The role of management is important in working capital management and in productivity growth. Specifically the role of management in total productivity growth is vital in terms of managerial efficiency and technological adoption as these two are the sources of productivity growth. Therefore, with working capital management, the analysis of total factor productivity growth is also performed in the present study.
principle in production by a firm. It is further assumed that firms take those decisions and actions which increase profit. For a manufacturing firm profit can be increased either by increasing the total revenue or by decreasing the total expenses. The present study focuses on output oriented model of productivity growth where the emphasis is on increasing the profits by increasing the productivity of the firm resulting in greater out put.
Total factor productivity growth is estimated using Data Envelopment Analysis approach because it allows construction of best frontier based on the data from different Decision Making Units (DMUs). Each DMU is compared to the best frontier and as a DMU gets closer to the frontier, it indicates that particular DMU is successful in ‘catching up’ which is because of better use of technology and equipment. In our analysis each sector is a Decision Making Unit (DMU).
The methodology used for estimation of total factor productivity growth and its components starts with the explanation of theoretical foundation followed by the measurement and explanation of input and output variables. Finally the model specification is explained in detail.
4.2.1: Theoretical Foundation/Justification
One of the major areas of research in economics has been to identify factors of output growth. There is ample literature on the subject matter. These factors differ from country to country. If these factors can be identified, it would be helpful to accelerate growth by focusing on the major leading sources of growth. In this regard, ever since growth accounting was discovered by Abramovitz (1956) and Solow (1956), most studies concentrated on productivity growth sources at national level. Solow (1956) initiated a new debate by identifying that economic growth involves technical change.
The same became known as total factor productivity growth (TFPG), in economic literature.
TFP remains important because it not only measures economic growth and cross-country growth differences, but also economic fluctuations and business cycle frequencies (Comin and Mark, 2006). Higher TFP indicates better level of
technology, higher per worker capital, and larger returns. It enhances an economy’s ability to produce more output from a given stock of inputs. Productivity growth plays a very important role in examining the economic growth sources and growth of factor efficiency and technological change. On one side, Improvement in productivity arises from technical progress alone only if a producer operates on its production frontier.
On the other side when operating below the production frontier, the change in the TFP growth could be attributable not only to technical change (or shifts in the production frontier), but also to changes in technical efficiency, or the catching up effects (Nishimizu and Page, 1982; Bauer, 1990 and Perelman, 1995). Technical efficiency can be further decomposed into scale efficiency and non-scale efficiency, often termed as pure technical efficiency.
In the pioneering study which decomposes productivity growth, Nishimizu and Page (1982) used the linear programming technique of Aigner and Chu (1968) and Timmer (1970, 1971), and applied it to the social sector panel data of Yugoslavia in order to construct parametric production frontiers. Nishimizu and Page measured the technical change as the movement of the best practice or frontier production function over time and recognized the rest of the productivity change as efficiency change or as ‘catch-up’ component. Nishimizu and Page (1982) showed that technical change and technical efficiency change together obtain the overall measure of TFP change. In the other study, Färe et al (1994) analyzed how one could apply DEA-like linear programs to construct non-parametric production frontiers and then compute Malmquist indices of TFP growth.
Productivity efficiency can be estimated by non parametric or parametric approach. In non parametric approach, Charnes et al (1979) suggested a mathematical programming approach called as Data Envelopment Analysis while most common parametric approach employed for estimation of productivity efficiency is the translog cost function approach proposed by Christensen et al (1973) and Brown et al (1979).
Both parametric (Nishimizu & Page, 1982) and non-parametric (Fare et al, 1994) methods have been widely used in literature. In this study, productivity growth in Pakistani manufacturing sector and sub-sectors has been estimated using Malmquist DEA methods proposed by Fare et al. (1994). The productivity and efficiency change
indices which include stochastic Tornqvist (1936) index and the non-stochastic Malmquist (1953) index. Under the first stochastic Tornqvist approach, the deviations from the frontier are recognized to purely random shocks and inefficiency. Under the non-stochastic approach all such deviations are recognized to inefficiency. In this study, the Malmquist index approach is used in analyzing productivity growth for manufacturing sector and sub-sectors of Pakistan. The Malmquist TFP index measures changes in total output relative to inputs. The idea was developed by the Swedish statistician Malmquist (1953). The Malmquist TFP index is one of the most frequently used methods to evaluate productivity growth.
Färe, Grosskopf and Russell (1998) and Coelli and Rao (2001) observe that Malmquist DEA methods have been widely applied to a variety of industries including banks, hospitals, transport, agriculture, insurance and electricity generation.
This growing popularity of Malmquist DEA methods owes to a number of attractive properties. Tornqvist or Fisher index approach of TFP measurement inherently assumes that all firms want to minimize the cost and maximize the revenue. On the other side and contrary to Fisher index approach, Malmquist Productivity Index (MPI) does not in itself require any assumption of optimal behavior on the part of the firm.
Therefore, Malmquist Productivity Index is especially useful in applications where TFP growth is calculated for industries or sectors. The Malmquist index has additional benefit as price data are not required if panel data on inputs and outputs are available. Also, using Malmquist index, TFP growth could be decomposed into technical change and technical efficiency change components. There are also separate advantages associated with the use of DEA technique. DEA envelops observed input-output data without requiring a priori specification of functional form of production or cost. Gong and Sickles (1992) argued that DEA is more appealing than parametric efficiency models as DEA eliminates the possibility of correlation between inefficiency and the inputs and hence is not prone to problems such as autocorrelation bias observed in parametric frontier models.
4.2.2: Measurement and Explanation of Variables
Data envelopment analysis approach can be applied to profit making and non profit organizations. In case of the firms which produce revenue, this process can be
performed by converting the financial performance measures into the firm’s technical efficiency equivalents. Following the methodology of Feroz et. al., (2003) and Wang (2006), who have converted the financial performance measures to the firm’s technical efficiency equivalent by disaggregating Return on Equity (ROE) using DuPont model. The DuPont model is a technique for analyzing a firm’s profitability using traditional performance management tools. For this purpose, DuPont model integrates income statement elements with balance sheet. Therefore, return on equity which measures the relation between net income and common equity can be divided into profit margin, total assets turnover and equity multiplier. This process of converting financial performance indicators to input and output variables is explained with the help of following equation.
The general return on equity formula using DuPont ratio can be written as