Value relevance of biological assets under IFRS

Accepted Manuscript Title: Value relevance of biological assets under IFRS Authors: Rute Gonc¸alves, Patr´ıcia Lopes, Russell Craig PII: DOI: Reference:

S1061-9518(17)30051-4 https://doi.org/10.1016/j.intaccaudtax.2017.10.001 ACCAUD 224

To appear in:

Journal of International Accounting, Auditing and Taxation

Received date: Revised date: Accepted date:

24-9-2015 5-10-2017 18-10-2017

Please cite this article as: Gonc¸alves, Rute., Lopes, Patr´ıcia., & Craig, Russell., Value relevance of biological assets under IFRS.Journal of the Chinese Institute of Chemical Engineers https://doi.org/10.1016/j.intaccaudtax.2017.10.001 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Value relevance of biological assets under IFRS Rute Gonçalves PhD School of Economic and Management, University of Porto, Rua Roberto Frias, 4200-464 Porto, Portugal [email protected]

Patrícia Lopes Professor School of Economic and Management, University of Porto, Rua Roberto Frias, 4200-464 Porto, Portugal [email protected]

Russell Craig Professor Portsmouth Business School, University of Portsmouth, Richmond Building, Portland Street, Portsmouth, Hampshire PO1 3DE, United Kingdom [email protected]

ABSTRACT This paper examines the value relevance of fair value accounting for biological assets under IAS 41 Agriculture, using 389 firm-year observations from 2011 to 2013 of listed firms in 27 countries. We adjust the Ohlson (1995) model to operationalize value relevance as the ability of book value to explain market equity value. The results confirm that recognized biological assets are value relevant at fair value, but are more value relevant in firms with higher levels of disclosure. The same results were obtained for bearer biological assets. However, for consumable biological assets, the results suggest that investors do not value recognized biological assets in firms with higher levels of disclosure. These findings should help regulators, accounting standard setters, and readers of financial statements to better understand the market valuation implications of IAS 41. Keywords: biological assets, fair value accounting, financial reporting, disclosure index, regulation.

1. Introduction Although fair value accounting has been discussed widely (for example, by Mala and Chand, 2012; Laux and Leuz, 2010; Hitz, 2007; Ball, 2006; Cairns, 2006; Barlev and 1

Haddad, 2003; Barth et al., 2001; and Holthausen and Watts, 2001) most of the academic literature on this topic addresses fair value in the context of financial instruments. Consideration of fair valuation issues in other contexts is much less common. To help redress this situation, this paper focuses on fair valuation in the context of International Accounting Standard (IAS) 41 Agriculture. Our intent is to enhance knowledge of fair valuation issues in the context of agriculture. In doing so, we are mindful of the claim that “IAS 41 is a ‘true’ fair value standard [in the sense that] the fair value of biological assets is reported on the firm’s balance sheet and any change in the fair value of the biological assets over the reporting period is recognized in the periodic income as an unrealized gain or loss” (Huffman, 2013: 2). Some claim that the introduction of fair value accounting for all biological assets in agriculture has led to the provision of more reliable information for the decision-making process of agents in the agricultural sector (Argilés et al., 2012). However, the standardization assured by IAS 41 is claimed to “not diminish the fact that the process [is relying on] a subjective valuation model” (Machado et al., 2015). The main disadvantage of fair value is the absence of active markets for some biological assets. Moreover, Martins et al. (2012) highlight the difficulties of valuation because each biological asset has its own attributes and life-cycle. This study focuses on the following research questions:  Are biological assets value relevant at fair value under IAS 41?  Does the value relevance of biological assets differ between listed firms in terms of their level of disclosure of biological assets? To address these questions, we adjust the accounting-based valuation model of Ohlson (1995). We analyze panel data drawn from 132 firms in 27 IFRS-adopting countries across eight sectors, focusing on the period 2011 to 2013. 2

