Pricing And Non Pricing Strategies

Pricing And Non Pricing Strategies This paper discusses about business proposal and details the pricing as well as non-pricing strategies. It also details the planning and operating decisions for the existing service that based upon the stage of economy in business cycle. It recommends an appropriate course for the service that based upon the projected credit markets and also evaluates how the current credit market affected the planning of goods and services. This paper recommends the business decisions in order to estimating the international economic conditions. It also concludes with this concept by providing internal economy effects for the planning of goods and services. Recommendation of pricing and non-pricing strategies The pricing strategies were the strategies that encompassed in order to improve the economic stage in the business cycle. The cost plus pricing was the strategy that can calculate the cost of producing goods and services to the business cycle and this strategy have taken the consideration in the case of fixed and variable costs of new good or services in business cycle. The market oriented pricing was also the strategy that can set the price of goods and this price was dependent upon the pricing of competitors. Target pricing was the one that based upon the economic stage in the business cycle for the existing goods or services. The non-pricing strategy occurred when the organization decided to distinguish its products from their competitor products in order to make the quality of service to the services. This strategy was also to maintain the market share without altering price. It included the advertising, service quality and longer opening hours for the exiting goods and services that based upon the stages of economic in business cycle (Mark Hirschey, 2008). Operating decisions for goods or services The operating decisions included the product, price distribution and advertising as well as promotion. The product was the operating decision that created as a result of a process and this product was the combination of tangible or intangible attributes for the existing goods and services that based upon the economic stages. The price was the second operating decision of goods and services in the case of providing the quantity of compensations that was given by one party to another party. In economic stages, the price was expressed in the form of currency. The distribution was the third operating decisions that were the process of making service that available for using consumption and this consumption based upon the economic stage in business cycle. The three types of distributions were intensive distribution, selective distribution and exclusive distribution. The advertising was the best suitable concern for goods and services that informed to the potential customers about services for obtaining them and the promotion included the advertising for the product line. Appropriate course for services The Xerox planning model was the appropriate course of action to make the business decisions on projected economic state in the business cycle. This planning model was based upon the financial simulations for creating the proposed planning alternatives. This Xerox planning model was also computed the financial implications of alternative marketing and production policies under various environmental conditions and generated financial statements for each set of inputs in goods and services. The optimum seeking capability was the form of Xerox planning model that developed for facilitating the selection, evaluation and alternatives of goods and services. This course of action recommended to the marketers in order to illuminate the indicators of forthcoming trends. The results of this course of action were affected by a continuation of macroeconomic trends in the global financial areas. The projected credit markets can get the innovation due to implementation of this course in the case of computing the financial goods and services (Tom Sant, 2012). Current credit market conditions The credit markets conditions could be used to raise the funds through debts issuance and the credit encompassed both investment grade bonds and short term commercial paper. The credit markets offered the bonds, securitized obligations to the goods and services that based upon the stages of economic in business cycle. The current credit markets affected the operating decisions positively by promoting the product, pricing, distribution as well as advertising to global marketing. The planning or the operating decisions of services were established by the current credit marketing conditions. This credit marketing increased the facilities to get more profit for services by the protection of good credit marketing reputation. It was used to increase the quality of operating decisions for getting more service opportunities in business cycle. The special function of planning could be served by the current credit marketing conditions in order to increasing the morale value of goods or services that based upon the stages of economics in business cycle. Business decisions Utilization of variety of sources for collection of data The business decisions were based upon the primary as well as secondary sources that could make the services effectively in order to enter into the business cycle. The primary sources included the survey methodology, sampling methods for the projected economic stages. The secondary resources included the internet research, published data and product data for the collection of data. This business was to store the information, protect the ethical issues. Understand a range of techniques to analyze the data This was also the effective business decision in order to analyze the data for making the business purposes effectively. This business decision represented the value of mean, median and mode for making the valid conclusion for the goods or services. The standard deviation of this data analysis was used in the case of measuring the dispersion. The business values were dependent upon this range of techniques in order to analyze the business data effectively. Effects of international economy The international economy was having influences on the business planning or operating decisions positively. The international economy provided the power and authority to business planning or operating decisions. The business planning could also be implemented by the effect of international economy and also this economy provided the contribution in the case of enhancing the business goods and services. The business goods and services could be reached into the global market due to the impact of international economy. The challenging influences could also mitigated by the effects of international economy thatà ¢Ã¢â€š ¬Ã¢â€ž ¢s why operating decisions of business will be protected effectively. The business goods and services has benefited with the attainment of cheap labour, technology as well as capital and this effect had promoted the planning and operating decisions for the better enhancement of business goods and services. The business growth was also based upon the arrival of international economy thatà ¢Ã¢â€š ¬Ã¢â€ž ¢s why this economy influenced on business planning positively. Additional recommendations for economic conditions Determine the pricing strategy to meet organizational goals This was the recommendation in order to ensure the organization smoothly and this recommendation was based upon the economic conditions. The utilization of cost pricing was essential to the organizational products and services. This strategy recommendation could set the price at the production cost of company that included the fixed cost at the current volume, cost of goods and certain margin profits. The pricing strategy was used to determine the product at lower prices and this recommendation was established in the case of addressing the new strategies for the development of organizational products and services. This recommendation also allowed the organization to capture the new clients in the market for building the image for the new products thatà ¢Ã¢â€š ¬Ã¢â€ž ¢s why the implementation of pricing strategies was essential for an organization. Conclusion The concepts of business proposal and pricing as well as non-pricing strategies were discussed. The operating decisions of organizational services were detailed and the recommendations of appropriate course for projected credit markets were described. The conditions of current credit market on organizational goods and the recommendations of business decisions were examined. The influence of international economy on organizational products and the additional recommendations for the organizational improvement were concluded.

Pipeline Risk Analysis

Pak. J. Engg. & Appl. Sci. Vol. 2 Jan 2008 Risk Analysis for Construction and Operation of Gas Pipeline Projects in Pakistan S. Mubin1 and G. Mubin2 1 2 Civil Engineering Department University of Engineering & Technology, Lahore, Pakistan Instructor, VTI, PVTC, Govt. of Punjab, Lahore, Pakistan Abstract In order to cater for its high energy demand, Pakistan is planning to import natural gas through pipelines from neighboring countries. For fully utilizing the imported gas, providing it to end customers, the infrastructure of gas pipeline needs to be developed.Therefore, huge investment has been done and proposed in this sector in coming future. Considering geological, topographical, geopolitical and climatic conditions of the country, there is added risk of earthquake, landslides and floods. Due to current geopolitical situation there is a persistent threat of unrest and terrorism in the country. Instable Government policies, high rate of inflation, rapid change in material prices ar e also important risk factors.All these factors make the situation very complex in quantifying the risk especially for a project in which the risk impact factor rises exponentially in case of risk occurrence. In this paper, most appropriate risk classification is made based on technological, organizational, political, natural climatic, security and environmental risk factors. Effort has been made to device a simpler risk management methodology to analyze and manage risks of gas pipeline project. In the proposed risk management model Monte Carlo simulation has been used to identify critical risks.Keywords: Oil and Gas pipelines; Risk Analysis and Management; Monte Carlo simulation 1. Introduction Oil and gas sector is considered as back bone of any country’s economy. In Pakistan industrialization, agriculture, transportation and even domestic utilization of the energy depends on oil and gas sector. Almost 80 % of power generation is oil and gas based (50% gas and 30% oil) [1]. For efficient energy production there is a need of efficient transportation system (main and distribution network of pipeline) in the country, which is not sufficient to fulfill the country’s requirement.As per World Bank Report only 21% of the total population of the country has access on natural gas. Due to the growing demands, pipeline network is expanding vigorously as during the last 10 years the network of main and distribution gas pipeline was expanded by 85% [2]. Currently Pakistan is meeting its gas demand by internal sources but by the year 2011 the difference between country’s gas demand and supply will be 1. 2 Bcfd which will rise to 3. 1 Bcfd by the year 2015 and ultimately to 11. 