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Title of Thesis

Muhammad Afzal
Institute/University/Department Details
National College of Business Administration & Economics
Applied Economics
Number of Pages
Keywords (Extracted from title, table of contents and abstract of thesis)
declining gdp, large-scale manufacturing (lsm), approaching limits, skilled labor, capital, simultaneous equation approach, productivity management, productivity, productivity measurement, vector auto-regressive models, three stage least square models

World is shrinking day by day with the advancement of technology. The expectations of human beings are rising. Economic liberalization is finding greater roots. Globalization of economy is becoming a worldwide phenomenon. It is, however established that survival and economic growth of any country will depend on increase of productivity. This is particularly important in developing countries because of higher population growth, declining GDP, growing debt, higher interest burden, rising inflation, domestic and international competition, scarcity of raw materials, balance of payment problem, fiscal deficit etc. Some of these problems can be overcome by paying greater attention to managing productivity. Productivity has different meanings to different people. It is perhaps one of the most widely discussed subjects. However, its real meaning and interpretation is still not clearly understood.

During 2005-06 the manufacturing sector of Pakistan is estimated to grow by 8.6 percent against the target of 11.0 percent. Large-scale manufacturing (LSM) is estimated to exhibit growth of 9.0 percent on the basis of July-March 2005-06 data. The growth shows a mixed trend during the current year. The items, which exhibited negative growth, are sugar (-2.40 %), jute goods (-2.47 %), coke (-77.39 %), pig iron (-43.99 %), billets (-47.95 %), HR/CR sheets (-22.83 %). Some of the items, which have shown positive growth during July-March 2005-06, are cotton yam (11.16 %), LCVs /Jeeps (33.3%), tractors (16.34 %), trucks (58.30%), cement (9.75 %), fertilizer phosphate (12.03 %), paper and paperboard (11.85 %), vegetable ghee (13.16 %). (Government of Pakistan, Economic survey of Pakistan and Annual Plan 2006-07, planning commission of Pakistan)

In the face of approaching limits to further availability of resources i.e. raw material, loan, skilled labor, capital, much of the future manufacturing growth has to come, from increased manufacturing productivity.

In this thesis, we estimate total factor productivity indices for the large scale-manufacturing sector to study the underlying sources of productivity growth from 1975 to 200 1. We have used different approaches to measure productivity. Different concepts of productivity being used by Economists and Management Scientists have been underlined in the thesis. We have used three approaches to measure the productivity. In the first approach classical models are being used and results of four models are compared. In Arrow model, productivity is measured with different intensities of labor. The estimates show that TFP has grown positively with changes occurring in intensity of labor. While the productivity gains in the mid period, taken for analysis were lower than the remarkable growth in the end period

In the second Phase simultaneous equation approach has been used for the large-scale manufacturing sector of Pakistan. The objective to use this approach is to measure the contribution of factors affecting the productivity of LSM. Positive contribution of labor force has been observed in Production while contribution of labor force in per capita is observed negative. Different variables like gross national product, and gross fixed capital formation are contributing positively to volume of production and per capita income.

In third Phase Autoregressive models are being used to forecast the productivity of large scale manufacturing sector of Pakistan. To illustrate the mechanics, we initially used two lags (k=2) of each variable. By using EVIEWS, we obtained the estimates of the parameters of the equations. Based on only two lags of each endogenous variable, the results show that the productivity regression, the one-period lagged productivity variable and both lagged per capita income terms are individually statistically significant. In the per capita income, both lagged productivity terms and lagged per capita terms (at about 5% level) are individually significant. On the basis of results, we conclude that some of the variables are not individually statistically significant, but collectively all the lagged terms are statistically significant.

Overall results show that productivity is affected by a number of factors like labor, capital, GNP, and per capita income. Causal relationship among different variables does exist. Our analysis depicts that different economic models are applicable and predictable to the data of LSM sector of Pakistan. Macroeconomic policies may help improving productivity of the LSM sector.

Productivity management encompasses all facts related to the art and practice of productivity. For its successful implementation it is necessary to have an organization, an audit system and a monitoring plan. For improvement of productivity an organization requires improvement plans identifying actions desired, fixing responsibilities and laying down of time schedules.

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5268.82 KB
S. No. Chapter Title of the Chapters Page Size (KB)
1 0 Contents
549.62 KB
2 1 Introduction 1
647.03 KB
  1.1 Productivity 7
  1.2 Rationale Of Productivity 11
  1.3 Productivity Some Perspectives 13
  1.4 Objective Of The Study 15
  1.5 Organization Of The Study 16
3 2 Productivity Measurement Models : Theoretical And Empirical Review 17
897.86 KB
  2.1 Introduction 17
  2.2 Models Of Productivity Measurement 17
  2.3 Empirical Studies On The Measurement Of Productivity 38
  2.4 Conclusion 46
4 3 Methodology And Data Sources 47
245.51 KB
  3.1 Introduction 47
  3.2 Methodology 47
  3.3 Data
  3.4 Explanation Of Variables 52
  3.5 Conclusion 72
5 5 Simultaneous Equations And Three Stage Least Square Models 73
275.23 KB
  5.1 Introduction 73
  5.2 Single Equation Regression Models 73
  5.3 Simultaneous Equations Regression Models 74
  5.4 Simultaneous Regression Models 75
  5.5 Conclusion 79
6 6 Vector Auto-Regressive Models 80
540.8 KB
  6.1 Introduction 80
  6.2 Vector Auto Regressive Models 80
  6.3 Estimation Of Var Models 82
  6.4 Forecasting With Var Models 86
  6.5 Vector Auto Regressive ( Var ) Models 86
  6.6 Conclusion 98
7 7 Conclusion And Policy Implications 99
211.92 KB
8 8 References 107
327.96 KB
9 9 Appendices 117
919.71 KB