ACCOUNTING FOR PRE-PRODUCTION COSTS IN THE AUSTRALIAN
EXTRACTIVE INDUSTRIES
Peter G. Gerhardy
Senior Lecturer in Accounting
School of Commerce
The Flinders University of South Australia
SCHOOL OF COMMERCE
RESEARCH PAPER SERIES: 98-2
ISSN: 1441-3906
Abstract
This paper examines the method used to account for pre-production costs by companies in the
Australian extractive industries. Within a costly contracting framework, hypotheses on factors
motivating the treatment chosen to account for these costs are developed and tested. Specifically,
the relationship of debt contracting, political sensitivity, income diversification, engineering risk
and auditor effect variables to three alternative continuous measures of the accounting treatment are
tested.
While, overall, traditional costly contracting and political sensitivity explanations of
accounting choice do not appear to hold well, the findings do suggest that the ability to explain
observed variation in accounting treatment is dependent upon the definition of pre-production costs
considered – particularly whether development costs are included with the early pre-production
costs relating to explanation and evaluation.
Further, the results are affected by whether
amortisation is included in the measure of pre-production costs written off. Some support is found
for an effect on the treatment of early stage pre-production costs of the company auditor’s
expressed preference for capitalisation of such costs.
Evidence is also found to support the
proposition that, at the time of the study, those companies in the gold mining sub-sector of the
extractive industries were subject to the effects of political sensitivity, related to the impending
removal of concessional taxation arrangements. Some support for a relationship between leverage
and the proportion of pre-production costs written off directly to the profit and loss statement is also
evident.
1
1.
Introduction
This paper examines the method used to account for pre-production costs by companies in the
Australian extractive industries. Availability of choice for this accounting policy has continued
despite the introduction of Australian accounting standard AAS 7 dealing with the matter in 1976,
and companies have and continue to utilise this ability to make a choice in relation to the treatment
of pre-production costs adopted in practice (see for instance, Ryan, Heazlewood and Andrew, 1977,
1980; Ryan, Andrew, Gaffikin and Heazlewood, 1990, 1993; Ryan and Heazlewood, 1995, 1997;
Gerhardy, 1998).
Costly contracting theory is used as the basis for explaining companies’
accounting choices. Hypotheses relating to factors motivating the treatment chosen to account for
pre-production costs in the Australian extractive industries are developed and tested within this
framework.
This paper adds to the literature on accounting method choice within the Australian
institutional framework. Its particular contribution to the area is twofold. First, it examines
companies within a particular sector of the economy, namely the mining sector, which has often
been regarded as being subject to differing influences and incentives, particularly in relation to the
political sensitivity of firms within the sector (see for example, Bowen, Noreen and Lacey, 1981,
pp. 168-9).1 Supporting the importance of taking a single industry approach to accounting choice
studies, Aitken and Loftus (1994, p. 2) suggest ‘that future developments in this research will
depend upon the eventual accumulation of research results across a number of separate industries’.
The second significant difference between this and many accounting choice studies conducted to
date relates to the measurement of accounting treatment adopted. The majority of studies in the
area have adopted a dichotomous measure of accounting policy.2 This paper suggests that surveys
of practice which attempt to categorise the choice of treatment for pre-production costs into discreet
1
2
The possibility of differing influences on sub-sectors of the extractive industries, particularly those in the gold
mining and petroleum sub-sectors, is discussed below.
Exceptions to this include Aitken and Loftus (1994) who measure the dollar effect of policy choices, and Walker
(1988) whose approach has been adopted and modified in this paper.
2
categories represent a major understatement of the variation found in practice. It therefore utilises
an alternative continuous measure of the accounting treatment adopted.
In addition to extending the scope of Australian research within the contracting cost
framework, the study is timely from the point of view of the usefulness of its results to standard
setters. Watts and Zimmerman (1986, p. 14) argue:
Positive accounting theory is important because it can provide those who must make
decisions on accounting policy (corporate manages, public accountants, loan officers,
investors, financial analysts, regulators) with predictions of, and explanations for, the
consequences of their decisions.
The Australian standard setters have recently indicated that extractive industries accounting
represented a high priority project, with the expectation that a discussion paper on the issue would
be prepared in the not too distant future, to be followed by an exposure draft and finally a revised
accounting standard (AARF & AASB, 1996, p. 16). In addition, at its April 1998 meeting the
International Accounting Standards Committee (IASC) placed on its agenda a new project dealing
with the Extractive Industries. A Steering Committee for the project was appointed in July, with the
first major step in the process to be preparation and issue of a discussion paper by the end of 1998
(IASC, 1998). Given the current program to harmonise Australian and International accounting
standards, this move by the IASC suggests that the Australian standard setters will soon commence
their foreshadowed reconsideration of this country’s extractive industries standards. Thus, this
study is able to provide further insights into this issue, particularly in relation to the factors that may
be associated with a preference for a particular treatment of pre-production costs.
Prior research conducted within the contracting cost framework has indicated three
significant motivations operating on firms’ accounting method choices, namely management
compensation contracts, debt constraints and political sensitivity. Due to measurement difficulties
the influence of the first of these factors, the existence of management compensation contracts tied
to earnings, is not investigated in this study. The influence of both debt constraints and political
sensitivity are investigated in detail. In addition, the effects on accounting treatment of two factors
hypothesised on the basis of ex ante efficient contracting arguments, namely income diversification
3
and engineering risk, are investigated. Finally, the existence of an auditor effect on accounting
method, as hypothesised by Deakin (1980), is investigated in the Australian context.
The remainder of this paper is organised as follows. The next section briefly outlines the
possible methods of accounting for pre-production costs, and the position adopted by the Australian
regulators in relation to this accounting choice. Flowing from this, the need for a continuous
measure of the accounting treatment adopted is explained. In Section 3 the formal hypotheses are
developed. Section 4 provides details of the research methods utilised in the study, with the results
being presented and discussed in Section 5. The final section summarises the major findings and
discusses the implications of the study.
2.
Accounting for Pre-production Costs
The fundamental issue distinguishing the different methods of accounting for pre-production costs
in the extractive industries relates to the choice of cost centre to be used in accumulating such costs
for external reporting purposes. There exist a large number of possible cost centres, varying greatly
in their size, which could be used to accumulate pre-production costs. Lourens and Henderson
(1972, p. 54) indicate that the cost centre can ‘be as small as an individual mineral deposit, mine,
quarry, well, lease or reserve, or it can be as large as several leases, areas, a State, or an entire
country’. Effectively the choice of a different cost centre leads to the choice of a different method
of accounting for pre-production costs. While numerous possible cost centres implies equally
numerous methods of accounting for pre-production costs, Table 1 summarises four of the major
methods available; viz., the full cost, area of interest, successful efforts and expense methods.
[INSERT TABLE 1 ABOUT HERE]
As can be seen from the table, the full cost method is conceptually the opposite of the expense
method. This is by virtue of their adoption of extreme positions as to the cost centre used to
accumulate pre-production costs. Both the successful efforts and area of interest methods lie
between the two extremes, with the distinction between the two being less than clear. It has for
instance been suggested that the area of interest method is a particular variety of the successful
4
efforts method, with the cost centre defined as an area of interest (Henderson and Peirson, 1995, p.
772; Whittred, Zimmer and Taylor, 1996, p. 395). Heazlewood (1987, p. 30) on the other hand
suggests that the area of interest method lies between the full cost and successful efforts method in
view of the fact that the former ‘could result in more expenditure being capitalised than under the
successful efforts method in any given geological area’. Davison (1979, pp. 22-23) and Wise and
Wise (1988, p. 30) express a similar view. Whichever view is accepted, it is suggested that while
Table 1 describes four particular methods of accounting for pre-production costs, this number belies
the wide variety of possible methods which could be used to account for pre-production costs in the
extractive industries, given that the outcome for the two intermediate methods will be dependent
upon how the cost centre is defined.
In the Australian regulatory context the essential accounting requirements relating to preproduction costs are contained in paragraphs 10-12 of the current version of Australian Accounting
Standard AAS 7.3 Paragraph 10 deals with accounting for pre-production costs incurred in the
earlier pre-production stages of exploration and evaluation, and states that:
Costs arising from exploration and evaluation related to an area of interest shall be
written of as incurred, except that they may be carried forward provided that rights to
tenure of the area of interest are current and provided further that at least one of the
following conditions is met:
(a)
such costs are expected to be recouped through successful development and
exploitation of the area of interest, or alternatively, by its sale; and
(b)
exploration and evaluation activities in the area of interest have not at reporting
date reached a stage which permits a reasonable assessment of the existence or
otherwise of economically recoverable reserves, and active and significant operations
in, or in relation to, the area of interest are continuing. (emphasis added)
This paragraph clearly provides a choice of accounting methods for exploration and evaluation
costs. While initially it appears to require the expense method, whereby all such costs are written
off to the profit and loss statement as incurred, it goes on to add that the costs may be capitalised,
subject to one of the stated conditions being met. This approach is described as the ‘area of interest
method’ (AAS 7, para. 16). Whittred, et al. (1996, p. 395) suggest that the conditions under which
3
Approved accounting standard AASB 1022 was issued in 1989, giving legislative force for companies to the
accounting requirements contained in AAS 7. Since this study refers to accounting practices of mining companies
5
this area of interest method may be applied allow ‘the option of deferral even in those cases where
the feasibility of operations … has not yet been established – all that is required is that rights to
tenure of the area of interest are current and that the actual and significant operations (not defined)
are continuing in that area’. In addition to having an explicit choice between use of the expense and
area of interest methods, the ‘area of interest’ is so broadly defined in geological terms that a wide
variety of possible cost centres may be adopted for the purpose of accumulating pre-production
costs. On this point AAS 7 (para. 18) admits that during the earlier pre-production stages ‘an area
may be difficult to delimit’. Similarly, Davison and Lourens (1978, p. 34) note that in formulating
the Australian accounting requirements ‘the critical size of the area of interest was, unavoidably,
left undefined in an operational manner, thus permitting a wide variety of options in practice’
(emphasis added). Thus, the available treatments of exploration and evaluation costs are much
broader than simply a dichotomous choice between two methods, being highly dependent upon how
the area of interest is defined.