Overall, we find that the recognized amount for biological assets under the fair value model is value relevant. Additionally, the recognized amount of biological assets under the fair value model is more value relevant for firms that exhibit higher disclosure levels. After splitting the sample according to the two classes of biological assets specified in IAS 41 (bearer and consumable), the same results were obtained for bearer biological assets. As for consumable biological assets, we conclude that investors’ valuation is formed independently of the corresponding disclosure level. The paper is structured as follows. Section 2 reviews relevant literature, in several parts. First, it describes the regulatory framework of IAS 41. Then it reviews literature related to fair value accounting in financial reporting, focusing on non-financial assets and biological assets. Two hypotheses are introduced. Section 3 describes research method, sample selection, analytical models, and disclosure index. Section 4 presents and discusses the empirical results. A brief conclusion follows. 2. Literature review 2.1. Regulatory framework of IAS 41 IAS 41 was issued in December 2000. It applies to annual periods beginning on or after January 1, 2003. This standard prescribes the accounting treatment for biological assets during their period of biological transformation, and for the initial measurement of agricultural produce at the point of harvest. IAS 41 requires that biological assets be measured on initial recognition, and at subsequent reporting dates, at fair value less costs to sell. Agricultural produce is to be measured at fair value less costs to sell at the point of harvest. The single exception allowed is only to be applied to initial recognition and in the particular context when a market-determined price is not available and the entity could not assure a reliable estimate of fair value [IAS 41.30]. In

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such conditions, an entity is permitted to use the “unreliability clause” and recognize the biological assets concerned at cost less depreciation and impairment. IAS 41 divides biological assets into bearer biological assets and consumable biological assets. In terms of the market valuation implications, fair value assessment diverges under such a classification. Unlike agricultural produce, bearer biological assets are selfregenerating (e.g. livestock from which milk is produced, grape vines, fruit trees, and trees from which firewood is harvested while the tree remains) (IAS 41.44). In contrast, consumable biological assets are harvested as agricultural produce or are sold as biological assets (e.g. livestock intended for meat production, livestock held for sale, fish in farms, crops such as maize and wheat, and trees grown for lumber) (IAS 41.44). There is often an active market for this class of biological asset. Thus, the corresponding fair value can be determined easily. Recently, the International Accounting Standards Board (IASB) amended IAS 41 in relation to bearer plants, with amendments effective for annual periods beginning on or after 1 January 2016. A bearer plant is defined in IAS 41 as a living plant that: is used in the production or supply of agricultural produce; is expected to bear produce for more than one period; and has a remote likelihood of being sold as agricultural produce, except as incidental scrap. The amendments consider bearer plants (prior to reaching maturity) and their measurement at accumulated cost. Entities are permitted to choose either the cost model or the revaluation model for mature bearer plants under IAS 16 Property, Plant and Equipment. Produce growing on bearer plants should be accounted for at fair value in accord with IAS 41.

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2.2 Fair value accounting under International Financial Reporting Standards (IFRS) In general, value relevance research infers how accounting information is reflected in share prices and how it influences investors’ decision-making (Barth et al., 2001). Incorporating more information into financial statements seems to be the most important advantage of fair value accounting (Mala and Chand, 2012: Ball, 2006; Barlev and Haddad, 2003; Barth et al., 2001). In particular, fair value provides more information than historical cost whenever there is an observable market price that managers cannot adjust or an independently observable and reliable estimate of market price (Ball, 2006). Nonetheless, there are several well-known disadvantages of fair value accounting. First, the recognized fair value changes in capital or in profit and loss are responsible for the higher volatility of reported results, thereby obscuring the value creation process (Mala and Chand, 2012). Second, when a liquid market price is not available, “mark to market” accounting leads to “mark to model” accounting. There are several valuation models (including the present value discounted cash flow model) and valuation methods adapted from the original Black-Scholes model (Black and Scholes, 1973). These fair value models are based on specific parameters and assumptions that are conducive to management manipulation (Mala and Chand, 2012; Hitz, 2007; Ball, 2006). 2.3 Fair value accounting of non-financial assets In the context of biological assets, Lefter and Roman (2007:15) argue that “the transformation process is immediately represented in the financial statements and then the investor has the possibility of estimating the future economic benefit.” The studies discussed in the following paragraph report mixed results regarding the impact of fair value accounting. Martins et al. (2012) analyze a sample of 45 firms for 2008. They conclude that fair value accounting for biological assets is not value relevant for investors; and that investors are more interested in the financial performance of firms as a whole. Conversely, based on an 5

