|Year : 2015 | Volume
| Issue : 2 | Page : 51-56
Evaluation of productivity in Iranian pharmaceutical companies: A DEA-based Malmquist approach and panel data analysis
Mehdi Varmaghani1, Amir Hashemi Meshkini1, Farshad Farzadfar2, Mehdi Yousefi3, Saeed Yaghoubifard4, Vida Varahrami5, Ehsan Rezaei Darzi2, Majid Anabi4, Abbas Kebriaeezadeh4, Hedieh-Sadat Zekri6
1 Department of Pharmacoeconomics and Pharmaceutical Administration; Non communicable Disease Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
2 Non-communicable Disease Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
3 Department of Health and Management, Mashhad University of Medical Sciences, Mashhad, Iran
4 Department of Pharmacoeconomics and Pharmaceutical Administration, Tehran University of Medical Sciences, Tehran, Iran
5 Department of Economics, Shahid Beheshti University, Tehran, Iran
6 Faculty of Economics, Allameh-Tabatabaiee University of Human Sciences, Tehran, Iran
|Date of Web Publication||24-Apr-2015|
Department of Pharmacoeconomics and Pharmaceutical Administration, Tehran University of Medical Sciences, Tehran
Source of Support: None, Conflict of Interest: None
Objective: In this study, we aimed to assess comparative productivity of 21 pharmaceutical companies in Iran during 2000-2013.
Methods: To evaluate the productivity trend of pharmaceutical companies in Iran, we used data envelopment analysis-based Malmquist index. "Total assets" and "capital stock" as inputs and "net sales" and "net profit" as outputs extracted from Tehran stock exchange, were selected to be included in the analysis. This method provides the possibility for analyzing the performance of each company in term of productivity changes over time. We also used an estimation generalized least square panel data model to identify the factors that might affect productivity of pharmaceutical companies in Iran using EViews 7 and Deep 2.1 software.
Findings: The mean total productivity during all years of the study was 0.9829, which indicates the improvement in their overall productivity. The results, over the 13-year period, indicated that the range of productivity changes in pharmaceutical companies, that were included in this study, was between 0.884 and 1.098. Panel data model indicated that age of company could positively (t = 4.765978, P < 0.001) and being located in cities other than Tehran (the capital) could negatively (t = −5.369549, P < 0.001) affect the productivity of pharmaceutical companies. The analysis showed the new policy (brand-generic scheme) and also the type of ownership did not have a significant effect on the productivity of pharmaceutical companies.
Conclusion: In this study, pharmaceutical productivity trends were fluctuated that could be due to the sub-optimal attention of policy makers and managers of pharmaceutical companies toward long-term strategic planning, focusing on productivity improvement.
Keywords: Data envelopment analysis; Iran; panel data model; pharmaceutical; productivity
|How to cite this article:|
Varmaghani M, Meshkini AH, Farzadfar F, Yousefi M, Yaghoubifard S, Varahrami V, Darzi ER, Anabi M, Kebriaeezadeh A, Zekri HS. Evaluation of productivity in Iranian pharmaceutical companies: A DEA-based Malmquist approach and panel data analysis. J Res Pharm Pract 2015;4:51-6
|How to cite this URL:|
Varmaghani M, Meshkini AH, Farzadfar F, Yousefi M, Yaghoubifard S, Varahrami V, Darzi ER, Anabi M, Kebriaeezadeh A, Zekri HS. Evaluation of productivity in Iranian pharmaceutical companies: A DEA-based Malmquist approach and panel data analysis. J Res Pharm Pract [serial online] 2015 [cited 2021 Apr 22];4:51-6. Available from: https://www.jrpp.net/text.asp?2015/4/2/51/155750
| Introduction|| |
To ensure population's access to medicines, one of the important goals of Iranian Ministry of Health (MOH) has been to produce pharmaceuticals and vaccines locally.  To support domestic manufacturers, government has set high-level tariffs and also limitations on medicines import. This has raised some concerns about the abilities of domestic manufacturers, facing real competitive situation with the presence of international companies after joining the World Trade Organization.  The cost-plus pricing model by MOH, in which the price of medicines is calculated by adding a predefined margin for manufacturers, distributors, and pharmacies to total cost of production, has also led companies to invest more on marketing and sale activities rather than research and development;  it could probably undermine their productivity. Evaluation of efficiency and productivity of pharmaceutical companies could provide valuable information for policy-makers about their former policies and guide them for further decisions in term of short and long-term effects on the domestic production body. The asset productivity of pharmaceutical companies in Iran has not been evaluated before, however, some studies have been published on different productivity-related topics in Iran: One study on the impact of intellectual capital efficiency on market value,  one study about the efficiency of regulatory departments in pharmaceutical companies,  an efficiency trend analysis of pharmaceutical companies before and after implementation of brand-generic scheme;  one study on labor productivity in pharmaceutical industry,  and a study on productivity indices in pharmaceutical industry  that none of these studies are direct evaluation of productivity in pharmaceutical manufacturers. Also in none of these published studies, the influencing factors on productivity of pharmaceutical companies have been evaluated.
