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.
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More recently, the notion that assets derive value in-use or in-exchange is present in several accounting standards (see IASB 2009, ¶ME30-¶ME32; FASB 2011, Accounting Standards Codification (ASC) 820–10-35-10E).
For example, under U.S. Generally Accepted Accounting Principles (GAAP), firms are required to measure investment property at cost, while under International Financial Reporting Standards (IFRS), firms can choose to measure investment property at fair value (KPMG 2012). Similarly, under IFRS, PP&E can be recognized at cost, but biological assets, a class of assets belonging to PP&E, must be recognized at fair value (KPMG 2012).
Both firms are examples from my sample.
Whether the separability criteria of the asset influence the way the asset is used by the firm, and thus the relevance of the fair value information, is a limitation of this study. I thank an anonymous reviewer for this comment.
This holds true to the extent that the unobserved factors do not vary systematically across periods.
Specifically, the IASB amended IAS 41 with respect to bearer plants, a type of in-use biological asset.
My sample predates the IASB’s amendment to IAS 41.
According to the issues paper (IASB 2012a, ¶27), the analysts felt that accounting for in-use (bearer) biological assets under IAS 41 was misleading to users because the standard required reporting price changes for assets that were not being held for sale (derive value in-use). In addition, analysts felt that accounting for in-use (bearer) biological assets under IAS 41 introduced volatility into earnings that was not useful in estimating the value of the in-use (bearer) biological assets (IASB 2012a, ¶33).
The IASB (2010, ¶6.13a) states: “The existing Conceptual Framework provides little guidance on measurement and when a particular measurement basis should be used.”
Specifically, IAS 41 prescribes accounting treatment for agricultural activity, or “management by the entity of the biological transformation of living animals and plants (biological assets) for sale, into agricultural produce, or into additional biological assets” (IASB 2009, ¶IN1).
This sample selection is consistent with Daly and Skaife (2016), who examine whether IAS 41 impacted firms’ cost of debt, and partition their sample into bearer and non-bearer biological assets, eliminating firm-year observations holding both asset groups.
Fifty-five percent of firms in the sample are cross-listed on a variety of other exchanges.
My results are not sensitive this design choice. Specifically, my results are unchanged if I substitute the market information from the firm’s largest equity exchange as opposed to summing across exchanges.
In my sample, 50 firms (27.3% of the sample), adopt IAS 41 and continue to measure their biological assets at historical cost.
The amendment to IAS 41 does not include bearer livestock such as dairy cattle, only plants.
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I thank the editor (Richard Sloan) and two anonymous reviewers for their helpful comments. This study is based on my dissertation at the University of Utah’s David Eccles School of Business. I am particularly grateful to Steve Stubben for all of his help and guidance on this project. In addition, I thank my dissertation committee: Christine Botosan (chair), Melissa Lewis-Western, Marlene Plumlee, Jim Schallheim, and Haimanti Bhattacharya. I also thank Gus DeFranco, Lynn Hannan, and workshop participants from the BYU Research Symposium, the University of Utah, the FDIC, Tulane University, LSU, and the 2015 FARS Conference. This study was a finalist for the Best Paper Award at the 2015 Financial Accounting Reporting Section Mid-Year Conference.
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:
NON CURRENT ASSETS
Property, plant and equipment
Investments in subsidiaries
Cash and cash equivalents
Trade and other receivables
Assets classified as held for sale
Amounts owed by group companies
Income Statement Disclosure:
Revenue from continuing operations
Cost of sales
Net (loss)/gain arising from changes in fair value of biological assets
9. BIOLOGICAL ASSETS
Oil palm trees
Balance at the beginning of the year
Increases due to expenditure to planted areas
Gain arising from changes in fair value
Decreases due to harvest (note 5)
Increases resulting from acquisition of subsidiary
Balance at the end of the year
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 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:
Country Fixed Effects
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Huffman, A. Asset use and the relevance of fair value measurement: evidence from IAS 41. Rev Account Stud 23, 1274–1314 (2018). https://doi.org/10.1007/s11142-018-9456-0