World Health Organization Dietary Norms: A Quantitative Evaluation of Potential Consumption Impacts in the United States, United Kingdom, and France

The member countries of the World Health Organization have endorsed its Global Strategy on Diet, Physical Activity, and Health. We assess the potential consumption impacts of these norms in the United States, France, and the United Kingdom using a mathematical programming approach. We find that adherence would involve large reductions in the consumption of fats and oils accompanying large rises in the consumption of fruits, vegetables, and cereal. Further, in the United Kingdom and the United States, but not France, sugar intakes would have to shrink considerably. Focusing on sub-populations within each country, we find that the least educated, not necessarily the poorest, would have to bear the highest burden of adjustment.     

Is agriculture the engine of growth?

We seek to address formally the question raised by Gardner (2003) in his Elmhirst lecture as to the direction of causality between agricultural value added per worker and Gross Domestic Product (GDP) per capita. Using the Granger causality test in the panel data analyzed by Gardner for 85 countries, we find overwhelming evidence that supports the conclusion that agricultural value added is the causal variable in developing countries, while the direction of causality in developed countries is unclear. We also examine further the use of the Granger causality test in integrated data and provide evidence that the performance of the test can be increased in small samples through the use of the bootstrap.     

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 pattern is repeated by the TFP indices. This result highlights the policy dilemma of the trade‐off between efficiency and equity objectives.     

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 TEP 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.