Profits and Productivity

In this study we consider the linkage between productivity change and profit change. We develop an analytical framework in which profit change between one period and the next is decomposed into three sources: (i) a productivity change effect (which includes a technical change effect and an operating efficiency effect), (ii) an activity effect (which includes a product mix effect, a resource mix effect and a scale effect), and (iii) a price effect. We then show how to quantify the contribution of each effect, using only observed prices and quantities of products and resources in the two periods. We illustrate our analytical decomposition of profit change with an empirical application to Spanish banking during the period 1987 - 1994.Profits, Productivity

A Quasi-Malmquist Index

Abstract The Malmquist productivity index is based on distance functions, which are reciprocals of radial Debreu-Farrell efficiency measures, and which have a number of desirable properties. Linear programming techniques are frequently employed to calculate the efficiency measures. However these techniques can leave slacks, which constitute a non-radial form of inefficiency which is not incorporated into the analysis. Thus a radial efficiency measure overstates true efficiency, the reciprocal distance function understates the distance to the relevant efficient subset, and the Malmquist productivity index is adversely affected, although in an analytically indeterminate direction. This has led us to consider a new definition of "one-sided" efficiency, and to develop a new nonradial efficiency measure which incorporates all slacks on the selected side. Replacing conventional radial efficiency measures with our new non-radial efficiency measures generates what we call a quasi-Malmquist productivity index. We illustrate our quasi-Malmquist productivity index with an application to productivity change in Spanish banking

A spatiotemporal Data Envelopment Analysis (S-T DEA) approach:the need to assess evolving units

One of the major challenges in measuring efficiency in terms of resources and outcomes is the assessment of the evolution of units over time. Although Data Envelopment Analysis (DEA) has been applied for time series datasets, DEA models, by construction, form the reference set for inefficient units (lambda values) based on their distance from the efficient frontier, that is, in a spatial manner. However, when dealing with temporal datasets, the proximity in time between units should also be taken into account, since it reflects the structural resemblance among time periods of a unit that evolves. In this paper, we propose a two-stage spatiotemporal DEA approach, which captures both the spatial and temporal dimension through a multi-objective programming model. In the first stage, DEA is solved iteratively extracting for each unit only previous DMUs as peers in its reference set. In the second stage, the lambda values derived from the first stage are fed to a Multiobjective Mixed Integer Linear Programming model, which filters peers in the reference set based on weights assigned to the spatial and temporal dimension. The approach is demonstrated on a real-world example drawn from software development

Who leads research productivity growth? Guidelines for R&D policy-makers

[EN] This paper evaluates to what extent policy-makers have been able to promote the creation and consolidation of comprehensive research groups that contribute to the implementation of a successful innovation system. Malmquist productivity indices are applied in the case of the Spanish Food Technology Program, finding that a large size and a comprehensive multi-dimensional research output are the key features of the leading groups exhibiting high efficiency and productivity levels. While identifying these groups as benchmarks, we conclude that the financial grants allocated by the program, typically aimed at small-sized and partially oriented research groups, have not succeeded in reorienting them in time so as to overcome their limitations. We suggest that this methodology offers relevant conclusions to policy evaluation methods, helping policy-makers to readapt and reorient policies and their associated means, most notably resource allocation (financial schemes), to better respond to the actual needs of research groups in their search for excellence (micro-level perspective), and to adapt future policy design to the achievement of medium-long term policy objectives (meso and macro-level).Jiménez Saez, F.; Zabala Iturriagagoitia, JM.; Zofio, JL. (2013). Who leads research productivity growth? Guidelines for R&D policy-makers. Scientometrics. 94(1):273-303. doi:10.1007/s11192-012-0763-0S273303941Abbring, J. H., & Heckman, J. J. (2008). Dynamic policy analysis. In L. Mátyás & P. Sevestre (Eds.), The econometrics of panel data (3rd ed., pp. 795–863). Heidelberg: Springer.Acosta Ballesteros, J., & Modrego Rico, A. (2001). 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COST AND PRODUCTIVITY COST AND PRODUCTIVITY*

Abstract We develop an analytical model capable of decomposing both intertemporal and multilateral cost variation. We begin by attributing cost variation to a price effect and a quantity effect. We then decompose the quantity effect into a productivity effect and an activity effect. The productivity effect in turn decomposes into a cost efficiency effect and, in the intertemporal context, a technical change effect. We also show how the intertemporal and multilateral cost decompositions can be implemented, using linear programming techniques. These techniques offer certain advantages over conventional econometric techniques whenever a substantial portion of cost variation is due to variation in cost efficiency. We illustrate the two cost decompositions with a pair of benchmarking exercises based on a panel of 93 US electric power generating companies, in which variation in cost efficiency does play a key role

Profit, productivity and distribution: Differences across organizational forms - The case of Spanish banks

We examine variations in financial and economic performance as a function of organizational form, and, over time. The forms we consider include Spanish commercial banks, savings banks, and financial cooperatives. We decompose multilateral variation in operating profit, our measure of financial performance, into price and quantity effects. We then decompose the latter into a margin effect and productivity change. Our measure of economic performance, productivity variation, is subsequently disaggregated into technical, cost efficiency, and scale. We find that deregulation and liberalization have acted to narrow performance gaps among organizational forms; this, despite less-than compelling evidence that increased competition has contributed to this convergence. For the Spanish banking system as a whole, the margin effect appears to deliver twice as much financial benefit as do improvements in productivity. Importantly, this finding does not vary across organizational form. Such regularity has an important implication: Incentives for growth are apparently provided by a positive margin rather than by the more elusive benefits of economies of scale and improved cost efficiency.Profit Productivity Income distribution Organizational form

A generalized Malmquist productivity index

Malmquist productivity index, Törnqvist productivity index, 90A99,