experiment involving students, farmers and accountants engaged in the agricultural sector in Spain, Argilés et al. (2012) compare the constraints that arise from using each of the two valuation methods for biological assets: historical cost and fair value. They observe that fair value is more useful than historical cost in the decision-making processes of agents in the agricultural sector, and in the preparation of financial statements. Silva et al. (2013) study 25 listed Brazilian firms for 2008 and 2009. They conclude that measurement of biological assets using fair value (rather than historical cost) was not relevant to users. Nonetheless, Silva Filho et al. (2013) argue that historical cost measurements and fair value measurements of biological assets are relevant to the Brazilian capital market. It is pertinent to explore, (as with Huffman, 2013), whether asset measurement related to asset use assures that more value relevant information is provided to investors. In terms of generated value, an asset can be classified in one of two ways. The first is as an in-exchange asset, such as a consumable biological asset (for example, a plantation to produce timber logs). The second is as an in-use asset, such as a bearer biological asset (for example, a plantation producing palm oil). Huffman (2013) analyze 183 firms from 35 countries that adopted IAS 41 for the periods 1999-2001 and 2007-2010. She concludes that book value and earnings information is more value relevant when consumable biological assets are measured at fair value and bearer biological assets are measured at historical cost. Little research has been carried out on the use of fair value accounting for the measurement of biological assets. The scope of the studies conducted has been narrow. Generally, they focus on comparisons between historical cost and fair value. Studies of other types of nonfinancial assets such as goodwill, investment property, research and development expenditure, have been conducted to test market valuation implications (Baboukardos and Rimmel, 2014; Lourenço and Curto, 2008). Most of these studies apply the same

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methodology – an adaptation of the Ohlson model (Ohlson, 1995). This model is further explained in section 3.2. Lourenço and Curto (2008) explore a sample of 224 European firms. They conclude that investors differentiate the recognized cost and fair value and the disclosed fair value of investment property. However, they do not differentiate between the valuation implications of recognized fair value in Germany, the United Kingdom, France and Sweden. Some studies suggest that the corresponding mandatory disclosure is also a disciplinary element of perception of the market (Baboukardos and Rimmel, 2014). Moreover, in the absence of a market price, managers usually act in their self-interest. In brief, in comparison to previous value relevance studies related to biological assets by Martins et al. (2012), Argilés et al. (2012), Silva et al. (2013), Silva Filho et al. (2013), and Huffman (2013), the present study tests the value relevance of recognized biological assets at fair value (instead of considering both types of valuation method, historical cost and fair value). Drawing from prior research into other non-financial assets (e.g., Baboukardos and Rimmel, 2014; Lourenço and Curto, 2008) this study also examines whether there is a “level of disclosure effect.” 2.4 Development of hypotheses The first research question seeks to determine whether biological assets explain market equity values. Previous empirical evidence on this matter is mixed. Although some studies confirm that accounting information related to biological assets influences decision-making of investors (Argilés et al., 2012; Silva Filho et al., 2013), other studies do not support the value relevance of biological assets (Martins et al., 2012; Silva et al. 2013). Thus, there is no strong expectation regarding the sign of the first hypothesis: H1.

Biological assets are value relevant at fair value under IAS 41. 7

To explore investors’ perceptions of the effect of incorporating more information into financial statements, it is important to differentiate recognition from disclosure (Al Jifri and Citron, 2009; Kun, 2013; Ball, 2006; Ahmed et al., 2006). Disclosure is not a substitute for recognition. To be recognized, an item must have “a cost or value that can be measured with reliability” (para 4.38b, IASB Conceptual Framework). The same criteria are not applied for disclosed items. In this respect, and by analogy to the FASB (Financial Accounting Standards Board), investors characterize recognized items as more reliable than disclosed items (Fried, 2012). Nonetheless, Choudhary (2011) and Holthausen and Watts (2001) argue that recognition suggests less reliability, since managers are encouraged to manipulate recognized items rather than disclosed items. Other arguments support a view that investors incorrectly underestimate disclosed items through lack of expertise or because of information processing costs (Al Jifri and Citron, 2009; Kun; 2013). Conversely, the efficient market hypothesis suggests that “recognition adds little when the information investors seek is disclosed” (Barth et al., 2003: 582). We justify introduction of the disclosure effect because of the controversial nature of the current debate (outlined above); and because we seek to extend previous value relevance studies that have dealt only with recognition of biological assets. A survey of biological assets in Australia, the UK and France concludes that there is a lack of comparability between disclosure practices; and that French firms tend to disclose less information on biological assets (Elad and Herbohn, 2011). Given the diverse levels of disclosure of biological assets, the impact on market valuation is predicted to differ. A positive signal is expected for the second hypothesis:

