Production Efficiency Study Of Statistics

Production efficiency study of statistics(PESS) is using cost-benefit analysis (CBA) to assesses different kinds of data's production methods and production processes, to balance the needs of data users against the burden placed on suppliers, with minimal data production costs to obtain satisfactory data quality -the best relative data quality. In this PESS's thesis, has described the corresponding analysis methods of assessing the costs and benefits of these data, to develop a production efficiency analytic framework.A key aim of this research has been to improve the relative quality of statistical data. By comparing the statistical production efficiency performance between the statistical input and output level, namely seeking a better balance between benefits and costs, for the maximum relative data quality, is the innovation point in this thesis. And one of the challenges for CBA in the PESS is to be able to shed light on such trade-offs. Introduced the CBA into PESS, the problems are as followed. First, how to define and breakdown the relative cost of statistical input and the relative benefit of data outputs. Second, as CBA is not usually applied to the provision of information or to macroeconomic policies, the approach needs to be adapted to this different environment. So that, developed ways of assessing the costs and benefits of these data production, to estimation of relative costs and benefits are very important in the cost-benefit analysis project.This paper constructs the production efficiency analytic framework of the official general statistics, followed by the outline of the study as "theory research-constitute assesses models and tools - Case Study - Prospects". Here is a brief summary for each of the six chapters in this paper:The first chapter introduces the background of the research. Then reviews the development of PESS, that the theory and application of CBA to statistics:the relative cost models and benefit assessment tool have been used in statistics reviews in foreign countries; but in China, this study has just begun, and the most similar research was to constructs the quality cost academic system of the official statistical data.In the chapter two, by defines and analyzes PESS, assumes that costs and benefits can be related to data quality in at least a moderately well-behaved way. In particular it is assumed that as data quality rises, improvements in data quality become progressively more costly to achieve, and they deliver fewer incremental benefits to users. Data collection inevitably imposes some costs upon producing institutions, and data users will gain benefit from the data outputs.Therefore, using CBA, to assesses different kinds of data's'production methods and production processes, to balance the needs of data users against the burden placed on suppliers, to search for the best production efficiency of statistics, is feasible.The third chapter focuses on how statistical data production input costs can be estimated. These monetary valuations of costs are difficult in practice, but we can consider developing a model to estimate the relative producing cost of the different forms used to collect statistical data. Also, data producing institutions incur set-up costs and costs in dealing with follow-up questions on their data production. Ways of reducing these costs are discussed.The fourth chapter discusses how benefits can be assessed, some of the problems encountered in trying to obtain a monetary estimate, and the approach that has been developed within the statistical data producing institutions to assess the relative benefits of different data collections. Monetary valuation of both costs and benefits has proved elusive, but estimation of elative costs and benefits has been more tractable.The previous chapters outlined how costs and benefits can be assessed. This fifth chapter brings together these analyses of relative costs and relative benefits, uses a simplified framework to illustrate the potential gains from applying CBA to statistics. In conventional CBA of production efficiency analytic framework, a decision would depend on whether the benefits exceed the costs, which requires a monetary valuation for both sides. Because the analysis here is in terms of relative costs and benefits, it does not necessarily follow that a data collection with high cost and low benefit should be discontinued. Rather, this indicates an area where it is more likely that data may be no longer required, or where an estimated alternative would suffice; also where the potential gains from action are largest. What's more, two examples have been given in chapter 5 about applies of PESS in Monetary and Financial Statistics in England. To sum up, this chapter has summarized some of the key questions to be asked, formed a production efficiency analytic framework, depending on the balance of costs and benefits.In the last chapter, has considered some other questions about PESS and the domestic PESS's current situation. At last, it gives the direction of the research in the future.The production efficiency analytic framework is dynamic and the tools and approaches will be updated as required, including any change indicated by greater understanding of the uses of statistical data and the key determinants of institutions'statistical producing costs. Over time, the PESS framework should help the statistical data producing institutions to focus its efforts on those data that are most important to users, while bearing down on the burdens imposed on data providers. At the same time, it will look for ways of strengthening further links with both users and providers of data. The principles underlying PESS:namely seeking a better balance point between benefits and costs, rather than the highest possible quality of data, regardless of cost. This is the point which maximizes the total net value of benefits less costs -the best relative data quality.