Exploration and evaluation comprise the initial and generally higher risk pre-production
phases in the extractive industries.
Accounting for costs incurred in the remaining two pre-
production phases, development and construction, also require consideration. Costs incurred in the
latter construction phase generally are in the nature of expenditure on fixed assets and therefore, as
recognised in paragraph 12 of AAS 7, are subject to the accounting requirements of Australian
Accounting Standard AAS 4, Depreciation. As such, construction costs are not considered further
in this study. In addition, Walker (1988, p. 40) suggests that an argument can be put for excluding
development costs when considering accounting for pre-production costs, on the grounds that
‘development typically relates to the preparation of the deposit or field for commercial production
and as such will take place after it has been determined that the exploration has been successful’.
This is reflected in the accounting treatment required by AAS 7 (para. 11) for such costs, which
‘shall be carried forward to the extent that [they] … are expected to be recouped through successful
prior to the release of AASB 1022, reference will be confined to the AAS series standard. The accounting
requirements of the two standards are the same.
6
exploitation of the area of interest, or alternatively, by its sale’. The choice of methods available for
exploration and evaluation costs is not available when accounting for development costs. As such,
they may be argued to be different in nature to the costs incurred in the exploration and evaluation
phases, and not subject to managerial discretion.
It could however be argued that to exclude development costs would unduly bias the study
since they represent an extra degree of freedom for managers in determining the amount of preproduction costs to carry forward/write off. Since, as recognised by Henderson and Peirson (1995,
p. 773), ‘Frequently, the boundaries between these phases are unclear as the … phases often occur
simultaneously in the same area’, consideration of development costs adds a further dimension to
managements’ decisions on the accounting treatment to be accorded pre-production costs.4 The
significance of this distinction is investigated in this paper by conducting tests which both include
and exclude development costs.
The above discussion suggests that specifying the dependent variable in this study as
dichotomous, or even as polychotomous, would not accurately represent firms’ accounting
treatments of pre-production costs. A refinement of Walker’s (1988) continuous proportionate
measure is used to represent the accounting treatment of pre-production costs. The specification of
this dependent variable is fully discussed in Section 4.
3.
Hypothesis Development
Research conducted within the contracting cost framework utilises the contracting and political
processes as the basis for investigating firms’ choices amongst accounting alternatives. This section
develops hypotheses relating to the influence of two motivations derived from the framework,
namely debt constraints and political sensitivity. Also derived from contracting theory, hypotheses
predicting engineering risk and income diversification as factors influencing the accounting
4
In addition, a practical argument in favour of considering development costs in this study is the frequent practice of
companies whereby all non-construction pre-production costs are included in the balance sheet as a single figure,
not allowing in such cases the distinction of development costs from exploration and evaluation costs.
7
treatment of pre-production costs in the Australian extractive industries are developed. Finally,
Deakin’s (1980) auditor effect hypothesis is investigated in the Australian regulatory context.
3.1
Debt Constraints
The general hypothesis that the closer is a business to breaching an accounting based debt constraint
the more likely is management to adopt accounting methods that increase profit has been well
documented in contracting cost theory and related empirical studies (see for instance Watts and
Zimmerman, 1986, pp. 257-259). This strategy is adopted by management in order to loosen such
covenants and therefore reduce the probability of a breach, and the incurrence of the consequential
costs.
The most commonly used proxy for closeness to debt covenant default in prior studies has
been a leverage ratio.5 Whittred and Zimmer (1986) and Stokes and Tay (1988) provide evidence
of the use of various types of restrictive covenants in trust deeds supporting listed public debt issues
by Australian companies. Both studies indicate that most commonly such covenants are specified
in terms of liabilities to total tangible assets. Whittred and Zimmer (1986, p. 27) suggest that
exclusion of intangibles means that for a number of accounting choices, including inter alia., the
treatment of pre-production costs, to a large extent ‘the choice of capitalizing or expensing cannot
affect a firm’s proximity to a leverage constraint’. This would seem to suggest that the general
hypothesis outlined above would not hold in the current study. Despite this argument, two factors
suggest that investigation in this study of the relationship between leverage and the choice of
accounting treatment for pre-production costs is warranted. First, both of the surveys mentioned
investigated only public debt agreements. The current study suggests that public debt is seldom
used in the Australian extractive industries, with only one company included in the study having
such debt outstanding. It is possible that covenants in private debt agreements differ from those for
public debt issues. In addition, Whittred and Zimmer’s (1986) survey includes only one company
with a stock exchange industry classification indicating involvement in the extractive industries,
8
with none of the companies in Stokes and Tay’s (1988) survey being involved in the pre-production
stages of the extractive industries. Thus, to draw the conclusion that pre-production costs are
routinely precluded when specifying leverage constraints in debt agreements may be somewhat
premature.
In addition, Press and Weintrop (1990) found evidence in the US context of an
association between firms’ level of leverage and closeness to debt constraints.
Based on the above, and recalling that capitalisation of pre-production costs will have the
effect of reducing the probability of breaching a debt covenant specified in terms of the debt to
equity ratio, the following hypothesis is tested:
H1:
The ratio of debt to equity will be positively correlated with capitalisation of
pre-production costs.
A further ratio often specified in debt covenants is the level of interest coverage. As
observed by Bowen, Noreen and Lacey (1981, p. 167), ‘Some debt agreements prohibit the firm
from issuing new debt unless a minimum prescribed ratio of income to interest charges is
maintained’. Whittred and Zimmer (1986, p. 25) provide evidence of the use of such interest
coverage constraints in Australia, although they were found in only 33 per cent of the debenture
trust deeds they examined. The potential difficulty of leverage ratios being unaffected by the
method of treatment of pre-production costs if intangible assets are excluded in the specification of
the constraint does not apply to constraints specified in terms of interest coverage. This therefore
suggests a possible observable relationship between the interest coverage ratio and the accounting
treatment of pre-production costs in the Australian extractive industries. Specifically, capitalisation
of pre-production costs in preference to writing them off will have the effect of increasing the level
of interest coverage above what it would be under the alternative, thereby reducing the probability
that the firm will violate such a constraint. Thus the lower the level of coverage the more likely is
the firm to capitalise such costs. The following hypothesis is tested:
H2:
5
The interest coverage ratio will be positively correlated with writing off or
expensing of pre-production costs.
As recognised by Press and Weintrop (1990, p. 65) such proxies for closeness to breach of covenants are used
because of the high cost of gaining access to actual contracts.
9
3.2
Political Sensitivity
Contracting cost theory establishes a link between the political sensitivity of firms and their
accounting method choices, hypothesising that the more politically sensitive is a firm the more
likely is management to choose income reducing accounting methods.
Traditionally studies
investigating political sensitivity have adopted firm size, measured in various ways, as the proxy for
political sensitivity.6
Malmquist (1990, p. 181) provides an additional ex ante argument as to why firm size may
be related to the accounting method choice of US oil and gas companies. He argues that while
choosing to capitalise pre-production costs rather than write them off has the effect of decreasing
variance in reported profits, the effectiveness of this strategy reduces as firm size increases. He
argues that larger firms will have a greater number of drilling projects, which leads to a ‘portfolio
effect’, by diversifying away variance due to drilling risk.7 Thus, for such firms the necessity to
capitalise pre-production costs to reduce earnings variance is diminished. Conversely, the potential
exists for smaller firms to gain much in the way of variance reduction by adopting a policy of
capitalisation, since they are less likely to have the number of separate drilling projects required to
gain variance reduction from the ‘portfolio effect’. This ex ante efficiency argument implies, as
does the ex post political sensitivity argument outlined previously, that firm size will be correlated
positively with the expensing of pre-production costs.
Revenue and total assets have been the two most frequently adopted proxies for firm size in
prior accounting choice studies. In their study Deegan and Hallam (1991, p. 11) adopt what they
consider to be an alternative and preferable measure of firm size, viz., net profit after tax and before
extraordinary items. They base their preference for this measure on Watts and Zimmerman’s
(1986, p. 239) view that a firm’s accounting earnings are ‘a better proxy for the negative/positive
corporate wealth transfers ... [than] firm size (total sales or assets)’. Thus, substituting a net profit
6
7
Lim (1996) provides a comprehensive review of the literature relating to the political sensitivity/costs hypothesis.
The concept of drilling or engineering risk and its likely effect on accounting treatment of pre-production costs in
the extractive industries is discussed further in Section 3.3 below.
10
measure for a measure of size such as revenue or total assets may provide a better proxy for
political sensitivity by providing a more direct measure of political costs. The following hypothesis
is tested:
H3:
Net profit after tax and before extraordinary items will be positively correlated
with writing off or expensing of pre-production costs.
