Replication package for: "Using global and local measures to decompose the Malmquist productivity index assuming a variable returns-to-scale technology"

Description

This replication package contains the files required for replicating the results presented in the paper “Using global and local measures to decompose the Malmquist productivity index assuming a variable returns-to-scale technology” co-authored by Juan Aparicio and Daniel Santín and published in Economic Modelling 2026. This paper proposes a novel five-way decomposition of the MPI assuming a VRS technology based on the Lebesgue measure and on the use of artificial units randomly generated within the normalized input–output space of a unit hypercube, ensuring robust calculations while avoiding infeasible solutions. The five compenents are: pure efficiency change (PEC), pure global technical change (PGTC), global scale efficiency change (GSEC), pure local technical change (PLTC) and local scale efficiency change (LSEC). The package contains: - The well-known database of 42 Swedish pharmacies producing four outputs from four inputs between 1980 and 1989 in Sweden previously analyzed in the seminal paper of Färe et al. (1992). The excel file is composed by ten sheets one for each year. - R code for reproducing results in Section 3.3. This includes Table 1, Table 2 and Fig. 1. "Annex A example biennial". - R code for obtaining results in Section 4. This includes Tables 3, 4 and 5. "Annex B MPI VRS Lebesgue" References Färe, R., Grosskopf, S., Lindgren, B., & Roos, P. (1992). Productivity changes in Swedish pharmacies 1980-1989: A non-parametric Malmquist approach. Journal of Productivity Analysis, 3(1), 85-101.

Steps to reproduce

Aparicio, J. and Santín, D. "Using global and local measures to decompose the Malmquist productivity index assuming a variable returns-to-scale technology". Accepted for publication by March 2026 in Economic Modelling. The general guidelines for using the R scripts are: 1) Open any of the two R scripts in RStudio. 2) The excel file pharmacy.xlsx is composed by ten spreadsheets, one for each year. 3) Replace in all the instructions "read_excel" your own paths to open the pharmacy.xlsx database from where you have saved it on your computer. For example, change the original path d80 <- read_excel("C:/Daniel/EM/pharmacy.xlsx", sheet = "R80") by d80 <- read_excel("C:/Manuela/Myproject/ReplicationEM/pharmacy.xlsx", sheet = "R80") 4) Run the R code

Institutions

• Universidad Complutense de Madrid

  Madrid, Madrid

Categories

Licence