In this study we aimed (1) to assess comparative productivity of Iranian pharmaceutical companies during a 13-year period of time and (2) to identify probable factors affecting pharmaceutical companies' productivity.
| Methods|| |
To evaluate the productivity trend in Iranian pharmaceutical companies, we used data envelopment analysis (DEA)-based Malmquist index. This method makes it possible to analyze the performance of each company in term of productivity changes over time. DEA is a nonparametric method based on linear programming which was developed in order to calculate the relative technical efficiency among organizations or firms that are called decision-making units (DMUs).  In DEA, for multiple input and output data, an efficiency frontier would be constructed, and then the efficiency of investigated DMUs would be calculated based on their position relative to this frontier, respectively.  This relative efficiency would be calculated in three main categories: Relative technical efficiency, relative pure (managerial efficiency), and relative scale efficiency. Malmquist productivity index (MPI) is a common index to compare total productivity changes over time. For the 1 st time, Caves et al.  used MPI for productivity measurement. Total productivity change could be decomposed to three main components.
We can calculate MPI through the following equation:
MPI = Technical efficiency change × technological changes.
Technical efficiency change could be decomposed to include two separate components: Pure efficiency change and scale efficiency change; therefore we can write:
M = (pure efficiency change × scale efficiency change) × technological change. ,,
This decomposition could help a company to realize the reason of productivity change in a particular time period.
According to input-oriented DEA approach, the MPI >1 indicates performance improvement of a company via minimization of inputs, and the index upper than 1 shows the worsening performance of a company. 
Input and output
To select variables as inputs and outputs for measuring DEA-based MPI change, an expert panel was held with the presence of experts in the pharmaceutical industry including three financial managers and three manager directors. They evaluated all variables which were accessible from financial statements of Iranian pharmaceutical companies. Finally, "total assets" and "capital stock" as inputs and "net sales" and "net profit" as outputs were selected to be included in the analysis. The companies' data were extracted from the "balance sheet," "profit and loss statement," and other financial statements available in Tehran stock exchange (TSE) database  for the fiscal years March 19, 2000-March 19, 2013.
There are 89 pharmaceutical production companies in Iran.  Because of the homogeneity assumption of DMUs in DEA, only the finished product manufacturers were included in the analysis. To assure the access to reliable data, another inclusion criterion for selecting companies for the study was being listed in TSE. Considering the time period of the study, (2000-2013) the companies whose data were available for this period of time and also matched our stated inclusion criteria were included in analysis (totally 21 companies).
Using Deep 2.1, a computer program which is designed to conduct DEA, we analyzed total factor productivity change and it's components including technological change, technical efficiency change, and scale efficiency change for each company during 2000-2013.
