Pub Date : 2018-09-07DOI: 10.1093/OXFORDHB/9780190226718.013.5
R. Färe, S. Grosskopf, D. Margaritis, William L. Weber
The focus of this chapter is to move the measurement of efficiency and productivity from a static to a dynamic approach using distance functions. Since distance functions represent technology, the authors first specify that technology in a dynamic framework is amenable to data envelopment analysis (DEA)–type estimation, explicitly allowing current (or past) decisions to affect future production possibilities. This includes notions of intermediate products, investment, time substitution, supply chain, networks and possible reallocations across time. The chapter shows how to estimate dynamic distance functions and specify a multi-period dynamic model in the spirit of Ramsey (1928), as well as an adjacent-period model familiar from the Malmquist productivity literature, providing an empirical illustration of the former. Extensions of these dynamic models is relatively straightforward for other distance function–based productivity indices, both parametric and nonparametric, as well as for production in the presence of good and bad outputs.
{"title":"Dynamic Efficiency and Productivity","authors":"R. Färe, S. Grosskopf, D. Margaritis, William L. Weber","doi":"10.1093/OXFORDHB/9780190226718.013.5","DOIUrl":"https://doi.org/10.1093/OXFORDHB/9780190226718.013.5","url":null,"abstract":"The focus of this chapter is to move the measurement of efficiency and productivity from a static to a dynamic approach using distance functions. Since distance functions represent technology, the authors first specify that technology in a dynamic framework is amenable to data envelopment analysis (DEA)–type estimation, explicitly allowing current (or past) decisions to affect future production possibilities. This includes notions of intermediate products, investment, time substitution, supply chain, networks and possible reallocations across time. The chapter shows how to estimate dynamic distance functions and specify a multi-period dynamic model in the spirit of Ramsey (1928), as well as an adjacent-period model familiar from the Malmquist productivity literature, providing an empirical illustration of the former. Extensions of these dynamic models is relatively straightforward for other distance function–based productivity indices, both parametric and nonparametric, as well as for production in the presence of good and bad outputs.","PeriodicalId":287755,"journal":{"name":"The Oxford Handbook of Productivity Analysis","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123082995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-07DOI: 10.1093/OXFORDHB/9780190226718.013.16
Per J. Agrell, P. Bogetoft
State-of-the-art benchmarking methods, and in particular data envelopment analysis (DEA) and stochastic frontier analysis (SFA), are well-established and informative tools in economic regulation to set reasonable revenue caps for energy network operators. However, regulatory benchmarking is not just another application of productivity analysis. This chapter reviews the economic theory upon which these applications are based and the additional stages in activity analysis, data analysis, and model development that are necessary. The chapter also provides detailed analyses of the applications in international electricity transmission and for the Norwegian electricity distribution networks.
{"title":"Theory, Techniques, and Applications of Regulatory Benchmarking and Productivity Analysis","authors":"Per J. Agrell, P. Bogetoft","doi":"10.1093/OXFORDHB/9780190226718.013.16","DOIUrl":"https://doi.org/10.1093/OXFORDHB/9780190226718.013.16","url":null,"abstract":"State-of-the-art benchmarking methods, and in particular data envelopment analysis (DEA) and stochastic frontier analysis (SFA), are well-established and informative tools in economic regulation to set reasonable revenue caps for energy network operators. However, regulatory benchmarking is not just another application of productivity analysis. This chapter reviews the economic theory upon which these applications are based and the additional stages in activity analysis, data analysis, and model development that are necessary. The chapter also provides detailed analyses of the applications in international electricity transmission and for the Norwegian electricity distribution networks.","PeriodicalId":287755,"journal":{"name":"The Oxford Handbook of Productivity Analysis","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121814547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-07DOI: 10.1093/OXFORDHB/9780190226718.013.10
Roberto García-Castro, J. Ricart, M. Lieberman, N. Balasubramanian
Productivity gains play a crucial role in value creation and distribution in firms. This chapter connects the strategy framework of value creation and value capture with the tools from the productivity literature in order to understand better how returns are distributed between different stakeholders in the business and how this distribution might evolve over time. The authors distinguish between business model innovation and replication as two genuine sources of value creation. The historical analysis of Southwest Airlines in the US airline industry illustrates the insights that can be gained using a formal model to measure productivity gains at the firm level.
