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Empirical studies in the area of sovereign debt have used statistical models singularly to predict the probability of debt rescheduling. Unfortunately, researchers have made few efforts to test the reliability of these model predictions or to identify a superior prediction model among competing models. This paper tested neural network, OLS, and logit models' predictive abilities regarding debt rescheduling of less developed countries (LDC). All models predicted well out-of-sample. The results demonstrated a consistent performance of all models, indicating that researchers and practitioners can rely on neural networks or on the traditional statistical models to give useful predictions.

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Farmers have benefited from unique tax treatment since the beginning of the income tax law. This paper explores agricultural influences on the passage of the income tax in 1913, using both qualitative and quantitative analysis. The results show that agricultural interests were influential in the development and passage of tax/tariff laws. The percentage of congressmen with agricultural ties explains the strong affection for agriculture. Discussion in congressional debates and in agricultural journals was passionate and patriotic in support of equity for farmers. The quantitative analysis reveals that the percentage farm population was a significant predictor of passage of the 16th Amendment by the states and of adoption of state income taxes in the 20th century.

No technique works equally well for every company. That's especially true for activity‐based costing and management (ABC/M). But which companies and industries have had the greatest success—and why? © 2001 John Wiley & Sons, Inc.