Academic literature on the topic 'Analysis of the efficiency of the use of fixed assets'

The textbook contains an overview of the general theory and methodology of economic analysis, production results, the use of material and labor resources, as well as fixed assets, production and sales costs, and an assessment of the effectiveness of an enterprise. Self-study questions and test material for each chapter are presented. Meets the requirements of the latest generation of federal state educational standards for higher education. It is intended for students, postgraduates, teachers of economic universities, managers, students of advanced training courses and business schools.

The textbook contains the main elements of the methodology for diagnostics and analysis of financial and economic activities of the organization. The chapters of the textbook consider the role, significance, types, methods of analysis of financial and economic activities of the organization, its mathematical tools, the history and new directions of analysis of the organization's activities in Russian and foreign analytical practice, as well as methods for analyzing the volume of production, sales and cost of production, the analysis of security and use of material and labor resources, fixed assets, analysis of financial results and financial condition of the organization.
 Meets the requirements of the Federal state educational standard of higher education of the latest generation.
 For University students in the field of training 38.03.01 " Economics "and other areas, studying the disciplines" Analysis and diagnostics of financial and economic activities"," Complex economic analysis", as well as students in the system of retraining.

AbstractSince the rapid growth of big data technology and the continuous development of information technology in recent years, the significance of network security monitoring is increasing consistently. As one of the major tools to secure the system environment, organizations use various monitoring devices to govern the utilities of networks, hardware and applications. Meanwhile, massive and redundant data are produced by these devices constantly, which make a huge problem for analysts and scientists who are willing to extract useful information from them, and even impact the accuracy and efficiency of the monitoring systems. In this paper, we employ random forest algorithm and propose an ensemble learning model under certain scenarios with fixed data features. We use a preprocessing method to balance positive and negative samples, and then use 6 different intrusion detection systems as weak classifiers, which satisfy the rules of “partial sampling” and “partial features selection” of ensemble learning. Finally, we test three combination strategies, including relative majority voting, weighted voting and stacking, to combine the predictions. Experiments show that stacking has a better performance than the other two, with a score of 98.25% in recall, and achieves a 47.91% precision.

An important factor in the sustainable development of the economy is the effective use of the enterprise's capital, which includes fixed assets. Fixed assets ensure the simultaneous implementation of interrelated business processes: supply, production of products and their implementation. The purpose of the study is to study the economic and accounting essence of fixed assets, the analysis of scientific approaches to their classification; determination of the place and role of fixed assets in the depreciation policy of the enterprise; development of alternative approaches to the process of documenting fixed assets. Methodology. In the context of modern scientific research, the definition of the category "fixed assets" was considered, which was formed in the process of the evolution of economic thought. The criteria for recognition of fixed assets are summarized, which include: economic benefit, reliability of value, material form, functional purpose, term of use. It has been proven that the results of research by Ukrainian scientists have expanded the economic nature of the essence of fixed assets. Scientific approaches to the construction of classifications are systematized, their rules are highlighted. Through the prism of the dialectical method and logic, the economic essence of the categories "depreciation" and "depreciation policy" was analyzed. Using a terminological approach, it is proven that depreciation of fixed assets is a complex multifaceted category. It includes economic, financial and accounting aspects that determine different approaches to its understanding. Results. Classification is a method of learning the object being studied: the more signs of classification, the higher the degree of their study, and this contributes to increasing the efficiency of management. Classification as a method of grouping objects involves three stages of creation: 1) determination of the characteristic by which division is carried out; 2) the order of construction of the classification; 3) adaptation – the procedure of its practical use. The conducted study considered and summarized the views of Ukrainian scientists on the classification of fixed assets. The expediency of their use in the accounting practice of enterprises is substantiated. It has been proven that scientific approaches to the grouping of fixed assets are more analytical and will provide management with the most detailed information. The study summarizes theoretical and practical approaches to the construction of depreciation policy of fixed assets, highlights its components, analyzes their elements and options for selection. The mechanism of depreciation and depreciation policy includes: 1) the process of gradually transferring the cost of fixed assets to the manufactured product; 2) monetary reimbursement of the cost of fixed assets as part of income from the sale of finished products (goods, works, services); 3) accumulation of depreciation resources and their further targeted use. Practical implications. Depreciation policy as a component of the company's accounting policy covers all areas of fixed asset accounting, regardless of the form of their operation. As practice shows, an effective depreciation policy enables the enterprise to stimulate investment processes, develop innovative technologies and increase its competitiveness. An effective fixed asset management system is inextricably linked to both the depreciation policy and the documentation process. On the one hand, documentation is an object of management that requires managerial influence on the methodology and organization of the processes of collection, registration, storage and transfer of information resources. On the other hand, it is a prerequisite for management decisions, providing their informational support. Value/originality. The study determined the stages of accounting information movement and built a conceptual model of information flows that are formed in the system of accounting for fixed assets. An alternative approach has been developed in documenting information about fixed assets. New accounting documents were developed, their purpose and feasibility of use in the accounting practice of enterprises was revealed. It is proved that the proposed documents are meaningful, informative and can be used in the construction of an electronic document circulation system.

