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.

The use of bootstrapped Malmquist indices to reassess productivity change findings: an application to a sample of Polish farms

This article assesses the extent to which sampling variation affects findings about Malmquist productivity change derived using data envelopment analysis (DEA), in the first stage by calculating productivity indices and in the second stage by investigating the farm-specific change in productivity. Confidence intervals for Malmquist indices are constructed using Simar and Wilson's (1999) bootstrapping procedure. The main contribution of this article is to account in the second stage for the information in the second stage provided by the first-stage bootstrap. The DEA SEs of the Malmquist indices given by bootstrapping are employed in an innovative heteroscedastic panel regression, using a maximum likelihood procedure. The application is to a sample of 250 Polish farms over the period 1996 to 2000. The confidence intervals' results suggest that the second half of 1990s for Polish farms was characterized not so much by productivity regress but rather by stagnation. As for the determinants of farm productivity change, we find that the integration of the DEA SEs in the second-stage regression is significant in explaining a proportion of the variance in the error term. Although our heteroscedastic regression results differ with those from the standard OLS, in terms of significance and sign, they are consistent with theory and previous research.

Toward a combined seasonal weather and crop productivity forecasting system: Determination of the working spatial scale

A methodology is presented for the development of a combined seasonal weather and crop productivity forecasting system. The first stage of the methodology is the determination of the spatial scale(s) on which the system could operate; this determination has been made for the case of groundnut production in India. Rainfall is a dominant climatic determinant of groundnut yield in India. The relationship between yield and rainfall has been explored using data from 1966 to 1995. On the all-India scale, seasonal rainfall explains 52% of the variance in yield. On the subdivisional scale, correlations vary between variance r(2) = 0.62 (significance level p < 10(-4)) and a negative correlation with r(2) = 0.1 (p = 0.13). The spatial structure of the relationship between rainfall and groundnut yield has been explored using empirical orthogonal function (EOF) analysis. A coherent, large-scale pattern emerges for both rainfall and yield. On the subdivisional scale (similar to 300 km), the first principal component (PC) of rainfall is correlated well with the first PC of yield (r(2) = 0.53, p < 10(-4)), demonstrating that the large-scale patterns picked out by the EOFs are related. The physical significance of this result is demonstrated. Use of larger averaging areas for the EOF analysis resulted in lower and (over time) less robust correlations. Because of this loss of detail when using larger spatial scales, the subdivisional scale is suggested as an upper limit on the spatial scale for the proposed forecasting system. Further, district-level EOFs of the yield data demonstrate the validity of upscaling these data to the subdivisional scale. Similar patterns have been produced using data on both of these scales, and the first PCs are very highly correlated (r(2) = 0.96). Hence, a working spatial scale has been identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modeling work for the case of groundnut production in India. Last, the change in correlation between yield and seasonal rainfall during the study period has been examined using seasonal totals and monthly EOFs. A further link between yield and subseasonal variability is demonstrated via analysis of dynamical data.

Bias from farmer self-selection in genetically modified crop productivity estimates: Evidence from Indian data

In the continuing debate over the impact of genetically modified (GM) crops on farmers of developing countries, it is important to accurately measure magnitudes such as farm-level yield gains from GM crop adoption. Yet most farm-level studies in the literature do not control for farmer self-selection, a potentially important source of bias in such estimates. We use farm-level panel data from Indian cotton farmers to investigate the yield effect of GM insect-resistant cotton. We explicitly take into account the fact that the choice of crop variety is an endogenous variable which might lead to bias from self-selection. A production function is estimated using a fixed-effects model to control for selection bias. Our results show that efficient farmers adopt Bacillus thuringiensis (Bt) cotton at a higher rate than their less efficient peers. This suggests that cross-sectional estimates of the yield effect of Bt cotton, which do not control for self-selection effects, are likely to be biased upwards. However, after controlling for selection bias, we still find that there is a significant positive yield effect from adoption of Bt cotton that more than offsets the additional cost of Bt seed.

Seeds of growth? Agricultural productivity and the transitional dynamics of the Ramsey model

A two-sector Ramsey-type model of growth is developed to investigate the relationship between agricultural productivity and economy-wide growth. The framework takes into account the peculiarities of agriculture both in production ( reliance on a fixed natural resource base) and in consumption (life-sustaining role and low income elasticity of food demand). The transitional dynamics of the model establish that when preferences respect Engel's law, the level and growth rate of agricultural productivity influence the speed of capital accumulation. A calibration exercise shows that a small difference in agricultural productivity has drastic implications for the rate and pattern of growth of the economy. Hence, low agricultural productivity can form a bottleneck limiting growth, because high food prices result in a low saving rate.

Productivity change in Polish agriculture: an illustration of a bootstrapping procedure applied to Malmquist indices

This article illustrates the usefulness of applying bootstrap procedures to total factor productivity Malmquist indices, derived with data envelopment analysis (DEA), for a sample of 250 Polish farms during 1996-2000. The confidence intervals constructed as in Simar and Wilson suggest that the common portrayal of productivity decline in Polish agriculture may be misleading. However, a cluster analysis based on bootstrap confidence intervals reveals that important policy conclusions can be drawn regarding productivity enhancement.