1 Bcfd by the year 2025 [3].To fill the gap between demands and supply Pakistan is planning to import natural gas through pipeline from neighboring countries. Options of Turkmenistan, Iran and Qatar are available for gas import. Figure 1 shows that route of future cross country pipeline. In Pakistan, expected investment in pipeline construction is within range of 7 to10 billion dollars during the next 5-10 years [4]. Structure and characteristics of risk are different in different mega project such as Iran-Pakistan-India pipeline due to multi-party involvement from different geographic locations and regulatory structure [5].These mega projects may be termed as international projects defined as those where the owner and/or contractor may be from a country different to that of where the project is situated typically involve a wider range of issues than domestic projects and in effect, moving outside of one’s usual business jurisdiction interjects many unknowns. Factors impacting owner investment decisions with international capital facilities can be quite complex and may vary significantly from region to region and project to project [18].Nature and impact of risk are different in different stages of project life cycle of pipeline projects. For most e ffective risk management it is recommended to plan, analyze and manage risk in all phases of project life cycle i. e. initializing, concept clearance and feasibility, design, construction and operation. Understanding the relationship between risk Corresponding Author: S. Mubin ([email  protected] edu. pk) Risk Analysis for Construction and Operation of Gas pipeline Projects in Pakistan management and project phases for capital projects can be a difficult task.For instance, some risks are negligible in construction phase but are of vital importance in design phase such as earthquake. While dealing with risk management of international projects, which are often first or one-time efforts and project progress and phasing decisions can be isolated from risk management. For most international projects, different participants are responsible for control of the various phases of a project’s life cycle. In many cases, the project owner is largely responsible for program analysis, a thirdparty is often hired to design (engineering), construct, manage and control to meet the initial onstraints set by the owner [6]. Contractor is hired to construct the project, which turns the results over to the owner for operations or production. Structuring projects with distinct phases and responsibilities can increase risk by isolating the project participants in such a manner that minimal attention is given to overarching project concerns. Individual project participants become concerned with only their own project risks and either willingly or unwillingly try to transfer these risks to other project participants.To limit the scope of this paper the discussion is confined to the risks occurring during construction and operation phase. Figure 1: The routes of future gas pipeline project in the region. The uncertainty in undertaking construction of a pipeline project comes from many sources and often involves many participants in the project. Since each participant tries to m inimize its own risk, the conflicts among various participants can be detrimental to the project. Systematic risk management of project activities is not fully recognized as valuable by practitioners in the construction industry.No common view of risk exists since the owner, investor, designer, and constructor have differing project goals and objectives, and historically adverse relationships are common. In recent years, the concept of â€Å"risk sharing/risk assignment† contracts has gained acceptance in pipeline design and construction. The distribution of risk between the client and contractor tends to overshadow effective management strategies and investigations show that contactors and owners give minimal consideration to risks outside the realm of their own concerns.The Federation Internationale des Ingenieurs Conseils (the International Federation of Consulting Engineers, FIDIC) and the International European Construction Federation (FIEC) publish two well-known and wi dely-accepted forms of conditions of contract for international construction projects (the Red and Yellow Books) that include provisions on the fair and equitable risk sharing between the owner and the contractor as well as risk responsibilities, liabilities, indemnity, and insurance [7].Considering technological point of view geographical conditions of Pakistan are very complex for the construction of pipeline projects. Almost 50 % of the total area of Pakistan is mountainous or semi-mountainous and in rest of the 23 Pak. J. Engg. & Appl. Sci. Vol. 2 Jan 2008 area there is wide network of rivers and canals (Figure 2). Therefore, for linear structure like pipelines there are extensive crossings and sometimes extreme site conditions are met, where degree of risk is increased as compare to normal conditions of construction.On the other hand, risks during operation of pipelines have different characteristics depending upon the strength and weakness of operating organization, topographi cal, geopolitical and climatic conditions of the country where project is executed. While dealing with natural risks, the geology and geographical characteristics of the regions must be thoroughly studied. For instance, the two continental plates i. e. Indian and Eurasian meet in Pakistan which highly impact on the eodynamics of the region which are the major source of earthquake [8]. In monsoon period there is high probability of floods. Typical topography, steep slopes, high rainfall in a specific period (JuneAugust) and high temperature (melting glaciers) are the dominating factors for intensifying the frequency of floods in a particular year. Considering geopolitics of the regions there is a persistent threat of unrest and terrorism.The economic instability has added the problem due to that there is frequent change in economic parameters. All these are in fact the potential risks for any construction project especially oil and gas pipelines in which risk are multiplied many fold and there is exponential rise in damage in case of occurrence of one or more risks resulting huge human and environmental losses. Figure 2: Map of Pakistan showing important geological and geographical features of the country . Classification of Risks For effective Risk Management, risk classification is of prime importance. There are many kinds of classifications have been made so far [10]. In general, risks associated with pipeline projects may be classified as broadly: †¢ †¢ Risk during Construction Risk during Operation However, in operation, risk are slightly different, in which emphasis is given to avoid those factor with hurdle safe and smooth operation/functioning of pipeline.Usually, in mega projects such as cross country trunk pipelines investment risk are considered most import followed by the security risk. More precisely, risk during construction and operation of oil and gas pipelines can be divided into following categories (Table 1): The type and causes of risk in each class are different. Risks during construction are time susceptible and the probability of occurrence of different risk are time dependent, more is the duration of project higher are the probabilities.These are generally related to execution of work processes, material availability, manpower, finances (budget), time frame, accidental, legal and environmental. 24 i. ii. iii. iv. v. vi. vii. viii. Political risk Socio-economical risk Technical risk Organizational risk Natural catastrophic risk Financial risk (investment risk) Safety and security risk Environment risk Risk Analysis for Construction and Operation of Gas pipeline Projects in Pakistan Table1: Risk Classifications No 1. Category Risk Political risks Unstable Govt. olicies Change in economic parameters Breach in contractual relationship Unrealistic cost baseline and financial delay Inefficient communication Accident during construction or operation Earthquake Risk Factors Change in labour policy Rise in inflati on and material prices Loss of venture or partnership Exchange rate risk and rise in interest rate Inefficient and conventional technologies Not use of HSE policies and standard floods Damage to surrounding environment Delay in approvals from regulatory bodies Seasonal unavailability of labour Unrealistic SWOT analysis Strikes, lockout, lawlessness Change in economic policies and tax system Fine or compensation 2. Socio-economical risks Organizational risks 3. 4. Investment risk Disinvestment from market Insufficient resources and equipment Terrorism or war Strong credit policy Quality risk and rework Human error (Damage or loss of machine or human resource) Weather conditions e. g. humidity, precipitation Damage to ecology and wildlife 5. Technological risk 6. Security risk 7. Natural and climatic risk Landslide, hurricanes Depletion of hydrocarbon resources 8. Damage to Environmental risk natural resources 2. 1 Political Risk The effect of country’s policies on the project directly impact on project success or failure.During the policy making process, technical factors are usually ignored and policies may be set in a way that operation of a project may not be economical or trade offing. This factor is also important in unstable governments, where there is more risk of change of economic, petroleum or labor policies, which are directly related to the pipeline projects. Delays can occur due to laborious and detailed procedure for approval from public safety regulation department, environmental regulation agencies and oil and gas regulatory bodies. Public health, safety and environmental concern are more important in the western countries as compare to developing countries like Pakistan. Policy and political risks are more concerned in international project risks, such as cross border pipeline projects.In international projects these risks 25 are sometimes overlooked or assessed haphazardly. Such risks include war, civil war, terrorism, expropriation, in ability to transfer currency across borders, and trade credit defaults by foreign or domestic customers [6]. Although risks such as civil unrest and economic stability are typically outside the scope, understanding and dealing with these risks are critical for companies working internationally. A 2001 study by AON Trade Credit discovered that, in the Fortune 1000, only about 26 percent of companies had in place systematic and consistent methodologies to assess political risks [6]. 2. 2 Socio-economical riskSocio-economical conditions further reinforced the climate of uncertainty with high inflation and interest rates. The deregulation of financial institutions has also generated unanticipated problems related to the financing of construction. These risks can be forecasted and linked with the economic indicators of the country. For instance, In Pak. J. Engg. & Appl. Sci. Vol. 2 Jan 2008 Pakistan, the economic indicators are tending to grow regardless of the political instability in t he country. The GDP of the country was 8. 4% prior to 2005 earthquake, which declined down to GDP 5. 6 or less currently. Earthquake and floods during the last two year costed government approximately $5. 4 B and expected to spend more $3. 6bn till 2010.Overall there is growth in the market and potential for foreign investment in construction sector [1]. 2. 3 Technical risk The risks related to technological problems are familiar to the design/construct professions which have some degree of control over this category. However, because of rapid advances in new technologies which present new problems to designers and constructors, technological risk has become greater in many instances. Certain design assumptions which have served the professions well in the past may become obsolete in present time. Site conditions, particularly subsurface conditions which always present some degree of uncertainty, can create an even greater degree of uncertainty during construction.Because constructi on procedures may not have been fully anticipated, the design may have to be modified after construction has begun. An example of facilities which have encountered such uncertainty is the nuclear power plant, and many owners, designers and contractors have suffered for undertaking such projects. There is a need of technological advancement to overcome this risk. statistics, geological surveys, sub surface investigation through various method has given rise to the development of such techniques which can not only quantify frequency of occurring of such phenomenon in a particular region but also their impact and destruction. Northern areas of Pakistan are considered in high seismic zone [8] particularly after incidence of 8th Oct. 005 earthquake, in which more than 86000 people died and one million got injured and 3 million became homeless, this factor is highly considered in planning, feasibility, design and construction of the any construction project in the region [9]. The major re ason is the plate tectonic motion in Himalaya, northern part of Pakistan. This plate tectonic motion is due to the uplift of Euro Asian plate by Indian plate (two plates are meeting in Pakistan) 2. 