CHAPTER 5: METHODOLOGY
5.6 Control Variables: Firm and CEO Level Determinants of CSS
Data for all firm-level control variables was sourced from the COMPUSTAT
database for each firm in the sample for the years 1990-2009 (An additional year of data
was necessary for the past performance measure).
8
Note that Adams et al. (2005) used the top and bottom 40%, excluding industries in the middle 20% as these were deemed more difficult to classify as either high or low discretion industries. Although this may be a legitimate concern, not wanting to lose data for entire industries, I split the sample above and below the median discretion score of the averaged ratings, which was 5.05.
9 To assess if this was an appropriate process, I compared the means of the capital intensity scores for the high vs. low discretion industries already established. The mean capital intensity for high discretion industries was 67.2, while the mean capital intensity for low discretion industries was 276.6, which was statistically significant in a t-test comparison of means (p<0.000). After categorizing the remaining industries as either high/low, these numbers remained virtually unchanged (63.2 and 278.2).
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Firm size. Prior research has shown that firm size will affect a firm’s CSR ratings (McWilliams & Siegel, 2000). Specifically, studies have shown that larger firms are more
likely to engage in the ‘people’ dimension of CSR, which includes community donations,
the hiring of women and minorities and the treatment of employees, but are less likely to
perform well in the ‘product’ dimension of CSR which includes product/service quality
and a firm’s stance toward the natural environment (Johnson & Greening, 1999). Strike et
al. (2006) also found that larger firms will show higher levels of both CSR and CSiR
(Corporate Social Irresponsibility) and Shropshire and Hillman (2007) found explicitly
that larger firms are more likely to experience significant shifts in stakeholder
management programs than smaller firms. I therefore control for firm size, measured as
the natural log of total assets. Note that although Waddock and Graves (1997) also
include both total sales and total number of employees as proxies for firm size in their
model of CSP-CFP, these variables were found to have extremely high variance inflation
factors (VIF >29) which indicate high degrees of multicollinearity. As such, only the log
of total assets was retained as a proxy for firm size in this analysis.
Past performance. Waddock and Graves (1997) also found that a firm’s previous financial performance positively affects the firm’s subsequent social performance and this
finding has been substantiated in a recent meta-analysis (Orlitzky et al., 2003). As such,
following previous studies, I control for past performance by accounting for the return on
assets (ROA) lagged by one year. ROA is considered an appropriate measure of firm
performance here given that it captures the profitability of the firm based on the strategic
use of the resources, or assets, under its control (Hull & Rothenberg, 2008).
Firm risk. Because investing in CSR issues may be associated with either potential savings (e.g., waste reduction) or possible incremental costs (e.g., pollution
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control equipment), a firm’s risk profile may influence the adoption of CSS. In line with
previous studies (McWilliams & Siegel, 2000; Waddock & Graves, 1997) therefore, I
control for firm risk by including the ratio of long term debt to total assets.
R&D intensity. McWilliams and Siegel (2000) argue that R&D intensity should be included as a control variable in all future CSR studies. Although this argument rests
on R&D expenditures as an explanatory variable in firm financial performance (not social
performance), recent studies have nonetheless demonstrated a strong relationship between
R&D intensity and CSP (Hull & Rothenberg, 2008; Padgett & Galán, 2010). Consistent
with these findings, I therefore include R&D intensity as a control variable.
Because this variable is notoriously plagued with missing data issues10, I follow
previous research by creating three separate measures to capture R&D Intensity: (1) total
R&D expenditures divided by total sales, (2) total R&D expenditures divided by total
sales where all missing values for R&D expenditures are treated as zero and (3) an R&D
missing dummy variable where missing values are coded 1, otherwise 0 (Henderson et al.,
2006). Because measure (1) greatly reduces the number of observations, models using
measures (2) and (3) in combination, allow the total number of observations to be
preserved, yet remove any bias that may be associated with the assigning of zero values to
missing data (Henderson et al., 2006).
10 If a company spends an insignificant amount on R&D (e.g. financial companies), they are not required to report this amount in financial statements captured by COMPUSTAT and are thus recorded as missing.
