A tractable means of correcting sector level TFP calculations in the face of biased technological change.

The measurement of the impact of technical change has received significant attention within the economics literature. One popular method of quantifying the impact of technical change is the use of growth accounting index numbers. However, in a recent article Nelson and Pack (1999) criticise the use of such index numbers in situations where technical change is likely to be biased in favour of one or other inputs. In particular they criticise the common approach of applying observed cost shares, as proxies for partial output elasticities, to weight the change in quantities which they claim is only valid under Hicks neutrality. Recent advances in the measurement of product and factor biases of technical change developed by Balcombe et al (2000) provide a relatively straight-forward means of correcting product and factor shares in the face of biased technical progress. This paper demonstrates the correction of both revenue and cost shares used in the construction of a TFP index for UK agriculture over the period 1953 to 2000 using both revenue and cost function share equations appended with stochastic latent variables to capture the bias effect. Technical progress is shown to be biased between both individual input and output groups. Output and input quantity aggregates are then constructed using both observed and corrected share weights and the resulting TFPs are compared. There does appear to be some significant bias in TFP if the effect of biased technical progress is not taken into account when constructing the weights

Measuring productivity growth when technological change is biased - a new index and an application to UK agriculture

Productivity growth is conventionally measured by indices representing discreet approximations of the Divisia TFP index under the assumption that technological change is Hicks-neutral. When this assumption is violated, these indices are no longer meaningful because they conflate the effects of factor accumulation and technological change. We propose a way of adjusting the conventional TFP index that solves this problem. The method adopts a latent variable approach to the measurement of technical change biases that provides a simple means of correcting product and factor shares in the standard Tornqvist-Theil TFP index. An application to UK agriculture over the period 1953-2000 demonstrates that technical progress is strongly biased. The implications of that bias for productivity measurement are shown to be very large, with the conventional TFP index severely underestimating productivity growth. The result is explained primarily by the fact that technological change has favoured the rapidly accumulating factors against labour, the factor leaving the sector. (C) 2004 Elsevier B.V. All rights reserved.

Dual technological development in Botswana agriculture: A Stochastic input distance function approach

To improve the welfare of the rural poor and keep them in the countryside, the government of Botswana has been spending 40% of the value of agricultural GDP on agricultural support services. But can investment make smallholder agriculture prosperous in such adverse conditions? This paper derives an answer by applying a two-output six-input stochastic translog distance function, with inefficiency effects and biased technical change to panel data for the 18 districts and the commercial agricultural sector, from 1979 to 1996 This model demonstrates that herds are the most important input, followed by draft power. land and seeds. Multilateral indices for technical change, technical efficiency and total factor productivity (TFP) show that the technology level of the commercial agricultural sector is more than six times that of traditional agriculture and that the gap has been increasing, due to technological regression in traditional agriculture and modest progress in commercial agriculture. Since the levels of efficiency are similar, the same patient is repeated by the TFP indices. This result highlights the policy dilemma of the trade-off between efficiency and equity objectives.

The British ‘failure’ that never was? The Anglo-American ‘productivity gap’ in large-scale interwar retailing—evidence from the department store sector

Interwar British retailing has been characterized as having lower productivity, less developed managerial hierarchies and methods, and weaker scale economies than its US counterpart. This article examines comparative productivity for one major segment of large-scale retailing in both countries—the department store sector. Drawing on exceptionally detailed contemporary survey data, we show that British department stores in fact achieved superior performance in terms of operating costs, margins, profits, and stock-turn. While smaller British stores had lower labour productivity than US stores of equivalent size, TFP was generally higher for British stores, which also enjoyed stronger scale economies. We also examine the reasons behind Britain's surprisingly strong relative performance, using surviving original returns from the British surveys. Contrary to arguments that British retailers faced major barriers to the development of large-scale enterprises, that could reap economies of scale and scope and invest in machinery and marketing to support the growth of their primary sales functions, we find that British department stores enthusiastically embraced the retail ‘managerial revolution’—and reaped substantial benefits from this investment.

The multiple team formation problem using sociometry

The Team Formation problem (TFP) has become a well-known problem in the OR literature over the last few years. In this problem, the allocation of multiple individuals that match a required set of skills as a group must be chosen to maximise one or several social positive attributes. Speci�cally, the aim of the current research is two-fold. First, two new dimensions of the TFP are added by considering multiple projects and fractions of people's dedication. This new problem is named the Multiple Team Formation Problem (MTFP). Second, an optimization model consisting in a quadratic objective function, linear constraints and integer variables is proposed for the problem. The optimization model is solved by three algorithms: a Constraint Programming approach provided by a commercial solver, a Local Search heuristic and a Variable Neighbourhood Search metaheuristic. These three algorithms constitute the first attempt to solve the MTFP, being a variable neighbourhood local search metaheuristic the most effi�cient in almost all cases. Applications of this problem commonly appear in real-life situations, particularly with the current and ongoing development of social network analysis. Therefore, this work opens multiple paths for future research.