Risk management in Canada's agricultural sector in light of COVID-19

The unexpected introduction and spread of COVID-19 has presented significant challenges for every aspect of Canadian society. Although the food and agricultural sector is positioned better than most, there are many risks that will need to be managed in the coming months. The suite of Federal-Provincial-Territorial Business Risk Management (BRM) programs delivered under the Canadian Agricultural Policy framework are meant to assist farmers in managing risks; however, there are no corresponding specialized programs for agribusinesses. The underlying structure of the BRM program was developed decades ago and certainly not with any thought to the possibility of a global pandemic. This article considers to what extent the BRM program and, more broadly, government programming will assist farmers in managing new risks. By default, the article is speculative in nature given that we are currently at the onset of the pandemic in Canada.     

Exceptional performance of multinational suppliers: theory and evidence

Review of World Economics - This paper focuses on an unexamined area of trade—the behaviour of heterogeneous intermediate suppliers facing final producers of different ability and pursuing...     

Biofuels technology development in Southern Africa

ABSTRACT

The benefits of biofuels depend on the feedstock, conversion pathway and local context. This paper assesses biofuels technology readiness and developments to provide foresight to biofuels development in Southern Africa. Efficient conversion pathways, coupled with biomass from waste or high-yielding energy crops, will reduce both the costs of biofuels production, and the environmental impacts. Compared to petroleum fuels, the current commercial biofuels (ethanol, biogas and biodiesel) typically offer carbon emission reductions of 30–50% but are marginally more expensive. The extent of biofuels market penetration will therefore be influenced by mandates (blending targets) and subsidies (green premium). Advanced biofuels promise greater efficiencies and carbon emission reductions at reduced cost but will require further research and development to reach commercialisation. If developed appropriately, biofuels can reduce carbon emissions and improve energy security, while enabling sustainable agriculture and improved natural resources management.     

Higher Intensity,Higher Profit? Empirical Evidence from Dairy Farming in New Zealand

Over the last two decades there have been significant and well‐documented changes in the nature and structure of New Zealand dairy production. One particular feature has been a marked shift in the ‘input intensity’ of dairy farming systems through increased use of supplementary feed. These changes have generated debate about the impact of dairy farm intensity on the performance of farm businesses and the competitiveness of the New Zealand dairy sector. Using a novel econometric approach, we assess statistically the impact of three types of dairy farming systems on milk production and financial performance, using farm business data provided by DairyBase^®. Our empirical results show that higher input systems perform significantly better physically than lower input systems, but not financially. The disaggregated analysis suggests that the average treatment effects differ by region and performance quantiles.     

Towards an Understanding of Social Responsibility Within the Church of England

This research explores the interplay of individual, organisational and institutional variables that produce the current pattern of social responsibility practices within a specific religious organisation, namely the Church of England. By combining elements primarily of neo-institutional theory with Bourdieu’s theory of practice, we construct a theoretical framework to examine the extent to which social responsibility activity is modified or informed by a distinctive faith perspective. Given that neo-institutional theory predicts a convergence of structures and practices between different organisations operating in the same institutional sector, this research draws on a single case study focusing on social responsibility activity in six different Church of England dioceses and shows that the opposite is actually the case: there is evidence of a significant degree of divergence in terms of organisational practice within the same institutional structure. Reasons for that were found to be the impact of human agency and loose institutional structures. This paper thus contributes to the understanding of social responsibility in religious organisations by exploring the dynamics between institutional pressures, organisational context and individual agents operating in the field of faith-based social responsibility. It thereby also contributes to neo-institutional theory.     

Introduction to the policy articles on Trump to Biden: Implications for Canadian Agriculture

President Biden was sworn in on January 20, 2021 after a 4‐year term by President Trump. Certainly, Trump's norm breaking presidency impacted Canada in significant ways and the agricultural sector was no exception. In this issue, we have a set of four short articles discussing the possible implications on Canadian agriculture of transitioning from a Trump to Biden presidency. Issues related to Canada‐U.S. agricultural trade, political norms and uncertainty, U.S. domestic support, participation in trade agreements, and U.S.‐China relations are all considered.     

Unpredictable times: the extent,characteristics and correlates of insecure hours of work in Britain

This article presents new British evidence that suggests that cutting working hours at short notice is twice as prevalent as zero-hours contracts and triple the number of employees are very anxious about unexpected changes to their hours of work. The pay of these employees tends to be lower, work intensity higher, line management support weaker and the threat of dismissal and job loss greater. In addition, the well-being of these employees is lower and they are less committed to the organisations that employ them. However, the prevalence of insecure working hours is reduced by workplace level employee involvement exercised individually or through collective representation.     

Random Effects Misspecification Can Have Severe Consequences for Random Effects Inference in Linear Mixed Models

There has been considerable and controversial research over the past two decades into how successfully random effects misspecification in mixed models (i.e. assuming normality for the random effects when the true distribution is non‐normal) can be diagnosed and what its impacts are on estimation and inference. However, much of this research has focused on fixed effects inference in generalised linear mixed models. In this article, motivated by the increasing number of applications of mixed models where interest is on the variance components, we study the effects of random effects misspecification on random effects inference in linear mixed models, for which there is considerably less literature. Our findings are surprising and contrary to general belief: for point estimation, maximum likelihood estimation of the variance components under misspecification is consistent, although in finite samples, both the bias and mean squared error can be substantial. For inference, we show through theory and simulation that under misspecification, standard likelihood ratio tests of truly non‐zero variance components can suffer from severely inflated type I errors, and confidence intervals for the variance components can exhibit considerable under coverage. Furthermore, neither of these problems vanish asymptotically with increasing the number of clusters or cluster size. These results have major implications for random effects inference, especially if the true random effects distribution is heavier tailed than the normal. Fortunately, simple graphical and goodness‐of‐fit measures of the random effects predictions appear to have reasonable power at detecting misspecification. We apply linear mixed models to a survey of more than 4 000 high school students within 100 schools and analyse how mathematics achievement scores vary with student attributes and across different schools. The application demonstrates the sensitivity of mixed model inference to the true but unknown random effects distribution.