Identifying factors affecting productivity
We also used an estimation generalized least square (EGLS) panel data model to identify the factors that might affect productivity of pharmaceutical companies in Iran. The independent variables used in the model included "age of company," "location" (whether it is located in capital or not), "generic-brand policy" (the effect of this policy on productivity), and "type of ownership" (public or private).
| Results|| |
With reference to the above-mentioned points, as shown in [Table 1], the results of our study over the 13-year period indicated that the range of productivity changes in pharmaceutical companies that were included in this study was between 0.884 and 1.098. The best and the worst companies in term of performance were B and J, respectively. The results also shown that seven companies had improved in term of pure efficiency (management efficiency) and nine companies had improved in term of technical efficiency over the years of study. Among these pharmaceutical companies, seven companies did not improve with regard to total factor productivity [Table 1].
|Table 1: Average productivity index of Iranian pharmaceutical companies during 2000-2013|
Click here to view
According to our analysis [Table 2], the mean total productivity during all the years of study was 0.9829; this showed that overall, there was an improvement in productivity in the years of study. There was a fluctuation in the trend of productivity over all the studied years [Figure 1] so that although productivity worsened in 2000, 2003, 2005-2007, 2009, and 2012, as observed, the productivity had improved in other years that might indicate the inconsistency of companies' policies regarding productivity.
|Figure 1: Trend of mean Malmquist index in studied companies during 2000-2013|
Click here to view
|Table 2: The mean of productivity changes in pharmaceutical companies calculated via MPI during 2000-2013|
Click here to view
The second section of our study [see [Table 3]] showed that productivity of pharmaceutical companies in Iran might be correlated with the age and location. The EGLS panel data model indicated that the age of company could positively (t = 4.765978, P < 0.001) and being located in the cities other than Tehran (the capital) could negatively (t = −5.369549, P < 0.001) affect the productivity of pharmaceutical companies. [Figure 2], which is about the trend of mean productivity index, shows that the Malmquist index is higher in companies located in Tehran, the capital of Iran. This analysis indicated no significant relationship between the implementation of the new policy (brand-generic scheme) and the type of ownership with the productivity of pharmaceutical companies.
|Figure 2: Trend of productivity during 2000-2013 considering the location and revolution|
Click here to view
|Table 3: Identification of variables affecting productivity of investigated pharmaceutical companies|
Click here to view
Only less than 50% of companies experienced improvement in managerial and technical efficiency. Moreover, of all investigated variables, age of company, being located in the capital, and being established before 1979 revolution showed significant relationships with productivity index of Iranian pharmaceutical companies. We also used this panel data model separately for the data collected before and after 2004, when the brand-generic scheme policy was introduced. This sub-analysis also did not change the former results.
| Discussion|| |
Regarding the findings, 15 out of 21 investigated companies had averagely improved in term of productivity in the years of study. It means that these companies had been able to improve their performance averagely via minimizing inputs during the period of study. This study also showed that there was no correlation between productivity and the net profit of companies: Two most profitable companies were at the end of productivity score rank.
In this study, technical efficiency change and pure efficiency change negatively affected the productivity; however, the technological change and scale efficiency change had a positive effect. The result of this study also indicated that during the period of investigation, total productivity of pharmaceutical companies had a fluctuating pattern. This could be due to frequent policy changes and instability in industrial and macroeconomic environment. As another reason, companies were not following a well-designed and long-term strategy focusing on productivity improvement.
Econometric models including panel data model were used to evaluate and report the effects of different variables on efficiency or productivity score.  The results of our panel data model present multiple likely reasons for higher level of productivity in pharmaceutical companies located in Tehran (the capital city) compared with those in other cities. These factors can lead to either lower production cost or more commercial opportunities. As a reason, higher level of technology and research funds may be obtainable in capital. It is also worth-mentioning that companies in Tehran may access distribution networks better. It is mainly because of concentration of distribution companies in Tehran as the capital of the country, in which a significant proportion of private and public pharmacies, hospitals, and other dispensers of pharmaceuticals exist. Another probable reason could be related to the access to experts and human sources with a higher level of education. Because of higher standards of living in the capital, these experts prefer to live in Tehran and companies in other cities are faced with human resource problems. The companies in capital have the potential to lobby to influence the MOH and Food and Drug Organization. ,,
Because almost all companies established before 1979 revolution were international companies with higher technology status compared with companies established after the revolution, the observed correlation between the establishment date of companies and productivity might be justified.
