Neoclassical consumer theory and genetically modified food

Three axioms underpin consumer choice in neoclassical theory: weak order, independence, and continuity. Two of these axioms may not hold, however, for consumers’ choices regarding genetically modified (GM) food. Consumers may evaluate product attributes differently depending on whether the food is GM or not, violating attribute independence. Some consumers may not want GM food at all, violating continuity. The axioms were empirically investigated with a choice experiment survey. The paper discusses evidence of violations of both independence and continuity, as well as a non-neoclassical approach to modelling consumer choice.genetically modified food, neoclassical, bounded rationality, choice modelling, Agricultural and Food Policy, Community/Rural/Urban Development, Consumer/Household Economics, Crop Production/Industries, Environmental Economics and Policy, Farm Management, Food Consumption/Nutrition/Food Safety, Research Methods/ Statistical Methods,

F-15 SMTD low speed jet effects wind tunnel test results

Key results from low speed wind tunnel testing of the F-15 STOL and Maneuver Technology Demonstrator (SMDT) with thrust reversers are presented. Longitudinally, the largest induced increments in the stability and control occur at landing gear height. These generally reflect an induced lift loss and a nose-up pitching moment, and vary with sideslip. Directional stability is reduced at landing gear height with full reverse thrust. Nonlinearities in the horizontal tail effectiveness are found in free air and at landing gear height

How We Can Apply AI, and Deep Learning to our HR Functional Transformation and Core Talent Processes?

[Excerpt] While organizations agree with the importance of AI, only 31% are ready to embrace or have already applied it to their HR process. There are varying levels of acceptance for AI across the HR function. Top areas of implementation are: recruiting and hiring (49%), HR strategy and employee management decisions (31%), analysis of workplace policies (24%), and automation of tasks previously performed by humans (22%)

Estimated Contribution of Four Biotechnologies to New Zealand Agriculture

The impact of biotechnology is an important consideration for New Zealand. The country depends significantly on agricultural production and exports (Ministry of Agriculture and Forestry, 2004), and has relied in part on modern biotechnology for productivity increases over the last 20 years (Evenson & Gollin, 2003; Jacobsen & Scobie, 1999; Ovenden, Anderson, Armstrong, & Mitchel, 1985). A recent survey of individuals in agriculture and biotechnology generated a comprehensive list of products and processes that are derived from four specific biotechnologies and are commercially significant in agriculture (Kaye-Blake, Saunders, Emanuelsson, Dalziel, & Wreford, 2005). This innovative research generated primary data on the actual impacts that biotechnology is currently having on agricultural production and produced a unique dataset of biotechnology products and processes and their value to New Zealand agriculture. Analysis found that these four biotechnologies are contributing approximately 206 million per year to agriculture. This analysis, however, assumed perfectly elastic international prices, and thus that New Zealand agricultural producers would capture the benefits of increased productivity. Literature on the impacts of productivity increases suggests that the distribution of benefits from increased productivity depends on how widely a technology is adopted. For example, genetic improvements in the crops of one country can allow domestic producers to increase producer surplus at the expense of producers in the rest of the world (Frisvold, Sullivan, & Raneses, 2003). By contrast, domestic farmers may be worse off if innovations are adopted in both the home country and the rest of the world (Moschini, Lapan, & Sobolevsky, 2000). The literature also suggests that specific impact of a novel technology is important to its impacts on agricultural producers. For example, technology that increases yields may be less beneficial for farmers than technology that reduces costs (Moschini et al., 2000). In addition, innovations that increase productivity of commodity products with low price elasticities of demand may not benefit farmers as much as innovations that increase consumer demand for agricultural products (Saunders & Cagatay, 2003). These findings are relevant because some features of New Zealand's primary sector suggest that international price impacts may be important. New Zealand is an open economy (Ministry of Agriculture and Forestry (MAF), 2004) and a significant exporter on world markets, particularly in pastoral products (Ministry of Agriculture and Forestry (MAF), 2004; Saunders & Cagatay, 2003). Modelling the movement of international prices may be done in several ways. The general equilibrium GTAP model (Hertel, 1997), for example, has been used to examine the potential impacts of biotechnology on producer and consumer welfare assuming different levels of adoption and consumer acceptance (e.g., Anderson & Jackson, 2005; Stone, Matysek, & Dolling, 2002). These impacts have also been analysed with partial equilibrium models, in particular models derived originally from the Uruguay Round of trade negotiations (Roningen, 1997), such as SWOPSIM (Frisvold et al., 2003; Roningen, Dixit, Sullivan, & Hart, 1991) and LTEM (Saunders & Cagatay, 2003). Partial equilibrium models are particularly appropriate for analysing impacts on a single sector of the economy: they allow substantial disaggregation by commodity and examination of the linkages that lead to model results (Gaisford & Kerr, 2001). In order to investigate the possible impact of biotechnological innovations on commodity prices and agricultural producers, the results of the original findings based on elastic prices were incorporated into a partial equilibrium model of world agricultural commodity trade (Cagatay & Saunders, 2003; Saunders & Cagatay, 2003). The model contained 19 commodities, including the major trade commodities for New Zealand (dairy products and meat). World trade was divided into 17 countries and the rest of the world, including New Zealand as a separate entity as well as the US, EU, Australia, Japan and others. As a partial equilibrium model, it examined the agricultural sector in isolation from other sectors of the economy. The base year was 2000, and impacts were modelled to 2005. The base solution modelled current production, which included biotechnological innovations. Alternative scenarios modelled the impact of the absence or loss of biotechnological innovations. The first scenario modelled the absence of innovations in all countries, while the second scenario examined the impact of innovations specific to New Zealand. The contribution of biotechnology to productivity was assessed separately for each commodity, using the original dataset (Kaye-Blake et al., 2005). For each commodity in the model, the analysis calculated the change in producer prices and total producer returns (price x quantity). The modelling results conformed to expectations. In the first scenario, a worldwide reduction in productivity in the primary sector led market prices to adjust upward in response to the lower production. For the second scenario, the price impacts were smaller for sectors with innovations specific to New Zealand. These changes were then combined with the original, constant-price estimate to calculate price-adjusted figures. The constant price analysis found that the contribution of the biotechnologies was 206 million. The first modelling scenario found that the economic benefit of the biotechnologies was only $19 million because increased productivity reduced commodity prices. The second scenario yielded an economic benefit of$191 million, suggesting that adopting New Zealandspecific innovations might not have a large impact on aggregate trade and might have allowed domestic producers to capture much of the increased welfare from innovations. Economic impacts, however, were spread unevenly across the commodities. In both trade scenarios, dairy producers increased producer returns through biotechnology, regardless of how widely the innovations were adopted. Meat producers, on the other hand, improved their returns when the innovations were specific to New Zealand, but were somewhat worse off when the innovations were available worldwide. This research contributes to understanding of the impacts of biotechnology in several ways. First, the productivity impacts were based on empirical findings regarding estimated impacts of actual commercially released biotechnologies; these were estimates of impacts that have actually occurred. Secondly, the productivity effects varied by commodity in the model, so that the impacts on different commodities could be estimated. Finally, by using a disaggregated, multi-commodity model, the cross-effects from resources shifting into other agricultural uses could be captured.Research and Development/Tech Change/Emerging Technologies,