6 Investment risk Pipelines are mega project. A lot of funding is required for the completion and safe operation of pipelines. Investment has been always a prime risk in construction project due to multi party involvement.But especially for the international pipeline project, this is always risk of payback and trade offing, because of the bilateral and diplomatic relationships. 2. 7 Safety and security risk In a broader sense, safety and security risks include factors due to that loss or damage of resources (manpower, machinery and financial resources) or facilities (pipeline, pipeline crossing, gas compressor station) can occur during construction or operation phase of a pipeline. It is very often that loss of work time, machinery and manpower occur due to accident on side because of the negligence of some worker. These risks involve all actions (accident, malfunctioning, terrorism, war etc) due to that loss of resources nd production of pipeline can occur. These risks are more likely to occur during operation phase however, these can be occurring in construction stage as well. To cater these risk to occur Health safety policy is strengthen so that to minimize on-site and offsite accidents during construction. It is generally accepted that the pipeline are the target in terrorists’ attacks and wars. For, instance, history prevails that in last five years the total terrorist attacks made on the pipelines in Pakistan were 103. It may be the result of internal political situation of the country but anywhere in the world this factor of risk is considered to be very important.For safe operations, state of the art methodology and technology has been developed which ensure safe exploitation of pipeline, which include remote sensing, Geographical Information System (GIS) and mapping techniques, Light detection and ranging (LIDAR), Global positioning system (GPS), data acquisition (SCADA) and In-line inspection (ILI) etc. 26 2. 4 Organizational risk The risks related to organization and organizational relationships may appear to be unnecessary but are quite real. Strained relationships may develop between various organizations involved in the design/construct process. When problems occur, discussions often center on responsibilities rather than project needs at a time when the focus should be on solving the problems.Cooperation and communication between the parties are discouraged for fear of the effects of impending litigation. This barrier to communication results from the illconceived notion that uncertainties resulting from technological problems can be eliminated by appropriate contract terms. The net result has been an increase in the costs of constructed facilities. 2. 5 Natural catastrophic risk Natural catastrophic risks are those on w hich there is no control. They are usually the ‘act of God’ and can occur at anytime and anywhere. Earthquake, floods, hurricanes are the common examples of these risks. However, due to the development of the science and technology in the field of simulation and modeling,Risk Analysis for Construction and Operation of Gas pipeline Projects in Pakistan 2. 8 Environmental risk Environmental concerns and awareness is increasing everywhere. The worldwide environmental protection movement has contributed to the uncertainty for construction because of the inability to know what will be required and how long it will take to obtain approval from the regulatory agencies. This delay in approval practically influence on total costs of the project. Public safety regulations have similar effects. The situation constantly change guidelines for engineers, constructors and owners, as projects move through the stages of planning to construction due to the change in govt. policies.These moving targets add a significant new dimension of uncertainty which can make it virtually impossible to schedule and complete work at budgeted cost . Risk management reduces the impact of negative risks and enhances positive risk to make opportunities. However, limiting our scope in this section to negative risks, risk management may be defined as a method to reduce the consequences of negative events (risk) tend to occur during construction and operation of pipeline by developing mechanisms and strategies (risk transfer, risk reduction, risk distribution, avoidance, risk enhancement) compatible to the system environment in which project is executed. The strategy of risk management is based on risk analysis results for a particular project.According to Project Management Institute (PMI) approach of risk management [11] the process includes: 1. 2. 3. 4. 5. Risk management planning Risk identification Qualitative risk analysis Quantitative risk analysis Risk reduction strategies 3. Ri sk Management Process Generally risk analysis and management had not been applied in construction industry and especially in pipeline projects. It is comparatively new area for pipeline projects, which is rapidly advancing due to the involvement of non native client or contractor. However, the concept of risk analysis and management is getting fame in pipeline project due to involvement of multinational contractor/organizations.Basically risk management deals with management of positive and negative events which occurs during realization of projects. 3. 1 Risk management planning Risk management process (PMI approach) starts with the planning of risk management, which includes a detailed risk management planning. In Risk management planning the proposed course of action for risk analysis is set. The input, output and process are shown in the table 2. Table 2: Process showing Risk Management Planning [19] Input Organizational environmental factor Organizational process of assets Proj ect scope management Project management plan Planning meeting and analysis Risk Management Plan Planning course of action Process Out put 3. 2 Risk Identification processFor effective risk analysis and management the identification of risk is very important carefully such that no important factor is left which can negatively impact on the project. The risk indemnification process input and output are shown in table 3, which include the following: Information Gathering Techniques: Examples of information gathering techniques used in identifying risk can include brainstorming, Delphi techniques, interviewing, root cause identification and SWOT (Strengths, weaknesses, opportunities, and threats) 27 analysis. Brainstorming is important data gathering technique for risk identification in which a group of team members or subject-matter experts (design, construction, purchase, finance etc) together identify expected risks.Delphi is another technique of information gathering used as a way t o reach a consensus of experts on a subject. Experts on the subject participate in this technique anonymously. A facilitator uses a questionnaire to solicit ideas Project Documentation Reviews: For risk identification project documentation are reviewed, including plans, assumptions, prior project files, and other information. The quality of the plans, as well as Pak. J. Engg. & Appl. Sci. Vol. 2 Jan 2008 consistency between those plans and with the project requirements and assumptions, can be indicators of risk in the project. Assumptions Analysis: Every pipeline project is conceived and developed based on a set of hypotheses, scenarios, or assumptions.Assumptions analysis is a tool that explores the validity of assumptions as they apply to the project. It identifies risks to the project from inaccuracy, inconsistency, or incompleteness of assumptions. Table 3: Process of Risk Identification Input Organizational environmental factor Organizational process of assets Project scope man agement Project management plan Risk Management plan Checklist Analysis: Risk identification checklists can be developed based on historical information and knowledge that has been accumulated from previous similar projects and from other sources of information. The lowest level of the RBS can also be used as a risk checklist.Diagramming techniques: Some Risk diagramming techniques may also be used for risk identification which includes cause-and-effect diagrams, system or process flow charts and influence diagrams. Process Information collection Documentation review Assumption analysis Checklist analysis Diagramming techniques Out put Risk Register 3. 3 Qualitative risk analysis There are several theories to quantify risks [12, 17]. Numerous different risk formulae exist, but perhaps the most widely accepted formula for risk quantification is: Rate of Occurrence i. e. , probability multiplied by the Impact of event equal to Risk Number, mathematically expressed in equation 7. The i nputs and output of qualitative risk analysis process is shown in table-4.PMI defined values of probability and impact factor can be used in risk analysis given in Table 5. However, the selection of one of the value of P for a particle risk from table 5, is based on expert judgment which may produce controversial results. The objective is to prioritize risk based on their probability and impact assessment. Probability and Impact matrix is used to visualize the impact of risk from least to maximum possibility. Another method called Risk Data Quality Assessment is used which requires accurate and unbiased data Analysis of the quality of risk data is a technique to evaluate the degree to which the data about risks is useful for risk management.It involves examining the degree to which the risk is understood. Risks to the project can be categorized by sources of risk (e. g. , using the RBS), the area of the project affected (e. g. , using the Work Breakdown Structure), or other useful c ategory (e. g. , project phase) to determine areas of the project most exposed to the effects of uncertainty. Table – 4 Process showing Qualitative risk analysis [19] Input Organizational process of assets Project scope management Project management plan Risk Management plan Risk Register Process Risk probability and impact assessment Probability and Impact matrix Risk data quality assessment Risk categorization Risk Register (updates) Out put 28Risk Analysis for Construction and Operation of Gas pipeline Projects in Pakistan Table 5: Standard values of frequency of occurrence and Impact factors [11] Possibility of occurrence very high chance High chance Greater chance Possible Likely Unlikely Probability (P) 90 % 75% 60% 45% 30% 15% Type and level of risk Impact When maximum impact on scope, time and cost High impact on scope, medium impact on time and lesser impact on cost High impact on time, medium impact on scope and lesser impact on cost When high impact on cost of the project, medium impact on time and lesser impact on scope Impact Factor (I) 0. 9 0. 6 0. 3 0. 1 3. 4 Quantitative risk analysisFor quantitative risk analysis any of the following method may be used as illustrated in Table 6. incorporates probabilities and the costs or rewards of each logical path of events and future decisions, and uses expected monetary value analysis to help the organization identify the relative values of alternate actions. See also expected monetary value analysis. Sensitivity analysis: Sensitivity analysis helps to determine which risks have the most potential impact on the project. It examines the extent to which the uncertainty of each project element affects the objective being examined when all other uncertain elements are held at their baseline value.One typical display of sensitivity analysis is tornado diagram, which is useful for comparing relative importance of variables that have a high degree of uncertainty to those that are more stable. Expected Mon etary Value (EMV) Analysis: It is a statistical technique that calculates the expected outcome of future scenarios in monetary form that may or may not happen. Modeling and simulation: Modeling and simulation is recommended for cost and schedule risk analysis because it is more powerful and less subject to misapplication than expected monetary value analysis. Simulation uses a model that translates the uncertainties specified at a detailed level of the project into their potential impact on project objectives. 