142 research outputs found
Indian farmer suicides. Is GM cotton to blame?
No full text“Thousands of Indian farmers are committing suicide after growingGM crops.” It is no minor claim. Genetically modified cropsrevolutionise agriculture – but are controversial. They will feed theworld, reduce the need for pesticides and fertilisers, and add healthprotectingnutrients to those who consume them, say some. They arean ecological disaster in the making, say others, and impoverish theThird World farmers who grow them. Often quoted is the exampleof suicides among Indian farmers who grow GM crops. Ian Plewisexamines the data and the conclusion
Pesticides and transgenerational inheritance of pathologies:Designing,analysing and reporting rodent studies
No full textSingle-centre studies examining the transgenerational inheritance of pathologies in rodents exposed to pesticides have not always taken important design and analysis issues into account. This paper examines these methodological and statistical issues in detail. Its particular focus is on the estimation of ‘litter effects’: the tendency for rodents within a litter to be more alike than rodents in different litters. Appropriate statistical models were fitted to publisheddata from a series of widely reported studies carried out at Washington State University. These studies were amalgamated into a single dataset in order to estimate these litter effects and associated treatment effects. Litter effects varied by outcome and were often substantial. Consequently, the effective sample size was often substantially less than the number of observations with implications for the power of the studies. Moreover, the reported precision of the estimates of treatment effects was too low. These problems are exacerbated by unexplained missing data across generations. Researchers in the life sciencescould be more cognisant of the guidelines established in medicine for reporting randomised controlled trials, particularly cluster randomised trials. More attention should be paid to the design and analysis of multi-generational rodent studies; their imperfections have important implications for assessments of the evidence relating to the risks of pesticides for public health.<br/
Modelling impact heterogeneity
No full textThe treatments embodied in social interventions are characterized by their heterogeneity, delivered as they often are by different individuals operating in different social and geographical contexts. One implication of this heterogeneity is that average treatment effects will often be less useful than estimates of differential impacts across contexts. The paper shows how multilevel models can be used to estimate variability of impact and to account for systematic effects. These models are specified for multisite interventions, for studies using cluster allocation and for designs that incorporate matching. The paper indicates how qualitative and quantitative approaches to evaluation could be linked
Contextual variations in ethnic group differences in educational attainments
No full textThe primary school population in England is becoming ethnically more diverse and differences in educational attainments between ethnic groups continue to be of interest. The paper applies multilevel modelling to an administrative database-the national pupil database-to assess the extent of these differences. It shows that the national picture hides considerable heterogeneity both within and between schools and that the models for attainment are complex. The analyses highlight the relative educational success of Chinese pupils and a cause for concern about the attainments of black Caribbean boys. The paper's most important message is that conclusions about ethnic group differences in attainments need to be related to individual, family, school and cognitive contexts. Methodological issues about model specification and the categorization of ethnic groups are discussed. © 2011 Royal Statistical Society
Multiple regression, longitudinal data and welfare in the nineteenth century: Reflections on Yule (1899)
No full textThe paper that G. U. Yule read to the Royal Statistical Society in 1899 is, by virtue of its application of multiple regression to observational data, a landmark in social statistics. It is also an illustration of the value of relating a change in an explanatory variable to a change in the response when wishing to draw causal conclusions. This paper returns to Yule's data and analysis from a 21st-century perspective. A range of multilevel and fixed effects models are fitted to the reconstructed data set and his conclusions are re-examined. The social and political contexts of Yule's work are also considered.<br/
"Adopting Hybrid Bt Cotton:Using Interrupted Time-Series Analysis to AssessIts Effects on Farmers in Northern India,"
No full textMore than a decade has passed since Bt cotton was introduced in India. It is now possible to use official cotton statistics to assess whether Bt cotton has had positive effects on farmers’ lives and livelihoods. I use interrupted time-series analysis of data on insecticide costs, yields, and profits to examine trends before and after the introduction of Bt cotton in the States of Haryana, Punjab, and Rajasthan, in northern India. The conclusions from these analyses are mixed. The more expensive Bt hybrid seeds have lowered insecticide costs in all three States, but only in Rajasthan did yields increase. An important message of this paper is that conclusions about the effectiveness of Bt cotton are more nuanced than many researchers and commentators recognise. The paper does not refute the assertions about the success of Bt cotton, but it does show that the benefits are not evenly distributed across India
GM cotton and suicide rates for Indian farmers
No full textThe arguments for and against genetically modified (GM) crops are spread across the academic literature and in the media. This paper focuses on one of these disputes: has the introduction of GM cotton in India led, as some have claimed, to an increase in the suicide rate for Indian farmers? Evidence on the numbers of suicides and the numbers of farmers is assembled from several sources, by state and over time for both male and female farmers. This evidence is, faute de mieux, at an aggregate level. The short time series are modelled to test whether there is any evidence of a break in the series that corresponds to the adoption of GM cotton. The analysis reveals considerable variation in trends in suicide rates across the nine cotton-growing states. The data, although not ideal, and the modelling do not, however, support the claim that GM cotton has led to an increase in farmer suicide rates: if anything the reverse is true
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