Abstract Investment information is often statistically insignificant. MV optimization is insensitive to investment insignificance, resulting in frequent but ineffective and costly portfolio rebalancings. In current investment practice, portfolio rebalancing and monitoring rules are largely ad hoc. For example, institutions commonly rebalance their portfolios on a calendar basis, such as monthly, quarterly, or annually. Another widely used rule is to rebalance a portfolio if asset weights exceed a predetermined fixed range, such as ±5%, of optimal or benchmark weights. Ad hoc rules exist to limit the number of rebalancings and to avoid trading on insignificant information. A proper and reliable portfolio rebalancing rule is necessarily statistical. An investor will want to know whether trading a portfolio for a presumably more optimal one is likely to significantly improve performance in the investment period. If the current portfolio is not statistically significantly different from the optimal, trading is unlikely to be productive. The challenge lies in identifying statistically significant differences. This chapter introduces statistically based rigorous portfolio trading rules for linear constrained MV optimized portfolios. Rigorous statistical rules have not been available until now because standard analytical techniques do not treat linear constrained MV optimization.

This chapter describes the environmental assessment of hybrid broadband satellite systems using the latest lifecycle analysis (LCA) techniques. The BATS (Broadband Access via integrated Terrestrial and Satellite systems) project has based its assessments on the GHG Protocol approach, with the ETSI LCA and ITU-T L.1400 methodology series used to provide more detailed guidance where this is needed. This assessment has shown that it is possible to employ cut off rules and approximations to reduce the cost or length of an assessment and still provide useful results. The assessment has shown that, using current technology for the IUG and satellite modem, the carbon footprint of a hybrid broadband satellite network can be comparable with that of a terrestrial (fixed or mobile) network for an equivalent level of service when the use of low-power states are considered. The implementation of these states in future commercial systems should be explored. Finally ways are suggested to reduce the environmental impact of hybrid broadband satellite systems, in particular by reducing energy (power) consumption during the use stage. Key issues include how to compare the energy efficiency of a hybrid broadband satellite network with other methods of broadband delivery, how to accurately assess the carbon embodied during the production of the equipment, and how to use low-power modes to reduce energy consumption without affecting the response time or quality of experience (QoE).