3. 5 Risk eduction strategies Risk register may be obtained from risk management procedure defined by Project Management Institute (PMI) [11], which is a document containing the results of the qualitative risk analysis and quantitative risk analysis. On the basis of risk analysis risk reducing strategy is set which is also given in risk register. The risk register in that way, presents all related information of identified risks including description, category, cause, probabil ity of occurring, impact(s), risk number and the possible strategy set for each risk. Decision Tree: The decision tree is a diagram that describes a decision under consideration and the implications of choosing one or another of the available alternatives. It is used when some future scenarios or outcomes of actions are uncertain.It Table 6: Process showing Quantitative risk analysis [19] Input Organizational process of assets Project scope management Project management plan Risk Management plan Risk Register Process Out put Quantitative risk analysis ( Sensitivity analysis, Decision Tree, Modeling and Simulation, Expected Monetary Value, EMV) 29 Risk Register (updates) Pak. J. Engg. & Appl. Sci. Vol. 2 Jan 2008 The common course of action of the any organization or participant (consultant, contractor, client or owner) participating in the construction process of oil and gas pipeline can adopt one or combination of course of action given below, depending upon the type of project, lo cation and circumstances.Distribution of risk between participants of the project can be made by: 1. Risk Transfer (insurance, contracts) 2. Contingency Budget 3. Risk mitigation (problem solving and root cause analysis) 4. Risk avoidance 4. Development of Risk Management Model for Pipeline Construction Projects Project Management Institute (PMI) approach of risk analysis and management may be complicated and laborious for construction project like pipeline. Therefore a model of risk analysis and management is developed which simplifies the process and produce more probable results with the implementation of Monte Carlo simulation (Figure 3). Project document review Market Analysis Client/Contractor reviewGeopolitical analysis and review 2. Risk Classification Risk Breakdown Structure (RBS) 3. Risk probability and impact factor Data collection (Authentic source) Data processing (Normal, Beta, Gamma, Log, etc distribution) Calculation of Frequency (P) and Impact factor (I) 4. Risk an alysis 5. Monte Carlo Simulation Identification of critical risk 6. Risk management strategy Risk Transfer (Contract, insurance) Risk Distribution (Between parties) Risk Mitigation (Eliminating risk causes) Risk Avoidance 7. Risk monitoring process Documentation Monitoring process and results Check and make changes Data Bank Figure3: Risk Management Model for pipelines construction project. 30Risk Analysis for Construction and Operation of Gas pipeline Projects in Pakistan STEP-1: Model starts with identification and classification of risks considering the type of construction project. Degree and frequency of risk varies from trunk pipeline to distribution line. Similarly it gives suitable approach for both the major parties i. e. Owner (client) and the Contractor. Before identifying the risk the market review, client/contractor capability and geopolitical conditions of the region are analyzed where project is expected to be executed. The types of risk are also depending upon the ty pe of contractual relationship between the owner and constructing firm. In different ypes of contract (Build-Operate and Transfer, Engineering-Purchase and Construction, Figure, Turnkey contracts, Labour contract, etc) between the owner and constructing body the level and intensity of risk differs [13]. STEP-2: On the basis of risk identification risk are categorized and Risk Breakdown Structure (RBS) is made as shown in Figure 4. Risk identification is the most important thing followed by the probability and impact calculations in whole risk analysis process. Figure 4: Risk Breakdown Structure of gas pipeline project STEP-3: Risk probability assessment investigates the likelihood that each specific risk will occur. Risk impact assessment investigates the potential effect on a project objective such as time, cost, scope, or quality.The selection of PMI defined the values of probability and impact factor given in Table 5 is based on expert judgment which may produce controversial res ults. For instance, it may be difficult some time to distinguish the possibility from â€Å"Higher Chance† to â€Å"Greater Chance† for that an expert can use 60% probability value however, another use 45%. In that way some negligible risk may be superseded to other important risk. Risk impact factor defined by Project Management Institute (PMI) are used in this study which range from 0,1 to 0. 9 depending upon the type and impact of event to the project. For risk Monte Carlo Simulation the precise value of probabilities are required.Therefore, probability and impact of each risk may be calculated based on historic data. In this 31 case we the values of probability of different risks are calculated by using different probability distribution curves, however, when the historical data is not available, the probability is judged by experts opinion (from SNGPL) or the direct value of probability for that particular risk published by the related government agency. It is ver y important to define the probability distribution of a risk on the basis of that the frequency of occurrence is calculated. It is observed that the probability distribution of different risk appearing in different stages of project life cycle is different.Therefore, during calculation of probability of each risk the characteristic of risk must be considered to find the appropriate distribution to get the more precise results. For example, figure 5 shows the 10 year data of flood [21] depicts that the a normal curve is best suited to find the probability of a given volume/time called as the flood flow may be calculated using Equation 1,2 and 3 [14]. Pak. J. Engg. & Appl. Sci. Vol. 2 Jan 2008 Figure 5: Graphical representation of flood data 1990-2001 where P – probability of occurrence Z – area under normal curves for a given value X (the probability of that area can be found out from charts) ? – mean value of the 10 year data of river flows. ? – standard deviation of the mean data.On the basis of historical data, obtained from IRSA, the probability of river flow more than 400 (MAF) (which is termed as flood flow) through river system of Pakistan (sum of river flow at a time on Mangla and Terbela) is calculated by using measured. Similarly other risks are also quantified based on the characteristic of data distribution curve. For instance, for earthquakes we are interested to find the probability of occurrence earthquake more than 5. 5 Richer Scale. According to construction codes, the earthquake between 3. 5-5. 4 Richer Scale is often felt, but rarely causes damage. A value of 5. 5 Richer Scale is selected to calculate probability of 32 occurrence under assumption that almost slight damage to well designed buildings can caused major damage to poorly constructed buildings over small regions.Pipelines can go under slight damage of residual. For a random variable X (x > 0 and elsewhere i. e. x < 0 the value of probability is zero) have an exponential distribution with parameter ? then probability distribution is defined as in equation (4), (5) and (6) [14]. Therefore either exponential or gamma distribution (with m =1) may be used for probability calculation of earthquake at a given value (in Richer Scale) using the historical data [9], as shown in the Figure 6. where P – probability of occurrence ? – mean value of historic data ? – standard deviation of the historic data e – 2. 718282 VAR is the variance at any value X. For 5. Richer Scale earthquake ? = 1 ? P (X > 5. 5) = ? 1. e – 1*5. 5 = 0. 000408 5. 5 Risk Analysis for Construction and Operation of Gas pipeline Projects in Pakistan Frequency of occurrence 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0,000 0 1 2 3 4 5 6 Earthquake Intensity (Richer scale) Figure 6: Graphical representation 45 year earthquake data STEP-4: On the basis of probability values for each risk a risk register (table 7) may be made which presents quantitative risk analysis for each risk. PMI defined impact factor can be used which clear cut defines the type and condition of risk impact. On the basis of this formula below qualitative risk analysis is made.The following relationship is used for risk analysis [11]: RN = P x I RN – Risk Number P – Probability of occurrence I – Impact factor of risk For parameters the data is not available expert judgment can be used for probability assessment. Risk Number (RN) can be found for all risk identified in Risk Breakdown Structure (RBS). Manually it can be identified critical risk having larger risk number, RN based on the one point calculation. However, the more authentic way to identify the critical risks associated to pipeline project is Monte Carlo Simulation approach which is discussed in next step. STEP 5: Monte Carlo simulation is a widely used computational method for generating probability distributions of variables that depend on other variables or param eters represented as probability distributions.Although Monte Carlo simulation has been used since the 1940s, development of computer technology has made it accessible and attractive for many new applications [15]. That availability has coincided with increasing dissatisfaction with the deterministic or point estimate calculations typically used in quantitative risk assessment; as a result, Monte Carlo simulation is rapidly gaining popularity. Monte Carlo simulation, which is a mathematical method used in risk analysis to approximate the distribution of potential results based on probabilistic 33 (7) inputs would involve many calculations of the intake rate rather than a single calculation; for each calculation, the computation would use a value for each input parameter randomly selected from the probability density function for that variable [16].Each simulation is generated by randomly pulling a sample value for each input variable from its defined probability distribution, e. g. uniform, normal, lognormal, triangular, beta, etc. These input sample values are then used to calculate the results, i. e. total project duration, total project cost, project finish time. The inputs can be task duration, cost, start and finish time, etc. This procedure is then repeated until the probability distributions are sufficiently well represented to achieve the desired level of accuracy. They are used to calculate the critical path, slack values, etc. Monte Carlo simulations have been proven an effective methodology for the analysis of project schedule with uncertainties.In Monte Carlo simulation any desired level of mathematical accuracy can be achieved by increasing the number of iterations. Risks are probable entities, it is possible that all the risk accrued at the same time during project execution and may be no identified risk appears. Therefore, it is desired to use Monte Carlo simulation technique to find the most critical and probable risk which can appear in the pi peline project. Risk analysis has been made by using program Riskyproject 1. 3. 3 [20] which is an advanced project management software with integrated risk analysis. RiskyProject is used for planning, scheduling, quantitative risk analysis, and performance measurement of projects with multiple risks and uncertainties.RiskyProject determines which parameters will have the most effect on the project: duration, cost, and finish time with and without risks, crucial tasks, critical risks, and success rate. RiskyProject helps to optimize the course of the project: track project performance and risk together and analyze the affect of mitigation efforts [22]. On the basis of Monte Carlo simulation critical risks are Pak. J. Engg. & Appl. Sci. Vol. 2 Jan 2008 Table 7: Risk input in risk register and their quantitative analysis for pre-defined risks Risk Identification and Categorization Cat. Risk 1 1 1 1 1 1 1 2 2 2 3 3 3 3 3 3 3 4 4 4 4 5 5 5 6 6 6 6 7 7 7 7 8 8 1. 