In this paper, we propose a reduced-bias estimator of the EVI for Pareto-type tails (heavy-tailed) distributions. This is derived using the weighted least squares method. It is shown that the estimator is asymptotically unbiased, asymptotically consistent and asymptotically normal under the second-order conditions on the underlying distribution of the data. The finite sample properties of the proposed estimator are studied through a simulation study. The results show that it is competitive to the existing estimators of the extreme value index in terms of bias and Mean Square Error. In addition, it yields estimates of g > 0  that are less sensitive to the number of top-order statistics, and hence, it alleviate the problem of selecting an optimal tail fraction to some extent. The proposed estimator is further illustrated using practical datasets from pedochemical and insurance.