In this paper, we introduce a robust estimator of the tail index of a Pareto-type distribution. The estimator is obtained through the use of the minimum density power divergence with an exponential regression model for log-spacings of top order statistics. The proposed estimator is compared to an existing estimator for Pareto-type tail index based on fitting an extended Pareto distribution with the minimum density power divergence. A simulation study is conducted to assess the performance of the estimators under different contaminated samples from different distributions. The results show that the proposed estimator has better mean square errors and less sensitivity to an increase in the number of top order statistics. In addition, the estimation of the exponential regression model yields estimates of second-order parameters that can be used for estimation of extreme events such as quantiles and exceedance probabilities. The estimators are illustrated with a practical dataset on insurance claims.