Productivity and optimum plant density of pigeonpea in different environments in Tanzania

The objective of the present study was to determine the optimum plant density of four pigeonpea genotypes, representing early, medium and late maturing types, grown in five contrasting environments in Tanzania. ICPL 86005 (early), Kat 50/3 and QP 37 (medium) and Local (late) were grown at four plant densities (40 000-320 000 plants/ha) in irrigated and rainfed conditions at Ilonga and under rainfed conditions at Kibaha, Selian and Ismani. At maturity, total above-ground biomass and seed yield (SY) were measured. The highest yields were obtained in the irrigated experiment at Ilonga, where the medium/late genotypes produced 25 t biomass/ha and 5 center dot 6 t seed/ha. The lowest SY were at Kibaha, 0 58 to 1 center dot 76 t/ha, where a severe drought occurred. In nearly all cases the response to density was linear or asymptotic. The response of ICPL 86005 was significantly different from the other three genotypes. The optimum density for SY varied from 37 000 to 227 000 plants/ha in ICPL 86005, compared with 3000 to 101000 plants/ha in the medium/late genotypes. The highest optimum density was at Selian and Ismani and the lowest at Ilonga and Kibaha, where drought occurred. Optimum densities therefore varied greatly with genotype (duration) and environment, and this variation needs to be considered when planning trials.

Effect of restricted suckling of calves on the productivity of crossbred dairy cattle

An experiment on restricted suckling of crossbred dairy cows was conducted at the Livestock Research Centre, Tanga in north-east Tanzania. The objective of the experiment was to evaluate the comparative productivity of Bos taurus x Bos indicus cows of medium and high levels of Bos taurus inheritance, whose calves were either bucket-reared or suckled residual milk. Lactation milk yield, length and persistency were 1563 L, 289 days, and 1.0, respectively, for the bucket-reared and 1592 L, 289 days and 1.4, respectively, for the suckling group. Days to observed oestrus, first insemination and conception for cows whose calves were bucket-reared were 47, 74 and 115 days, respectively, and 57, 81 and 126 days, respectively, for the suckling cows. The calf weights were similar at 1 year of age. The productivity of the cows, measured as the annual milk offtake, was not significantly higher for those that suckled their calves than for those whose calves were bucket-reared.

Productivity of crossbred dairy cows suckling their calves for 12 or 24 weeks post calving

An experiment on restricted suckling of crossbred dairy cows was conducted at the Livestock Research Centre, Tanga in northeast Tanzania. Thirty-six Bos taurus (Holstein Friesian and Jersey) x Bos indicus (East African Zebu) cows were allocated alternately as they calved to suckling their calves for either 12 or 24 weeks after calving. Cows grazed improved pastures and were offered 4 kg concentrate daily. Milking occurred twice daily by hand; calves were allowed to suck residual milk for 30 min following each milking. Calves were also allowed access to grazing and were offered a maximum of I kg concentrate daily to 24 weeks of age. Weaning age had no significant effect on lactation milk yield for human consumption, the mean (SE) yield being 1806 (102.0) L and 1705 (129. 1) L for 12- and 24-week weaning, respectively. Cows from the two treatments suffered similar losses of live weight and body condition score during lactation and neither group had returned to the original body condition score 40 weeks following calving. Post-partum anoestrous intervals were prolonged. Although not significant, cows suckling calves to 24 weeks had a mean interval to first oestrus extended by 38 days compared with cows suckling calves to 12 weeks. The mean (SE) daily live weight gains of the calves to 52 weeks were 263 (14.1) g/day and 230 (18.1) g/day for calves weaned at 12 and 24 weeks, respectively, such that 12-month weights were 119 (5.6) kg and 110 (7.3) kg, respectively. Twelve-week-weaned calves consumed more concentrate (p < 0.05) from 13 to 24 weeks than did 24-week weaned calves. Calculation of residual milk consumption removed by calves from birth to 12 weeks indicated that it accounted for 28% of total yield. No benefits in cow and calf performance and welfare were found to justify prolonging the suckling period to 24 weeks.

Pesticide productivity and transgenic cotton technology: The South African smallholder case

This paper empirically investigates how the productivity of pesticide differs in Bt versus non-Bt technology for South African cotton smallholders, and what the implications for pesticide use levels are in the two technologies. This is accomplished by applying a damage control framework to farm-level data from Makhathini flats, KwaZulu-Natal. Contrary to findings elsewhere, notably China, that farmers over-use pesticides and that transgenic technology benefits farmers by enabling large reductions in pesticide use, the econometric evidence here indicates that non-Bt smallholders in South Africa under-use pesticide. Thus, the main potential contribution of the new technology is to enable them to realise lost productivity resulting from under-use. By providing a natural substitute for pesticide, the Bt technology enables the smallholders to circumvent credit and labour constraints associated with pesticide application. Thus, the same technology that greatly reduces pesticide applications but only mildly affects yields, when used by large-scale farmers in China and elsewhere, benefits South-African smallholder farmers primarily via a yield-enhancing effect.