 Dependent variable: COMP Coeff. (t-stat) Coeff. (t-stat) R&D -0.029 -0.117 (-0.20) (-0.81) Industry fixed effects Yes Yes Year fixed effects No Yes N 21,632 21,632 Adjusted R2 0.42 0.45 N o t e : This table presents results comparing differences in the effects on financial statement comparability for firms that switch from having male CFOs to female CFOs when compared to those firms that retain male CFOs after the executive change. Variable descriptions can be found in the Appendix. ***, **, and * indicate statistical significance at the 0.01, 0.05, and 0.10 levels, re- spectively. S o u r c e : tests are developed by authors, using data from Compustat, CRSP, and Execucomp. To summarize our results, the empirical results show that, as our hypothesis predicts, female CFOs are associated with significantly higher levels of FSC than male CFOs. Our findings are corroborated through examining sets of firms that experience CFO turnover transitions from female to male CFOs, as well as by testing a sample of male to female CFO transitions against a control sample of male to male CFO transition firms in a DiD setting. Firms with male to female CFO transitions experience significant increases in accounting comparability relative to those with male to male CFO turnover transitions.  Conclusions Conclusions Despite Financial Statement Comparability (FSC) being regarded as an impor- tant component of financial reporting quality and although existing research shows that female executives are associated with improved components of fi- nancial reporting quality, no studies have examined the effects of female exec- utives on FSC in a U.S. listed firm setting. This paper fills the significant gap in existing academic literature by examining whether female CFOs in U.S. listed firms are associated with increased FSC. Table 4. FSC… Xiang (Gail) Gao, Emmanuel Sequeira, Qifeng (Charles) Wu4242 We test our hypothesis using a U.S. listed firm CFO sample obtained from Execucomp between the years 1992 and 2023, where both pre- and post-transi- tion CFOs must be in office for at least three years excluding the transition year. Using pooled panel regressions and difference-in-difference regression mod- els, we document significant evidence that U.S. firms with female CFOs have higher levels of FSC than those with male CFOs. Our pooled panel regression results show that female CFOs have 1.1% higher FSC than male CFOs. Further, firms with female to male CFO switches experience decreases in FSC of 7.6%. Finally, our tests also show that firms that switch from having a male CFO to a female CFO have increases in FSC of 17.5%. Our results are robust to the inclu- sion of industry and year fixed effects. Our results provide further evidence describing the positive effects of fe- male executives on financial reporting quality and in the overall corporate fi- nance environment, and our findings are in line with several other studies that document the positive impact of women in various finance and accounting set- tings (Francis et al., 2014a; Francis et al., 2015; Gupta et al., 2020; Wang et al., 2023; Sinha, 2023). Our findings have significant implications for legislators and regulators who seek to improve corporate financial reporting quality, and also add further evidence supporting the push for increased female represen- tation in the C-suite. We invite future researchers to expand on our findings by examining the impact of gender on components of financial reporting qual- ity in a non C-suite setting. 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EARNINGS Net Income (NI) divided by (PRCC_F × CSHO); measured quarterly. COMP Measure of accounting comparability over the past 16 quarters between two firms as Appendix. Variable Descriptions Variables Description FCFO An indicator variable coded as 1 if a company’s CFO is female and zero otherwise. POST An indicator variable coded as 1 if the observation falls in the years after the CFO turnover transition and 0 if the observation falls before the CFO turnover. EARNINGS Net Income (NI) divided by (PRCC_F × CSHO); measured quarterly COMP Measure of accounting comparability over the past 16 quarters between two firms as � � ��∑ �𝐸𝐸�𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸���� � 𝐸𝐸�𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸���������� , where 𝐸𝐸�𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸���� is the predicted earnings for firm i obtained by regressing EARNINGS on firm returns every quarter, and a similar estimation for firm j (De Franco et al., 2011). We multiply this number by -1 to obtain a positive number for ease of readability. SIZE A measure of a firm’s size, computed as logarithm of total assets (AT) ROA A measure of a firm’s return on assets computed as NI / AT CASH A measure of a firm’s total cash holdings (CH) TA A measure of the firm’s total accruals (TA) LEV A measure of the firm’s leverage measured as (DLTT + DLC) / AT MB Ratio of the market value of the firm to book value (PRCC_F × CSHO)/CEQ LOSS An indicator variable is coded as 1 if a company reports a loss and zero otherwise. R&D Measure of a firm’s research and development expenses scaled by total sales (XRD/REVT), less the two-digit SIC industry mean value of the same measure. Source: developed by authors. , where Appendix. Variable Descriptions Variables Description FCFO An indicator variable coded as 1 if a company’s CFO is female and zero otherwise. POST An indicator variable coded as 1 if the observation falls in the years after the CFO turnover transition and 0 if the observation falls before the CFO turnover. EARNINGS Net Income (NI) divided by (PRCC_F × CSHO); measured quarterly COMP Measure of accounting comparability over the past 16 quarters between two firms as � � ��∑ �𝐸𝐸�𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸���� � 𝐸𝐸�𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸���������� , where 𝐸𝐸�𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸���� is the predicted earnings for firm i obtained by regressing EARNINGS on firm returns every quarter, and a similar estimation for firm j (De Franco et al., 2011). We multiply this number by -1 to obtain a positive number for ease of readability. SIZE A measure of a firm’s size, computed as logarithm of total assets (AT) ROA A measure of a firm’s return on assets computed as NI / AT CASH A measure of a firm’s total cash holdings (CH) TA A measure of the firm’s total accruals (TA) LEV A measure of the firm’s leverage measured as (DLTT + DLC) / AT MB Ratio of the market value of the firm to book value (PRCC_F × CSHO)/CEQ LOSS An indicator variable is coded as 1 if a company reports a loss and zero otherwise. R&D Measure of a firm’s research and development expenses scaled by total sales (XRD/REVT), less the two-digit SIC industry mean value of the same measure. Source: developed by authors. is the predicted earnings for firm i obtained by regressing EARNINGS on firm returns every quarter, and a similar estimation for firm j (De Franco et al., 2011). We multiply this number by -1 to obtain a positive number for ease of readability. SIZE A measure of a firm’s size, computed as logarithm of total assets (AT). ROA A measure of a firm’s return on assets computed as NI / AT. CASH A measure of a firm’s total cash holdings (CH). TA A measure of the firm’s total accruals (TA). LEV A measure of the firm’s leverage measured as (DLTT + DLC) / AT. MB Ratio of the market value of the firm to book value (PRCC_F × CSHO)/CEQ. LOSS An indicator variable is coded as 1 if a company reports a loss and zero otherwise. R&D Measure of a firm’s research and development expenses scaled by total sales (XRD/REVT), less the two-digit SIC industry mean value of the same measure. S o u r c e : developed by authors.