Watts and Zimmerman (1986, p. 235) suggest that ‘The most direct way to transfer
corporate assets [from firms to the government] is via the tax system, and therefore, income taxes
are one component of political costs borne by firms’ (emphasis in original). However, it is not a
complete measure of political costs since, inter alia., it does not take into account the possibility
that wealth transfers through the tax system may be offset by various measures, such as the granting
of subsidies. Companies in the Australian extractive industries have been subject to particular
concessional taxation treatment, the main concession being the extensive capital expenditure
deductions for the cost of exploration, prospecting and mining operations (Income Tax Assessment
Act 1936, Divisions 10, 10AA and 10AAA). Further, income from gold mining and sales was, until
1 January 1991, exempt from tax. Also, as noted by Walker (1988, p. 23), companies in the
Australian extractive industries, and in particular those involved in the petroleum industry, may
have been subject to political threat from the late 1970s to the mid 1980s by a proposed resource
rent tax. This tax was finally introduced in 1987 (Petroleum Resource Rent Tax Act 1987). It may
therefore be argued that the accounting treatment adopted during 1986, the year considered in the
current study, would reflect managers’ motivations in relation to the economic consequences to be
incurred due to impending introduction of the new tax, outlined in detail at that time in the
Petroleum Resource Rent Tax Bill 1986. Since the tax was to be levied on ‘economic profits’ (Ball
and Bowers, 1984, in Walker, 1988, p. 23), managers of those firms potentially affected by the new
tax may have been expected to adopt income decreasing accounting methods.
Following from the above discussion, it is argued that the effective tax rate may be used as a
measure of the political sensitivity of companies in the study. Companies with higher effective tax
rates, it is argued, are subject to greater political costs, and so would have an incentive to reduce
11
their political sensitivity by writing off pre-production costs rather than capitalising them. The
following hypothesis is tested:
H4:
The ratio of tax expense (net of deferred taxes) to net profit before tax and
extraordinary items will be positively correlated with writing off or expensing of
pre-production costs.
In addition, the preceding discussion suggests that the imposition of political costs may fall
unevenly upon firms in different sub-sectors of the extractive industries. For instance, companies
involved in gold mining and exploration, and those in the petroleum sector, would appear to be
distinguishable from other sectors of the extractive industries due to extant or potential differential
tax treatments. To the extent that the political sensitivity variables discussed above do not fully
capture the effects on pre-production cost method choice arising due to industry sub-sector
membership, this may be controlled for by the use of categorical/dummy variables. 8 The discussion
suggests that managers of firms involved in the petroleum sector of the extractive industries would
be more likely to adopt a method of accounting for pre-production costs which is income
decreasing, in order to reduce the political visibility associated with the impending imposition of the
resource rent tax. Similarly, the incentive for managers of firms in the gold sector to adopt such
income decreasing methods is likely to be particularly strong. Such firms would wish to protect
their privileged position with respect to the concessional tax treatment received by reducing their
political visibility. The following hypotheses, which capture these industry sub-sector effects, are
tested:
3.3
H5:
Involvement in the petroleum sector of the extractive industries will be
positively correlated with writing off or expensing of pre-production costs.
H6:
Involvement in the gold sector of the extractive industries will be positively
correlated with writing off or expensing of pre-production costs.
Engineering Risk and Income Diversification
Studies by Deakin (1979) and Lilien and Pastina (1982) suggest the possible influence of factors
such as aggressiveness in exploration and exploratory risk upon the method chosen to account for
12
pre-production costs in the US oil and gas industry.
The former study found no significant
relationship between the surrogates used for aggressiveness in exploration and the accounting
methods adopted.
Lilien and Pastina (1982) hypothesise that firms undertaking extensive
exploration activities incur proportionately more unsuccessful activities involving relatively large
amounts of pre-production costs. Such firms are argued to have a greater incentive to capitalise
these costs in preference to writing them off, in order to avoid significant deterioration in their
reported income and asset balances. Further, such action would have the effect of decreasing the
variability of reported income, and therefore possibly the perceived level of risk. Their study
supports this hypothesis by finding exploratory risk, measured as the proportion of dry wells to total
wells, to be significantly related to choice of an income maximising accounting policy. More
recently Malmquist (1990) argued that the relative magnitudes of two types of risk, namely drilling
(or engineering) risk and product market risk, determine the degree of variation in a firm’s earnings.
He defines engineering risk (p. 181) as ‘the probability of failure in exploration activities’. This
risk will be greater for firms with activities concentrated in exploration vis-à-vis those with their
resources concentrated in production.9 Firms in the former group seek to report higher income by
adopting an accounting treatment that maximises the pre-production costs capitalised.10 Malmquist
(1990) tests the proposition that the proportion of a firm’s resources devoted to drilling and
exploration is positively related to the likelihood of choosing an accounting method which
capitalises the maximum amount of pre-production costs (in the US context, the full cost method).
8
9
10
See the full specification of the model in Section 4.
Malmquist (1990) argues that depending upon their age, size and experience firms will develop a comparative
advantage in either the exploration or production phases of operations, and therefore direct their resources in order
to exploit this comparative advantage.
Firms with resources concentrated in production will report higher income by adopting an accounting method which
writes off pre-production costs earlier. This keeps the amounts of such costs to be amortised against production
revenue to a minimum, thereby maximising the amount of profit reported.
13
He adopts as a measure of the importance of exploration ‘the ratio of the firm’s world-wide
exploration costs to the year-end market value of its equity’. In this study the measure used to test
the influence of engineering risk uses the book rather than market value of equity. The following
hypothesis is tested:11
H7:
The ratio of exploration costs incurred to the book value of total equity will be
positively correlated with capitalisation of pre-production costs.
In her study of the 1974 accounting practices of companies in the Australian extractive
industries, Walker (1988, p. 30) suggests an alternative measure which could be used to capture the
incentive for managers to minimise negative income effects. She proposes that the more diversified
are the operations of a firm the more likely they are to expense pre-production costs, since the
existence of diversified sources of income would increase the capacity of the firm to bear the write
off without producing undue negative earnings effects. The results of Walker’s (1988, pp. 55-57)
study do not support the diversification hypothesis. However, this may, in part, be due to the timing
of the study, which related to 1974. Undiversified firms at this time may not have felt it necessary
to mitigate negative earnings effects by capitalising pre-production costs rather than expensing them
since, as suggested by Walker (1988, p. 57), ‘the revenues generated from the production of oil, gas
and minerals, are of such a magnitude that the income effects of a write off … of pre-production
costs will be mitigated’. Such an incentive may be related, at least in part, to the state of the
oil/mineral market at the time, which is known to change markedly over time. For instance, the
year 1986, of concern in the current study, saw substantial falls in the price of petroleum products,
which may increase the incentive to diversify revenue sources. It would therefore seem worthwhile
to retest the hypothesis in the context of the current study. However, measuring a company’s
11
Malmquist’s hypothesis relating product market risk to the propensity to choose a method which capitalises less preproduction costs is not tested in this study due to the difficulty of constructing an appropriate surrogate with
accessible data. Production figures for Australian firms in the extractive industries are not available on a firm-byfirm basis and, due to their commercially sensitive nature, are unlikely to be disclosed upon request. This omission
may not unduly bias the model given that firms with high engineering risk will have relatively low product market
risk, and vice versa. Use of a single variable to represent engineering risk may therefore be sufficient to capture the
effects of risk on the accounting treatment adopted.
14
diversification opportunities based on the number of lines of business does not take into account the
relative significance of these lines in earning revenue for the company. A finer measure of the
diversification effect, which provides a better indication of the firm’s capacity to bear the write off
of pre-production costs than simply the number of additional lines of business, is the proportion of
total revenue earned from sources other than the extractive industries. The following hypothesis is
tested:
H8:
3.4
The ratio of revenue earned from extra lines of business, other than those
involving extractive industries operations, to total revenue will be positively
correlated with writing off or expensing of pre-production costs.
Auditor Effect
Prior studies have investigated the audit function in a number of respects within the contracting cost
framework.12 In general such studies have relied on the occurrence of auditor changes or lobbying
on a proposed accounting standard, neither of which is relevant to the time period under
investigation in the current research. A study by Deakin (1980) investigates the possibility of an
auditor effect on accounting method choice of full cost versus successful efforts in the US oil and
gas industry, testing ‘for an association between auditor and companies using each accounting
method’ (p. 79). Deakin (1980, pp. 79-80) concludes that ‘there is a very strong auditor effect
which serves to indicate that managements of full cost and successful efforts companies tend to
select auditors advocating similar positions on oil exploration accounting’. Two possible reasons
are suggested by Deakin (1980, p. 80) for the observed association between auditor preference and
the method adopted by the client. First, it may arise from a shared philosophy in relation to the
appropriateness of each method of accounting for pre-production costs. Alternatively, he suggests
that consultation by client management with their auditors as to the most appropriate method of
accounting for pre-production costs, and the subsequent acceptance of this advice by management,
would explain the correlation. While suggesting that the former reason would imply that ‘there are
differences in the production/investment decisions of managements employing the alternative
15
methods’, it is not possible to discern from the study whether this is, in fact, the appropriate
explanation for the observed association.
Australian context in the current study.
Deakin’s (1980) hypothesis is investigated in the
Specifically, it is suggested that auditor’s expressed
preference for either the expensing or capitalisation of pre-production costs will be associated with
the treatment of such costs by their clients in the Australian extractive industries. The following
hypothesis is tested:13
H9:
4.
Auditor preference for writing off vis-à-vis capitalising pre-production costs will
be positively correlated with their clients’ accounting treatment of such costs.
Research Method
4.1
The Data
The population of concern in this study is all companies listed on the mining board of the Australian
Stock Exchange during 1986. As indicated in the previous section, the differential tax treatment of
income from gold mining operations and the influence upon the petroleum industry of the proposal
to introduce a resource rent tax can both be investigated by choosing 1986 as the year of
investigation. Also, previous extensive studies of accounting for pre-production costs in Australia
have concentrated around the time of the original issue of AAS 7, in 1976. The persistence of
variation in practice since release of the standard, and the likelihood of an impending review of the
standard, both discussed in Section 1, suggests choice of a more recent period may provide
additional useful insights.