H2:

The value relevance of biological assets is higher in listed firms with high disclosure levels for biological assets. 8

The research model includes market value per share (MV) as the dependent variable. MV corresponds to the month-end market price (three months after the fiscal year-end 1 ). The model analyzes factors that are expected to affect MV: book value per share (BV), biological assets per share (BA), earnings per share (E), disclosure index ranking with respect to IAS 41, and two control variables, firm size and industry sector. BV is the common equity divided by outstanding shares at the firm's fiscal year-end. A positive sign is expected. BA is the ratio between biological assets (given by the Datastream variables WS18277 – net book value, WS18278 – gross value or WS18258 – current value) and common shares outstanding. No directional sign is expected. E represents the net income available to common equity holders, divided by common shares outstanding. A positive sign is expected. Disclosure index ranking (Dindex) is a dummy variable. This is coded one if the disclosure index regarding the biological assets of each firm is in the first quartile, two if it is in the second or third quartiles, and three if it is in the fourth quartile of the distribution of the disclosure index of the sample. A positive sign is expected. The annual reports of each firm were analyzed to calculate the disclosure index. 2 The control variables were firm size (SIZE) and industry sector (SECTOR). A positive sign was expected for each. Previous literature measures SIZE in different ways. Here, firm size corresponds to the logarithm of total assets. Industry sector (SECTOR) refers to SIC code classification (2-digit division): sector 1 – agriculture, forestry, fishing and mining (01-14), sector 2 – manufacturing (20-39), and other sectors.3 The introduction of these two control variables is supported by the findings of studies which explore the value relevance of other 1

The fiscal year-end differs among the firms in the sample (31 March, 30 April, 31 May, 30 June, 31 August, 30 September, 30 November and 31 December). MV was calculated three months after these year ends. 2

This examination was performed by one coder. To minimise possible coding bias, the researcher coded the information twice, resolving any discrepancies. “Others” as an industry sector is represented by Wholesale Trade (7), Transportation & Public Utilities (6), Retail Trade (5), Finance, Insurance & Real Estate (2), Services (2) and Construction (1). 3

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non-financial assets for size (Tsalavoutas and Dionysiou, 2014) and for sector (Dahmash et al., 2009).

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3. Method 3.1 Sample The data analyzed comprise 389 firm-year observations of listed firms that adopted IFRS before 2010. 4 They range accross 27 countries and 8 industry sectors, during the period 2011 to 2013. 5 The sample firms contain different levels of recognized biological assets in the body of financial statements, and different amounts of disclosed information in the notes to the financial statements under fair value measurement. Some exceptions and exemptions allowed by IFRS 1 First-time Adoption of International Financial Reporting Standards can constrain analysis and inference about information pertaining to the year of adoption (Gastón et al., 2010). As a consequence, 2010 was defined as the limit year for considering firms that had adopted IFRS (or equivalent standards). Data were collected from Datastream. Countries that adopted IFRS before 2010 were selected first. Then, 164 firms with biological assets were selected from the corresponding sample for these countries. We explored whether one of the following biological asset variables provided by Datastream (WS18277 - net book value; WS18278 – gross value, WS18258 – current value) were present, in order to assure that each firm had biological assets for the entire period 2011 to 2013. Thirty firms using historical cost valuation, and two firms without an available annual report, were removed. This left an analysis group of 132 firms. 3.2 Research models Value relevance research examines the association between accounting amounts and equity market values. This study examines the value relevance of biological assets. Specifically, we

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Seven firm-year observations are missing, as well as six observations regarding the market value per share variable (the corresponding data is not available in Datastream). One observation was removed from the earnings per share variable because it was identified as an outlier. 5