{"title":"Business Model Innovation and Replication","authors":"Roberto García-Castro, J. Ricart, M. Lieberman, N. Balasubramanian","doi":"10.1093/OXFORDHB/9780190226718.013.10","DOIUrl":"https://doi.org/10.1093/OXFORDHB/9780190226718.013.10","url":null,"abstract":"Productivity gains play a crucial role in value creation and distribution in firms. This chapter connects the strategy framework of value creation and value capture with the tools from the productivity literature in order to understand better how returns are distributed between different stakeholders in the business and how this distribution might evolve over time. The authors distinguish between business model innovation and replication as two genuine sources of value creation. The historical analysis of Southwest Airlines in the US airline industry illustrates the insights that can be gained using a formal model to measure productivity gains at the firm level.","PeriodicalId":287755,"journal":{"name":"The Oxford Handbook of Productivity Analysis","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130857335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-07DOI: 10.1093/OXFORDHB/9780190226718.013.7
W. Diewert
Governments either provide various goods and services at no cost or at highly subsidized prices. It is usually possible to measure the quantities of these government-sector outputs and inputs as well as input prices, but the problem is how to estimate the corresponding output prices. Once meaningful output prices have been estimated, the measurement of productivity growth using index numbers can proceed in the usual manner. This chapter suggests three possible general methods for measuring public-sector output prices and quantities. Specific measurement issues in the health and education sectors are discussed. Similar output and productivity measurement issues arise in the regulated sectors of an economy since regulated producers are forced to provide services at prices that are not equal to marginal or average unit costs. Finally, the problems associated with measuring capital services are discussed.
{"title":"Productivity Measurement in the Public Sector","authors":"W. Diewert","doi":"10.1093/OXFORDHB/9780190226718.013.7","DOIUrl":"https://doi.org/10.1093/OXFORDHB/9780190226718.013.7","url":null,"abstract":"Governments either provide various goods and services at no cost or at highly subsidized prices. It is usually possible to measure the quantities of these government-sector outputs and inputs as well as input prices, but the problem is how to estimate the corresponding output prices. Once meaningful output prices have been estimated, the measurement of productivity growth using index numbers can proceed in the usual manner. This chapter suggests three possible general methods for measuring public-sector output prices and quantities. Specific measurement issues in the health and education sectors are discussed. Similar output and productivity measurement issues arise in the regulated sectors of an economy since regulated producers are forced to provide services at prices that are not equal to marginal or average unit costs. Finally, the problems associated with measuring capital services are discussed.","PeriodicalId":287755,"journal":{"name":"The Oxford Handbook of Productivity Analysis","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131880708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-07DOI: 10.1093/OXFORDHB/9780190226718.013.8
F. Førsund
The materials balance tells us that matter cannot be created or destroyed. The mass contained in inputs must either be contained in the outputs or contained in residuals. Residuals are discharged to the external environment and are pollutants if harmful effects arise. Damages are measured by the willingness to pay for environmental qualities. Static productivity is measured by a ratio of an output index, subtracted damages measured in money, on a multifactor input index. Static productivity will decrease when considering damages, but the social productivity change may go both up and down. A model considering both desirable and undesirable outputs should contain two types of relations: a production function for the desirable outputs, and one for the undesirable outputs. One way of doing this is to specify the functions to have the same set of inputs. This is the factorially determined multi-output model of Frisch. Productivity change measures can be calculated for each type of output separately using a Malmquist index.
{"title":"Productivity Measurement and the Environment","authors":"F. Førsund","doi":"10.1093/OXFORDHB/9780190226718.013.8","DOIUrl":"https://doi.org/10.1093/OXFORDHB/9780190226718.013.8","url":null,"abstract":"The materials balance tells us that matter cannot be created or destroyed. The mass contained in inputs must either be contained in the outputs or contained in residuals. Residuals are discharged to the external environment and are pollutants if harmful effects arise. Damages are measured by the willingness to pay for environmental qualities. Static productivity is measured by a ratio of an output index, subtracted damages measured in money, on a multifactor input index. Static productivity will decrease when considering damages, but the social productivity change may go both up and down. A model considering both desirable and undesirable outputs should contain two types of relations: a production function for the desirable outputs, and one for the undesirable outputs. One way of doing this is to specify the functions to have the same set of inputs. This is the factorially determined multi-output model of Frisch. Productivity change measures can be calculated for each type of output separately using a Malmquist index.","PeriodicalId":287755,"journal":{"name":"The Oxford Handbook of Productivity Analysis","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116891781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-07DOI: 10.1093/OXFORDHB/9780190226718.013.21
M. Timmer, Xianjia Ye
Fragmentation of production is posing new challenges to the analysis and measurement of productivity. Traditional approaches focus on firms, industries, or countries as the unit of analysis. This chapter argues that studies of global value chains (GVCs) are needed in situations where production is fragmented across firms and geographical borders. The chapter outlines how existing tools for measuring productivity, factor substitution, and (biased) technological change can be modified to analyze GVC production. A key concept is a production function where final output is generated based on factor inputs only, including both domestic as well as foreign factors. The chapter outlines what type of data would be needed and provides illustrative analyses of GVCs of manufacturing products based on the WIOD (world input-output database).