2. Risk Register Risk An alysis Freq. Risk reducing StrategyRanking 27 3 23 25 8 14 29 1 12 21 16 8 9 12 4 5 3 2 24 18 19 11 10 5 20 11 12 5 15 3 12 2 6 8 Risk Avoidance Risk Risk Transfer Mitigation Remarks Risk Delay in approvals from regulatory bodies Unstable Government policies Change in regulations Change in labor policy Change in petroleum policy Political instability Lawlessness, strikes, lockouts Change in economic parameters Hike in material prices Unavailability of skilled laborers Change in project scope Insufficient technology Completion of construction not on time Not realistic planning of resources and volume of work Request for increase in project budget In sufficient specialist and engineers Strains in contractual relationships Financial delays Disinvestment from the market Loss ofPartnership Change in credit policy (increase interest rate) Design not completed in time Unexpected obstacle on site (dewatering, rock excavation) Slow communication between team members War Terrorism Accident on site during construction Loss of human life Earthquake Flood Landslides Unexpended weather condition, precipitation wind storms Damage to environment Degradation of natural resources (P) 5,15% 8% 2,10% 2,90% 5% 4% 4,50% 8,10% 8,03% 6,80% 3,9 % 10% 9,50% 8,10% 13,13 % 6,50% 5,30% 6. 1 % 4,40% 3,01% 5,10% 7,80% 7,80% 5,90% 0,10% 2,20% 2% 3,90% 0,04% 3,07% 2,1 % 4,72% 3,75% 1,10% Impact (I) 0,32 0,6 4 0,9 0,6 0,6 0,6 0,3 0,9 0,3 0,3 0,6 0,3 0,3 0,3 0,3 0,6 0,9 0,9 0,1 1Risk Number 1,55% 4,80% 1,89% 1,74% 3,00% 2,40% 1,35% 7,29% 2,41% 2,04% 2,34% 3,00% 2,88% 2,43% 3,94% 3,90% 4,77% 5,49% 0,44% 1,81% 1,53% 2,34% 2,34% 3,54% 0,09% 1,98% 1,80% 2,34% 0,12% 2,76% 0,63% 2,82% 2,25% 0,66% E?5 E? E? GO E? E? E? 6 3 GO, EO SA 7 GO E? EO EO EO SA GO SA EO EO SA EO SA EO EO EO SA SA SA SA SA SA SA GO GO 0,6 0,3 0,3 0,3 0,6 0,9 0,9 0,9 0,6 0,9 0,9 0,3 0,6 0,6 0,6 0. 1- When high impact on cost of the project, medium impact on time and lesser impact on scope. 0. 3- High impact on time, medium impac t on scope and lesser impact on cost. 3. 0. 6- High impact on scope, medium impact on time and lesser impact on cost. 4. 0. 9- When maximum impact on scope, time and cost. 5.EO- Frequency of risk is based on expert’s opinion. 6. GO- Frequency of risk is based on statistic available by relevant Government organization. 7. SA- Frequency of risk is based on statistical analysis. 34 Risk Analysis for Construction and Operation of Gas pipeline Projects in Pakistan Figure-7(a): Monte Carlo Simulation conducted for risk analysis of Muree Rawat gas pipeline project presents most probable cost and duration to complete project. It also presents most probable date of completion of the project considering all identified risks. Figure-7(b): Result obtained from simulation identifying most critical risk impacting scope, duration and cost Muree Rawat gas pipeline project dentified impacting on scope, cost and duration of project [Figure 7 (a) and (b)]. Strategy for risk management is set ac cordingly. The following analysis and results was produced by the programme: 35 Sensitivity analysis Success rate of completion Critical risks affecting cost Critical risks affecting duration of project Pak. J. Engg. & Appl. Sci. Vol. 2 Jan 2008 Critical activities. Most probable duration Most probable cost of the project Most probable date of completion of project. STEP 6: On the basis of critical risk identification by Monte Carlo simulation, risk reduction strategy is set, which may be risk transfer, mitigation, avoidance, distribution and etc.During construction process the impact of risk can be lowered by changing the schedule of construction for example 95% of probability of flood occurrence is in period from June to August. In flood, the area comes under water and may not be possible to continue the construction process. Therefore, schedule may be set in a way that ground related activities should be set accordingly to avoid the occurrence. STEP 7: The results or set methodol ogy for risk management must be periodically monitored and checked for improvement. Lesson learned and recommendation should be send to â€Å"Data Bank† which may be useful for risk analysis and management of another pipeline project of similar nature. organizational capacity for design, construction and operation. Organizational or technological risk like insufficient resource planning or project management, change in scope etc can be eliminated by improving the process or application of new technologies available in this field. New state of the art technologies are helpful in managing change at any stage of the project. Historical data of river flows shows that the flood has probability of 95% of occurrence between June and August. This risk can be minimized during construction phase by rearranging the construction schedule. Other risks like landslides are associated with floods, rain fall or earthquakes. Earthquake risk during construction phase depends on the length of ex ecution of project and only impact on the construction cost of the project. As the duration of the execution increases probability of occurrence of risk also increase.However, in operation phase this risk must be eliminated by practicing design based on earthquake/horizontal forces. †¢ †¢ 5. Conclusion and Recommendations †¢ Probability of risk occurrence â€Å"P† comes out to be the function of project duration â€Å"T† both during construction and operation phase. However Intensity of destruction or Impact is a function of enterprise internal and external environment. Three most critical tasks calculated by Risky Project are Excavation, Transportation of Material and Stringing of pipelines. The most critical risks come out to be change in economic parameters, Change in design and scope, earthquake and terrorism during construction and operation of gas pipelines.Considering all risks the probable values to project completion calculated by Risky project is 460 days however the base project duration is 390 days. Similarly the project cost without risks is 350,00,000 however, with risks it is 391,00,000. On the basis of that contingency budget of project can be formulated to cater the risk. The secondary risks like change in material prices, construction not finished in time or budget and design not in time can be reduced or transferred to the other party or organization by contract. However SNGPL is designing, constructing and operating gas pipelines so risk can be eliminated by strengthening the internal Acknowledgement Mr. Pervair, Senior General Manager and Engr.Waqar Ashraf, Deputy General Manager (Projects), SNGPL are acknowledged for their contribution and support in providing data and relevant material. †¢ REFERENCE [1] Economic Survey of Pakistan, Ministry of Finance, Chapter 15, Energy Sector of Pakistan, Islamabad, Pakistan. (2006), 219-225. [2] Annual Report; Sui Northern Gas Pipelines Limited (SNGPL), Lahore, Pakist an (2006), 511. [3] Iran-Pakistan-India (IPI) Pipeline Pre-feasibility report by Hagler Bailly Pakistan. Islamabad, Pakistan, (2006), 111-119. [4] Syed Hassan Nawab; Proc. 3rd Pakistan oil & gas conference, Islamabad, Pakistan, (2007), 136-145 [5] Amberish K. D. ; A pipeline through Pakistan, Dehli, India (2004), 131-137. [6] John W. , Edward G. ; International Project Risk Assessment: Methods, Procedures, and Critical †¢ †¢ 36Risk Analysis for Construction and Operation of Gas pipeline Projects in Pakistan Factors (Center Construction Industry Studies, Report No. 31, The University of Texas at Austin) Austin, Texas. (2003), 41-49. [7] FIDIC  © Conditions of contract for construction. (Multilateral Development Bank Harmonized Edition). Geneva, Switzerland. (2005), 217-229. [8] Armbruster J. ; Research Journal, 83(1978) 8891. [9] Mahdi S. , Muhammad S. ; Proc. 1st International Conference on Earthquake Engineering (ICCE), Lahore, Pakistan, (2006), 177-182. [10] D'Appoloni a E. ; Proc. of 9th International Conference on Soil Mechanics and Foundation Engineering,Tokyo, Japan, 4(1979), 410-414. 11] PMBOK ® Guide; A Guide to the Project Management Body of Knowledge, 3rd Edition, PA, USA, (2004), 237-264. [12] Peter C. , Robert P. ; Proc. 2nd International Deepwater Pipeline Technology Conference. London, UK, (1999), 291-297. [13] Conditions of Contract for EPC/Turnkey Projects, Guidance for the preparation of the particular Conditions Forms of Tender, Contract Agreement and Dispute Adjudication Agreement, USA, (1999), 4-12. [14] Sher Muhammad Ch. ; Introduction to statistical theory, Ilmi Kitab Khana, Urdu Bazar, Lahore, Pakistan, 6th Ed, (1996), 361-370. [15] Susan R. P. ; Proc. Int. Conference on Risk Assessment and Policy Association meeting in Alexandria, Virginia, (1997), 245-255. [16] Brenda McCabe; Proc. Int.Conference on Simulation, Toronto, Canada, (2003), 15611565. [17] Jack R. , Meredith, Samuel J. , Mantel Jr. ; Project Management, 5th Ed, NJ, USA, (2002), 191105. [18] Wells Louis, Gleason Eric; Harvard Business Review Journal, 73(5)(1995) 44-54. [19] CPM 128: Project Management Boot Camp, (2006), 11. 1-11. 30. [20] www. intaver. com/accessed on 10th March, 2007 [21] Annual Flood Report; Ministry of Water and Power, Islamabad, Pakistan, (2006), 1-5 also available online on http://www. pakistan. gov. pk/ministries/index. jsp ? MinID=24=291. [22] User’s Guide to RiskyProject Professional 1. 3, Intavar Institute Inc. , USA, (2006), 31-38. 37

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Displacement Effect And Economic Growth In The Uk Finance Essay - Free Essay Example

Sample details Pages: 20 Words: 5914 Downloads: 9 Date added: 2017/06/26 Category Finance Essay Type Analytical essay Did you like this example? In this chapter of the research, will discuss the assumption made by both the Peacock and Wiseman (1961) displacement hypothesis to explain the increases in the proportion of time government expenditure to economic growth in the United Kingdom. They found that government expenditure in the United Kingdom did not follow a smooth trend, but instead, it seems to jump up in separate times. Peacock and Wiseman (1961) proposed the displacement effect hypothesis. It had related to the Wagners law even though there are a few differences between them. Thus, they contend that under normal conditions of peace and economic stability, changes in public expenditure are quite limited. Don’t waste time! Our writers will create an original "Displacement Effect And Economic Growth In The Uk Finance Essay" essay for you Create order The effect of the public expenditure on the time pattern of the general government expenditure is that public sector size will tend to be constant over time, rather than increasing, unless same major crisis periods occur, which require an increase in government intervention. The equivalent expansion of the public sector will not be just temporary, since the new levels of government expenditure and taxation will be accepted by the electors, and therefore public sector size will remain stable at an higher level until the next shock. The data used in this study is the time series Quarterly data for two periods of (1980q1 to 1990q2), and (1990q3 to 2007q4), have utilized to analyze the relationship between government expenditures and economic growth by measuring the gross domestic product in the Saudi economy. The rest of the chapter is organizing as following: section one, presents some empirical results of relevant theoretical and empirical literature on the relationship between government expenditure and economic growth. Section tow, presents the version of Peacock and Wiseman and their formula to explain the Displacement Effect. Section three, investigates the data and empirical results and analysis by using the methods. In addition, Section four, presents the results of analysis by using the time series techniques , such as the Ordinary Least Square (OLS), Augmented Dickey-Fuller for stationary Unit Root Tests, co-integration test , Causality Granger test , and Error Correction Model (ECM) , that for real GDP and Non-Oil GDP . While section five, concludes the chapters and presents. 