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5.6.2 CEO Level Control Variables
Data for all CEO-level control variables was sourced from the COMPUSTAT
Execucomp database for each firm in the sample for the years 1991-2009. In those cases
were data was missing, the information was sourced as detailed in Section 5.5.
CEO tenure. Research has shown that CEOs who have served within a firm or an industry for an extended period of time are more likely to conform to the norms of the
industry and less likely to deviate from industry conventions (Finkelstein & Hambrick,
1990; Hambrick, Geletkanycz & Fredrickson, 1993). For example, several studies have
shown that longer tenured CEOs are more likely to engage in defender strategies
characterized by stability and efficiency than shorter tenured CEOs who were more likely
to undertake prospector strategies related to increased levels of innovation (Finkelstein et
al., 2009). Similarly, tenure has also been found to be negatively related to organizational
change, with shorter-tenured CEOs willing to take more strategic risks, yet longer tenured
CEOs demonstrating a greater commitment to the status quo (Miller, 1991). Geletkanycz
and Black (2001) found that the longer an executive has spent in a particular functional
track, the greater his/her commitment to the status quo, suggesting that CEO perspectives
and views become increasingly narrow and fixed rendering the ability to conceive of new
alternatives or solutions difficult.
However, despite the fact that the impact of CEO tenure on different aspects of
firm performance has been extensively studied (Finkelstein & D’Aveni, 1994), few
studies have looked at the relationship between CEO tenure and any measure of CSR.
Only Thomas et al. (1994) found that CEOs who have a longer tenure in the role and
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current position were more likely to score higher on measures of corporate social
performance (CSP) than CEOs with shorter organizational tenures (Thomas & Simerly,
1994).
Taking these diverse perspectives into account, CEO tenure may have a complex
relationship to CSS. On the one hand, longer organizational tenures should be associated
with higher measures of social performance (Thomas et al., 1994). On the other, CEOs
with long tenures are less equipped to adjust to ambiguous and complex changes in the
operating environment, thus rendering them “stale in the saddle” (Miller, 1991) or
“obsolete” (Henderson et al., 2006). The relationship between CEO tenure and CSS thus
appears to be contingent on organizational experience such that CEO tenure in the firm
may be negatively related to CSS in that the longer a CEO has served in his/her position
the more committed they are to the status quo, yet tenure in the role may be related to
organizational change such that the shorter the time as CEO, the more likely they are to
instigate change. As such, I control for CEO tenure both as tenure in the role (Henderson
et al., 2006; Herrmann & Datta, 2002) as well as tenure in the organization (Thomas &
Simerly, 1994).
CEO age. Hambrick and Mason (1984) argue that a CEO’s age can influence his/her attitude toward risk, with older managers being more risk-averse than younger
ones. Similarly, Bertrand and Schoar (2003) found that executives from earlier birth
cohorts were, on average, more conservative, than executives from later birth cohorts. If
the adoption of CSS policies is considered risky or requires a more liberal worldview
(i.e., especially programs in contested areas such as the adoption of formal policies to
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1
(measured in years) as is the norm in upper-echelon research (Musteen, Barker III &
Baeten, 2010),
CEO gender. Lastly, there is some limited research that suggests that gender may play a role in determining a firm’s propensity to engage in CSS. Williams (2003), for
example, found that women on corporate boards are strongly linked to both the total
amount of firm philanthropic contributions, as well as the type of charity supported
(community services and the arts). Barnett and Karson (1989) found that women are
significantly more likely than men to chose the ethical over the economic option in a
presentation of various work-related ethical dilemma scenarios. More recently, Simga-
Mugan et al. (2005) also found support that women are more ethical than men in
scenarios in which respondents had responsibility towards agents such as employees (e.g.
demotion after maternity leave) concluding that the difference may be in the cognitive
rules (knowledge structures) accessed by the different genders: “Females are argued to
typically utilize ethics of care, which emphasizes social virtues and caring for others. On
the other hand, males are found to utilize ethics of justice, emphasizing equal treatment
and playing by the rules” (p. 150). Building on the large body of work that suggests
women are more ethically sensitive than men, I control for CEO gender by coding female
CEO’s as 1 and males as 0.