Corporate Financing Decisions and Non-Debt Tax Shields: Evidence from Italian Experiences in the 1990s

Between 1995 and 1999, Italy experienced three episodes of fiscal reform during which different categories of non-debt tax shields were introduced, including a classical investment tax credit, a system of dual income taxation, and an investment tax credit restricted to equity financed investments. Using the balance sheets of a large sample of Italian companies, we construct a data set which allows us to evaluate the impact of the different fiscal interventions. We apply MacKie-Mason's (1990) method to study incremental financing decisions using discrete choice analysis. The analysis shows that the measures introduced were successful in reducing the advantage of debt financing relative to equity financing. We relate the findings to the current literature on the determinants of capital structure. JEL Code: G32, H25     

Motives for sharing in social networks

What motivates people in rural villages to share? We first elicit a baseline level of sharing using a standard, anonymous dictator game. Then using variants of the dictator game that allow for either revealing the dictator's identity or allowing the dictator to choose the recipient, we attribute variation in sharing to three different motives. The first of these, directed altruism, is related to preferences, while the remaining two are incentive-related (sanctions and reciprocity). We observe high average levels of sharing in our baseline treatment, while variation across individuals depends importantly on the incentive-related motives. Finally, variation in measured reciprocity within the experiment predicts observed ‘real-world’ gift-giving, while other motives measured in the experiment do not predict behavior outside the experiment.     

Autocorrelation and masked heterogeneity in panel data models estimated by maximum likelihood

In a panel data model with random effects, when autocorrelation in the error is considered, (Gaussian) maximum likelihood estimation produces a dramatically large number of corner solutions: the variance of the random effect appears (incorrectly) to be zero, and a larger autocorrelation is (incorrectly) assigned to the idiosyncratic component. Thus heterogeneity could (incorrectly) be lost in applications to panel data with customarily available time dimension, even in a correctly specified model. The problem occurs in linear as well as nonlinear models. This article aims at pointing out how serious this problem can be (largely neglected by the panel data literature). A set of Monte Carlo experiments is conducted to highlight its relevance, and we explain this unpleasant effect showing that, along a direction, the expected log-likelihood is nearly flat.     

Ecosystem Service Value and Agricultural Conversion in the Amazon: Implications for Policy Intervention

We explore the welfare implications of agricultural expansion in the Brazilian Amazon by comparing spatially explicit estimates of soybean rents and the value of ecosystem services. Although these estimates are generated from different datasets, models, and estimation techniques, the values are comparable, such that the value of ecosystem services is greater than soybean rents for about 61% of the total area and 24% of the area where soybean rents are positive if protected areas are well enforced. Based on the balance between the benefits and costs of conversion, failure to value ecosystem services reduces total social welfare by 7.13 billion dollars annually relative to an optimum. Policy instruments that internalize the value of ecosystem services via protected lands, land conversion taxes, conservation subsidies, or excise taxes can avoid much of this loss. Regardless of intervention regime, policy makers should be cognizant of the diminishing net benefits of converting natural ecosystems to agriculture. Realizing the final 3.8% requires the conversion of an additional 15% natural ecosystems to soybean production.     

Diversity and Communication in Teams: Improving Problem-Solving or Creating Confusion?

Despite the rich and interdisciplinary debate on the role of diversity and communication in group problem-solving, as well as recognition of the interactions between the two topics, they have rarely been treated as a joint research issue. In this paper, we develop a computational approach aimed at modeling problem-solving agents and assess the influence of various levels of diversity and communication in teams on agents’ performance in problem-solving. By communication, we intend a conversation on the persuasiveness of the features characterizing problem-setting. By diversity, we mean differences in how agents build problem representations which allow them to access various solutions. We deploy the concept of diversity along two dimensions: knowledge amplitude, which accounts for the level of available knowledge allowing access to poorer or richer problem representations (compared with complete problem representations), and knowledge variety, which pertains to the differences in the constituents of agents’ representations. We define performance as the frequency with which diverse agents choose the same alternative representation of an agent displaying complete representations of the problem. Our results indicate that communication is more effective when agents elaborate from relatively richer problem representations, as this provides a basis for integrating the variously diverse beliefs of their teammates. Conversely, poorer diverse representations may lead to worse performance when knowledge variety also applies. Lastly, we show that the influence of communication is not monotonically positive, as increasing communication intensity performance may worsen at any level of knowledge availability and knowledge variety.     

Research and development target‐setting difficulties addressed through the emergent method: technology forecasting using data envelopment analysis

The aim of this study is to provide a foundation for researchers and managers to discuss and resolve difficulties associated with research and development (R&D) target‐setting. While multiple studies mention the difficulty of R&D target‐setting, few have compiled reasons for these difficulties or addressed this issue in detail. This paper provides one of the first studies outlining reasons for R&D target‐setting difficulties through a literature review. It also provides an initial set of analyses and results after applying an emerging quantitative method, Technology Forecasting Using Data Envelopment Analysis (TFDEA), to illustrate these difficulties step‐by‐step to commercial airplanes. Results include determining the state of art in commercial airplane technology and technological rate‐of‐change variants in setting R&D targets.     

Environmental evaluation of agri-environment schemes using participatory approaches: Experiences of testing the Agri-Environmental Footprint Index

The Agri-Environment Footprint Index (AFI) has been developed as a generic methodology to assess changes in the overall environmental impacts from agriculture at the farm level and to assist in the evaluation of European agri-environmental schemes (AES). The methodology is based on multi-criteria analysis (MCA) and involves stakeholder participation to provide a locally customised evaluation based on weighted environmental indicators. The methodology was subjected to a feasibility assessment in a series of case studies across the EU. The AFI approach was able to measure significant differences in environmental status between farms that participated in an AES and non-participants. Wider environmental concerns, beyond the scheme objectives, were also considered in some case studies and the benefits for identification of unintentional (and often beneficial) impacts of AESs are presented. The participatory approach to AES evaluation proved efficient in different environments and administrative contexts. The approach proved to be appropriate for environmental evaluation of complex agri-environment systems and can complement any evaluation conducted under the Common Monitoring and Evaluation Framework. The applicability of the AFI in routine monitoring of AES impacts and in providing feedback to improve policy design is discussed.