{"title":"Productivity and Substitution Patterns in Global Value Chains","authors":"M. Timmer, Xianjia Ye","doi":"10.1093/OXFORDHB/9780190226718.013.21","DOIUrl":"https://doi.org/10.1093/OXFORDHB/9780190226718.013.21","url":null,"abstract":"Fragmentation of production is posing new challenges to the analysis and measurement of productivity. Traditional approaches focus on firms, industries, or countries as the unit of analysis. This chapter argues that studies of global value chains (GVCs) are needed in situations where production is fragmented across firms and geographical borders. The chapter outlines how existing tools for measuring productivity, factor substitution, and (biased) technological change can be modified to analyze GVC production. A key concept is a production function where final output is generated based on factor inputs only, including both domestic as well as foreign factors. The chapter outlines what type of data would be needed and provides illustrative analyses of GVCs of manufacturing products based on the WIOD (world input-output database).","PeriodicalId":287755,"journal":{"name":"The Oxford Handbook of Productivity Analysis","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116985397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-07DOI: 10.1093/OXFORDHB/9780190226718.013.20
D. Jorgenson
The World KLEMS Initiative generates industry-level data on outputs, inputs, and productivity. Productivity is output per unit of all inputs. The inputs consist of the primary factors of production—capital (K) and labor (L)—and the intermediate inputs: energy (E), materials (M), and services (S). Industry-level data are indispensable for analyzing the sources of economic growth. Productivity gaps between two countries are defined in terms of differences in productivity levels. These differences are measured by linking productivity levels for each country by purchasing power parities for outputs and inputs. The large productivity gap between the United States and Japan in 1955 gradually closed until 1995. Since then, Japanese productivity has been stagnant, while US productivity has continued to grow. The widening productivity gap can be traced to a small number of sectors, mainly in trade and services.
{"title":"The World KLEMS Initiative","authors":"D. Jorgenson","doi":"10.1093/OXFORDHB/9780190226718.013.20","DOIUrl":"https://doi.org/10.1093/OXFORDHB/9780190226718.013.20","url":null,"abstract":"The World KLEMS Initiative generates industry-level data on outputs, inputs, and productivity. Productivity is output per unit of all inputs. The inputs consist of the primary factors of production—capital (K) and labor (L)—and the intermediate inputs: energy (E), materials (M), and services (S). Industry-level data are indispensable for analyzing the sources of economic growth. Productivity gaps between two countries are defined in terms of differences in productivity levels. These differences are measured by linking productivity levels for each country by purchasing power parities for outputs and inputs. The large productivity gap between the United States and Japan in 1955 gradually closed until 1995. Since then, Japanese productivity has been stagnant, while US productivity has continued to grow. The widening productivity gap can be traced to a small number of sectors, mainly in trade and services.","PeriodicalId":287755,"journal":{"name":"The Oxford Handbook of Productivity Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130281990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-07DOI: 10.1093/OXFORDHB/9780190226718.013.4
R. Robert Russell
Theoretical productivity indices provide a framework for comparing productivity levels over time or across economic units. Prominent approaches—generalizations of the classic Solow model of technical change to encompass multiple outputs—have come to be known as the Malmquist index and the Hicks-Moorsteen index. The former employs radial distance functions to measure productivity change in either input or output space, whereas the latter employs ratios of radial distance functions in each space to measure the relative change. When production below the frontier is taken into account, the Malmquist index decomposes naturally into the effects of shifts in the production frontier and changes in inefficiency. Other decomposition concepts encompass components like scale effects. Nonradial measures of productivity change in the full input–output space have been formulated using hyperbolic and directional distance functions. Also discussed are dual productivity indices (employing cost and revenue functions) and aggregation of productivity indices across economic units.