9.2. The Displacement Effect Hypothesis 9.2.1. Structural Break Theory As we mentioned before in chapter three, wars are capable of displacing this notion of tolerable tax rates. In addition, expenditure may fall again, but not to their previous levels. Therefore, public expenditure grows in a discontinuous and stepwise fashion, the steps coming at times of major social upheavals (Safa, 1998). According to, Tussing and Henning (1991:397) the upward displacement effect by Peacock and Wiseman is an example, but obviously not the only one of such a structural change. Nelson and Plosser (1982) examined the relationship between the unable to reject the null of a unit root against trend stationary alternatives their data set. They found that impact on the way economic series have viewed and treated subsequently, which have further discussed by Perrons (1989). Zivot and Andrews (1992) pointed out the specification argued in favour of the need to view break points as endogenous and to develop procedures, which considered this endogenously. Diamond (1977) presented the displacement effect as a theory of structural break, which means that the usual ceteris paribus assumption of unchanged tastes, preferences and institutions after the upheaval has denied. He has used the Chow test comparing two periods separated by a social upheaval, and he found that, if this shows significant structural change and there has been displacement. 9.2.2. A Ratchet Effect As mentioned previously, the main argument of the ratchet effect is that if there a crisis and GNP decline, then the public expenditure decline but less than GNP. According to Bird (1972), he has explained the displacement effect and called it the ratchet effect. Moreover, Bird (1972) has argued that crises are likely to have short-term implications for (E / GNP) rather than crises lead to a permanent upward displacement for (E / GNP). Henrekson (1992) argued that the (E / GNP) is fall in the short run in times of unexpectedly rapid GNP growth. Other study for Peacock and Wiseman (1979) they argued that at the extreme, the ratchet effect interpretation of the displacement effect leads to the denial of its very existence. 9.3. Empirical Testing of Displacement Effect: Previous Studies Gupta (1967) was the first attempt to subject the displacement effect to empirical testing. He found significant displacement after the world wars in all cases except for Sweden after World War II. However, this result seems to be due to an estimation error, he also found significant displacement caused by the Great Depression in the case of the U.S. and Canada. According to, Henrekson (1990:246) Peacock and Wiseman (1961), adopt a clearly inductive approach to explaining the growth of government expenditure. When Peacock and Wiseman observed that expenditures over time appeared to outline a series of plateaus separated by peaks, and that these peaks coincided with periods of war and preparation for war they were led to expound the displacement effect hypothesis. Legrenzi (2003) argued that the displacement effect for Italy within a multivariate revenue-expenditure model of government growth. His result for long-run analysis shows an effect of GDP on the governments growth. Otherwise, the short-run analysis shows some evidence for the displacement effect, in terms of a lower resistance against tax financing of government expenditures in the war. The similar test of Guptas version in many ways is for Bonin, Finch and Waters (1969); they have tested displacement effect in the U.K. after the two world wars. In addition, Peacock and Wiseman investigated that both citizens and government hold divergent views about the desirable size of public expenditures and the possible level of government taxation. This divergence can adjust by social disturbances that destroy established conceptions and produce a displacement effect. People will accept, in a period of crisis, tax levels and methods of raising revenue that in quieter times would have though intolerable, and this acceptance remains when the disturbance itself has disappeared. As a result, the revenue and expenditure statistics of the government show a displacement after periods of social disturbance. Expenditures may fall when the disturbance is over, but they are less likely to return to the old level. The state may begin doing some of the things it might formerly have wanted to, but for which it had hitherto felt politically unable to raise the necessary revenues (Peacock and Wiseman 1961: 26). Other study for Henry and Olekalns (2000), investigated the Peacock and Wisemans displacement effect to explain the increases in the ratio of government expenditure to GDP in the United Kingdom. They used a data set extending back to 1836; they found instances where displacement may say to have occurred. 9.4. The formulating of the versions of Displacement Effect We tested the Displacement Effect by reversing the Peacock-Wiseman version of Wagners, which are with real GDP (9.1): Table 9.1: The original Version of Peacock-Wiseman with real GDP No Function Version Year 1 L(GE) = ÃÆ'Ã… ½Ãƒâ€šÃ‚ ± + L(GDP) Peacock-Wiseman 1967 Moreover, we will use non-oil sector of Growth Domestic Product (GDP) table (9.2). Table 9.2: The Version of Peacock-Wiseman with real Non-Oil Sector of GDP No Function Version Year 1 L(GE) = ÃÆ'Ã… ½Ãƒâ€šÃ‚ ± + L(Non-Oil GDP) + e Peacock-Wiseman 1967 9.5. The Econometric Methodology and Analysis 9.5.1. Ordinary least square test (OLS) The ordinary Least Square test (OLS), has used to estimate the coefficients in the equations. The Durbin-Watson statistic indicates the absence of the serial correlation among the residuals; the closer the DW statistic and better result are to (2). Test reflects the regression equations ability to determine the dependent variables performance. In contrast, the coefficients of the logarithm model have an interpretation, as elasticises. The logarithm transformation is applicable only when all the observations in the data set are positive. In contrast, the parameters of the logarithm model have an interpretation as elasticises. The logarithm transformation is applicable only when all the observations in the data set are positive. According to, Gujarati (1995), the normal regression model by taking logs of both sides of the equation: Y = ÃÆ'Ã… ½Ãƒâ€šÃ‚ ± + X + e (9.1) To be: Log Y = ÃÆ'Ã… ½Ãƒâ€šÃ‚ ± + Log X + e (9.2) The slope is: Slope = (9.3) The elasticity is: Elasticity = = (9.4) For simplification, E can write as: = (9.5) The normal equation of Peacock and Wiseman version is: GE = f (GDP) f 0 f (9.6) Where: GE = Total Government Expenditure level in real terms. GDP= Gross Domestic Product in real terms. GE = ÃÆ'Ã… ½Ãƒâ€šÃ‚ ± + GDP + e (9.7) The equation by using logarithm model: L (GE) = ÃÆ'Ã… ½Ãƒâ€šÃ‚ ± + L (GDP) + e (9.8) E (Peacock Wiseman) = (9.9) 9.5.1.1. Structural Break Chow Test with Real GDP To find out whether there is a structural break between two periods we divide the observations, we need to calculate the chow test, which is like a F- test, the test statistic from the following formula (9.10): (9.10) The hypotheses tests are: Source RSSc RSS1 RSS2 df Model 9.33377 0.123032 4.65830 1 Residual 0.7410185 0.0067273 0.4136198 110 By using the formula above we can conclude, F-test (1, 110) = 83.914, and the critical value from the F-Table (5%) = 3.92. We have found that since the test F test (1, 110) = 83.914 is greater than the critical F- table = 3.92, we can reject the null hypothesis that there is no structural break and instead accept the alternative hypothesis that there is structural break, It means we have a structural break in the data. Thus, we need to divide the data into tow sup-samples. In the case of Saudi Arabia, we can analysis the Peacock-Wiseman version as: 9.5.1.1.1. Ordinary Least Square (OLS) with Real GDP Peacock-Wiseman (1980Q1 TO 1990Q2) The Peacock and Wiseman version would present as following: L(GE)= 6.43204+ 0.3737 L(GDP) (9.11) (14.44) (8.58) The numbers between parentheses are (t- statistics) for each estimated parameter and intercept. In the equation (9.11), we will get elasticity value directly as (E=0.3737) 0, that means an increase of (99.63%) unit in Government Expenditure (GE) generates a (99.63%) unit increase Gross Domestic Products (GDP). Moreover, the Government Expenditure (GE) explains (65%) change in Gross Domestic Products (GDP) (table (9.3). Table 9.3: Regression results for Peacock Wiseman Version for (OLS) test from (1980Q1) to (1990Q2) with Real GDP Versions D-Variable Constant In-Variable Coefficient R ² Peacock Wiseman L(GE) 6.43204 L (GDP) 0.3737203 0.6480 Peacock-Wiseman (1990Q2 TO 2007Q4) The Peacock and Wiseman version would present as following: L(GE)= 0.554041+ 0.94752 L(GDP) (9.12) (1.51) (27.67) The numbers between parentheses are (t- statistics) for each estimated parameter and intercept. In the equation (9.12), we will get elasticity value directly as (E = 0.94752) 0 , that means an increase of (99.05%) unit in Government Expenditure (GE) Gross Domestic Products (GDP) generates a (99.05%) unit increase Gross Domestic Products (Non-Oil GDP). Moreover, the Government Expenditure (GE) explains (91.8%) change in Gross Domestic Products (GDP) (table 9.4). Table 9.4: Regression results for Peacock Wiseman Version for (OLS) test from (1990Q3) to (2007Q4) with Real GDP Versions D-Variable Constant In-Variable Coefficient R ² Peacock Wiseman L(GE) 0.554041 L (GDP) 0.9475297 0.918 9.5.1.2. Structural Break Chow Test with Real Non-Oil-GDP To find out whether there is a structural break between two periods we divide the observations, we need to calculate the chow test, which is like a F- test, the test statistic from the following formula: The hypotheses tests are: Source RSSc RSS1 RSS2 df Model 8.62851 0.0943032 4.040055 1 Residual 1.44628 0.096802 1.0318692 110 By using the formula above we can conclude, F-test (1, 110) = 30.953, and the critical value from the F-Table (5%) = 3.92. We have found that since the test F test (1, 110) = 30.953 is greater than the critical F- table = 3.92, we can reject the null hypothesis that there is no structural break and instead accept the alternative hypothesis that there is structural break, It means we have a structural break in the data. Thus, we need to divide the data into tow sup-samples. 9.5.1.2.1. Ordinary Least Square (OLS) with Real NON-OIL-GDP Peacock-Wiseman (1980Q1 TO 1990Q2) The Peacock and Wiseman version would present as following: L(GE)=6.664974+0.3234 L(Non-Oil GDP) (9.13) (11.59) (6.24) The numbers between parentheses are (t- statistics) for each estimated parameter and intercept. In the equation (9.13), we will get elasticity value directly as (E=0.3234) 0, that means an increase of (99.68%) unit in Government Expenditure (GE) generates a (99.68%) unit increase Gross Domestic Products (GDP). Moreover, the Government Expenditure (GE) explains (65%) change in Gross Domestic Products (GDP) (table (9.5). Table 9.5: Regression results for Peacock Wiseman Version for (OLS) test from (1980Q1) to (1990Q2) with Real Non-Oil GDP Versions D-Variable Constant In-Variable Coefficient R ² Peacock Wiseman L(GE) 6.664974 L (Non-Oil GDP) 0.32340 0.6480 Peacock-Wiseman (1990Q3 TO 2007Q4) The Peacock and Wiseman version would present as following: L(GE)= -0.568392+ 0.9721244 L(Non-Oil GDP) (9.14) (-0.82) (16.32) The numbers between parentheses are (t- statistics) for each estimated parameter and intercept. In the equation (9.14), we will get elasticity value directly as (E = 0.9721244) 0 , that means an increase of (99.03%) unit in Government Expenditure (GE) Gross Domestic Products (Non-Oil GDP) generates a (99.03%) unit increase Gross Domestic Products (Non-Oil GDP). Moreover, the Government Expenditure (GE) explains (79.7%) change in Gross Domestic Products (Non-Oil GDP) (table 9.6). Table 9.6: Regression results for Peacock Wiseman Version for (OLS) test from (1990Q3) to (2007Q4) with Real Non-Oil GDP Versions D-Variable Constant In-Variable Coefficient R ² Peacock Wiseman L(GE) -0.568392 L (Non-Oil GDP) 0.9721244 0.797 9.5.2. Unit Roots Test Unit Root test aims to examine the properties of time series quarterly data for each of the Government Expenditures (LGE), Gross Domestic Product (LGDP), during the period from (1980q1-1990q2) to (1990q3-2007q4). To test the stationary