Updating and Estimating a Social Accounting Matrix Using Cross Entropy Methods

The problem in estimating a social accounting matrix (SAM) for a recent year is to find an efficient and cost-effective way to incorporate and reconcile information from a variety of sources, including data from prior years. Based on information theory, the paper presents a flexible 'cross entropy' (CE) approach to estimating a consistent SAM starting from inconsistent data estimated with error, a common experience in many countries. The method represents an efficient information processing rule-using only and all information available. It allows incorporating errors in variables, inequality constraints, and prior knowledge about any part of the SAM. An example is presented, applying the CE approach to data from Mozambique, using a Monte Carlo approach to compare the CE approach to the standard RAS method and to evaluate the gains in precision from utilizing additional information.     

Board diversity and firm performance: evidence from the U.K. SMEs

This study examines the relationship between board diversity and firm performance in small- and medium-sized enterprises (SMEs) in the U.K. In particular, we investigate the role of gender and age as two dimensions of diversity. Using a large sample of SMEs (34,798 firms) located in the U.K. and focusing on the period from 2005 to 2013, our results show a significant negative association between each of gender diversity and age diversity, and firm performance. Our evidence yields important insights on the association between board diversity and firm performance, and calls suggestions for increased board diversity into question. A possible explanation for our findings could be due to the fact that our sampled firms are SMEs, whereas those in previous studies have been large enterprises. This research provides insights to entrepreneurs on how to enhance their performance, and to governments and policymakers on the development of rules that would achieve better performance in the SME sector.