AN ANALYSIS OF SMALL BUSINESS LOAN GUARANTEE FUNDS

Small businesses are considered important engines for job growth and economic development by policy makers worldwide. One of the most commonly cited constraints of small businesses is a lack of access to capital. To address this constraint, small business loan guarantee programs have been established in over 100 countries. There are a variety of types of guarantee funds, with the most significant differences being which borrowers are eligible for guarantees, and how borrowers are approved for guarantees. There is currently no clear delineation between types of programs and the economic conditions they operate in, though some trends are becoming apparent. However, these trends may not be leading to the best economic outcomes possible. By better matching the structure of the guarantee fund to the economic conditions it operates in, the program’s success in meeting economic development goals may be greatly improved. Many programs in developing countries may not be taking advantage of bank expertise and may be limiting the scope of their effectiveness. At the same time, programs in developed countries may be wasting resources by scattering their efforts too thinly and subsidizing less competitive firms to the detriment of local economic development.

‘Gene shaving’ as a method for identifying distinct sets of genes with similar expression patterns

Background: Large gene expression studies, such as those conducted using DNA arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called ‘gene shaving’. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be ‘unsupervised’, that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful groupings. Results: We illustrate the use of the gene shaving method to analyze gene expression measurements made on samples from patients with diffuse large B-cell lymphoma. The method identifies a small cluster of genes whose expression is highly predictive of survival. Conclusions: The gene shaving method is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worth further investigation.