Pub Date : 2021-01-12DOI: 10.1093/oso/9780190919474.003.0003
Elvira Silva, S. Stefanou, A. Lansink
This chapter develops dynamic production analysis within the context of the adjustment cost model of the firm, where adjustment costs are associated with changes in the level of the quasi-fixed factors, also known as internal adjustment costs. The chapter characterizes axiomatically several primal representations of the adjustment cost production technology. The axiomatic approach is a cornerstone to model production technology both in theoretical and empirical work. The existence of several representations of the adjustment cost production technology is essential in the analysis of the firm’s decisions and its adjustment path that are conditioned by the technology. Three set representations of the adjustment cost production technology are discussed and characterized axiomatically. Two functional representations of the technology are also addressed using an axiomatic approach.
{"title":"Primal Analytical Foundations of Dynamic Production Analysis","authors":"Elvira Silva, S. Stefanou, A. Lansink","doi":"10.1093/oso/9780190919474.003.0003","DOIUrl":"https://doi.org/10.1093/oso/9780190919474.003.0003","url":null,"abstract":"This chapter develops dynamic production analysis within the context of the adjustment cost model of the firm, where adjustment costs are associated with changes in the level of the quasi-fixed factors, also known as internal adjustment costs. The chapter characterizes axiomatically several primal representations of the adjustment cost production technology. The axiomatic approach is a cornerstone to model production technology both in theoretical and empirical work. The existence of several representations of the adjustment cost production technology is essential in the analysis of the firm’s decisions and its adjustment path that are conditioned by the technology. Three set representations of the adjustment cost production technology are discussed and characterized axiomatically. Two functional representations of the technology are also addressed using an axiomatic approach.","PeriodicalId":158653,"journal":{"name":"Dynamic Efficiency and Productivity Measurement","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125514617","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 : 2021-01-12DOI: 10.1093/oso/9780190919474.003.0005
Elvira Silva, S. Stefanou, A. Lansink
This chapter discusses three concepts of the directional distance function in the presence of internal adjustment costs, designated as adjustment cost directional distance functions. These functions are the building blocks of technical inefficiency measures. Duality between an adjustment cost directional distance function and an indirect optimal value function allows the construction of economic measures of inefficiency. Duality is established between the adjustment cost directional input function and the optimal current value function of the intertemporal cost minimization problem. From this dual relation, a dynamic cost inefficiency measure is derived and decomposed into technical inefficiency and allocative inefficiency. Similarly, dynamic input-output measures of inefficiency are derived from the adjustment cost directional technology distance function and duality between this function and the current profit function.
{"title":"Dynamic Decision Making, Distance Functions, and Productive Efficiency","authors":"Elvira Silva, S. Stefanou, A. Lansink","doi":"10.1093/oso/9780190919474.003.0005","DOIUrl":"https://doi.org/10.1093/oso/9780190919474.003.0005","url":null,"abstract":"This chapter discusses three concepts of the directional distance function in the presence of internal adjustment costs, designated as adjustment cost directional distance functions. These functions are the building blocks of technical inefficiency measures. Duality between an adjustment cost directional distance function and an indirect optimal value function allows the construction of economic measures of inefficiency. Duality is established between the adjustment cost directional input function and the optimal current value function of the intertemporal cost minimization problem. From this dual relation, a dynamic cost inefficiency measure is derived and decomposed into technical inefficiency and allocative inefficiency. Similarly, dynamic input-output measures of inefficiency are derived from the adjustment cost directional technology distance function and duality between this function and the current profit function.","PeriodicalId":158653,"journal":{"name":"Dynamic Efficiency and Productivity Measurement","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133790631","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 : 2021-01-12DOI: 10.1093/oso/9780190919474.003.0002
Elvira Silva, S. Stefanou, A. Lansink
This chapter characterizes production in a dynamic decision-making environment. The classic characterization of static firm decision making is contrasted with the dynamic decision environment where not all inputs are freely adjusted. The latter characterization is motivated by the conjecture that transaction costs are associated with adjusting the capital stock at a rapid rate per unit of time and these costs increase rapidly with the absolute rate of investment. In fact, these costs increase so rapidly that the firm may never attempt to achieve a jump in its capital stock at any given moment. Such transaction (or adjustment) costs have implications for the nature of the technology. This interplay is introduced in this chapter and serves as a foundation for the dynamic structure that follows throughout the book.