Implementation of strategic and current tasks of operational management is impossible without the use of appropriate production capacity. An important place in its structure is occupied by fixed assets. Due to their availability and use in accordance with the production program, a resource base is formed to increase production, provide services or perform work, diversify them, improve quality characteristics. This contributes to gaining significant advantages in ensuring the competitiveness of the enterprise and its products. To reveal the purpose and objectives of the research, the article uses general scientific (analysis, synthesis, induction, deduction) and special research methods (system approach, graphical method, comparative analysis, modeling). The scientific article examines the existing methods of analysis of fixed assets. Its critical assessment is given, the shortcomings of use in the conditions of market economy and information needs of managers and investors of agrarian business enterprises are pointed out. Theoretical and methodological principles of improving the analysis of fixed assets of agricultural enterprises are formulated. A model of the methodology of analysis of fixed assets in the system of real investment management is proposed. To reveal the content of the proposed method, the practical aspects of the implementation of the method of analysis of fixed assets in the management of real investments of agricultural enterprises are considered. Analytical indicators for information support of management of each stage of real investment (at the stage of real investment planning and the stage of realization of investments in fixed assets of agricultural enterprise) are identified.

Strategic management is one of the most effective modern tools that allows taking into account, in addition to long-term planning methods, a set of possibilities for their adaptation to external business conditions by adjusting the reserves for the formation and use of current assets of a trade enterprise. The variety of strategic management tools allows applying them to different areas of management, types of enterprises and their elements. However, so far, the management of current assets of domestic enterprises has been limited exclusively to current management, which carried out using traditional methods. The purpose of the paper is to demonstrate the theoretical and methodological foundations of current assets management based on developing methodological provisions and practical recommendations for the implementation of strategic management of current assets as an element of the overall strategy of trade enterprise. Methodology based on general research methods of analysis and synthesis, induction and deduction, observation and abstraction, which used to systematize achievements in the theory and practice of strategic management of current assets as an element of the overall strategy of trade enterprise. Results of the survey showed that the expediency of calculating the synergistic effect of the return on current assets as one of the most significant indicators of assessing the efficiency of their use in the process of strategic management substantiated. Practical implications. The substantiation of the main provisions, methods, recommendations and tools for the application of strategic management of current assets at trade enterprises, which will ensure the increase in the effectiveness of their use and can be applied in practical activities. Value/originality. The system of indicators for evaluating the efficiency and effectiveness of the strategic management of the current assets of a trade enterprise, which includes the main and auxiliary indicators and the indicators proposed by the author. The structural and logical sequence of evaluating the effectiveness of the strategy of managing the current assets of trade enterprises, which will allow improving the evaluation process and orient it to determining the degree of achievement of target levels of performance indicators.

Inoculation of suitable rhizobia enhances biological nitrogen fixation in soybean production and are economically viable for use among smallholder farmers due to its low price over inorganic commercial fertilizer blends. In Mozambique, inoculants are available in liquid or solid form (powder/peat or granular). Field studies were conducted in 2017 and 2018 seasons in three agroecologies (Angonia, Nampula and Ruace) in Mozambique to evaluate the performance of inoculants when applied directly to soil and on seed before planting. Data on nodulation, plant growth, nitrogen fixed, 13C isotope discrimination related water use efficiency, yield and yield components were analyzed in Statistical Analysis System® 9.4. Nodulation, yield, and yield components were significant for the different application methods, and solid form tended to be better than liquid form. The nitrogen derived from atmosphere (%Ndfa) were 45.3%, 44.2% and 43.6% with a yield of 2672, 1752 and 2246 kg ha−1 for Angonia, Nampula and Ruace, respectively. Overall, inoculants applied on soil or seed increase the amount of biologically fixed nitrogen and has the potential of improving soybean productivity in Mozambique.

Evolution of technology summons risks. With the use of complex prototypes and methods, not only the decision-making propensity of the machines increases but also the risk assessment reduces and frauds increased. Machine learning (ML) is considered an appropriate solution for the management of risks as it can produce the desired solution with less human effort. So, to minimize the possibility of risks, certain methods are adopted that benefited through ML. The chapter provides an insight into various applications of ML techniques in the field of risk analysis. The application of ML in this sector has led to a fact that these methods can be used to analyze huge amounts of data with efficient predictive analysis. Moreover, the future of machine learning in risk analysis and management is presented bringing out the positive picture. As a conclusion, one can just say that humans will be seeing an era which will make even complex problems easy to solve with efficiency. The chapter concludes with some limitations which need to be fixed for better risk management.

