The article shows that when estimating productivity using linear programming, the results can change based on how detailed the analysis is and whether certain constraints are applied. By looking at U.S. state data from 1960-2004, the researchers found that as the analysis becomes more detailed, productivity changes appear to decrease. Additionally, the values assigned to inputs and outputs can affect the productivity measures, leading to differences in results.