{"title":"Overview","authors":"Elvira Silva, S. Stefanou, A. Lansink","doi":"10.1093/oso/9780190919474.003.0002","DOIUrl":"https://doi.org/10.1093/oso/9780190919474.003.0002","url":null,"abstract":"This chapter characterizes production in a dynamic decision-making environment. The classic characterization of static firm decision making is contrasted with the dynamic decision environment where not all inputs are freely adjusted. The latter characterization is motivated by the conjecture that transaction costs are associated with adjusting the capital stock at a rapid rate per unit of time and these costs increase rapidly with the absolute rate of investment. In fact, these costs increase so rapidly that the firm may never attempt to achieve a jump in its capital stock at any given moment. Such transaction (or adjustment) costs have implications for the nature of the technology. This interplay is introduced in this chapter and serves as a foundation for the dynamic structure that follows throughout the book.","PeriodicalId":158653,"journal":{"name":"Dynamic Efficiency and Productivity Measurement","volume":"210 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122695651","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 : 2021-01-12DOI: 10.1093/oso/9780190919474.003.0008
Elvira Silva, S. Stefanou, A. Lansink
This chapter focuses on the nonparametric data envelopment analysis (DEA) framework of structural linear programming models underlying the estimation of efficiency. The nonparametric approach to measuring technical and cost inefficiency has been adopted in the examples in earlier chapters. Chapter 3 introduced the notions of inner- and outer-bound technologies. The inner-bound technology representation has dominated the nonparametric empirical applications in the literature on measuring efficiency and productivity. However, as this chapter shows, the outer-bound representation of the technology presents a viable alternative to measuring technical and cost efficiency as well. The chapter also develops an application to farm-level panel data.
{"title":"Nonparametric Approaches","authors":"Elvira Silva, S. Stefanou, A. Lansink","doi":"10.1093/oso/9780190919474.003.0008","DOIUrl":"https://doi.org/10.1093/oso/9780190919474.003.0008","url":null,"abstract":"This chapter focuses on the nonparametric data envelopment analysis (DEA) framework of structural linear programming models underlying the estimation of efficiency. The nonparametric approach to measuring technical and cost inefficiency has been adopted in the examples in earlier chapters. Chapter 3 introduced the notions of inner- and outer-bound technologies. The inner-bound technology representation has dominated the nonparametric empirical applications in the literature on measuring efficiency and productivity. However, as this chapter shows, the outer-bound representation of the technology presents a viable alternative to measuring technical and cost efficiency as well. The chapter also develops an application to farm-level panel data.","PeriodicalId":158653,"journal":{"name":"Dynamic Efficiency and Productivity Measurement","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122555022","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 : 2021-01-12DOI: 10.1093/oso/9780190919474.003.0007
Elvira Silva, S. Stefanou, A. Lansink
Econometric approaches provide another avenue to implementing the frameworks and concepts of dynamic efficiency and productivity measurement. This chapter addresses both structural and reduced-form econometric approaches to estimating the dynamic directional distance function directly as well as to estimating the cost function that accommodates technical inefficiency. An application to a farm-level panel data set is presented that estimates the decomposition of dynamic cost inefficiency into technical and allocative inefficiency measures presented in Chapter 4 and then determines the components of primal and dual Luenberger total factor productivity change based on the elaboration of these concepts in Chapter 5. In addition to the discussion of empirical issues, this chapter provides an empirical illustration using micro-level data.
{"title":"Econometric Approaches","authors":"Elvira Silva, S. Stefanou, A. Lansink","doi":"10.1093/oso/9780190919474.003.0007","DOIUrl":"https://doi.org/10.1093/oso/9780190919474.003.0007","url":null,"abstract":"Econometric approaches provide another avenue to implementing the frameworks and concepts of dynamic efficiency and productivity measurement. This chapter addresses both structural and reduced-form econometric approaches to estimating the dynamic directional distance function directly as well as to estimating the cost function that accommodates technical inefficiency. An application to a farm-level panel data set is presented that estimates the decomposition of dynamic cost inefficiency into technical and allocative inefficiency measures presented in Chapter 4 and then determines the components of primal and dual Luenberger total factor productivity change based on the elaboration of these concepts in Chapter 5. In addition to the discussion of empirical issues, this chapter provides an empirical illustration using micro-level data.","PeriodicalId":158653,"journal":{"name":"Dynamic Efficiency and Productivity Measurement","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114473386","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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