Abstract Most assets in the oil and gas sector that use metal substrates e.g., pipe supports, or structures are susceptible to corrosion problems primarily because of continual exposure of damaged areas to severe environmental conditions. The proliferation and merging of corrosion issues can cause catastrophic failure of materials, resulting in a reduction in the service life of the materials. To avoid these issues, it is crucial to protect the scratch or minor crack area as quickly as possible so that it does not spread too far. In this case, self-healing as a smart material can be applied to the coating system to repair coating damage instantaneously. Self-healing works in multiple functions where it can be used to protect against corrosion and at the same time heal the damaged area itself. In this research, the experimental work includes boronic ester (BE) and polyurethane (PU) synthesis, BE-PU coating specimen preparation, characterization and performance analysis. BE was introduced in the polyurethane (PU) and applied as an additional coating layer to the existing coating system according to PETRONAS technical standard (PTS) requirement with a thickness of approximately 300-400 μm. The healing performance of the scribed coating at room and 60°C with 80% humidity level was initially evaluated using 3D profilometer microscopy and electrical impedance spectroscopy (EIS). Corrosion resistance performance was evaluated by performing long term 3000 hours salt spray and 4200 hours cyclic UV. The mechanical performance or compatibility of the self-healing with current coating system was also assessed using a pull-off adhesion test and abrasion test. The assessment of the scratched BE-PU self-healing using 3D profilometer microscopy demonstrated remarkable healing efficiency of more than 85% and has excellent corrosion resistance performance through salt spray and cyclic UV test. In addition, BE-PU self-healing showed significant adhesion efficiency with more than 300 psi and abrasion performance.

Abstract Sand production and erosion pose significant challenges in the oil and gas industry, leading to equipment damage, production loss, and costly repairs. Existing sand control techniques such as well completion, sand screens, sand separation, or predictive models for sand production are not 100% effective. This paper presents a comprehensive approach to sand management utilizing complimentary non-intrusive erosion monitoring solutions. The focus is on the integration of acoustic sensors and ultrasonic thickness sensors to detect and assess sand particles in pipelines and process equipment. The use of acoustic sensors enables the detection and characterization of particles, including sand, through their unique acoustic signatures. These sensors provide real-time data, allowing for proactive monitoring of sand production and erosion. In combination with ultrasonic thickness sensors, which provide accurate measurements of material thickness, a risk and impact calculation can be derived. By analysing the risk value obtained from the acoustic sensor and the impact value obtained from the thickness sensor, an overall assessment of asset health can be determined. To facilitate effective sand management, the proposed solution incorporates the integration of these sensors into a single package, accompanied by specialised software. The software allows for comprehensive data analysis, trending, and future predictions. By utilising this integrated system, operators can gain actionable insights into the condition of their assets, enabling timely intervention and mitigation strategies. The benefits of this approach include enhanced asset integrity, reduced downtime, and improved maintenance planning. Additionally, the ability to monitor sand production and erosion in real-time empowers operators to optimize production rates while ensuring the integrity of their infrastructure. This paper presents a practical and cost-effective solution for effective sand management in the oil and gas industry. By leveraging complimentary non-intrusive erosion monitoring solutions, operators can mitigate risks associated with sand production, extend the operational life of their equipment, and optimize production efficiency.

The article presents an analysis of indicators of the state, movement, efficiency of using the fixed assets of LLC PEK. It is shown that in the enterprise there is a decrease in the cost and aging of fixed assets. Proposed priority measures to increase the efficiency of the use of property, plant and equipment of LLC PEK

<p>Sund & Bælt is a technology-driven infrastructure company that owns and operates the fixed links across Storebælt and Øresund and, within a few years, the link across the Fehmarnbelt to Germany. These facilities were built under the framework of the Danish state-guarantee model and have significantly improved daily travel for more than 250,000 customers.<p>Our current challenges are that our assets are aging with increasing costs and the learning curve for our knowledge of the assets is becoming more flat. This means that we will have to increase our use of technology to lower costs and to analyse our processes and methods more detailed and that we will have to analyse and redesign our assets through optimisation of our procurement processes.<p>Sund & Bælt is currently using a digital asset management system and is further digitising operation of our major bridges and infrastructure facilities in partnership with external technology experts to deliver unique solutions that will increase efficiency within the infrastructure area - not only at Sund & Bælt, but also within the industry as a whole.<p>The following paper describes, in details, how the bridges are managed, the ambition, the plan, results achieved so far, the technologies involved and finally the perspectives and conclusions.

