Wildlife damage, insurance/compensation for farmers and conservation: Sri Lankan elephants as a case

The interference with agriculture has been recognised as the main cause for the current conflict between farmers and wild elephants in Sri Lanka, as elsewhere in the Asian elephant range. Thus compensating farmers for the damages caused by elephants is essential, if this endangered species is to survive in the long run. This paper explores the practicality of establishing an improved publicly funded insurance/compensation scheme to recompense farmers for the elephant damages. It does so by analysing results from two contingent valuation surveys undertaken in Sri Lanka. We find that possible public support of farmers plus urban dwellers significantly exceeds the financial requirement of the insurance scheme proposed in this study for perpetuity. The article also shows that it is often inappropriate from an economic viewpoint to analyse crop insurance as if it only involves the insurance of a private good because important positive externalities can arise from ‘crop’ damages by wildlife, e.g. elephants. The use of agricultural land by some species is essential for their long-term survival and this is often positively valued by the community as a whole.

The effects of normalisation on the ability of business end-users to detect data anomalies: An experimental evaluation

With the proliferation of relational database programs for PC's and other platforms, many business end-users are creating, maintaining, and querying their own databases. More importantly, business end-users use the output of these queries as the basis for operational, tactical, and strategic decisions. Inaccurate data reduce the expected quality of these decisions. Implementing various input validation controls, including higher levels of normalisation, can reduce the number of data anomalies entering the databases. Even in well-maintained databases, however, data anomalies will still accumulate. To improve the quality of data, databases can be queried periodically to locate and correct anomalies. This paper reports the results of two experiments that investigated the effects of different data structures on business end-users' abilities to detect data anomalies in a relational database. The results demonstrate that both unnormalised and higher levels of normalisation lower the effectiveness and efficiency of queries relative to the first normal form. First normal form databases appear to provide the most effective and efficient data structure for business end-users formulating queries to detect data anomalies.