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L. B. Mahjoub and H. Khamoussi
A. Descriptive statistics for EPS

EPSt
EPSt 1

Obs.
767
640

EPS
EPSt 1
Constant

Coeff.
0.579
1.080

Mean
Std dev.
2.498
5.181
2.438
5.229
B. Regression of time-series model
Std err.
t
0.215
2.69
0.712
1.52
Adj. R2 31.10%

Min.
55.01
55.01

Max.
37.75
37.75

P > |t|
0.007
0.129

Table 2. Descriptive statistics for earnings per share and regression of time-series regression model

Variables Description
Dependent Variable
Following previous studies (Lev, 1983; Ali and Zarowin, 1992; Francis et al., 2004; Laksmana and Yang, 2009; Gaio,
2010), we measure earning persistence (PERST) as the negative slope coefficient estimate, a1i, from an autoregressive
model of order one5 (AR1) for annual earnings per share:
EPSi;t ¼ ai;0 þ a1;i EPSi;t 1 þ ei;t

(1)

For each firm i and year t, EPS is net income before extraordinary items divided by the weighted average number
of outstanding shares.
Larger (smaller) values of PERST correspond to less (more) persistent earnings. Persistent earnings are viewed as
higher quality earnings because they are sustainable (Francis et al., 2004; Laksmana and Yang, 2009).
From an accounting perspective, earning is an extremely important measure of periodic financial performance.
We take the view that the degree to which current reported earnings persist into the next period is an important
measure of earning quality.
In relation to annual earnings, Sloan (1996) shows that in US companies in the period 1962–1991 the a value in
the model regression is approximately 0.84. That is, if a company earns $US 1 of earnings in year t, then $US 0.84
would be expected to persist into the next year.
The lower persistence of the accrual component of earnings indicates that the amount of accruals in current earnings
is inversely related to the persistence of earnings in the future and is an inverse measure of earning quality.
The majority of the earning persistence literature employed a time-series regression (Lev, 1983; Kormendi and
Lipe, 1987; Peng, 2011) such as the auto-regressive, integrated moving average (ARIMA) model to estimate a
measure of earning persistence. However, in order for a time-series model to have effective power there should
be a relatively long history of earnings (e.g., many studies utilize a time series of 20 successive years of earnings
data, for example Fama and French, 2000).
We present in Table 2 the measurement of the variable PERST and related statistics.
Explanatory Variable
For measuring environmental and social disclosure, a review of past research shows several techniques. The
majority of studies, in the field, have used the content analysis method based on indexing and weighting scales
(Wiseman, 1982; Patten, 1991, 1992; Patten and Nance, 1998).
According to Wiseman (1982), the simplest structure of content analysis techniques notes whether or not a
particular event is brought up in a document (annual report for example).
Previous studies assessed environmental disclosures mainly from annual reports and other regulatory filings
such as 10-Ks, and many of those studies rely on a Wiseman (1982) based content analysis index to measure the
5

We use an autoregressive model with order one rather than the higher order specification suggested by Baginski et al. (1999) because we wish to
estimate firm-specific persistence measure for a broad sample of firms.

Copyright © 2012 John Wiley & Sons, Ltd and ERP Environment

Bus. Strat. Env. (2012)
DOI: 10.1002/bse