time series model for the study variables, it requires the unit root test (Enders: 1995). Despite the multiplicity of the unit root tests, but we will use Augmented Dickey-Fuller for stationary Unit Root Tests, through the following equation:   (9.15) Where: = the first difference of the series. is the series under consideration (GDP, government expenditures, or government revenues), t = the time trend. k= the number of lag. is a t is a stationary random error (white noise residual). The hypotheses tests are: If we fail to reject the , then we have a unit root process. On the other hand , if the outcome indicates that the series are stationary after the first difference , in other words , the series integrated of order one I(1) , then we have to proceed to perform a co-integration test. Augmented Dickey-Fuller for stationary Unit Root Tests have used to test for unit roots. If the null hypothesis that the variable contains a unit root cannot be rejected, In this section we have to test the Unit Root Tests for Peacock and Wiseman version for real GDP and Non Oil GDP during two periods, firstly from (1980q1) to (1990q2) and from (1990q3) to (2007q4). Table (9.7) presents the calculated t-value from Augmented Dickey-Fuller for stationary Unit Root Tests on each variable. Table 9.7: Augmented Dickey-Fuller for stationary Unit Root Tests for Real GDP and Non Oil GDP from (1980Q1) to (1990Q2) Variables Augmented Dickey-Fuller for stationary Unit Root Test Statistics L(GDP) -2.725 L(GE) -3.514 L(Non-Oil GDP) -3.426 Critical Values 1% level -2.431 Critical Values 5% level -1.687 Critical Values 10% level -1.305 For the period during (1980Q1 to 1990Q2), according to the result in table (9.7), while all variables under examination are time-series variables, we needed first to test the properties of the series. In order to avoid the problem of spurious regression, each series has tested for stationary. To do so, we apply Augmented Dickey-Fuller for stationary Unit Root Tests, considering 5% level of significance, for the unit root test whether to accept or reject the null hypothesis. However, we found the results of each variable used in Peacock Wiseman version in Saudi Arabia indicate that the series are non-stationary in level but stationary after the first difference. The number of observation is 41 for Saudi Arabia and the following table (9.7) summarize the results of the unit root test for Saudi Arabia. Based on these test it can concluded that all variables tested (LGDP, LGE, LNON OIL GDP) are contained a unit root insignificant level of 5% for Augmented Dickey-Fuller for stationary Unit Root Tests. These results are consistent with the standard theory, which assumes that most macroeconomic variables are not static level, but become stationary in first difference (Enders: 1995).The next step would be to test for co-integration by testing the residual from the co-integrating regression. Table 9.8: Augmented Dickey-Fuller for stationary Unit Root Tests for Real GDP and Non Oil GDP from (1990Q3) to (2007Q4) Variables Augmented Dickey-Fuller for stationary Unit Root Test Statistics L(GDP) -4.199 L(GE) -7.332 L(Non-Oil GDP) -6.301 Critical Values 1% level -4.110 Critical Values 5% level -3.482 Critical Values 10% level -3.169 On the other hand, for the period during (1990Q3 to 2007Q4), according to the result in table (9.8), while all variables under examination are time-series variables, we needed first to test the properties of the series. In order to avoid the problem of spurious regression, each series has tested for stationary. To do so, we apply Augmented Dickey-Fuller for stationary Unit Root Tests, considering 5% level of significance, for the unit root test whether to accept or reject the null hypothesis. However, we found the results of each variable used in Peacock Wiseman version in Saudi Arabia indicate that the series are non-stationary in level but stationary after the first difference. The number of observation is 69 for Saudi Arabia and the following table (9.8) summarize the results of the unit root test for Saudi Arabia. Based on these test it can concluded that all variables tested (LGDP, LGE, LNON OIL GDP) are contained a unit root insignificant level of 5% for Augmented Dickey-Fuller for stationary Unit Root Tests. These results are consistent with the standard theory, which assumes that most macroeconomic variables are not static level, but become stationary in first difference (Enders: 1995).The next step would be to test for co-integration by testing the residual from the co-integrating regression. 9.5.3. Co-integration Test In this section we have to test the Co-integration Test for Peacock and Wiseman version for real GDP and Non Oil GDP during two periods, firstly from (1980q1) to (1990q2) and from (1990q3) to (2007q4). As mentioned previously, the concept of integration common that if the level variables of the form are non-stationary any package of first class, if possible, to generate a linear combination of these variables is characterized by a static zero-class integrated I (0). It is in this case, the  integrated real-time variables of the same rank co-integrated, and thus it can use the level variables in the regression, nor is the decline in this case a false spurious, (Rau, 1994). The null hypothesis is that the variables under investigation are not co-integrated. The rejection of the null hypothesis requires that the trace value of the co-integration test to be greater than at least one of the different critical values. Therefore, failing to reject the null hypothesis of no co-integration leads us to conclude that no relationship in the long-term equilibrium between government spending and national income. Co-integrating test in this study are conducted using the method developed by Johansen (1988), and Johansen and Juselius (1990). Many studies used the Engle Granger two-step, but there are those who used a Johansen and Juselius )1990) , for so many advantages, such as first, that tests for all of the variables and, secondly, all variables are treated as internal variables, so that the choice of the variable is not arbitrary. This procedure is the most reliable test for co-integration. To determine whether stochastic trends in series have related to each other or not, we will test for co-integration in Peacock Wiseman version. In addition, after determining the order of integration by Augmented Dickey-Fuller for stationary Unit Root Tests, we test whether the series are co-integrated or not, and if they are, we have to identify the co-integrating long-run equilibrium relationship (Brooks, 2008). In this section, we have to test the Co-integration Test with (Real GDP) and Co-integrati on Test with (Real Non-Oil GDP). 9.5.3.1. Co-integration Test with (Real GDP) In the case of Real GDP for the period during (1980q1 to 1990q2), table (9.9) shows that co-integration relationship were found and the test support the existence of one co-integration equation in the relationship between LGE and LGDP. By looking at the Trace Statistic value in table (9.9), we conclude that we must reject the null hypothesis of no co-integration in of Peacock Wiseman version with, because the Trace Statistic values are greater than the critical values at 5% levels. The existence of co-integration vector has pointed out by trace test since t-test value exceeds critical value in 5% level of significant. This means the co-integration tests are statistically significant at five percent level for determining the long-run relationship between LGE and LGDP. Otherwise, there is long run equilibrium relationship between Real GDP and Government Expenditures has found in Peacock Wiseman version that the trace tests indicates at 5%. At the Trace Statistic value in table (9.9), we can reject the null hypothesis of co-integration in Peacock Wiseman version with respect to real GDP, because the Trace Statistic values are greater than the critical values at 5% levels. Table 9.9: Johansen Co-integration Test results with (Real GDP) from (1980Q1) to (1990Q2) Versions Hypothesized No. of CE(s) Eigen value Trace Statistic (Long Run) Critical Value 5% Prob Peacock Wiseman None 0.51356   33.2534   15.41   0.0000 At most 1 0.08645   3.79   3.76   0.0000 On the other hand , In the case of Real GDP for the period during (1990q3 to 2007q4), table (9.10) shows that co-integration relationship were found and the test support the existence of one co-integration equation in the relationship between LGE and LGDP. By looking at the Trace Statistic value in table (9.9), we conclude that we must reject the null hypothesis of no co-integration in of Peacock Wiseman version with, because the Trace Statistic values are greater than the critical values at 5% levels. The existence of co-integration vector has pointed out by trace test since t-test value exceeds critical value in 5% level of significant. This means the co-integration tests are statistically significant at five percent level for determining the long-run relationship between LGE and LGDP. Otherwise, there is long run equilibrium relationship between Real GDP and Government Expenditures has found in Peacock Wiseman version that the trace tests indicates at 5%. At the Trace Statistic value in table (9.10), we can reject the null hypothesis of co-integration in Peacock Wiseman version with respect to real GDP, because the Trace Statistic values are greater than the critical values at 5% levels. Table 9.10: Johansen Co-integration Test results with (Real GDP) from (1990Q3) to (2007Q4) Versions Hypothesized No. of CE(s) Eigen value Trace Statistic (Long Run) Critical Value 5% Prob Peacock Wiseman None 0.75275   105.5668   15.41   0.0000 At most 1 0.12419 9.1496   3.76   0.0000 9.5.3.2. Co-integration Test with (Real Non-Oil GDP) In the case of Real Non-Oil GDP for the period during (1980q1 to 1990q2), table (9.11) shows that there is long run equilibrium relationship between Real GDP and Government Expenditures has found in Peacock Wiseman version with respect to real non-oil gross GDP at 5% levels . We can reject the null hypothesis of co-integration in Peacock Wiseman version with respect to real non-oil gross GDP table (9.11), because the Trace Statistic values are greater than the critical values at 5% levels. Table 9.11: Johansen Co-integration Test results with (Real Non-Oil GDP) from (1980Q1) to (1990Q2) Versions Hypothesized No. of CE(s) Eigen value Trace Statistic Critical Value 5% Prob Peacock Wiseman None   0.70444   79.2146   15.41   0.0000 At most 1   0.50992   29.2407   3.76   0.0000 On the other hand , In the case of Real Non-Oil GDP for the period during (1990q3 to 2007q4), table (9.12) shows that there is long run equilibrium relationship between Real GDP and Government Expenditures has found in Peacock Wiseman version with respect to real non-oil gross GDP at 5% levels . We can reject the null hypothesis of co-integration in Peacock Wiseman version with respect to real non-oil gross GDP table (9.12), because the Trace Statistic values are greater than the critical values at 5% levels. Table 9.12: Johansen Co-integration Test results with (Real Non-Oil GDP) from (1990Q3) to (2007Q4) Versions Hypothesized No. of CE(s) Eigen value Trace Statistic Critical Value 5% Prob Peacock Wiseman None   0.73329   158.7948   15.41   0.0000 At most 1   0.62460 67.6036   3.76   0.0000 9.5.4. Causality Test: After making sure of the time series model to study the variables that they are not stationary in the level and stationary in the difference, and then check it all-integrated joint, it is clear that there is a long-term equilibrium relationship. According to, Engle and Granger (1987), the variables that integrate common equilibrium reflects a long-term, it should be a representation of Error Correction Model (ECM), which has the potential to test and assess the relationship in the short and long term between the variables of the form, as it avoids  problems arising from the spurious correlation.   To apply the Error Correction Model (ECM) for Peacock Wiseman version in Saudi Arabia, we must employ Granger-causality as follows: In the context of error correction model (ECM) of the variables that are co-integrated. Standard Granger-Causal for the variables that do not co-integrated. 