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This study develops an empirical analysis of the relevance of accounting information when biological assets are measured at fair value. We use an international sample of firms with biological assets. We find that biological assets influence unpredictability when they are measured at historical cost (HC). In this case, the ability of accounting data to predict future cash flows diminishes as the proportion of biological assets on total assets increases. The valuation at fair value (FV) switches this negative influence of biological assets to a positive one. We find that when they are measured at FV the prediction accuracy of future cash flows improves as the ratio of biological assets to total assets increases. This evidence is robust to different measures of prediction accuracy, as well as to the improvement of accounting standards, regardless of FV, over time. The evidence is weaker for bearer plants.

We investigate the relationship between auditor-provided tax services (APTS) and tax avoidance strategies of their clients in the Spanish market. As a result of a recently enacted EU legislation, APTS is seriously restricted within the EU. The evidence available so far for the US provides consistent support for a positive relationship between APTS and tax avoidance. However, given the importance of country-specific institutional issues, such as litigation risk, to understand the relationship between auditors and clients, the possibility of generalizing the US evidence to other countries is limited. Supporting this view, our results indicate that the positive relationship between APTS and tax avoidance observed in the US does not hold in the Spanish market. In fact, the univariate analysis shows that firms which buy tax services from their auditors present significantly higher mean and median effective tax rates. Subsequently, in the multivariate analysis, we do not observe any significant relationship between APTS and tax avoidance. This result seems robust, as it holds independently of the proxy utilized for measuring tax avoidance, as well as across an array of sensitivity checks. This study has potentially interesting implications at both theoretical and practical levels.

RESUMEN La investigación contable ha abordado el problema de la histéresis de los costes mediante modelos en los que las variaciones en los costes y en la producción o los ingresos están expresados mediante logaritmos de cambios relativos. Pero esta aproximación es incapaz de detectar adecuadamente dicho fenómeno en sectores, como por ejemplo el agrícola, caracterizados por el predominio de pequeñas empresas incapaces de separarse de la tendencia del sector. La tradición de investigación económica sobre transmisiones de precios defi ne los cambios en los precios mediante diferencias. Este trabajo encuentra evidencia empírica de que los modelos que utilizan diferencias son más precisos que los que toman logaritmos de cambios relativos para detectar la histéresis de los costes en el sector agrícola. Estos últimos solamente la detectan en las explotaciones más grandes, mientras que los primeros la hacen independientemente del tamaño. Los resultados son robustos controlando por diferentes variables. PALABRAS CLAVE Histéresis de los costes; Contabilidad agrícola; Agricultura; Tamaño; Pequeñas empresas; Costes basados en las actividades.ABSTRACT Accounting research has usually approached studies on cost stickiness through models with costs and output variations in logarithms of relative changes. But this approach is unable to detect cost stickiness properly in sectors characterized by small business units with scarce infl uence and chances to depart from sector trends, such as agriculture. The well-established economic tradition of research on price stickiness express changes in prices as differences in prices of a given period with respect to previous periods. This study fi nds empirical evidence, with a sample of farms, that models expressing changes in costs and outputs as differences are more precise in detecting stickiness than those expressing variables as logarithms in relative terms. The latter only detected it for the biggest farms, while the former did it for the whole spectrum of farm sizes. Results are also robust after controlling for changes in transactions, time, type of farming and location.

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