Abstract The large quantity of batch wells typical in unconventional fields results in per well incremental optimisation gains compounding and significantly improving asset economics. A disposable, cost effective, fibre optic intervention system safely and efficiently provides high quality data to optimise completion design and hydraulic fracturing, calibrate reservoir models and inform future well design. CO₂ efficiency estimates and technology applications in unconventional fields including Vertical Seismic Profiling (VSP), cement cure analysis and hydraulic fracture optimisation are outlined. The upper end of one or two bare fibre optic cables are fixed inside a light-weight pressure control cap, while the downhole ends are deployed along the wellbore as they unspool from a disposable probe which is pumped along horizontal sections. Distributed Acoustic Sensing (DAS) is used for seismic and micro-seismic fracture analysis, whereas Distributed Temperature Sensing (DTS) is used to monitor well and near wellbore fluids. The high-quality data is used both in real time and subsequent enhanced analysis with disposable fibre having been successfully deployed in over 200 unconventional wells. The high level of acoustic and strain coupling as well as the elevated sensitivity of disposable bare optic fibre produce high quality data, with recent studies favourably comparing disposable fibre data with that obtained by retrievable fibre optic intervention. When compared with well monitoring methods utilising Wireline or Coiled Tubing, the disposable fibre system is extremely light weight with a micro footprint, requires only one person to deploy, has static seals, and does not require a BOP. The result is a low risk, efficient, method of highly sensitive, complete wellbore, fibre optic sensing. The transportation weight saving, reduced survey time, power consumption and reduced personnel requirements result in significant operational CO₂ reductions. Examples of unconventional well VSP, cement cure analysis, well integrity and fracture optimisation applications demonstrate efficiency gains and impact on asset economics. Unconventional field lifecycle applications of a new disposable fibre optic system are presented along with field optimisation and economic benefits. In addition, an example operation illustrating comparative CO₂ emissions for a hydraulic fracture monitoring application demonstrates disposable fibre CO₂ emissions at 8% of comparable wireline operations.

Abstract At the request of a major offshore producer in Thailand, an analytic edge-based leak detection vision technology was developed and implemented. The solution was the result of modifications made to an onshore system that was engineered in collaboration with a midstream operator and has been proven in various aboveground facilities and other industrial monitoring applications. One of the key changes involved making the product explosion-proof (Ex). The modified AI vision system addresses 1) the unique challenges of offshore platform environments (remoteness, harsh climate, network limitations, and high safety risks), and 2) customer coverage requirements: continuous, autonomous monitoring of distributed assets with automated detection and alarming on early-stage leaks and/or leaks of a particular size. Aside from leak detection, the system can also be implemented for exhaust vents monitoring prior to well pad remote start up, fire detection and other applications. It is anticipated that the solution will deliver the benefits and advantages intended by design as well as those that were gained by onshore operators with the use of the original AI leak detection vision system. These include higher operational efficiency, improved event detection and alarm validation capabilities, enhanced automation (e.g.: remote shutdown), up to 90% workload reduction as a direct result of significantly lowered false alarms along with 50% decrease in monitoring related costs and site visits. The success of the first installation in 2019 was followed by system deployment at additional well pads, with continued expansion planned. The growing demand and the positive experience with the technology together demonstrates the viability, value and potential of artificial intelligence powered cameras for remote monitoring of offshore platform assets and processes.