The specific companies included in the study were determined by the source of much of the
data used in the study, namely the AGSM Annual Report File. All companies with 1986 annual
reports on the file and which were listed on the mining board of the stock exchange were identified.
As indicated in Table 2, of the 123 such companies found on the file, 104 were finally included in
12
13
See for instance Watts and Zimmerman (1981), Chow (1982) and DeAngelo (1982).
The methods used to operationalise this hypothesis are detailed in the next section.
16
the study. The reasons for exclusion of the other 19 are indicated in the Table.14, 15
[INSERT TABLE 2 ABOUT HERE]
4.2
Dependent Variable
The dependent variable in this study is the accounting treatment of pre-production costs. Section 2
of the paper outlined the rationale for using a continuous proportionate measure of the dependent
variable, rather than the dichotomous, or even polychotomous classification of accounting treatment
frequently adopted in accounting choice studies.16 As argued previously, firms have a great deal of
latitude in the amount of pre-production costs to be expensed and/or capitalised in any year. In this
study the continuous measure used by Walker (1988, p. 40), the ratio of all pre-production costs
expensed or written off for the year to the opening balance of pre-production costs carried forward
at the beginning of the year, has been modified by adopting a different denominator in the ratio. 17
In this research the measure used is the ratio of all pre-production costs expensed or written off for
the year to the closing balance of pre-production costs carried forward plus pre-production costs
expensed or written off during the year. The denominator therefore represents the total amount of
pre-production costs which theoretically could have been written off during the year. Thus, the
dependent variable can be described as the proportion of the total amount of pre-production costs
available to be written off which was actually written off during the year.
Initially the dependent variable is defined to include all non-construction pre-production
cost; that is, exploration, evaluation and development costs (WRITEOFF1). However since, as
outlined in Section 2, there are arguments supporting the exclusion of development costs from the
14
15
16
A list of the companies in the study is available from the author.
Utilisation of the AGSM Annual Report File as the data source for the study may affect the external validity of the
results obtained. By design, the file, being is based on the top 500 Australian companies by market capitalisation,
contains proportionately more larger companies than would be included in the general population of extractive
industries companies.
A detailed accounting policy review of the 104 companies included in the study was undertaken to determine the
accounting method for pre-production costs adopted by each company. While the vast majority adopted the ‘area of
interest method’ required by the standard, many of these companies developed their own interpretations of the
appropriate way to implement the method in practice. This review therefore confirmed the belief expressed
previously, that in practice it would be an oversimplification to say that the area of interest method represents a
single standard method of accounting for pre-production costs. Detailed results of this policy review are not
reported here due to space limitations.
17
definition, additional tests are performed using the more restricted definition (WRITEOFF2),
utilising the reduced sample of companies for which it is possible to distinguish exploration and
evaluation costs from development costs.
A further issue relating to the definition of the dependent variable is the extent of preproduction costs written off to include in the numerator. Specifically, in addition to those preproduction costs written off directly, should it also include amounts of pre-production costs
amortised as a cost of production? Walker (1988, p. 55) suggests that ‘a finer measure’ of the
dependent variable, which excludes those costs expensed which represent amortisation of costs
carried forward, may provide a different result. On the other hand, amortisation of pre-production
costs represents an accounting technology over which management has some degree of discretion,
and is an integral part of the overall treatment of pre-production costs and the decision to expense or
capitalise such costs. It can therefore be argued that it is appropriate to include any amortisation of
pre-production costs in the numerator of the dependent variable. In this study the initial dependent
variable (WRITEOFF1) adopts the broader measure of pre-production costs written off, with
additional tests, using the restricted measure of the numerator (WRITEOFF3), conducted to
determine the sensitivity of the results to this distinction. The primary measure of the accounting
treatment of pre-production costs adopted in this study is WRITEOFF1, the definition of which is
provided in Table 3. In addition the two more restricted measures, labelled WRITEOFF2 and
WRITEOFF3, are defined and are utilised for comparison throughout. As for all variables in this
study, unless otherwise indicated, the data used to calculate their values are taken from the financial
statements and notes contained in the companies’ 1986 annual reports.
[INSERT TABLE 3 ABOUT HERE]
4.3
Independent Variables
Two proxies are used in this study to capture the effects of debt constraints on managements’
treatment of pre-production costs, one based on leverage constraints and one on interest coverage
17
As noted by Walker (1988, p. 40) a proportionate measure is required since the absolute amount of such costs
18
constraints. These are defined in Table 4 as LEVERAGE and INTCOVER respectively. The
predicted signs for these variables are indicated in line with the hypothesised relationship of the
variables to the dependent variable, as detailed in hypotheses H1 and H2.
[INSERT TABLE 4 ABOUT HERE]
While both of these variables relate to the use of debt in a company’s capital structure, they
represent different aspects of the impact of debt constraints, one related to balance sheet amounts
and one to profit and loss statement amounts. Both LEVERAGE and INTCOVER are to some
extent interdependent with the dependent variable measures outlined previously. In the case of the
former variable, ceteris paribus, the lower is the amount of pre-production costs written off the
greater will be the company’s equity. Thus, the lower will be the calculated value of LEVERAGE.
Given that the dependent variable is continuous in nature, rather than involving a discrete choice
between well defined accounting methods, it is not possible to sensibly adjust for this dependency;
however, as pointed out by Zimmer (1986, p. 47), the effect of undoing the difference in accounting
treatment in such cases would be to increase the likelihood of leverage being related to the
accounting treatment adopted. That is, the unadjusted measure used in this study errs on the side of
conservatism by making a finding of significance more difficult to achieve. A similar dependency
is present in the definition of INTCOVER.
Again the unadjusted measure can be seen as
conservative in that it makes it more difficult to achieve a finding of the hypothesised relationship.
It was suggested in Section 3 that net profit may provide a better measure of political
sensitivity than the size measures traditionally used in accounting choice studies. The independent
variable PROFIT, defined in Table 4, is therefore used as the prime proxy for political costs in this
study.18 The amount of PROFIT is also affected by the treatment of pre-production costs adopted
by the company, and again the unadjusted measure used is conservative in that it makes a finding of
significance less likely.
18
expensed in a year (the numerator) will be closely related to firm size.
For completeness all tests involving the PROFIT variable were re-run using in its place first the book value of total
assets (ASSETS) and second total revenue (REVENUE). Any significant change in results is reported in the
relevant section.
19
Another political sensitivity variable utilised in this study is TAXRATE, argued in Section 3
to represent a partial measure of political costs imposed on firms. TAXRATE is dependent on the
accounting treatment of pre-production costs adopted to the extent that net profit will be lower the
greater is the amount of such costs written off. This implies that not undoing this dependency
makes a finding of a significant positive relationship more likely. Thus, any results finding such a
relationship need to be interpreted with this in mind. Forty-three companies in the study reported a
net loss before tax.
Of these, eight reported positive income tax expense.
For such firms
TAXRATE was set to one. The remaining 35 loss companies reported either zero or negative tax
expense for the year. For these TAXRATE was set to zero.19
The final two political costs variables defined in Table 4 are PETROL and GOLD. These
are both (0,1) dummy variables relating to industry involvement, since participation in the
petroleum and gold sectors of the extractive industries are hypothesised to be related to the
treatment of pre-production costs (H5 and H6). Three separate sources of data were used to
determine companies’ involvement, or otherwise, in the petroleum and gold sectors. First, the then
AASE industry classification codes for each company in the sample was checked. A code of 015
indicated ‘gold’ involvement, while any code from 031 to 034 inclusive indicated involvement in
‘oil and gas’. While this represented a first attempt at determining industry involvement, the
published AASE code alone does not provide a sufficient guarantee of correct and complete
classification for a number of reasons. First, there may be errors in the published AASE code, or
the published code may be out of date if companies have recently changed or expanded their
involvement in particular sectors. Also generic codes, such as 019 ‘Mineral Exploration’ and 243
‘Diversified Resources’, do not specify the particular type of mineral/petroleum product involved.
For this reason, information on corporate activities contained in Jobson’s Mining Yearbook 19871988 (30th edition), which includes information on companies up to 1986, was used to crosscheck
the industry involvement of sample companies. As a final check, information contained in the
19
This follows a similar procedure adopted by Wong (1988, p. 157).
20
annual report was used to check the classification provided by the two prior sources. Based on this
process industry involvement was determined and values of zero or one were assigned to the two
dummy variables.
Table 4 provides definitions of the explanatory variables relating to engineering risk and
income diversification. ENGNRISK is designed to measure the relative importance of exploration
expenditure, as discussed in relation to hypothesis H7.20 The variable EXTRALNS measures the
importance of extra lines of business, other than extractive industries activities, as contributors to
the companies’ revenue stream, calculated from segment information provided in the companies’
annual reports.
The final two variables contained in Table 4 are definitions of the two (0,1) dummy
variables used to model the effect of auditors’ preference for capitalisation of pre-production costs
vis-à-vis immediate expensing of such costs as incurred. While the definitions are self-explanatory,
the method of determining auditor preferences requires explanation. This study is concerned with
1986 accounting practices. As such, inquiries of audit firms, by mail or telephone survey, some
years later are unlikely to provide an accurate indication of their preferences at the time in question.
A secondary source of this information was therefore sought.