The countries are Australia, Belgium, Brazil, Chile, Cyprus, Denmark, Finland, France, Germany, Greece, Hong Kong, Ireland, Italy, Kenya, Lithuania, Netherlands, New Zealand, Norway, Papua New Guinea, Peru, Philippines, Portugal, South Africa, Spain, Sweden, Ukraine and the UK. 11

adjust the accounting-based valuation model of Ohlson (1995) to determine firm value. In general, this model represents firm value as a linear function of the book value of equity and earnings per share. For non-financial assets (such as goodwill, investment property and tangible and intangible assets) this approach has been followed by Baboukardos and Rimmel (2014), Oliveira et al. (2010), Lourenço and Curto (2008), and Barth and Clinch (1998). Martins et al. (2012) also adopt this approach with regard to biological assets. They argue that this model can effectively measure the sensitivity, and cause and effect, between the book value and market value of a given firm. Following Baboukardos and Rimmel (2014) and Al Jifri and Citron (2009), we test the valuation model by assessing the effects of disclosure levels. Results are provided in Table 3.6 The presence of heteroskedasticity is taken into account with White diagonal standard errors and covariance (White, 1980). Additionally, to reduce heteroskedasticity, all the variables (except control variables) are deflated by the number of common shares outstanding (Barth and Clinch, 2009).7 Value relevance of recognized biological assets is tested in a regression where a firm’s market value is a function of the book value of equity and earnings. This relation is tested in the first equation with market value per share (MV) as the dependent variable, and book value per share (BV) and earnings per share (E) as the independent variables. Moreover, this first model includes two control variables: firm size (SIZE) and industry sector (SECTOR). MV it= b0 +b1BVit +b2Eit+ b3SIZEit+ b4∑ j=1,2,3 SECTORjit +uit

(2)

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We submitted the dataset to a random effects model (Greene, 2012). The Hausman test was then applied, in which the null hypothesis is that the coefficients estimated by the random effects estimator are the same as those estimated by the fixed effects estimator. Based on the results, we infer that a fixed effects model is the appropriate model for this sample. By following the panel least squares method, three equations are considered for the adjusted Ohlson model (Ohlson, 1995). 7

A return model could be used in a complementary study to investigate whether annual share returns are associated with current-year revaluations (Barth and Clinch, 1988). However, this would necessitate splitting firm earnings from the unrealized valuation gains/losses of biological assets. 12

To test H1 in Model 2, BV is divided into two variables: BV excluding BA (BV-BA), and BA. The coefficient b2 assures a response to the value relevance of biological assets under IAS 41. MVit = b0 +b1(BV-BA) it +b2BAit+b3Eit+ b4SIZEit+ b5∑ j=1,2,3 SECTORjit +uit (3) To test H2, Model 3 adds the effect of disclosure. The aim is to investigate whether there is a systematic difference in biological asset valuation effects between firms with relatively high and relatively low, levels of disclosure of biological assets. The coefficient b5 assures a response to the value relevance of biological assets regarding mandatory and voluntary disclosure. Dindex is a dummy variable based on whether the disclosure index for the biological assets of each firm is below the first quartile, in the middle quartiles, or in the fourth quartile of the sample’s disclosure index distribution. MVit = b0 +b1(BV-BA)

it

+b2BAit+b3Eit+ b4∑

j=1,2,3

Dindexjit+ b5∑

j=1,2,3

Dindexjit x BAit+

b6SIZEit+ b7∑ j=1,2,3 SECTORjit +uit (4) 3.3 Disclosure index Given the disclosures required by IAS 41, a disclosure index is structured and calculated for the sampled firms for the period 2011 to 2013. This index is divided into three sections: mandatory items, non-mandatory but recommended items (both sections represent all items required to be disclosed by IAS 418), and non-mandatory and non-recommended items (that is, voluntary information in excess of mandatory information). The last category is constructed according to a report on the impact of adopting IAS 41 in the timber sector, developed by PriceWaterhouseCoopers (PWC, 2011). This report identifies three approaches being followed by their clients in terms of disclosure practices:

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To construct the disclosure index, the disclosure items from IAS 41 that focus on historical cost were omitted. 13



revealing the complexity of valuation parameters;



providing more information on the effects of variations in key valuation factors;



exposing firm assumptions on future prices and costs, and using them in a sensitivity analysis with multiple parameters, and disclosing the results.

The disclosure index we construct is dichotomous, unweighted and adjusted for nonapplicable items (Santos et al., 2013; Lopes and Rodrigues, 2007; Owusu-Ansah, 1998). A score of one is assigned to an item if it is disclosed, or zero if it is undisclosed. Each item is given equal importance in all three categories. We exclude items with no information in the notes about any disclosure items required by IAS 41. The only exemption allowed is in respect of IAS 41.49, “financial risk management strategies related to agricultural activity”. Risk strategy related to biological assets is highly important because a firm is obliged to declare its overall strategy in the annual report. Therefore, if this item contains no information, it is considered in the index to be a non-disclosed item. The maximum number of items in the disclosure index is 27.9 Consequently, the total score of the mandatory and voluntary disclosure index for biological assets (Index) in a firm is: 𝐼𝑛𝑑𝑒𝑥𝑖 = ∑𝑚 𝑖=1 𝑑𝑖 /𝑚

(1)

where di = 1 if the item is disclosed; and di = 0 otherwise; and m = maximum number of applicable items a firm may disclose.