The article develops measures to improve the efficiency of use of fixed assets of agrarian formations. The main problems of efficiency of using fixed assets at the enterprises of agroindustrial complex in the south of the Rostov region have been identified within the framework of the study. The relationship of forecasting and planning with other functions of management has been revealed, and also stages of implementation of planning system at the enterprise and sequence of their realization directed on increase of efficiency of use of fixed assets of the agricultural enterprise have been determined.

Abstract Major Rotating equipment assets availability and reliability is critical for oil and gas companies. Current practice to ensure high availability and reliability is carrying out fixed running hours based preventive maintenance, which then dictates the Plant shutdown intervals, and increase the operating cost. Operational excellence, high efficiency and reliability, can be maintained and even enhanced further with latest digital technology enablers, Digital technology that uses advanced analytical and modeling techniques that understand equipment and operating behavior – to predict deviations in "optimal performance" versus "current performance" months ahead of time ("hidden" plant opportunities early identified). The company in its pursuit to operation excellency has identified and deployed solutions to enhance efficiency, reliability, improve performance and reduce operating costs related to Major rotating equipment operation and maintenance (Centrifugal, screw & Reciprocating & Compressors, Gas turbines, Steam turbines, Pumps, Power turbine and turbo Expander). The solution installed, commissioned and operates Smart Predictive analytics & diagnostics solutions (CPAD) to monitor analysis, anomaly predict and assess the conditions of Major Rotating Equipment. The Predictive diagnostics are based on modelling techniques coupled with machine learning algorithms deployed to achieve the early prediction of conditions and performance degradation causes & symptoms, ensures timely intervention by making small changes on line to prevent subsequent failures and extend the equipment remnant live, expand the planned shutdown intervals and reduce unplanned shutdowns. The solution is a data-driven to leverage existing data and infrastructure, provide insights and actionable warnings on equipment performance, reliability and process problems using the existing instrumentation on any type of equipment or processes, regardless of equipment manufacturer or agnostic to equipment age or class.

