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

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.

A novel approach to national technological accumulation and absorptive capacity: aggregating Cohen and Levinthal

The paper develops a more precise specification and understanding of the process of national-level knowledge accumulation and absorptive capabilities by applying the reasoning and evidence from the firm-level analysis pioneered by Cohen and Levinthal (1989, 1990). In doing so, we acknowledge that significant cross-border effects due to the role of both inward and outward FDI exist and that assimilation of foreign knowledge is not only confined to catching-up economies but is also carried out by countries at the frontier-sharing phase. We postulate a non-linear relationship between national absorptive capacity and the technological gap, due to the effects of the cumulative nature of the learning process and the increase in complexity of external knowledge as the country approaches the technological frontier. We argue that national absorptive capacity and the accumulation of knowledge stock are simultaneously determined. This implies that different phases of technological development require different strategies. During the catching-up phase, knowledge accumulation occurs predominately through the absorption of trade and/or inward FDI-related R&D spillovers. At the pre-frontier-sharing phase onwards, increases in the knowledge base occur largely through independent knowledge creation and actively accessing foreign-located technological spillovers, inter alia through outward FDI-related R&D, joint ventures and strategic alliances.