Designing Policy for Reducing the Off‐farm Effects of Soil Erosion Using Choice Experiments

Soil erosion produces both on‐site private costs and off‐site social costs, such as desertification, rural depopulation, siltation of waterways and reductions in biodiversity. To design efficient policies, land use planners and decision makers need information on the relative weights of changes in these consequences, since policy alternatives, such as different management restrictions, will have varying impacts on these consequences of erosion. The research presented here uses the choice experiment method to evaluate these relative weights, using a case study in the Alto Genil and Guadajoz watersheds in southern Spain. We find that reductions in desertification, protection of water quality, protection of biodiversity, the area covered by the scheme, and the number of rural jobs safeguarded are all significant determinants of preferences over alternative policy designs.     

Testing Choice Experiment for Benefit Transfer with Preference Heterogeneity

Benefit transfer is a cost-effective method for estimating the value of environmental goods that relies on information obtained in previous studies. The multiattribute approach of choice experiments should provide advantages in terms of benefit transfer, allowing differences in environmental improvements between sites as well as differences in socioeconomic and attitude characteristics between respondent populations. This article investigates the capability of choice experiment method to be used in environmental benefit transfer when a random parameters approach is used to allow for preference heterogeneity: we find that the inclusion of respondents' taste heterogeneity reduces the magnitude of the transfer error.     

An alternative method to estimate parameters in modelling the behaviour of commodity prices

Measuring the risk of investment projects involving commodities and modelling its price dynamics behaviour is usually implemented with Kalman filtering techniques. However, because the use of these techniques has high implementation requirements, recent literature has employed approximate models. This paper proposes a new and simpler spreadsheet implementation procedure which presents lower implementation requirements than the widely used Kalman filtering estimation procedure. The proposal needs to estimate fewer parameters than usual and does not directly estimate sequences but considers the relationship between the states implicitly when defining the regression matrices. This translates into a significant reduction in processing time. We apply the proposal to estimate the parameters of a 4-factor model for four commercial commodities: crude oil, heating oil, unleaded gasoline and natural gas; we then compare the accuracy with results using the Kalman filter method. Results indicate that error measurements are approximately equal for the actual model and the approximation proposed in this paper, for both the in- and out-of-sample data-sets.     

Proposing new variables for the identification of strategic groups in franchising

The identification of strategic groups in the Spanish franchising area is the main aim of this study. The authors have added some new strategic variables (not used before) to the study and have classified franchisors between sectors and distribution strategy. The results reveal the existence of four perfectly differentiated strategic groups (types of franchisors). One of the major implications of this study is that the variables that build a strategic group vary depending on the respective sector the network operates in and its distribution strategy. This fact indicates that including sector and distribution strategy is absolutely necessary to achieve good classifications of franchisor types.     

Using Remote Sensing for Agricultural Statistics

Remote sensing can be a valuable tool for agricultural statistics when area frames or multiple frames are used. At the design level, remote sensing typically helps in the definition of sampling units and the stratification, but can also be exploited to optimise the sample allocation and size of sampling units. At the estimator level, classified satellite images are generally used as auxiliary variables in a regression estimator or for estimators based on confusion matrixes. The most often used satellite images are LANDSAT-TM and SPOT-XS. In general, classified or photo-interpreted images should not be directly used to estimate crop areas because the proportion of pixels classified into the specific crop is often strongly biased. Vegetation indexes computed from satellite images can give in some cases a good indication of the potential crop yield.     

Choice of Product Variety for the Durable-goods Monopolist

Received January 10, 2001; revised version received June 25, 2001     

Accounting measures and international pricing models: Justifying accounting homogeneity

We study the importance of homogeneous accounting data when testing international versions of asset pricing models. Specifically, we focus on a pricing model commonly used by practitioners – the Fama–French three-factor model – which uses accounting information and has traditionally performed poorly at the cross-country level. We show that international versions of the model perform significantly better if the accounting information is homogeneous across firms. We apply the model to a set of firms that follow common accounting standards – the IAS/IFRS – and also to firms that have issued ADRs in the US – and therefore must report following both US GAAP and their own domestic standards. In both cases our results show that the accounting dimension is relevant: the use of homogeneous accounting measures allows for much higher goodness-of-fit of international versions of the three-factor model, at levels similar to those of domestic versions and superior to those of non-homogeneous versions. This suggests that further accounting homogeneity could lead to more accurate pricing and valuation of international assets and to an improvement of the efficiency of international fund allocation.     

Bank earnings and regulatory capital management using available for sale securities

Based on a large sample of publicly listed and non-listed US commercial banks from 1996 to 2011, we find robust evidence consistent with banks using realized available for sale (AFS) securities gains and losses to smooth earnings and increase low regulatory capital. We also find that (i) banks with positive earnings smooth earnings, and banks with negative earnings generally take big baths; (ii) regulatory capital constrains big baths; (iii) banks with more negative earnings and more unrealized beginning-of-quarter losses (gains) take big baths (smooth earnings); and (iv) banks with low regulatory capital and more unrealized gains realize more gains. Also, banks with negative earnings take big baths (avoid or reduce the earnings loss) if their unrealized gains are insufficient (sufficient) to offset the negative earnings. Our inferences apply to listed and non-listed banks, which indicates that the earnings management incentives do not derive solely from public capital markets. Our findings reveal that the accounting for AFS securities gains and losses enables banks to manage regulatory capital and earnings in a variety of ways.