Segmenting the Italian coffee market: marketing opportunities for economic agents working along the international coffee chain

Globalization, either directly or indirectly (e.g. through structural adjustment reforms), has called for profound changes in the previously existing institutional order. Some changes adversely impacted the production and market environment of many coffee producers in developing countries resulting in more risky and less remunerative coffee transactions. This paper focuses on customization of a tropical commodity, fair-trade coffee, as an approach to mitigating the effects of worsened market conditions for small-scale coffee producers in less developed countries. fair-trade labeling is viewed as a form of “de-commodification” of coffee through product differentiation on ethical grounds. This is significant not only as a solution to the market failure caused by pervasive information asymmetries along the supply chain, but also as a means of revitalizing the agricultural-commodity-based trade of less developed countries (LDCs) that has been languishing under globalization. More specifically, fair-trade is an example of how the same strategy adopted by developed countries’ producers/ processors (i.e. the sequence product differentiation - institutional certification - advertisement) can be used by LDC producers to increase the reputation content of their outputs by transforming them from mere commodities into “decommodified” (i.e. customized and more reputed) goods. The resulting segmentation of the world coffee market makes possible to meet the demand by consumers with preference for this “(ethically) customized” coffee and to transfer a share of the accruing economic rents backward to the Fair-trade coffee producers in LDCs. It should however be stressed that this outcome cannot be taken for granted since investments are needed to promote the required institutional innovations. In Italy FTC is a niche market with very few private brands selling this product. However, an increase of FTC market share could be a big commercial opportunity for farmers in LDCs and other economic agents involved along the international coffee chain. Hence, this research explores consumers’ knowledge of labels promoting quality products, consumption coffee habits, brand loyalty, willingness to pay and market segmentation according to the heterogeneity of preferences for coffee products. The latter was assessed developing a D-efficient design where stimuli refinement was tested during two focus groups.

Integrating drivers influencing the detection of plant pests carried in the international cut flower trade

This paper analyses the cut flower market as an example of an invasion pathway along which species of non-indigenous plant pests can travel to reach new areas. The paper examines the probability of pest detection by assessing information on pest detection and detection effort associated with the import of cut flowers. We test the link between the probability of plant pest arrivals as a precursor to potential invasion, and volume of traded flowers using count data regression models. The analysis is applied to the UK import of specific genera of cut flowers form Kenya between 1996 and 2004. There is a link between pest detection and the Genus of cut flower imported. Hence, pest detection efforts should focus on identifying and targeting those imported plants with a high risk of carrying pest species. For most of the plants studied efforts allocated to inspection have a significant influence on the probabilty of pest detction. However, by better targetting inspection efforts, it is shown that plant inspection effort could be reduced without increasing the risk of pest entry. Similarly, for most of the plants analysed, an increase in volume traded will not necessarily lead to an increase in the number of pests entering the UK. For some species, such as conclude that analysis at the rank of plant Genus is important both to understand the effectiveness of plant pest detection efforts and consequently to manage the risk of introduction of non-indigenous species.

Strategic use of private standards to enhance international competitiveness: Vegetable exports from Kenya and elsewhere

The paper examines how European retailers are using private standards for food safety and,quality as risk management and competitive tools and the strategic responses of leading Kenyan and other developing country supplier/exporters to such standards. Despite measures to harmonize a 'single market', the European fresh produce market is very diverse in terms of consumer preferences, structural dynamics and attention to and enforcement of food safety and other standards. Leading Kenyan fresh produce suppliers have re-positioned themselves at the high end, including 'high care', segments of the market - precisely those that are most demanding in terms of quality assurance and food safety systems. An array of factors have influenced this strategic positioning, including relatively high international freight costs, the emergence of more effective competition in mainstream product lines, relatively low labor costs for produce preparation, and strong market relationships with selected retail chains. To succeed in this demanding market segment, the industry has had to invest substantially in improved production and procurement systems, upgraded pack house facilities, and quality assurance/food safety management systems. (C) 2005 Elsevier Ltd. All rights reserved.

SORMA - business cases for an open grid market: concept and implementation

Economic mechanisms enhance technological solutions by setting the right incentives to reveal information about demand and supply accurately. Market or pricing mechanisms are ones that foster information exchange and can therefore attain efficient allocation. By assigning a value (also called utility) to their service requests, users can reveal their relative urgency or costs to the service. The implementation of theoretical sound models induce further complex challenges. The EU-funded project SORMA analyzes these challenges and provides a prototype as a proof-of-concept. In this paper the approach within the SORMA-project is described on both conceptual and technical level.

Gauging the investment potential of international real estate markets

Investing in real estate markets overseas means venturing into the unknown, where you meet unfamiliar political and economic environments, unstable currencies, strange cultures and languages, and so although the advantages of international diversification might appear attractive, the risks of international investment must not be overlooked. However, capital markets are becoming global markets, and commercial real estate markets are no exception, accordingly despite the difficulties posed by venturing overseas no investor can overlook the potential international investment holds out. Thus, what strategies are appropriate for capitalising on this potential? Three issues must be considered: (1) the potential of the countries real estate market in general; (2) the potential of the individual market sectors; and (3) the investment process itself. Although each step in foreign real estate investment is critical, the initial assessment of opportunities is especially important. Various methods can be used to achieve this but a formal and systematic analysis of aggregate market potential should prove particularly fruitful. The work reported here, therefore, develops and illustrates such a methodology for the over 50 international real estate markets.

Back from beyond bid-ask spread: estimating liquidity in international markets

Research on the topic of liquidity has greatly benefited from the improved availability of data. Researchers have addressed questions regarding the factors that influence bid-ask spreads and the relationship between spreads and risk, return and liquidity. Intra-day data have been used to measure the effective spread and researchers have been able to refine the concepts of liquidity to include the price impact of transactions on a trade-by-trade analysis. The growth in the creation of tax-transparent securities has greatly enhanced the visibility of securitized real estate, and has naturally led to the question of whether the increased visibility of real estate has caused market liquidity to change. Although the growth in the public market for securitized real estate has occurred in international markets, it has not been accompanied by universal publication of transaction data. Therefore this paper develops an aggregate daily data-based test for liquidity and applies the test to US data in order to check for consistency with the results of prior intra-day analysis. If the two approaches produce similar results, we can apply the same technique to markets in which less detailed data are available and offer conclusions on the liquidity of a wider set of markets.

International evidence on the predictability of returns to securitized real estate assets: econometric models versus neural networks

The performance of various statistical models and commonly used financial indicators for forecasting securitised real estate returns are examined for five European countries: the UK, Belgium, the Netherlands, France and Italy. Within a VAR framework, it is demonstrated that the gilt-equity yield ratio is in most cases a better predictor of securitized returns than the term structure or the dividend yield. In particular, investors should consider in their real estate return models the predictability of the gilt-equity yield ratio in Belgium, the Netherlands and France, and the term structure of interest rates in France. Predictions obtained from the VAR and univariate time-series models are compared with the predictions of an artificial neural network model. It is found that, whilst no single model is universally superior across all series, accuracy measures and horizons considered, the neural network model is generally able to offer the most accurate predictions for 1-month horizons. For quarterly and half-yearly forecasts, the random walk with a drift is the most successful for the UK, Belgian and Dutch returns and the neural network for French and Italian returns. Although this study underscores market context and forecast horizon as parameters relevant to the choice of the forecast model, it strongly indicates that analysts should exploit the potential of neural networks and assess more fully their forecast performance against more traditional models.