This study is the first assessment of productivity in Iranian pharmaceutical industry in which Malmquist index and panel data model were applied, and its results could be used by industrial policy-makers and managers of the pharmaceutical industry. This method had been previously used to analyze the productivity of pharmaceutical companies in India  and Spain  In Iran, a similar analysis has been conducted in other industries and fields such as cement,  health care sector, and hospitals. 
In this study, technical efficiency change and pure efficiency change negatively affected the productivity; however, the technological change and scale efficiency change had a positive effect. This analysis indicated that the managerial index in the proper utilization of resources in pharmaceutical companies seems to be undesirable. To overcome this issue, some measures are suggested, such as: Continuous assessment of performance by managers and board of directors, using internal capacities for reconstruction of processes, more investment on facilities and equipment, using economies of scale, and designing incentives aligned with productivity improvement objectives.
Considering the results of our study, productivity trend in the pharmaceutical companies in this study showed a fluctuating pattern that could be probably due to the sub-optimal attention of policy-makers and managers of pharmaceutical companies toward long-term strategic planning and focusing on productivity improvement. It seems to be effective and useful to apply strategies and techniques for productivity improvement in pharmaceutical companies including appropriate management, using internal capacities for reconstruction of processes, more investment on facilities and equipment, using economies of scale, and providing an ideal workplace for productivity improvement.
This study had some limitations such as its limited number of investigated pharmaceutical companies. There are 89 pharmaceutical manufacturers in Iran  from which we only studied 21 companies because of limitations in accessing reliable financial data for them. Hence, there might be a challenge in generalizing the results to all other companies working in the pharmaceutical industry in Iran. In this method, the results are strongly dependent on the selection of inputs and outputs for measuring productivity. It is suggested to conduct further similar studies using other inputs and outputs for the pharmaceutical companies studied in this paper, and to compare the results with each other. The other probable limitation may be about the quality of input data. In this analysis, we only included the companies listed in TSE, because their data were likely more reliable; however, it is likely that for-profit companies report false financial data.
In this analysis, we studied only a 13-year time period because of lack of available data for included companies, however analyzing longer periods could provide more reliable results.
| Authors Contribution|| |
MV: Study design and manuscript writing and data collection, AHM: Study design and manuscript writing, FF: Supervision on all study process, checking the manuscript, MY: Study design analysis, SY: Data collection, MA: Study design analysis, VV: Study design analysis, ERD: Study design analysis, AK: Study idea and supervision on all study process.
| References|| |
Cheraghali AM. Iran pharmaceutical market. Iran J Pharm Res 2006;1:1-7.
Hashemi Meshkini A, Kebriaeezadeh A, Dinarvand R, Nikfar S, Habibzadeh M, Vazirian I. Assessment of the vaccine industry in Iran in context of accession to WTO: A survey study. Daru 2012;20:19.
Hashemi-Meshkini A, Keshavarz K, Nikfar S, Vazirian I, Kebriaeezadeh A. Pharmacists remuneration models in Iran and selected countries: A comparative study. Iran J Pharm Res 2013;12:995-64.
Mehralian G, Rasekh HR, Akhavan P, Sadeh MR. The impact of intellectual capital efficiency on market value: An Empirical Study from Iranian Pharmaceutical Companies. Iran J Pharm Res 2012;11:195-207.
Mostafavi SH. Evaluation of Pharmaceutical Regulatory Review Process in Iran and Its Impact on Patients Access to Medicines. PhD thesis, Cardiff University, UK.
Hashemi-Meshkini A, Varmaghani M, Yousefi M, Yaghoubifard S, Zekri HS, Nikfar S, et al.
From generic scheme to brand-generic scheme: Have new policy influenced the efficiency of Iranian pharmaceutical companies? J Res Pharm Pract 2014;3:88-93.
Keighobadi MH, Saeedi P. Determining the degree of labor productivity and comparing it in two industries of food and pharmacy based on the companies accepted in Tehran stock exchange. Adv Res Econ Manage Sci 2014;18:95-107.
Annabi M, Kebriaeezadeh A, Shoshtari SN, Ghodsi SH. Priority setting for productivity indices in Iranian Pharmaceutical Companies Introduction. J Pharmacoecon Pharm Manage 2014;1:27-31.