A Dynamic Structural Model for Stock Return Volatility and Trading Volume

This paper seeks to develop a structural model that lets data on asset returns and trading volume speak to whether volatility autocorrelation comes from the fundamental that the trading process is pricing or, is caused by the trading process itself. Returns and volume data argue, in the context of our model, that persistent volatility is caused by traders experimenting with different beliefs based upon past profit experience and their estimates of future profit experience. A major theme of our paper is to introduce adaptive agents in the spirit of Sargent (1993) but have them adapt their strategies on a time scale that is slower than the time scale on which the trading process takes place. This will lead to positive autocorrelation in volatility and volume on the time scale of the trading process which generates returns and volume data. Positive autocorrelation of volatility and volume is caused by persistence of strategy patterns that are associated with high volatility and high volume. Thee following features seen in the data: (i) The autocorrelation function of a measure of volatility such as squared returns or absolute value of returns is positive with a slowly decaying tail. (ii) The autocorrelation function of a measure of trading activity such as volume or turnover is positive with a slowly decaying tail. (iii) The cross correlation function of a measure of volatility such as squared returns is about zero for squared returns with past and future volumes and is positive for squared returns with current volumes. (iv) Abrupt changes in prices and returns occur which are hard to attach to 'news.' The last feature is obtained by a version of the model where the Law of Large Numbers fails in the large economy limit.

Modelling Climate Change Impacts on Agriculture and Forestry with the Extended LTEM (Lincoln Trade and Environment Model)

In the land-based sectors, agricultural production generally is a source of carbon, while forestry may be thought to act as a sink. This paper focuses on new research examining the interaction of the two. The core of the research is the Lincoln Trade and Environment model (LTEM), a partial equilibrium model which links trade in NZ with the main trading countries overseas, through to production and associated environmental consequences . This paper reports on research expanding the model to include forestry from incorporating the capabilities of the Global Forest Products Model (GFPM) into the LTEM and hence producing an integrated model of agricultural and forestry land-uses for NZ and overseas. The paper extends the environmental modelling capabilities of the LTEM to include the impacts of climate change. The paper thereby reports on the development of a model of international trade that encompasses major agricultural commodities and forestry, complete with linkages and feedback with the environment and differentiated international markets. The paper then presents results of scenarios around changes in consumer behaviour and production using the new model.Environmental Economics and Policy,