One source of the views of
individuals or organisations on accounting policies is the submissions made to the AARF when an
exposure draft is released and comments on its contents are invited. In relation to Statement of
Accounting Standards AAS 7, the most recent opportunity for submissions to be made on the issue
of the treatment of pre-production costs occurred with the issue in January 1989 of Release
416/Exposure Draft 47, which, inter alia., invited comments on the appropriateness of the standard
permitting a choice of methods. While it is recognised that the timing of these submissions to the
AARF is some three years after the year examined in this study, they represent the most reliable and
contemporaneous source of information on preferences available.
20
As noted in Table 4, when the dependent variable is measured as WRITEOFF2, which excludes development costs,
an alternative measure of engineering risk, ENGNRISK2, which also excludes such costs from its numerator, is
used.
21
Eleven of the 95 submissions made were classified as by large accounting firms, including
all of the then ‘Big-8’ firms. Since all of these firms acted as auditor for at least one company
included in this study, their submissions were examined to determine whether a preference for a
particular treatment to account for pre-production costs was expressed.21 Preferences were divided
into three categories, viz., those favouring the immediate write off of such costs, those favouring
allowing their capitalisation (possibly subject to certain conditions being met) and those which were
neutral on the matter. Submissions which did not mention the matter were classified as neutral, as
were those which favoured the status quo of allowing a choice between the two treatments. None
of the firm’s submissions indicated a preference for the immediate expensing of pre-production
costs. In addition to those firms making submissions to the AARF, 19 companies included in the
study employed accounting firms which did not make such submissions. Since such firms did not
feel compelled to express their views on the issue when given the opportunity they were regarded as
being neutral on the matter of the preferred treatment of pre-production costs, and were classified as
such. Table 5 indicates the number of sample companies for which each of the accounting firms
acted as auditor, and indicates whether each firm was classified as preferring capitalisation of preproduction costs (C) or as being neutral on the issue (N).22
[INSERT TABLE 5 ABOUT HERE]
It should be noted that Table 5 lists 12 accounting firms, rather than 11, in the category of firms
making submissions to the AARF. The reason for this anomaly is the merger of the firms KMG
Hungerfords and Peat Marwick Mitchell & Company between 1986 and 1989. Thus, while the two
separate firms were in existence and acted as auditor for sample companies in 1986, by 1989 when
submissions were made to the AARF they had become a single firm, KPMG Peat Marwick
Hungerfords. Since determination of the separate firm’s preferred treatment of pre-production costs
21
22
Thirty-eight of the submissions were classified under the heading ‘Small Practices’, but none of these firms acted as
auditor to any company in this study.
Since no accounting firm acting as an auditor for a sample company is classified as having a preference for the
immediate expensing of pre-production costs, the variable AUDITEXP becomes redundant. All the relevant
accounting firms are classified as either preferring capitalisation or being neutral on the matter. Thus a
22
was not possible, it was decided to classify both the individual firms as having the preference
expressed in 1989 by the merged firm, viz., capitalisation of such costs. It is recognised that this
classification may misrepresent what might have been the separate preferences of the two firms,
introducing measurement error into the variable AUDITCAP. Therefore, while including the firms
on this basis, as a check all tests were re-run excluding sample companies for which either of the
pre-merger firms acted as auditor.
4.4
Statistical Tests
Both univariate and multivariate methods are used to test the hypotheses developed above.
Distributional characteristics of the data, discussed in the next section, suggest that non-parametric
univariate tests are appropriate. Spearman rank correlation coefficients are used to investigate the
relationship between the non-categorical explanatory variables and the dependent variables, while
Mann-Whitney U tests and a Kruskal-Wallis one-way analysis of variance are used in the case of
the independent variables which are categorical in nature. The multivariate test used in this study is
ordinary least squares regression. The general form of the regression model used is as follows:
WRITEOFFk = a + b1LEVERAGE + b2INTCOVER + b3PROFIT + b4TAXRATE + b5PETROL +
b6GOLD + b7ENGNRISK + b8EXTRALNS + b9AUDITCAP + i
where the random error term i is normally distributed with mean zero and constant variance.
5.
Results
5.1
Descriptive Statistics
Table 6 contains the descriptive statistics for the independent variables (Panel A) and the noncategorical dependent variables (Panel B) defined in the previous section.
[INSERT TABLE 6 ABOUT HERE]
dichotomous, rather than trichotomous, classification results, with the single (0,1) dummy variable AUDITCAP
being sufficient to fully describe this situation.
23
It was possible to calculate the dependent variable WRITEOFF2 for only 38 of the total 104
companies included in the study, since the other 66 did not fully separate development costs from
exploration and evaluation costs in their financial statements. The variable INTCOVER is not
defined for cases where the denominator, interest expense, equals zero. Following Daley and
Vigeland (1983, p. 200), in such cases the variable was assigned a value exceeding that of any
company in the sample with a non-zero denominator. The value assigned was 11,065. Sixteen of
the companies had this value assigned to INTCOVER.23
The skewness and kurtosis coefficients of all variables included in Table 6 indicate
departures for normality.24 All are positively skewed and leptokurtic; that is, skewed to the right
and more peaked than normal. In addition, the Kolmogorov-Smirnov one-sample test statistics are
all highly significant, indicating that the hypothesis of normality is rejected for all the variables.
The results of the univariate test will not be affected by non-normality of the variables since these
tests are nonparametric and therefore ‘distribution free’. To bring the variables closer to normality
for the purpose of the multivariate tests, transformation of the variables was undertaken.
Commonly used transformations that may improve normality of positively skewed distributions are
the natural logarithmic transformation, the square root transformation and the fourth root
transformation. In this study each variable was transformed using all three of these transformations,
with the one reducing the departure from normality the most being chosen for inclusion in the
multivariate tests.25 Table 7 contains the descriptive statistics for the transformed variables, where
23
24
25
Due to the extremely large size of the calculated or assigned values of the interest coverage variable for some
companies, it was decided, following a similar procedure used by Daley and Vigeland (1983), to test the sensitivity
of the results to this coding by testing using two alternative coding rules for interest coverage. The first was to
recode interest coverage for firms with very large absolute values of the ratio, including the 16 which were
undefined, to  50. The second was to drop all observations where interest coverage was undefined because of zero
interest expense. The results of this sensitivity analysis are discussed in the relevant sections below.
Foster (1986, p. 106) suggests that for sample sizes of the order of those in this study the benchmark values of the
skewness coefficient for suspecting positive and negative skewness are greater than 0.5 and less than -0.5
respectively. Similarly, he suggests (p. 107) that violations from normality may be suspected for kurtosis
coefficients greater than 1.0 (leptokurtic) and less than -1.0 (platykurtic).
In the case of the logarithmic transformation the variables were first normalised by dividing each observation by the
mean of the series. As observed by Malmquist (1990, p. 187, n.32), this aids in interpretation of coefficients and
eliminates possible difficulties associated with variables being on widely differing scales.
24
the first two characters SR indicates the square root transformation, 4R the fourth root
transformation and LN the natural logarithmic transformation.26
[INSERT TABLE 7 ABOUT HERE]
As can be seen from Table 7 the departures from normality, as reflected in the skewness and
kurtosis coefficients, is less for all the transformed variables than for the corresponding
untransformed variables (Table 6), with the exception of SRINTCOVER, where the skewness
coefficient has decreased but the kurtosis coefficient has slightly increased over that of the raw
variable INTCOVER. Four of the transformed independent variables, namely SRINTCOVER,
4RPROFIT, LNTAXRATE and 4REXTRALINES, continue to exhibit some departure from
normality. This fact is also reflected in the Kolmogorov-Smirnov statistics for these variables,
which have p values of less than 0.001.
5.2
Univariate Tests
Correlation analysis was utilised to test the relationship between the dependent variables and the
non-categorical independent variables. Table 8 shows the relevant Spearman rank correlation
coefficients.
[INSERT TABLE 8 ABOUT HERE]
As can be seen from the first column of the table, the correlation coefficients do not support
any of the hypotheses relating to the expected relationship between the dependent variable
WRITEOFF1 and explanatory variables included in the table. Only two of the coefficients with
WRITEOFF1 are of the predicted sign, viz., those with LEVERAGE and ENGNRISK; however,
neither of these correlation coefficients is significant. All other coefficients in this column are of
26
For a substantial number of companies the PROFIT and INTCOVER variables took negative values, for which the
transformations are not defined. Foster (1986, p. 111) and Gujarati (1995, p. 387, n.33) suggest that for such cases
the variables may be recentred. This was done by adding to each observation the absolute value of the minimum
taken by the variable plus one. Thus, the amount added to recentre PROFIT prior to transformation was 22,595,001.
The amount used to recentre INTCOVER was 4,366. A similar problem was encountered in applying the natural
logarithmic transformation to the dependent variable measures WRITEOFF1, WRITEOFF2 and WRITEOFF3 and
to the independent variables TAXRATE, ENGNRISK and EXTRALN. All these variables may take the value of
zero, for which this transformation is not defined. Thus, a shift factor of 0.5 was added to the raw variables prior to
calculating their natural logarithms. In all cases except TAXRATE the square root and fourth root transformations
25
the incorrect sign, with INTCOVER and TAXRATE significant at the 10% level. The second
column of Table 8 indicates that the dependent variable WRITEOFF2 is significantly correlated (p
= 0.014) with the independent variable PROFIT, but not in the predicted direction.
The final dependent variable definition utilised in this study, WRITEOFF3, differs from the
dependent variable WRITEOFF1 in that it excludes amortisation of pre-production costs from the
numerator in the calculation of the ratio. The Spearman rank correlation coefficients for this
dependent variable are shown in the final column of Table 8. Correlations between WRITEOFF3
and the two debt covenant variables LEVERAGE and INTCOVER are significant (p = 0.022 and
0.016 respectively), but only the coefficient with leverage is in the predicted direction.
