Index Models and Land Allocation Reconsidered

In contrast with previous index model land applications, this article shows that the land allocation problem is a portfolio model with two constraints, namely, investable funds and land. The two-constraint model implies a drastic reinterpretation of what previous studies have quantified as diversifiable and systematic risks in agricultural production. The article also argues that the constant marginal-rate-of-product-substitution (MRPS) technology implicit in financial portfolio models is unlikely to hold in a production context such as the land allocation problem, and that the index model must be modified accordingly. Farm-level data are used to illustrate and test the hypotheses advanced. Empirical results indicate that most of the risk for corn and soybeans is diversifiable, and that corn and soybeans are characterized by decreasing MRPS. The MRPS effect is found to be large from an economic standpoint and implies that crop diversification may be optimal, even for risk-neutral farmers.     

Long-period Positions and Economic Change. A rejoinder

Roncaglia and Nell have criticised the interpretation of Sraffa's normal prices as centres of gravitation for actual prices. Roncaglia mainly focuses on the interpretation of the notion of gravitation in Smith and on the meaning of the ‘given outputs’ in Sraffa. Nell attempts to confine the relevance of Sraffa's theory to specific historical circumstances and to find a role for Sraffa's prices in his version of post-Keynesian price theory. This rejoinder criticises the case made by both Roncaglia and Nell, and defends Sraffa's centre of gravitation view.     

Local banking and local economic growth in Italy: some panel evidence

This article provides new evidence on the contribution of local banking to local economic growth (i.e. at county level – the Italian ‘province’) in Italy. A comprehensive data set is used, which includes control variables for social capital and human capital as well as indicators of the quality of local infrastructures and the production structure of the local economy. A linear within-estimator technique with fixed effects is applied to a modified version of the so-called Barro regression in order to address the well-known econometric issues of reverse causality and estimation bias resulting from unobserved district-specific influences.     

B Corp Certification and Its Impact on Organizations Over Time

Journal of Business Ethics - This study explores the impact of B Corp certification and its associated impact assessment on four case studies of small and medium-sized Brazilian companies certified...     

Customer satisfaction and service domains: a further development of PROSERV

Quality & Quantity - This paper aims to represent that levels of customer satisfaction of tourist accommodation are an important prerequisite when the goal is building a loyal relationship with...     

Modeling node heterogeneity in latent space models for multidimensional networks

Multidimensional network data can have different levels of complexity, as nodes may be characterized by heterogeneous individual-specific features, which may vary across the networks. This article introduces a class of models for multidimensional network data, where different levels of heterogeneity within and between networks can be considered. The proposed framework is developed in the family of latent space models, and it aims to distinguish symmetric relations between the nodes and node-specific features. Model parameters are estimated via a Markov Chain Monte Carlo algorithm. Simulated data and an application to a real example, on fruits import/export data, are used to illustrate and comment on the performance of the proposed models.