{"title":"Theoretical Productivity Indices","authors":"R. Robert Russell","doi":"10.1093/OXFORDHB/9780190226718.013.4","DOIUrl":"https://doi.org/10.1093/OXFORDHB/9780190226718.013.4","url":null,"abstract":"Theoretical productivity indices provide a framework for comparing productivity levels over time or across economic units. Prominent approaches—generalizations of the classic Solow model of technical change to encompass multiple outputs—have come to be known as the Malmquist index and the Hicks-Moorsteen index. The former employs radial distance functions to measure productivity change in either input or output space, whereas the latter employs ratios of radial distance functions in each space to measure the relative change. When production below the frontier is taken into account, the Malmquist index decomposes naturally into the effects of shifts in the production frontier and changes in inefficiency. Other decomposition concepts encompass components like scale effects. Nonradial measures of productivity change in the full input–output space have been formulated using hyperbolic and directional distance functions. Also discussed are dual productivity indices (employing cost and revenue functions) and aggregation of productivity indices across economic units.","PeriodicalId":287755,"journal":{"name":"The Oxford Handbook of Productivity Analysis","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126051946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-07DOI: 10.1093/oxfordhb/9780190226718.013.22
R. Inklaar
Industry-level productivity analysis can be a useful diagnostic tool to better understand why some countries show faster overall productivity growth and to direct research attention to parts of the economy that warrant more detailed scrutiny. This chapter illustrates these strengths in three applications, namely the Europe-US productivity growth gap since the mid-1990s, the sectoral sources of rapid convergence of productivity levels between advanced and emerging economies, and an analysis of the determinants of productivity growth and convergence. One conclusion is that a better understanding of productivity growth (or lack thereof) in services industries should still be an important goal of researchers aiming to understand cross-country growth differences.
{"title":"The Industry Sources of Productivity Growth and Convergence","authors":"R. Inklaar","doi":"10.1093/oxfordhb/9780190226718.013.22","DOIUrl":"https://doi.org/10.1093/oxfordhb/9780190226718.013.22","url":null,"abstract":"Industry-level productivity analysis can be a useful diagnostic tool to better understand why some countries show faster overall productivity growth and to direct research attention to parts of the economy that warrant more detailed scrutiny. This chapter illustrates these strengths in three applications, namely the Europe-US productivity growth gap since the mid-1990s, the sectoral sources of rapid convergence of productivity levels between advanced and emerging economies, and an analysis of the determinants of productivity growth and convergence. One conclusion is that a better understanding of productivity growth (or lack thereof) in services industries should still be an important goal of researchers aiming to understand cross-country growth differences.","PeriodicalId":287755,"journal":{"name":"The Oxford Handbook of Productivity Analysis","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130839105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-07DOI: 10.1093/OXFORDHB/9780190226718.013.9
E. Grifell-Tatjé, C. A. Knox, Lovell C. A. Knox Lovell
This chapter explores the relationship between productivity and financial performance, primarily at the level of an individual business. It begins by decomposing profit change into price and quantity drivers, under alternative accounting treatments of operating surplus. The chapter considers a range of related issues, including the drivers of productivity change, the distribution of the value productivity that change creates, problems associated with missing or distorted prices, complications caused by fluctuating exchange rates, and the use of price change to measure productivity change. In addition to profit, it considers alternative measures of financial performance, such as return on assets and unit cost. The chapter concludes by pointing to some topics deserving of further research.
{"title":"Productivity and Financial Performance","authors":"E. Grifell-Tatjé, C. A. Knox, Lovell C. A. Knox Lovell","doi":"10.1093/OXFORDHB/9780190226718.013.9","DOIUrl":"https://doi.org/10.1093/OXFORDHB/9780190226718.013.9","url":null,"abstract":"This chapter explores the relationship between productivity and financial performance, primarily at the level of an individual business. It begins by decomposing profit change into price and quantity drivers, under alternative accounting treatments of operating surplus. The chapter considers a range of related issues, including the drivers of productivity change, the distribution of the value productivity that change creates, problems associated with missing or distorted prices, complications caused by fluctuating exchange rates, and the use of price change to measure productivity change. In addition to profit, it considers alternative measures of financial performance, such as return on assets and unit cost. The chapter concludes by pointing to some topics deserving of further research.","PeriodicalId":287755,"journal":{"name":"The Oxford Handbook of Productivity Analysis","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131353907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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