Additionally, correlations with two of the political cost variables, PROFIT and TAXRATE, are
significant but are not in the predicted direction. It therefore appears that limited support for
hypothesis H1 is provided by this test, that the use of debt in a company's capital structure is
negatively correlated with the relative amount of exploration and evaluation costs written off
directly to the profit and loss statement. The test does not, however, support any of the other
hypotheses. It appears from Table 8 that, contrary to hypotheses H3, companies with greater profits
write off relatively less exploration and evaluation costs directly, as do companies with higher
levels of interest coverage. Similarly the significant negative correlation of TAXRATE (p = 0.022)
is not directionally consistent with hypothesis H4, and suggests companies with higher effective tax
rates write off a lesser proportion of early pre-production costs than those with lower tax rates. A
possible explanation for this result is outlined in Section 6.27
The relationship between the dependent variables and each of the categorical explanatory
variables, PETROL, GOLD and AUDITCAP, were investigated using nonparametric MannWhitney U tests, the results of which are presented in Table 9. They indicate that it is not possible
to reject the null hypothesis of no difference in distributions for the dependent variable PETROL for
of the raw data, which did not require the addition of the shift factor, performed better than the logarithmic
transformation of the raw data plus the shift factor.
26
all three dependent variable definitions. This suggests that political costs do not fall differentially
upon companies involved in this sub-sector of the extractive industries, contrary to the prediction of
hypothesis H5.
However, this variable is designed to capture that part of the effect on the
accounting treatment of pre-production costs not captured by other political cost proxies included in
the study. Thus, it is possible that it may be important in interaction with the other political cost
variables in multivariate analysis. In the case of the dependent variable GOLD, there is support for
hypothesis H6 at the 10% level when the dependent variable is defined as WRITEOFF1 and
WRITEOFF3. This suggests that when all pre-production costs are considered those companies
involved in the gold sector of the extractive industries were likely to write off a greater proportion
of exploration, evaluation and development costs during 1986, supporting the proposition that at
this time such firms were more politically sensitive than those not involved in the gold mining
sector. Rejection of the null hypothesis of no difference in relation to the variable AUDITCAP
suggests that auditor preference for capitalisation of pre-production costs does not influence the
actual treatment of such costs adopted by the sample companies. This may be due to limitations
associated with the measurement of the variable AUDITCAP alluded to previously.28
[INSERT TABLE 9 ABOUT HERE]
An additional nonparametric test, the Kruskal-Wallis one-way analysis of variance, was conducted
to test whether companies audited by different firms have differing median values of the dependent
variables. This test did not therefore rely on classification of audit firms as preferring a particular
method of accounting for pre-production costs, as do tests involving the variable AUDITCAP. The
fourteen separate audit firms listed in Table 5 formed the basis of the 14 samples used in the test.
Those firms included in the ‘other’ category were not included in the test. The results of the tests
are shown in Table 10.
27
28
The results of the correlation analysis were not affected by the alternative specifications of the INTCOVER variable.
Nor were the results of correlations of ASSETS and REVENUE significantly different from those with the PROFIT
variable.
As indicated in footnote 28, the tests for the AUDITCAP variable were rerun excluding those companies audited by
either Peat Marwick Mitchell and Company or KMG Hungerfords, which merged between 1986 and 1989. The
27
[INSERT TABLE 10 ABOUT HERE]
The large probability values in Table 10 indicate that for all three dependent variable
definitions the null hypothesis of equal medians for the 14 samples could not be rejected, indicating
that the presence of an auditor effect on accounting treatment adopted, irrespective of the auditor's
method preference, is not supported.
5.3
Regression Tests
Ordinary least squares regression was used for multivariate testing of the hypotheses. Each of the
three independent variables, transformed using the fourth root transformation, was regressed against
the transformed continuous independent variables in Panel B of Table 7 and the three categorical
independent variables PETROL, GOLD and AUDITCAP.
The results of these three base
regressions are reported in Table 11.
[INSERT TABLE 11 ABOUT HERE]
5.3.1
4RWRITEOFF1
The first regression reported in Panel A of Table 11 uses the dependent variable 4RWRITEOFF1.
While five of the explanatory variables in the model are of the correct sign (LNLEVERAGE,
4REXTRALNS, PETROL, GOLD and AUDITCAP) none are significant. The only significant
coefficient relates to LNTAXRATE, but it is of the incorrect sign. These results are broadly
consistent with the univariate correlation results reported above. In addition, the values of R 2 and
the F statistic for the model indicate that it has very low explanatory power.29
The model was re-estimated replacing the political sensitivity proxy 4RPROFIT with first a
total assets proxy (LNASSETS) and then total revenue (LNREVENUE).30 The results for these
models do not differ significantly from that where 4RPROFIT is used, except that in both models
29
30
results do not lead to a change in decision to not reject the null hypothesis of no difference in distributions for the
two groups.
All regression models reported were tested for heteroscedasticity and multicolliniarity. Neither was found to be a
significant factor affecting the reliability of the results.
For both these size measures the best performing normalising transformation was the natural logarithm, taken after
division by the mean of the variable in question.
28
the variable LNTAXRATE is no longer significant. As indicated previously, the model reported in
Table 11 was also tested for sensitivity to the coding of the interest coverage variable. The first
alternative system, which coded extremely large absolute values of the variable as plus or minus 50,
resulted in an improvement in the performance of the model (R2 = 0.105, F = 1.221 with p = 0.292).
In this model both the tax rate and alternative interest coverage coefficients are significant at the
0.05 level, but both are negative while their predicted sign is positive. Where the model is reestimated excluding those companies for which interest coverage is not defined (as recorded interest
expense was zero), the results for the reduced sample of 88 companies do not change from those
reported in Table 11, with only the tax rate variable significant, but negative, with p = 0.023. The
final sensitivity analysis was to re-estimate all the regression models discussed in relation to
4RWRITEOFF1 but excluding those companies audited by either KMG Hungerfords or Peat
Marwick Mitchell and Company (N = 88), to control for measurement error in the variable
AUDITCAP, as discussed previously. The results of these additional tests did not differ in any
significant way from those of the regressions that included these companies.
5.3.2
4RWRITEOFF2
The model was re-estimated using the dependent variable 4RWRITEOFF2. The results of this
regression, reported in Panel B of Table 11, indicate that considering only the write off of early preproduction costs, exploration and evaluation expenditure, does not enhance the performance of the
model. Using data for the 37 companies that provided the required information, again five of the
coefficients, those for LNTAXRATE, 4RENGNRISK2, 4REXTRALNS, PETROL and
AUDITCAP, are of the correct sign, but none are significant. 31 Re-estimation of the model using
the alternative political sensitivity measures, LNASSETS and LNREVENUE, led to no significant
change in results. Similarly, the results were not found to be sensitive to the coding of the interest
31
Collinearity in this subgroup of data was evident between the two dummy variables PETROL and GOLD, with a
correlation coefficient of -0.734, which falls only marginally short of Gujarati's (1995, p. 335) rule of thumb of 0.80
for severe multicollinearity. However, Variance Inflation Factors in the primary regression for the two variables
were of 5.125 and 5.384 respectively, well below his rule of thumb of 10 (p. 339). All regressions reported in this
sub-section were re-estimated excluding each and both of the correlated variables.
29
coverage variable, with no significant change in results for the two alternative coding systems used
in this study. However, significant changes occurred when the model was retested to control for
error in measuring the AUDITCAP variable, by using only the 27 companies with auditors other
than KMG Hungerfords and Peat Marwick Mitchell and Company. While overall the model
remained insignificant, in the model utilising the standard set of independent variables (as listed in
Panel B of Table 11), the coefficient of the AUDITCAP variable was –0.463 with a significance
level of p = 0.105.
When the alternative political sensitivity proxies LNASSETS and
LNREVENUE were used significance levels of the AUDITCAP coefficient were p = 0.091 and p =
0.10 respectively. Thus, there is a suggestion that where only exploration and evaluation costs are
considered, the treatment of such costs is influenced at least to some extent by whether the
company’s audit firm has expressed a preference for capitalisation of such costs. In testing the
sensitivity of the results for this reduced sample to the coding of interest coverage, where the model
was re-estimated by coding extremely large absolute values of interest coverage as plus or minus
50, the AUDITCAP coefficient was –0.812, with a significance level of p = 0.056. In this case the
interest coverage variable was also just significant at the 10% level, but was of the wrong sign. Reestimating the model excluding those companies for which interest coverage is not defined reduced
the number of companies to 17. In this case the model has an R 2 = 0.620 and F statistic of 1.269
(with p = 0.3851), with the variables 4RPROFIT, 4REXTRALNS, PETROL AND GOLD all
having significant coefficients at the 5% level, with all of the correct sign except that for
4RPROFIT. The coefficient of the AUDITCAP variable is not significant. These results are,
however, probably being driven by severe collinearity in this sub-group of companies between the
PETROL and GOLD variables, which have a correlation coefficient of –0.859 and VIFs of 22.897
and 23.752 respectively. When the regression is re-estimated excluding these variables singly and
together none of the remaining variables have significant coefficients.
30
5.3.3
4RWRITEOFF3
The final dependent variable used in the study differs from the first only in that it excludes
amortisation of pre-production costs from the numerator, providing arguably a finer measure of preproduction costs written off. The results for the base regression are reported in Panel C of Table 11.