9.5.4.1. Granger Causality Test The Engle and Granger approach have two phases, the first: Assessing the relationship model equilibrium in the long term, called the decline of joint integration.  The second: an assessment error correction model to reflect the relationship in the short term or short-term volatility on the direction of the relationship in the long run, this model is estimated by the introduction of short-term residuum estimated long-term decline in the independent variable Decelerated for a single. In this section we have to test the Granger Causality for Peacock and Wiseman version for real GDP and Non Oil GDP during two periods, firstly from (1980q1) to (1990q2) and from (1990q3) to (2007q4). 9.5.4.1.1. Granger Causality Test from (1980q1) to (1990q2) with Real GDP Table (9.13) shows the probability values from Granger Causality Test for Peacock and Wiseman Version during periods from (1980q1) to (1990q2) with Real GDP. The reported F-statistics are standard test for the joint hypothesis that LGE does not Granger Cause LGDP. In the case of Saudi Arabia, the probability for accepting the Null-Hypothesis was only 0.1% while 99.9% rejecting this hypothesis, which means LGE, cause LGDP by around 99.9% all the time in Peacock and Wisemans Version. In table (9.13) the feedback of causality from LGDP to LGE has presented where the probability for accepting the Null-Hypothesis was, only 2.8% while 97.2% rejecting the hypothesis, which means LGDP cause LGE by about 97.2% all of them in the case of Saudi Arabia. Table 9.13: Granger Causality test for Peacock and Wiseman Version from (1980q1) to (1990q2) with Real GDP Null Hypothesis F-Statistic Prob. LGE does not Granger Cause LGDP 40.212 0.0010 LGDP does not Granger Cause LGE 7.1809 0.0280 The probability values from Granger Causality Test, table (9.14) present the causality test result from (1990q3) to (2007q4) with Real GDP. The reported F-statistics are standard test for the joint hypothesis that LGE does not Granger Cause LGDP. In the case of Saudi Arabia, the probability for accepting the Null-Hypothesis was only (1%) while 99% rejecting this hypothesis, which means LGE, cause LGDP by around 99% all the time in Peacock and Wisemans Version. In table (9.14) the feedback of causality from LGDP to LGE presented where the probability for accepting the Null-Hypothesis was, only 0.1% while 99.9% rejecting the hypothesis, which means LGDP cause LGE by about 99.9% all of them. Table 9.14: Granger Causality test for Peacock and Wiseman Version from (1990q3) to (2007q4) with Real GDP Null Hypothesis F-Statistic Prob. LGE does not Granger Cause LGDP 115.16 0.010 LGDP does not Granger Cause LGE 48.24 0.001 9.5.4.1.2. Granger Causality Test with Real Non-Oil GDP from (1990q3) to (2007q4) The probability values from Granger Causality Test, table (9.15) present the causality test result from (1980q1) to (1990q2) with (Real Non-Oil GDP). The reported F-statistics are standard test for the joint hypothesis that LGE does not Granger Cause LNON_OIL_GDP. In the case of Saudi Arabia, the probability for accepting the Null-Hypothesis was only 41% while 59% rejecting this hypothesis, which means LGE, cause LNON_OIL_GDP by around 59% all the time in Peacock and Wisemans Version. In table (9.15) the feedback of causality from LNON_OIL_GDP to LGE presented where the probability for accepting the Null-Hypothesis was, only 0.1% while 99.9% rejecting the hypothesis, which means LNON_OIL_GDP cause LGE by about 99.9% all of them. Table 9.15: Granger Causality test for Peacock and Wiseman Version from (1980q1) to (1990q2) with (Real Non-Oil GDP) Null Hypothesis F-Statistic Prob. LGE does not Granger Cause LNON_OIL_GDP 1.7821 0.410 LNON_OIL_GDP does not Granger Cause LGE 32.534 0.001 The probability values from Granger Causality Test, table (9.16) present the causality test result from (1990q3) to (2007q4) with (Real Non-Oil GDP). The reported F-statistics are standard test for the joint hypothesis that LGE does not Granger Cause LNON_OIL_GDP. In the case of Saudi Arabia, the probability for accepting the Null-Hypothesis was only 0.9% while 99.1% rejecting this hypothesis, which means LGE, cause LNON_OIL_GDP by around 99.1% all the time in Peacock and Wisemans Version. In table (9.16) the feedback of causality from LNON_OIL_GDP to LGE presented where the probability for accepting the Null-Hypothesis was, only 0.2% while 99.8% rejecting the hypothesis, which means LNON_OIL_GDP cause LGE by about 99.8% all of them. Table 9.16: Granger Causality test for Peacock and Wiseman Version from (1990q3) to (2007q4) with (Real Non-Oil GDP) Null Hypothesis F-Statistic Prob. LGE does not Granger Cause LNON_OIL_GDP 9.5193 0.009 LNON_OIL_GDP does not Granger Cause LGE 40.708 0.002 9.5.4.2. Error Correction Model (ECM) The Error Correction Model (ECM) differs as discussed by Granger (1988) for the number of error correction terms. The concept of error correction is related to co-integration because the co-integration relationship describes the long run equilibrium. If a set of variables are co-integrated, then there exists an error correction model to describe the short run adjustment to equilibrium Engle and Granger (1987). The incidence of mutual co-integration between the variable indicates that the Granger must be Causal in one direction, at least, but the rules of engagement did not refer to the direction of causality between the variables. To verify the rules of engagement we are conducting tests of causation in the context of Error Correction Model (ECM). With regard to periods of lag length, and use the same lag length for Peacock Wiseman version, which we were when we tested for co-integration. In addition, the t-statistics on the coefficients of the lagged error correction term (ECTt-1 (indicate the significance of the long-run causality between the two variables. The statistical significance of the t-statistics is in our tests should be at most 5% level. These analyses regarded as usual analyses of the displacement hypothesis and the hypothesis that the part of national income constant to government expenditure increases with income (Gupta 1967, Diamond 1977, Nomura 1991, 1995). Moreover, Peacock and Wiseman agree with Wagners version of Wagners law. In this section we have to test The Error Correction Model (ECM) for Peacock and Wiseman version for real GDP and Non Oil GDP during two periods, firstly from (1980q1) to (1990q2) and from (1990q3) to (2007q4). 9.5.4.2.1. Error Correction Model (ECM) from (1980q1) to (1990q2) with (real GDP) In the table (9.17), the results from (1980q1) to (1990q2) indicate that there is long-run unidirectional causality that runs from GDP to GE (Peacock Wiseman Version). We draw this conclusion because the sign for GE is positive, and at the same time, the coefficient is statistically significant at the 5%level, Thus, Peacock Wiseman version has found to hold for GDP in the case of Saudi Arabia. Table 9.17: Causality with Error Correction Model (ECM) test from (1980q1) to (1990q2) with (Real GDP) Versions Variables ECTt-1 T-Stat Peacock Wiseman L(GE) 0.0094398 7.69 L(GDP) 0.1036134 1.56 In the table (9.18), the results from (1990q3) to (2007q4) indicate that there is long-run unidirectional causality that runs from GDP to GE (Peacock Wiseman Version). We draw this conclusion because the sign for GE, positive, and at the same time it coefficient is statistically significant at the 5%level, while the signs for GDP is either positive, and/or the coefficient is statistically insignificant at the 5% level. Thus, Peacock Wiseman version has found to hold for GDP in the case of Saudi Arabia. Table 9.18: Causality with Error Correction Model (ECM) test from (1990q3) to (2007q4) with (Real GDP) Versions Variables ECTt-1 T-Stat Peacock Wiseman L(GE) 0.0086968 9.99 LGDP 0.2657211 3.53 9.5.4.2.2. Error Correction Model (ECM) from (1990q3) to (2007q4) with (real Non-Oil GDP) In the table (9.19), the results from (1980q1) to (1990q2) indicate that there is long-run unidirectional causality that runs from Non-Oil-GDP to GE (Peacock Wiseman Version). We draw this conclusion because the sign for GE is positive, and at the same time, the coefficient is statistically significant at the 5%level, Thus, Peacock Wiseman version has found to hold for Non-Oil-GDP in the case of Saudi Arabia. Table 9.19: Causality with Error Correction Model (ECM) test from (1980q1) to (1990q2) with (Real Non-Oil GDP) Versions Variables ECTt-1 T-Stat Peacock Wiseman L(GE) 0.0101674 1.73 L(Non-Oil GDP) 2.316124 6.43 In the table (9.20), the results from (1990q3) to (2007q4) indicate that there is long-run unidirectional causality that runs from Non-Oil-GDP to GE (Peacock Wiseman Version). we draw this conclusion because the sign for GE, is incorrect, negative, and at the same time it coefficient is statistically significant at the 5%level, while the signs for Non-Oil-GDP is either positive, and/or the coefficient is statistically insignificant at the 5% level. Thus, Peacock Wiseman version has found to hold for Non-Oil-GDP in the case of Saudi Arabia. Table 9.20: Causality with Error Correction Model (ECM) test from (1990q3) to (2007q4) with (Real Non-Oil GDP) Versions Variables ECTt-1 T-Stat Peacock Wiseman L(GE) -0.0037897 -3.42 L(Non-Oil GDP) 0.8948453 6.33 9.6. Conclusion According to, (Gupta, 1967) and (Diamond 1977,) argued that the displacement effect led to the share of national income devoted to government expenditures increases with GDP. In this chapter, we examined the relationship between the expenditures and economic growth of Peacock Wiseman version for Saudi Arabia by using time series quarterly data for the periods during (1980Q1 to 1990Q2) and during (1990Q3 to 2007Q4) . It has applied three distinct time series techniques. We have examined the regressions for Peacock Wiseman version by using Ordinary Least Square (OLS) for Real GDP and Non Oil GDP. The displacement literature surveys have shown that the earlier empirical tests of displacement suffer from several methodological compare between the studies has impaired by different choices of periods and data series. The next step is the Unit Root tests by using the Augmented Dickey-Fuller for stationary Unit Root Tests for Real GDP and Non Oil GDP, also we have used Co-integrating test for Real GDP and Non Oil GDP. Finally, Causality tests by using Granger causality tests and Error Correction Model (ECM). First, the regressions for Peacock Wiseman version by using Ordinary Least Square (OLS) for Real GDP and Non Oil GDP, to presents the probability of the equations and to analysis the R-square and DW, for Peacock Wiseman version. Second, The Unit Root tests by using the Augmented Dickey-Fuller for stationary Unit Root Tests for Real GDP and Non Oil GDP. In the case of the levels of the series, the null-hypothesis of non-stationary cannot reject for any of the series. Third, these results suggest that there is a co-integrating relationship between the share of government spending in national output and per capita income. In this situation, if co-integration exists between government expenditure and GDP, then Peacock Wiseman version holds. The equilibrium relationship indicates that the major determinant of government expenditure in Saudi Arabia, in the long run, is national income. Fourth, Granger causality tests have used to confirm the causality direction between the Variables. In the long run we found statistically significant evidence in favour of per capita GDP Granger-causing the share of government Expenditures in GDP, which is consistent with Peacock Wiseman version. The result of causality test indicate that the existence of strong feedback causality for Peacock Wiseman version in the long run. On the other hand, by using Error Correction Model (ECM), the concept of error correction, this has related to co-integration because the co-integration relationship describes the long run equilibrium. If a set of variables are co-integrated, then there exists an error correction model to describe the short run adjustment to equilibrium. Overall studies with the exception of Pryor (1968), the time dimension has completely suppressed, despite the fact that the Peacock and Wiseman hypothesis purports to explain the development of government expenditure over time.