According to (EU) Commission Regulation 1255/2012 (11 December 2012), IFRS 13 – Fair value Measurement is applied when another IFRS requires or permits fair value measurement or disclosures about fair value measurements. Therefore, this standard sets out amendments in several standards, such as in IAS 41, by deleting paragraphs 47 and 48. 9

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4 Results 4.1 Descriptive analysis Table 1 presents descriptive statistics for the independent and control variables. The disclosure index (Index) presents a wide range of results, between 0.13 and 1.00.10 Given that the annual report of each firm was analyzed to calculate the disclosure index, the biological assets presented in the balance sheet and in the notes of the financial statements, were compared to the information obtained from DataStream. This was done for validation purposes and, in some cases, to help provide missing values. We did not attempt to identify and remove outliers for BA, since BA is the main variable determining the number of firms in the sample. We retain all firms with biological assets, regardless of materiality. Insert Table 1 here The most frequently reported disclosure item is: “A reconciliation of changes in the carrying amount of biological assets between the beginning and the end of the period” (n=368; IAS 41.50). This result is consistent with that reported by Silva et al. (2012) for Brazilian firms. The least reported item is: “An aggregate gain or loss arising during the period on initial recognition of biological assets” (n=9; IAS 41.40). Mindful that some disclosures exceed the mandatory (and recommended) requirements, the results suggest there is an opportunity for improving biological asset disclosure – as concluded by PWC (2011). Such improvements could, in general, accord with those proposed by Tsalavoutas et al. (2014), whose recommendations were based on their study of compliance with mandated disclosures for mergers and acquisitions, intangibles and impairment assets for a sample of 544 firms across the world, for the financial year 2011. Overall, Tsalavoutas et al. (2014)

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Four firms had a negative book value. Consequently, BV is negative. The corresponding data were not removed, given that these four firms are still active, without any bankruptcy proceedings current or anticipated. There are also four firms with a higher level of biological assets compared to book value. This justifies the negative minimum value for the variable (BV-BA). 15

conclude that levels of information between firms diverge considerably; and that there is some level of non-compliance with these three standards. They call for investigation of whether firms deliberately refrain from making some transactions sufficiently material; whether the standards are sufficiently clear; or whether firms intentionally fail to follow mandatory disclosure requirements. This draws attention to the need for preparers, regulators and enforcement bodies to focus on improving the extent of firms’ disclosures and to eliminate ambiguity in interpreting standards so as to assure greater comparability of information disclosures. The Pearson rank correlation coefficients reported in Table 2 show that all independent variables are correlated positively with stock price. In particular, BV and (BV-BA) are highly positively correlated with MV. The correlation coefficient of BA is a preliminary sign that biological assets are value relevant on a univariate basis, consistent with H1. In multivariate analysis, it is frequently accepted that correlations between independent variables are not risky unless they exceed 0.80 or 0.90 (Gujarati, 1995). The correlation coefficients are higher than 0.90 for the BV and BV-BA variables, but this has no effect on the analysis because both variables are not used in the same regressions. Insert Table 2 here 4.2 Research model Table 3 shows the estimated coefficients of the least square regressions for the three models. Overall, the adjusted R2 statistic in each of Models 1, 2 and 3 are high and confirm that when biological assets and the corresponding disclosure level are introduced separately, the explanatory power of the model improves, albeit slightly. Insert Table 3 here