While overall the model remains insignificant, the model performs best using this version of the
dependent variable vis-à-vis the other two versions already discussed. In this case the coefficients
for LNLEVERAGE and GOLD are both of the expected sign and significant at the 5% level. These
results are consistent with those of the univariate tests reported earlier, and suggest that more highly
levered companies tend to write off a lesser proportion of pre-production costs incurred directly to
the profit and loss statement. Further, firms involved in the mining of gold also tended, in 1986, to
write off a greater proportion of such costs direct to the profit and loss statement, offering support
for the proposition that such firms were, at that time, a politically sensitive sub-group of companies
in the extractive industries.
Where LNASSETS is used as the proxy for political sensitivity LNLEVERAGE and GOLD
maintain significant coefficients with p = 0.10 and p = 0.052 respectively, with PETROL also
having a significant positive coefficient with p = 0.10. When LNREVENUE is used as the political
sensitivity proxy only GOLD has a significant coefficient with p = 0.065. Where large absolute
values of interest coverage are coded as plus or minus 50, LNLEVERAGE has a significant
coefficient at p = 0.023, and the coefficient of GOLD remains significant with p = 0.053. The
interest coverage coefficient is significant (p = 0.013) but of the wrong sign. Including only those
companies for which interest coverage is defined reduces the number of companies used to estimate
the model to 88. Here, in addition to the coefficient for GOLD being significant at p = 0.053,
PETROL has a significant positive coefficient with p = 0.085. As with the other dependent
variables, this model was retested to control for error in measuring the AUDITCAP variable by
using only the 81 companies with auditors other than KMG Hungerfords and Peat Marwick
Mitchell and Company. Little change in the results occurred, except that p values for those
31
coefficients that were significant when all companies were used were consistently slightly higher,
and thus somewhat less significant.
6.
Conclusions
This study examines explanations for variation in the treatment of pre-production costs by
companies in the Australian extractive industries. It finds that the ability of the hypothesised
explanations to account for the observed variation is dependent, to some extent, upon the definition
of pre-production costs considered, particularly whether development costs are included with the
early pre-production costs relating to exploration and evaluation. Further, the results are affected by
whether amortisation is included in the measure of pre-production costs written off. This suggests
that the availability of a choice of accounting treatment for early pre-production costs, but not for
development costs, may create differing incentives for companies’ treatment of costs incurred in
different phases of operations. As such, accounting standard setters in the impending review of the
extractive industries standard may wish to consider the desirability of maintaining such differential
treatments across different pre-production phases.
Further, the fact that only 37 per cent of
companies in the study provided sufficient information to separate exploration and evaluation costs
from total pre-production costs suggests that consideration ought to be given of whether the
standard’s disclosure requirements are adequate to meet the needs of financial statement users.
In summary, the results provide limited support for some of the factors hypothesised to
affect the accounting treatment of pre-production costs adopted. No support for interest coverage,
nor traditional political sensitivity proxies (PROFIT, ASSETS and REVENUE), as influences on
the accounting treatment is found. A significant negative correlation was found between the
political sensitivity proxy TAXRATE and both dependent variables incorporating all pre-production
costs, namely WRITEOFF1 and WRITEOFF3. The reason for the unexpected direction of this
relationship may lie in consideration of potential or future political costs relative to companies’
extant political sensitivity. Companies with high effective tax rates are already subject to high
political costs, and as such the potential for further such costs to be imposed in the future may be
32
regarded as low. Such companies may therefore have little incentive to adopt profit decreasing
accounting practices. On the other hand, lowly taxed firms may be seen as potential targets for
politically motivated wealth transfers in the future, since they could be argued to be ‘under taxed’ in
some sense.
Such firms therefore have an incentive to adopt income decreasing accounting
methods to reduce their extant political visibility. Such an explanation is consistent with the
observed negative association between the TAXRATE variable and the proportion of preproduction costs written off.
The results of this study also suggest that when only early stage pre-production costs are
considered (WRITEOFF2) the accounting treatment of such costs appear to be influenced by
whether the company’s audit firm has expressed a preference for capitalisation of pre-production
costs. It seems that, in relation to costs incurred in the more risky early stages of exploration and
evaluation, companies are either likely to be advised by their audit firm or have selected an audit
firm which favours their preferred treatment of such costs. This result only occurred when error in
the AUDITCAP variable was controlled for by excluding those companies audited by the two firms
which merged into KPMG Peat Marwick Hungerfords. The study also finds evidence that when all
pre-production costs are considered (WRITEOFF1 and WRITEOFF3) companies involved in the
gold mining sector of the extractive industries tended to write off (and/or amortise) a greater
proportion of such costs than companies which were not. This supports the proposition that such
companies were attempting to protect their privileged position in relation to the taxation treatment
of income generated from gold mining operations. In contrast, there is no strong evidence to
suggest that companies in the petroleum sector took actions consistent with the predicted sensitivity
caused by the impending introduction of the resource rent tax.
It is possible that by 1986
introduction of the tax was regarded as a ‘foregone conclusion’, and so action to reduce political
visibility by affected companies was expected to be ineffective and therefore unnecessary. This is
consistent with the suggestion by Walker (1988, p. 23) that the timing of such political factors needs
to be identified. She suggests that in this case it is difficult to determine in which year or years,
33
from its first proposal in 1975 up to its introduction, the resource rent tax was likely to affect the
accounting treatment adopted. Finally, there is support for a relationship between companies’
degree of leverage and the proportion of pre-production costs written off directly to the profit and
loss statement (WRITEOFF3). More highly levered companies tend to write off directly to the
profit and loss statement a lower proportion of pre-production costs. It was suggested that such
action might be undertaken in an effort to loosen debt covenants. To determine the plausibility of
such an explanation, and given that only one company in the study had public debt on issue, further
research into the nature of covenants in private debt agreements of companies in the extractive
industries in needed.
Overall, traditional costly contracting and political sensitivity explanations of accounting
choice do not appear to hold well in the case of pre-production cost accounting in the Australian
extractive industries. It seems that the mining sector may be subject to differing influences and
incentives to those that have been found to apply in studies which have ranged across numerous
industries. Further, it appears that incentives may apply differently to different sub-sectors of the
extractive industries. In the case of political sensitivity, this is likely to be highly dependent on
economic and political conditions at the time of the study – those in the gold sub-sector in 1986
appear to have been influenced by political visibility considerations, while those in the petroleum
sub-sector were not. Further investigation of the operation of differential incentives across the subsectors of the extractive industries would shed further light on this issue.
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Cost Centre
Table 1
Summary of Methods of Accounting for Pre-production Costs
Full Cost Method
Area of Interest
Successful Efforts Expense Method
Method
Method
All embracing
Area of Interest
Less than all
Smallest possible
(company/
(broadly defined in embracing
(individual
reporting entity)
AAS 7, para. 7, in
mine/well)
terms of
‘individual
geological areas’)
Basic
Description
All pre-production
costs capitalised
and progressively
amortised against
revenue from areas
yielding
economically
recoverable
reserves
Carry forward preproduction costs
where there is a
reasonable
probability of
success in the area.
Costs written off if
it becomes clear
the area will not
produce
economically
recoverable
reserves
Capitalise those
pre-production
costs which lead
directly to
discovery of
economically
recoverable
reserves; other
costs written off as
incurred
All pre-production
costs expensed as
incurred
Relative
Asset
Effect
Highest
Intermediate
Intermediate
(between full cost
and expense
methods;
dependent on
choice of cost
centre)
Lowest (none)
Relative
Profit
Effect
Highest in early
years
Intermediate
Intermediate
(between full cost
and expense
methods;
dependent on
choice of cost
centre)
Lowest in early
years
Table 2
Numbers of Extractive Industries Companies with 1986 Annual Reports on the
AGSM Annual Report File but Excluded from the Study, by Reason for Exclusion
Total extractive industries entities with 1986 annual reports on the File
Less:
123
Excluded companies by reason for exclusion
Foreign currency financial statements
6
Trusts
2
Investment companies
5
No 1986 annual report
2
Incomplete data
2
In rehabilitation phase
1
1986 report covers 18 months
1
Total excluded
19
Total companies included in study
104
Table 3
Dependent Variables
Label
WRITEOFF1
Definition
Exploration, evaluation and development costs written off and amortised during
the year/(Closing balance of exploration, evaluation and development costs
carried forward as at the end of the year + Exploration, evaluation and
development costs written off and amortised during the year)
WRITEOFF2
Exploration and evaluation costs written off during the year/(Closing balance of
exploration and evaluation costs carried forward as at the end of the year +
Exploration and evaluation costs written off during the year)
WRITEOFF3
Exploration, evaluation and development costs written off during the
year/(Closing balance of exploration, evaluation and development costs carried
forward as at the end of the year + Exploration, evaluation and development
costs written off during the year)
Table 4
Independent Variables
Label
LEVERAGE
(H1)
INTCOVER
(H2)
PROFIT
(H3)
TAXRATE
(H4)
PETROL
(H5)
Predicted
Sign
Definition
–
Book value of debt/Book value of total equity as at the end of the
period
+
Net profit before extraordinary items, interest and tax/Interest
expense for the year
+
Net profit before tax and extraordinary items for the year.