Charnes A, Cooper WW, Rhodes EL. Measuring the efficiency of decision making units. Eur J Oper Res 1978;2:429-44.
Fare R, Grosskopf S, Lovell CA. Production Frontiers. United Kingdom: Cambridge University Press; 1985.
Caves DW, Christensen LR, Diewert WE. The economic theory of index numbers and the measurement of input, output and productivity. Econometrica 1982;50:1393-414.
Nishimizu M, Page JM. Total factor productivity growth. Technological progress and technical efficiency change: Dimensions of productivity change in Yugoslavia, 1965-78. Econ J 1982;92:920-36.
Fare R, Grosskopf S, Norris M, Zhang Z. Productivity growth, technical progress and efficiency change in industrialized countries. Am Econ Rev 1994;84:66-83.
Jacobs R, Smith PC, Street A. Measuring Efficiency in Health Care: Analytic Techniques and Health Policy. 1 nd
ed. USA: Cambridge University Press; 2006. p. 124-38.
Emamimeibodi A. Principles of efficiency and productivity measurement. Tehran: Institute of Trade Studies and Research; 2004. p. 48-51.
Kebriaeezadeh A, Koopaei NN, Abdollahiasl A, Nikfar S, Mohamadi N. Trend analysis of the pharmaceutical market in Iran; 1997-2010; policy implications for developing countries. Daru 2013;21:52.
Biørn E, Hagen TP, Iversen T, Magnussen J. The Effect of Activity-based Financing on Hospital Efficiency: A Panel Data Analysis of DEA Efficiency Scores 1992-2000. Health Economics Research Programe at the University of Oslo HERO; 2002.
Ahmad N, Awan MU, Raouf A. Development of a service quality scale for pharmaceutical supply chains. Int J Pharm Healthc Mark 2009;1:26-45.
Mehralian GH, Gatarib A, Morakabatic M, Vatanpour H. Developing a suitable model for supplier selection based on supply chain risks: An Empirical Study from Iranian Pharmaceutical Companies, Services. Iran J Pharm Res 2012;11:209-19.
Lee H. CMS oversight. J Manag Care Pharm 2008;14:22-4.
Mazumdar M, Rajeev M. Comparing the efficiency and productivity of the Indian pharmaceutical Filrms: A Malmquist - Meta-Frontier Approch. Int J Bus Econ 2009;8:159-81.
Gonzalez E, Gascon F. Sources of productivity growth in the Spanish pharmaceutical industry (1994-2000). Res Policy 2004;33:35-745.
Mohammadi A, Ranaei H. The Application of DEA based Malmquist productivity index in organizational performance analysis. Int Res J Finance Econ 2011;62:68-76.
Safarnia H, Zeynali S, Bastani R. Measuring productivity of hospitals Affiliated to Iran′s Social Security Organization using Malmquist Index during 2006-2009. Hakim Res J 2013;16:65-71.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3]
|This article has been cited by|
||Size and productivity: a conditional approach for Italian pharmaceutical sector
| ||Pierluigi Toma |
| ||Journal of Productivity Analysis. 2020; |
|[Pubmed] | [DOI]|
||Analysis on the Spatiotemporal Characteristics of the Postearthquake Reconstruction Efficiency of the Extremely Earthquake-Stricken Areas by the Wenchuan Earthquake Based on the DEA-Malmquist Index
| ||Pin Lv,Bin Liu,Mingkang Yuan,Suyue Han,Di Zhang,Lv Zhang,Ricardo López-Ruiz |
| ||Discrete Dynamics in Nature and Society. 2020; 2020: 1 |
|[Pubmed] | [DOI]|
||Validation of Molecular Typing for Endometrial Screening Test That Predicts Benign and Malignant Lesions
| ||Xiaoqian Tuo,Lanbo Zhao,Qi Wang,Lu Han,Yiran Wang,Sijia Ma,Xue Feng,Qing Li,Chao Sun,Qing Wang,Guizhi Shi,Huilian Hou,Guanjun Zhang,Qiling Li |
| ||Frontiers in Oncology. 2019; 9 |
|[Pubmed] | [DOI]|