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In Model 1, BV and E are statistically and positively significant. The inference is that both variables are associated with the firms’ market value. To test H1, Model 2 excludes biological assets per share (BA) from book value per share (BV). Book value excluding biological assets per share (BV-BA), biological assets per share (BA) and earnings per share (E) are statistically and positively significant, confirming H1. In respect of H2, Model 3 tests whether there is a difference in the value relevance of biological assets between listed firms with high disclosure levels of biological assets, and those with low levels. This regression includes the interaction variable between the dummy variable for the disclosure level (Dindex) and biological assets per share (BA). BV-BA, together with BA, E and the interaction variable (Dindex3 x BA) are statistically and positively significant, confirming H2. We tested for robustness in three ways. First, we find that the inferences of these equations are not sensitive to using prices three or six months after fiscal year-end, except in Model 3. In this case, the interaction variable is not statistically significant when considering price six months after fiscal year-end. Second, and given that BA displays a wide range of values, Models 2 and 3 are re-estimated, using a sub-sample in which firms in the first quartile of biological assets intensity in the sample, are excluded. The results are the same. Third, the sample is divided according to the biological asset classifications under IAS 41; that is, into bearer biological assets and consumable biological assets. Model 3 was re-estimated using these two sub-samples. Table 4 reports the corresponding results. The estimated coefficients of the sub-sample of bearer biological assets are statistically similar to those of the initial multivariate analysis. For consumable biological assets, the interaction variable is not supported. Therefore, it seems that investors do not value recognized biological assets in firms that exhibit higher disclosure levels. There is usually an available market price for consumable biological assets and they are often sold in the short term. Therefore, the fair value of consumable biological assets is captured by the market 17

faster than for bearer biological assets. Moreover, bearer biological assets are held for an extended period. Typically, it is more difficult to access the corresponding fair value for them. Consequently, the provision of any further information is useful. For that reason, investors value bearer biological assets for firms that exhibit higher disclosure levels of biological assets. To reinforce the above analysis, the Chow test is used to determine whether the coefficients differ across subgroups (Chow, 1960). Because the F statistic is higher than its critical value of 2.369 at the 1% level of significance, we reject the null hypothesis of structural stability11. Therefore, we conclude there is a structural change in this model, and it is necessary to split the data into two sub-samples. This means that independent variables have a different impact on both bearer biological assets and consumable biological assets in the entire sample. Insert Table 4 here Based on this result, we infer that when recognizing biological assets under fair value, investors clearly distinguish between bearer biological assets and consumable biological assets. This evidence lends support to recent amendments to IAS 41 which prescribe a different accounting treatment for bearer plants than for other biological assets.12

5. Conclusions In general, the recognized amount of biological assets under the fair value model is value relevant. The present study extends previous value relevance studies by including the 11

The results are the same at the 5% and the 10% level of significance with a critical value of 1.856 and 1.616, respectively. 12

Adjustments to IAS 41 for annual periods beginning on/or after 1 January 2016 permit firms to choose either the cost or the revaluation models for mature bearer plants under IAS 16 Property, Plant and Equipment. 18

disclosure effect. We find that the recognized amount of biological assets under the fair value model is more value relevant for firms exhibiting higher disclosure levels. According to the classification of biological assets used in IAS 41, we partition our data into bearer biological assets and consumable assets in order to explore the effect of disclosure levels on value relevance. In the case of consumable biological assets, the interaction variable (introducing the effect of different disclosure levels in biological asset valuation) is not supported. It seems that investors value recognized biological assets, but do so independently from the corresponding disclosure level. One probable explanation is that there is typically an available market price for consumable biological assets because they are usually sold in the short term. In contrast, bearer biological assets are held for an extended period and thus it is not as easy to access the corresponding fair value. Consequently, any further information disclosed about bearer biological assets is useful. Thus, investors value bearer biological assets for firms that exhibit higher disclosure levels of biological assets. The above results should be read with several cautions and opportunities for future research in mind. First, one cannot guarantee the same results would be obtained with a larger or different sample. To assure more robust results, a longer time period should be analyzed. Second, it would be beneficial to ascertain whether the results hold across additional countries that adopted IFRS after 2010. Third, note that this study tests for Ohlson’s (1995) price model, not the alternative return model. Fourth, control variables other than size and industry sector could be explored. Fifth, the construction and calculation of the disclosure index is affected by subjectivity: for example, in deciding which paragraphs of IAS 41 should be grouped to represent one index item, and in deciding whether an item is applicable to a specific firm or not. Sixth, it would be useful to investigate the extent to which market assessments of recognized versus disclosed biological asset amounts depend on the method of valuation (historical cost versus fair value). 19

