Statistical Inference for the Aggregate Sources of Productivity Change Measured by Malmquist Productivity Indices
(2026) , 31 pages
Files
ISBA_DP_2026-24.pdf
Open Access - Adobe PDF
- 786.04 KB
Details
- Authors
- Abstract
- The Malmquist productivity index (MPI) is one of the most widely used tools for measuring productivity change over time. A recent contribution by Pham et al. (2024) develops statistical inference procedures for the aggregate (or weighted) MPI of a group of firms. Building on this framework, the present paper develops statistical inference for the aggregate sources of productivity change measured by MPI. In particular, we derive the central limit theorems of the aggregated components, thereby enabling hypothesis testing for changes in technology, efficiency, and related sources. We illustrate the developed theoretical results by analyzing the sources of productivity change for 84 countries. Moreover, analogous theoretical results can be established for alternative productivity measures and their decompositions, including the Hicks–Moorsteen productivity index, the Luenberger productivity index, the Malmquist–Luenberger productivity index, etc.
- Affiliations
Citations
Simar, L., Zelenyuk, V., & Zhao, S. (2026). Statistical Inference for the Aggregate Sources of Productivity Change Measured by Malmquist Productivity Indices (LIDAM Discussion Paper ISBA 2026/24). https://hdl.handle.net/2078.5/277253