Earnings management is generally defined as the practice of using discretionary accounting methods to attain desired levels of reported earnings (Gavious, Segev, & Yosef, 2012). Earnings management includes choosing accounting methods which provide reporting income that is advantageous for managers and the company but detrimental for external stakeholders (Krishnan & Parsons, 2008). The issue of earnings quality is discussed extensively in the accounting literature, and is an important area of concern for stakeholders. Earnings quality shows the extent to which stated earnings reveal an organization’s financial situation to interested parties. If users of financial data are “misled” by the level of reported income, then investors’ allocation of resources may be inappropriate when based on the financial statements provided by management (Healy & Wahlen, 1999). Managers are professionally responsible and ethically obliged to make sure that concerned parties receive high quality earnings reports in a timely manner (Krishnan & Parsons, 2008). Following the uncovering of major accounting scams involving large organizations (e.g. Enron), scholars have focussed on managers’ motives for engaging in earnings management (Gavious et al., 2012). The literature mentions various factors, for example, debt covenants, pending litigation and the existence of performance-based compensation plans for management, that can motivate earnings management (Jones, 1991). All stakeholders and users of financial information require tools that can moderate managers’ tendency to engage in earnings management (Krishnan & Parsons, 2008). Several researchers have explored the impact of gender diversity on both financial reporting quality and earnings management (Arun, Almahrog, & Aribi, 2015; Gavious et al., 2012; Krishnan & Parsons, 2008; Labelle, Gargouri, & Francoeur, 2010; Peni & Vah€ € amaa, 2010; Srinidhi, Gul, & Tsui, 2011). However, this issue requires further investigation. Equivocal methodologies and inconsistent findings have left researchers and managers perplexed. The main cause of this uncertainty is the excessive use of the agency hypothesis, which states that statutory diversity alone is enough to control management and provide motives to defend shareholders’ interests (Fama & Jensen, 1983). Prior studies have also focused on the number or percentage of female directors in examining the relationship between board gender diversity and earnings management. Our study broadens this approach and extends beyond research on gender difference by exploring the channel through which female directors exert influence on earnings management. Following the approach proposed by Ben-Amar, Francoeur, Hafsi, and Labelle (2013), we consider both statutory and demographic attributes of female board members. Statutory diversity is a measure of heterogeneity in the process of board composition and is essential for effective monitoring of management (Fama & Jensen, 1983). Demographic diversity (e.g. education, skills and experience) leads to better decision-making by nurturing candidness and analytical decision-making among board members (Erhardt, Werbel, & Shrader, 2003). Our initial sample consists of companies belonging to the CAC All-Shares index listed on Euronext Paris over the period 2001e2010, during which time women were appointed to boards on voluntary basis. Our sample period ends prior the amendment of the gender quota law by the French parliament in 2011. This legislation requires that, as from 2014, 20% of a firm’s board members be women, rising to 40% as from 2016. Apart from the benefits of setting gender quotas, our study highlights the importance of female directors’ attributes, as a consequence of which they are more effective in monitoring the corporate financial accounting process. In our paper, both gender diversity and the attributes of female directors are endogenously determined. We then employ a carefully formulated methodology for dealing with firm level differences, omitted variables, self-selection bias and endogeneity issues. We use propensity score-matching to match gender-diverse firms and non-gender-diverse firms with very similar characteristics. The analysis serves to determine whether sample firms differ in firm-specific characteristics, regardless of the role of gender diversity. We apply the system GMM regression estimation approach to the matched sample to correct for endogeneity bias.