Recent amendments to IAS 41 (whereby agricultural produce will still be measured at fair value), will not completely remove volatility in profit or loss. Initially, bearer plants are recognized at accumulated cost. Depreciation begins once the corresponding asset is in place and ready to work. “The point at which depreciation begins is subjective and is likely to depend on the type of plant. This judgment should be clearly disclosed” (PWC, 2015, p.6). Firms will need to measure biological assets separately under fair valuation from the bearer plants on which they are growing. This seems likely to “increase the complexity and subjectivity of the measurement” (Ernst & Young, 2015, p.11). Exploration of the market valuation implications of biological assets before, and after, 2016 seems likely to offer beneficial insights. This study will inform policy makers and international standard setters involved in future reviews of IAS 41. The empirical evidence supports the propriety of recent amendments of IAS 41. We believe that further academic research (or maybe further projects commissioned by the IASB) should be extended to other bearer biological assets (dairy cattle, for example), rather than plants, to determine whether the amendments are effective for annual periods beginning on after 1 January 2016.

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Figures and tables

Table 1. Descriptive statistics n=389 Variable Mean MV 7.62 BV 6.59 BV-BA 4.89 BA 1.70 E SIZE Index

0.50 5.84 0.60 Frequency

Median 1.41 1.59 1.23 0.16

Maximum 126.53 148.13 92.10 56.68

Minimum 0.01 -0.47 -1.64 0.0(1)

0.06 5.74 0.60 Percent

13.88 7.42 1.00

-13.08 3.96 0.13

SECTOR Manufacturing Agriculture, forestry, fishing and mining

63

47.73

Dindex Below 1st quartile (Index=0.50)

Stand. dev. 16.19 15.68 11.59 5.94 1.86 0.76 0.18 Frequency

Percent

98

24.75

202

51.01

Between 1st and 4th quartiles 46

34.85

Others

23

17.42

Total

132

100.00

Table 2. Correlations (Pearson) MV BV

BV-BA

Above 4th quartile (Index=0.74) Total

BA 25

E

96

24.24

396

100.00

SIZE

MV 0.887*** BV 0.812*** 0.949*** BV-BA 0.757*** 0.789*** 0.554*** BA *** *** 0.787 0.733 0.642*** E *** *** 0.304 0.245 0.297*** SIZE *** Statistical significance at: 1% level

0.682*** 0.067***

0.180***

Table 3. Regression results Model 1 Model 2 Variables Coef. SE Coef. SE Constant -10.499 2.943*** -11.774 2.457*** BV 0.668 0.119*** BV-BA 0.596 0.122*** BA 0.881 0.274*** E 2.608 1.080*** 2.406 1.062*** Dindex1 Dindex3 Dindex1 x BA Dindex3 x BA Controls SIZE 1.963 0.523*** 2.258 0.394*** SECTOR1+SECTOR2=1 1.136 0.887*** 0.711 0.838*** N 389 389 F-stat 327.428*** 287.464*** Adj. R2 0.835 0.838 Statistical significance at: *** 1% level; ** 5% level; * 10% level

Table 4. Robustness test (Model 3) Panel A: Regression results Bearer Consumable Variables Coef. SE Coef. SE Constant -20.759 5.246*** -3.394 1.953*** BV-BA 0.549 0.164*** 0.821 0.095*** BA 0.649 0.334*** 1.148 0.353*** E 2.497 1.038*** 1.781 1.016*** Dindex1 3.667 2.062*** -0.410 0.373*** Dindex3 -0.912 0.881*** -0.312 0.569*** Dindex1 x BA 4.180 3.149*** -1.239 0.900*** Dindex3 x BA 1.029 0.280*** 0.040 0.345*** Controls SIZE 3.745 0.838*** 0.689 0.344*** SECTOR1+SECTOR2=1 1.295 1.450*** -0.012 0.513*** N 167 222 F-stat 98.907*** 145.717*** Adj. R2 0.866 0.878 Panel B: Chow test of model 3 regression (all, bearer and consumable) Sum squared residuals All Bearer Consumable 14077.14 9880.421 2285.219 26

Model 3 Coef. SE -10.270 2.599*** 0.668 0.507 2.432 1.212 -0.622 1.402 0.797

0.158*** 0.298*** 0.948*** 0.855*** 0.505*** 1.537*** 0.258***

1.863 0.881

0.402*** 0.855***

389 213.358*** 0.858

Number of parameters 10 Number of observations 389 167 F-statistic (10,369) 5.798 Statistical significance at: *** 1% level; ** 5% level; * 10% level

27

222