+
+
GOLD
(H6)
+
ENGNRISK*
(H7)
EXTRALNS
(H8)
AUDITCAP
(H9)
–
AUDITEXP
(H9)
+
–
+
Tax expense for the year/Net profit before tax and extraordinary
items
(0,1) dummy variable = 1 if the company is involved in the
petroleum (oil and/or gas) sector of the
extractive industries,
= 0 otherwise
(0,1) dummy variable = 1 if the company is involved in the gold
sector of the extractive industries,
= 0 otherwise
Exploration, evaluation and development costs incurred during the
year/Book value of total equity at the end of the year
Revenue from industry segments other that those directly related to
the extractive industries/Total revenue for the year
(0,1) dummy variable = 1 if the company is audited by a firm
expressing a preference for carrying
forward of pre-production costs,
= 0 otherwise
(0,1) dummy variable = 1 if the company is audited by a firm
expressing a preference for the immediate
write off of pre-production costs as
incurred,
= 0 otherwise
* Where the dependent variable under consideration is WRITEOFF2 this variable is replaced by ENGNRISK2,
defined as the ratio of exploration and evaluation costs incurred during the year, to the book value of total equity at
the end of the year.
Table 5
Choice of Audit Firms by Sample Companies
Firm
Companies Audited
Audit Firm
Preference
No
%
Firms Making Submissions to the AARF
Peat Marwick Mitchell & Co
Coopers & Lybrand
Price Waterhouse
Ernst & Whinney
Arthur Young
KMG Hungerfords
Pannell Kerr Forster
Deloitte Haskins & Sells
Touche Ross
Arthur Andersen & Co.
Hendry Rae & Court
Duesburys
Total Firms Making Submissions
Firms Not Making Submissions to the AARF
Bentley & Co.
Nelson Wheeler
Other
Total
C
N
N
N
N
C
C
C
N
C
N
C
15
14
13
9
7
7
6
5
3
3
2
1
85
14
13
13
9
7
7
6
5
3
3
2
1
83
N
N
N
4
3
12*
104
4
3
12
102**
C = expressed preference for capitalisation of pre-production costs
N = neutral on treatment of pre-production costs
* Includes 12 separate accounting firms each acting for a single sample company
** Greater than 100% due to rounding of individual percentages
Table 6
Descriptive Statistics - Untransformed Variables
Panel A: Independent Variables
WRITEOFF1 WRITEOFF2 WRITEOFF3
N
104
38
104
Minimum
0
0
0
Maximum
1
1
1
Mean
0.1352
0.2065
0.0966
Standard deviation
0.1987
0.3308
0.2023
Skewness
3.074
1.724
3.400
Kurtosis
10.270
1.548
11.821
Kolmogorov-Smirnov Z statistic
2.530
1.926
3.228
(2-tailed p)
(<0.001)
(0.001)
(<0.001)
Panel B: Non-Categorical Independent Variables
LEVERAGE INTCOVER
PROFIT
N
104
104
104
Minimum
0.0025
-4,365.24
-22,595,000
Maximum
18.2693
11,065
338,100,000
Mean
0.7724
1,841.2640
10,209,290
Standard deviation
1.8645
4,240.1431
39,578,423
Skewness
8.254
1.658
6.438
Kurtosis
76.875
1.047
48.537
Kolmogorov-Smirnov Z statistic
3.466
4.442
3.448
(2-tailed p)
(<0.001)
(<0.001)
(<0.001)
n
Minimum
Maximum
Mean
Standard deviation
Skewness
Kurtosis
Kolmogorov-Smirnov Z statistic
(2-tailed p)
TAXRATE
104
-0.3125
1.2470
0.1647
0.3202
1.809
2.400
3.541
(<0.001)
ENGNRISK
104
0
1.2410
0.1847
0.2256
2.402
6.761
2.106
(<0.001)
EXTRALNS
104
0
1
0.0841
0.2364
2.879
7.130
4.751
(<0.001)
Table 7
Descriptive Statistics - Transformed Variables
Panel A: Independent Variables
4RWRITEOFF1
N
Minimum
Maximum
Mean
Standard deviation
Skewness
Kurtosis
Kolmogorov-Smirnov Z statistic
(2-tailed p)
104
0
1
0.4984
0.2224
-0.350
0.372
1.011
(0.259)
4RWRITEOFF2
38
0
1
0.4802
0.3064
0.169
-0.750
0.486
(0.972)
4RWRITEOFF3
104
0
1
0.3644
0.2718
0.276
-0.581
1.337
(0.056)
Panel B: Non-Categorical Independent Variables
LNLEVERAGE
N
Minimum
Maximum
Mean
Standard deviation
Skewness
Kurtosis
Kolmogorov-Smirnov Z statistic
(2-tailed p)
104
-5.7392
3.1635
-1.0565
1.6506
-0.607
0.140
1.157
(0.137)
LNTAXRATE
N
Minimum
Maximum
Mean
Standard deviation
Skewness
Kurtosis
Kolmogorov-Smirnov Z statistic
(2-tailed p)
104
1.1106
2.5261
1.7390
0.2732
1.335
1.175
3.560
(<0.001)
SRINTCOVER
104
1.3251
124.2256
75.1917
23.6612
1.069
1.477
4.108
(<0.001)
4RENGNRISK
104
0.0836
1.0555
0.5728
0.1988
0.219
0.087
0.413
(0.996)
4RPROFIT
104
1.4953
137.8114
72.0283
12.9020
0.452
15.629
2.477
(<0.001)
4REXTRALNS
104
0
1
0.1314
0.3031
2.059
2.602
5.043
(<0.001)
Table 8
Spearman Rank Correlation Coefficients
WRITEOFF1
WRITEOFF2
(N=104)
(N=38)
WRITEOFF3
(N=104)
LEVERAGE
(one-tail p)
-0.089
(0.184)
0.117
(0.242)
-0.197**
(0.022)
INTCOVER
(one-tail p)
-0.132***
(0.092)
-0.158
(0.172)
-0.211**
(0.016)
PROFIT
(one-tail p)
-0.075
(0.225)
-0.357**
(0.014)
TAXRATE
(one-tail p)
-0.154***
(0.059)
0.115
(0.246)
-0.197**
(0.022)
ENGNRISK
(one-tail p)
-0.074
(0.228)
-0.024†
(0.443)
0.120
(0.112)
EXTRALNS
(one-tail p)
-0.019
(0.425)
0.195
(0.120)
-0.013
(0.448)
-0.362*
(<0.001)
* significant at p = 0.01; ** significant at p = 0.05; *** significant at p = 0.10
† Correlation of WRITEOFF 2 and ENGNRISK2. N=37 as one company did not separate types of pre-production
expenditure incurred for the year.
Table 9
Mann-Whitney U Test Results
n1
n2
U-statistic
WRITEOFF1 (n=104)
PETROL
GOLD
AUDITCAP
62
49
68
42
55
36
1289.0
1142.0
1129.0
WRITEOFF2 (n=38)
PETROL
GOLD
AUDITCAP
28
12
27
10
26
11
139.5
132.5
127.0
WRITEOFF3 (n=104)
PETROL
GOLD
AUDITCAP
62
49
68
42
55
36
1264.0
1100.0
1085.0
z-statistic
-0.0861
-1.3384***
-0.6492
-0.0166
-0.7392
-0.6932
-0.2531
-1.6205***
-0.9549
* significant at p = 0.01; ** significant at p = 0.05; *** significant at p = 0.10
Dependent Variable
WRITEOFF1
WRITEOFF2
WRITEOFF3
* Adjusted for ties
Table 10
Kruskal-Wallis Test Results
Kruskal-Wallis
Probability
H Statistic*
8.8087
15.0367
10.7793
0.7872
0.1307
0.6293
One-tail p
0.466
0.090
0.258
0.493
0.230
0.244
0.400
0.053
0.170
Table 11
OLS Regression Test Results
Explanatory
Variable
Expected Sign
Coefficient
Panel A: Dependent variable = 4RWRITEOFF1
Constant
0.721
LNLEVERAGE
-0.005
SRINTCOVER
+
-0.001
4RPROFIT
+
-0.0008
LNTAXRATE
+
-0.144
4RENGNRISK
0.034
4REXTRALNS
+
0.032
PETROL
+
0.047
GOLD
+
0.060
AUDITCAP
-0.047
R2 = 0.077; F = 0.871; p = 0.554 ; n = 104
Panel B: Dependent variable = 4RWRITEOFF2
Constant
1.286
LNLEVERAGE
0.018
SRINTCOVER
+
-0.0003
4RPROFIT
+
-0.012
LNTAXRATE
+
0.094
4RENGNRISK2
-0.110
4REXTRALNS
+
0.081
PETROL
+
0.011
GOLD
+
-0.036
AUDITCAP
-0.115
2
R = 0.098; F = 0.327; p = 0.958 ; n = 37
Panel C: Dependent variable = 4RWRITEOFF3
Constant
0.357
LNLEVERAGE
-0.031
SRINTCOVER
+
0.0001
4RPROFIT
+
-0.001
LNTAXRATE
+
-0.060
4RENGNRISK
0.094
4REXTRALNS
+
0.062
PETROL
+
0.085
GOLD
+
0.120
AUDITCAP
-0.042
R2 = 0.106; F = 1.237; p = 0.282 ; n = 104
* significant at p = 0.01; ** significant at p = 0.05
t-statistic
p (one-tail)
3.002*
-0.375
-1.251
0.424
-1.675**
0.263
0.413
0.791
1.013
-1.015
0.002
0.355
0.107
0.336
0.049
0.397
0.341
0.216
0.157
0.157
1.354
0.449
-0.134
-1.065
0.429
-0.319
0.302
0.039
-0.130
-0.754
0.094
0.326
0.448
0.149
0.336
0.376
0.383
0.485
0.449
0.229
1.238
-1.851**
0.115
-0.491
-0.580
0.610
0.672
1.189
1.680**
-0.755
0.110
0.034
0.454
0.326
0.282
0.272
0.252
0.119
0.048
0.226