With the proliferation of Free Trade Agreements (FTAs) over the past decade, demand for quantitative analysis of their likely impacts has surged. The main quantitative tool for performing such analysis is Computable General Equilibrium (CGE) modeling. Yet these models have been widely criticized for performing poorly (Kehoe, 2002) and having weak econometric foundations (McKitrick, 1998; Jorgenson, 1984). FTA results have been shown to be particularly sensitive to the trade elasticities, with small trade elasticities generating large terms of trade effects and relatively modest efficiency gains, whereas large trade elasticities lead to the opposite result. Critics are understandably wary of results being determined largely by the authors’ choice of trade elasticities. Where do these trade elasticities come from? CGE modelers typically draw these elasticities from econometric work that uses time series price variation to identify an elasticity of substitution between domestic goods and composite imports (Alaouze, 1977; Alaouze, et al., 1977; Stern et al., 1976; Gallaway, McDaniel and Rivera, 2003). This approach has three problems: the use of point estimates as “truth”, the magnitude of the point estimates, and estimating the relevant elasticity. First, modelers take point estimates drawn from the econometric literature, while ignoring the precision of these estimates. As we will make clear below, the confidence one has in various CGE conclusions depends critically on the size of the confidence interval around parameter estimates. Standard “robustness checks” such as systematically raising or lowering the substitution parameters does not properly address this problem because it ignores information about which parameters we know with some precision and which we do not. A second problem with most existing studies derives from the use of import price series to identify home vs. foreign substitution, for example, tends to systematically understate the true elasticity. This is because these estimates take price variation as exogenous when estimating the import demand functions, and ignore quality variation. When quality is high, import demand and prices will be jointly high. This biases estimated elasticities toward zero. A related point is that the fixed-weight import price series used by most authors are theoretically inappropriate for estimating the elasticities of interest. CGE modelers generally examine a nested utility structure, with domestic production substitution for a CES composite import bundle. The appropriate price series is then the corresponding CES price index among foreign varieties. Constructing such an index requires knowledge of the elasticity of substitution among foreign varieties (see below). By using a fixed-weight import price series, previous estimates place too much weight on high foreign prices, and too small a weight on low foreign prices. In other words, they overstate the degree of price variation that exists, relative to a CES price index. Reconciling small trade volume movements with large import price series movements requires a small elasticity of substitution. This problem, and that of unmeasured quality variation, helps explain why typical estimated elasticities are very small. The third problem with the existing literature is that estimates taken from other researchers’ studies typically employ different levels of aggregation, and exploit different sources of price variation, from what policy modelers have in mind. Employment of elasticities in experiments ill-matched to their original estimation can be problematic. For example, estimates may be calculated at a higher or lower level of aggregation than the level of analysis than the modeler wants to examine. Estimating substitutability across sources for paddy rice gives one a quite different answer than estimates that look at agriculture as a whole. When analyzing Free Trade Agreements, the principle policy experiment is a change in relative prices among foreign suppliers caused by lowering tariffs within the FTA. Understanding the substitution this will induce across those suppliers is critical to gauging the FTA’s real effects. Using home v. foreign elasticities rather than elasticities of substitution among imports supplied from different countries may be quite misleading. Moreover, these “sourcing” elasticities are critical for constructing composite import price series to appropriate estimate home v. foreign substitutability. In summary, the history of estimating the substitution elasticities governing trade flows in CGE models has been checkered at best. Clearly there is a need for improved econometric estimation of these trade elasticities that is well-integrated into the CGE modeling framework. This paper provides such estimation and integration, and has several significant merits. First, we choose our experiment carefully. Our CGE analysis focuses on the prospective Free Trade Agreement of the Americas (FTAA) currently under negotiation. This is one of the most important FTAs currently “in play” in international negotiations. It also fits nicely with the source data used to estimate the trade elasticities, which is largely based on imports into North and South America. Our assessment is done in a perfectly competitive, comparative static setting in order to emphasize the role of the trade elasticities in determining the conventional gains/losses from such an FTA. This type of model is still widely used by government agencies for the evaluation of such agreements. Extensions to incorporate imperfect competition are straightforward, but involve the introduction of additional parameters (markups, extent of unexploited scale economies) as well as structural assumptions (entry/no-entry, nature of inter-firm rivalry) that introduce further uncertainty. Since our focus is on the effects of a PTA we estimate elasticities of substitution across multiple foreign supply sources. We do not use cross-exporter variation in prices or tariffs alone. Exporter price series exhibit a high degree of multicolinearity, and in any case, would be subject to unmeasured quality variation as described previously. Similarly, tariff variation by itself is typically unhelpful because by their very nature, Most Favored Nation (MFN) tariffs are non-discriminatory in nature, affecting all suppliers in the same way. Tariff preferences, where they exist, are often difficult to measure – sometimes being confounded by quantitative barriers, restrictive rules of origin, and other restrictions. Instead we employ a unique methodology and data set drawing on not only tariffs, but also bilateral transportation costs for goods traded internationally (Hummels, 1999). Transportation costs vary much more widely than do tariffs, allowing much more precise estimation of the trade elasticities that are central to CGE analysis of FTAs. We have highly disaggregated commodity trade flow data, and are therefore able to provide estimates that precisely match the commodity aggregation scheme employed in the subsequent CGE model. We follow the GTAP Version 5.0 aggregation scheme which includes 42 merchandise trade commodities covering food products, natural resources and manufactured goods. With the exception of two primary commodities that are not traded, we are able to estimate trade elasticities for all merchandise commodities that are significantly different form zero at the 95% confidence level. Rather than producing point estimates of the resulting welfare, export and employment effects, we report confidence intervals instead. These are based on repeated solution of the model, drawing from a distribution of trade elasticity estimates constructed based on the econometrically estimated standard errors. There is now a long history of CGE studies based on SSA: Systematic Sensitivity Analysis (Harrison and Vinod, 1992; Wigle, 1991; Pagon and Shannon, 1987) Ho