Asset use and the relevance of fair value measurement: evidence from IAS 41

Abstract

This study investigates whether asset use influences the relevance of fair value measurement. Specifically, I examine whether fair value is more relevant when it is applied to in-exchange assets than when it is applied to in-use assets. I test the framework on a sample of international firms that adopt International Accounting Standard 41. Using a difference-in-differences approach, I find that earnings information is significantly more relevant when firms measure in-exchange biological assets at fair value, but book value and earnings information is significantly less relevant when firms measure in-use biological assets at fair value. Consistent with these results, in cross-sectional analyses I find that investors discount the fair value of in-use biological assets and their associated unrealized gains and losses relative to the fair value of in-exchange biological assets. At present, the Conceptual Framework provides little guidance on asset measurement, resulting in inconsistencies across measurement standards. Thus, my findings may provide insight to standard setters and those interested in conceptually based asset measurement.

Appendices

Appendix A IAS 41 disclosure example

Below are examples of the balance sheet, income statement, and footnote disclosures required under IAS 41. The examples are from the New Britain Palm Oil’s 2011 Annual Report.

Balance Sheet Disclosure:

	Consolidated
	Notes	2011 USD,000	2010 USD,000
NON CURRENT ASSETS
Property, plant and equipment	8	779,688	577,081
Biological assets	9	435,173	558,965
Intangible assets	10	55,834	45,477
Investments in subsidiaries	11	–	–
		1,270,695	1,181,523
CURRENT ASSETS
Cash and cash equivalents	12	60,148	10,181
Trade and other receivables	13	152,474	89,885
Biological assets	9	32,559	12,216
Inventories	14	189,953	133,443
Assets classified as held for sale		–	8502
Amounts owed by group companies	15	–
		435,134	254,227
TOTAL ASSETS		1,705,829	1,435,750

Income Statement Disclosure:

	Consolidated
	Notes	2011 USD1100	2010 USD’000
Revenue from continuing operations	4	780,073	461,175
Cost of sales	5	(390,2981	(227,642)
Gross profit		389,775	233,533
Net (loss)/gain arising from changes in fair value of biological assets	9	(216,138)	245,292
Other Income	4	2714	2877
Other gains	4	61,268	27,4–47
Distribution costs		(77,146)	(53,933)
Administrative expenses		(91,375)	(73,722)
Operating profit	6	69,098	381,494

1. Footnote Disclosure:

9. BIOLOGICAL ASSETS
	Consolidated
	2011	2010
	USD’000	USIY000
Oil palm trees
Balance at the beginning of the year	556,239	180,819
Increases due to expenditure to planted areas	2070	1080
Gain arising from changes in fair value	40,441	399,167
Decreases due to harvest (note 5)	(271,019)	(155,003)
Increases resulting from acquisition of subsidiary	–	118,506
Exchange differences	105,957	11,670
Balance at the end of the year	433,688	556,239

Table 9 Construct validity for in-exchange versus in-use biological assets

Appendix B

Table 10 Variable definitions

Appendix C – Heckman (1979) procedure

A true DiD research design requires a random sorting of observations into the treatment and control samples. In my study, the control group comprises firms that adopt IAS 41 but continue to measure their biological assets at historical cost. As this is a non-random sorting, selection bias is a concern since its presence has the potential to produce biased coefficients in the main estimations (see Lennox et al. 2012). In order to address this concern, I use a standard procedure for controlling for selection bias: the two-stage Heckman (1979) procedure (Lennox et al. 2012).

In the first stage, I model firms’ “choice” of measuring their biological assets at historical cost upon adoption of IAS 41. I follow Christensen and Nikolaev (2013), who examine the characteristics of firms that adopt IFRS and voluntarily choose to measure their PP&E at fair value, but modify their approach to include variables that capture accounting enforcement, as the choice to continue measuring biological assets at historical cost appears to be a country-level enforcement issue. Specifically, I estimate the following logit model on all observations included in the DiD sample:

$$ {HC}_i={\mu}_0+{\mu}_1{INUSE}_i+{\mu}_2{SIZE}_{i,t}+{\mu}_3{PPE}_{i,t}+{\mu}_4{CURRENT\ DEBT}_{i,t}+{\mu}_5{LOSS}_{i,t}+{\mu}_6{EARLY}_i+{\mu}_7{ENFORCEMENT}_{i,t}+{\mu}_8 COMMON\ {LAW}_i+{\mu}_9{CHL}_{i,t}+ COUNTRY\ FIXED\ EFFECTS+{\varepsilon}_t\kern0.5em $$

(A1)

HC is an indicator variable that takes the value of one if firm i continues to measure its biological assets at historical cost upon adoption of IAS 41; SIZE is the log of the firm’s average assets in year t; PPE is the firm’s net PP&E scaled by total assets; CURRENT DEBT is the firm’s current portion of debt in year t, scaled by the firm’s average assets; LOSS indicates whether the firm had negative operating income in year t; and EARLY indicates whether the firm early adopted IFRS. To control for variation in the enforcement of accounting standards across countries, I include three variables in addition to country fixed effects. The first variable, ENFORCEMENT, indicates whether the firm’s country exceeds the sample median enforcement level index constructed by Brown et al. (2014), an index which captures the degree of accounting enforcement at the country level. Next, I include an indicator variable, COMMON, that takes a value of one if the firm operates in a common law country, as prior research finds that investor protection laws are stronger in common law countries (La Porta et al. 1997, 1998), and that common law financial reporting systems are perceived to be higher in quality than civil law systems are (Ball et al. 2000; Leuz et al. 2003). Finally, I include an indicator variable, CHL, that takes a value of one for year-country observations where Christensen et al. (2013), Appendix A find substantive changes in accounting enforcement.

The exclusion restrictions in model A1, that is, the independent variables included in the first-stage model but excluded from the second-stage models, are PPE, CURRENT DEBT, ENFORCEMENT, COMMON, and CHL. In the first-stage I model firms’ “choice” to continue measuring their biological assets at historical cost as a function of the proportion of the firm’s assets held in PP&E, of the current portion of the firm’s debt, and of the level at which the firm’s country enforces its accounting standards. I exclude these variables from the second-stage models, as I do not expect the percentage of the firm’s PP&E and current debt or the country’s enforcement of accounting standards to be first-order drivers in affecting the relevance of firm’s book value and earnings information. Nevertheless, in robustness analyses, I investigate the sensitivity of my results to this assumption, and find that including the portion of the firm’s PP&E and current debt or any of the enforcement variables in my second-stage models does not affect my inferences.

The results from the first-stage estimation, i.e., model A1, appear below:

	(1)
Variables	HC
INUSE	2.813***
	(3.63)
SIZE	−0.301
	(−1.42)
PPE	0.166
	(0.71)
CURRENT DEBT	1.578
	(1.20)
LOSS	0.991**
	(2.21)
IFRS	16.366***
	(16.92)
ENFORCEMENT	−0.039
	(−0.10)
COMMON LAW	−14.525***
	(−12.81)
CHL	0.034
	(0.36)
Constant	−3.367**
	(−2.39)
Country Fixed Effects	Yes
Observations	1012
R-squared	0.599

1. The results suggest that firms with in-use biological assets that earn losses, and that early adopt IFRS, are significantly more likely to measure their biological assets at historical cost upon adoption of IAS 41. In contrast, firms in common law countries are significantly less likely to measure their biological assets at historical cost upon adoption of IAS 41. After the first-stage estimation, I calculate an inverse Mills’ ratio for each firm-year observation in the above sample (MILLS), and I include it in all second-stage DiD estimations