EURANDOM: a decade of European stochastics

Conception, founding, start-up and development of EURANDOM are described by Willem van Zwet, Frank den Hollander (former scientific directors of EURANDOM) and Wim Senden (former managing director of EURANDOM).     

Using the Analytical Hierarchy Process (AHP) to Construct a Measure of the Magnitude of Consequences Component of Moral Intensity

The purpose of this work is to elaborate an empirically grounded mathematical model of the magnitude of consequences component of “moral intensity” (Jones, Academy of Management Review 16(2),366, 1991) that can be used to evaluate different ethical situations. The model is built using the analytical hierarchy process (AHP) (Saaty, The Analytic Hierarchy Process, 1980) and empirical data from the legal profession. One contribution of our work is that it illustrates how AHP can be applied in the field of ethics. Following a review of the literature, we discuss the development of the model. We then illustrate how the model can be used to rank-order three well-known ethical reasoning cases in terms of the magnitude of consequences. The work concludes with implications for theory, practice, and future research. Specifically we discuss how this work extends the previous work by Collins (Journal of Business Ethics 8, 1, 1989) regarding the nature of harm variable. We also discuss the contribution this work makes in the development of ethical scenarios used to test hypotheses in the field of business ethics. Finally, we discuss how the model can be used for after-action review, contribute to organizational learning, train employees in ethical reasoning, and aid in the design and development of decision support systems that support ethical reasoning.     

POST‐TRANSITION POVERTY TRENDS BASED ON AN ALTERNATIVE DATA SOURCE1

This paper analyses a previously unused source of data – the All Media and Product Survey (AMPS) – to arrive at alternative estimates of the post‐transition poverty path. The motivations for using this non‐official data source are twofold: concern over the comparability of the existing official post‐transition datasets – the Income and Expenditure Survey (IES) and Population Census – and a desire to extend analysis of poverty trends beyond 2001. While official data sources are generally preferred for purposes of poverty analysis, the IES and Census collect data at long (5 or 10 year) intervals, and additional years pass before these datasets become available to the public. In some cases there is also concern about data comparability between surveys. The expenditure data contained in the General Household Survey are available annually, although data are captured in a small number of categories that are not very conducive to analysis at the lower end of the income distribution. Analysis on AMPS data confirms the large decline in poverty implied by an increase of R18 billion (in 2000 Rand) in social grant payments between 2000 and 2004. The direction of this trend is consistent with recent research findings based on more frequently analysed data sources, including the work done by Agüero, Carter and May (2005), Seekings (2006 ) and Meth (2006 ).     

Forecasting US recessions with various risk factors and dynamic probit models

This paper extends probit recession forecasting models by incorporating various recession risk factors and using the advanced dynamic probit modeling approaches. The proposed risk factors include financial market expectations of a gloomy economic outlook, credit or liquidity risks in the general economy, the risks of negative wealth effects resulting from the bursting of asset price bubbles, and signs of deteriorating macroeconomic fundamentals. The model specifications include three different dynamic probit models and the standard static model. The out-of-sample analysis suggests that the four probit models with the proposed risk factors can generate more accurate forecasts for the duration of recessions than the conventional static models with only yield spread and equity price index as the predictors. Among the four probit models, the dynamic and dynamic autoregressive probit models outperform the static and autoregressive models in terms of predicting the recession duration. With respect to forecasting the business cycle turning points, the static probit model is as good as the dynamic probit models by being able to flag an early warning signal of a recession.     

What Do Farmers Want From Agri‐Environmental Scheme Design? A Choice Experiment Approach

Agri‐environmental schemes (AES) have had a limited effect on European agriculture due to farmers’ reluctance to participate. Information on how farmers react when AES characteristics are modified can be an important input to the design of such policies. This article investigates farmers’ preferences for different design options in a specific AES aimed at encouraging nitrogen fixing crops in marginal dry‐land areas in Spain. We use a choice experiment survey conducted in two regions (Aragón and Andalusia). The analysis employs an error component random parameter logit model allowing for preference heterogeneity and correlation amongst the non‐status quo alternatives. Farmers show a strong preference for maintaining their current management strategies; however, significant savings in cost or increased participation can be obtained by modifying some AES attributes.     

The Who,Where, What,How and When of Market Entry

This introductory, along with the eight articles contained within this Special Issue, highlights and brings greater clarity to entrant‐incumbent interactions and to firm movement – when entrants traverse market territories for the creation and/or delivery of offerings, where ‘markets’ include service or product categories, technology or resource spaces, industries, sectors and/or geographies. Collectively, this Special Issues explains that firm movement across market boundaries is highly consequential, influencing resource‐capability mixes inside firms, interfirm relations, market logic and industry value chains, and of course, people, communities and even nations. Specifically, we develop a field‐wide perspective of market entry by expanding on the framework of market entry that Zachary and his colleagues developed (Zachary et al., 2015) – i.e., the who (players such as incumbents, entrants, suppliers, etc.), when (the timing and sequence of entry), how (the strategy, resources, capabilities, etc.), where (the space of entry) and what (product, service, business model, etc.) – to include two additional categories: complements (networks, platforms, ecosystems) and non‐market elements (government, political, social and cultural arrangements). We also summarize the eight highly diverse and insightful articles that make this Special Issue, and conclude with a discussion to highlight foundational questions that point to new directions in future research in this field. In sum, we hope to inspire scholars to go beyond counting outcomes (e.g., entry/exit rates, or profiling successful versus unsuccessful entrants), to consider contexts, processes and contingencies (e.g., cost, time, collaboration, competition, interfirm relations, etc.) and to discover boundary conditions that inform a theory of market entry.     

Getting the Unemployed Back to Work: The Role of Targeted Wage Subsidies

This paper examines alternative approaches to wage subsidy programmes. It does this in the context of a recent active labour market reform for the young unemployed in Britain. This New Deal reform and the characteristics of the target group are examined in detail. We discuss theoretical considerations, the existing empirical evidence and propose two strategies for evaluation. The first suggests an ex-post trend adjusted difference in difference estimator. The second, relates to a model based ex-ante evaluation. We present the conditions for each to provide a reliable evaluation and fit some of the crucial parameters using data from the British Labour Force Survey. We stress that the success of this type of labour market programmes hinge on dynamic aspects of the youth labour market, in particular the pay-off to experience and training.     

Mind your pricing cues

For most of the items they buy, consumers don't have an accurate sense of what the price should be. Ask them to guess how much a four-pack of 35-mm film costs, and you'll get a variety of wrong answers: Most people will underestimate; many will only shrug. Research shows that consumers' knowledge of the market is so far from perfect that it hardly deserves to be called knowledge at all. Yet people happily buy film and other products every day. Is this because they don't care what kind of deal they're getting? No. Remarkably, it's because they rely on retailers to tell them whether they're getting a good price. In subtle and not-so-subtle ways, retailers send signals to customers, telling them whether a given price is relatively high or low. In this article, the authors review several common pricing cues retailers use--"sale" signs, prices that end in 9, signpost items, and price-matching guarantees. They also offer some surprising facts about how--and how well--those cues work. For instance, the authors' tests with several mail-order catalogs reveal that including the word "sale" beside a price can increase demand by more than 50%. The practice of using a 9 at the end of a price to denote a bargain is so common, you'd think customers would be numb to it. Yet in a study the authors did involving a women's clothing catalog, they increased demand by a third just by changing the price of a dress from $34 to $39. Pricing cues are powerful tools for guiding customers' purchasing decisions, but they must be applied judiciously. Used inappropriately, the cues may breach customers' trust, reduce brand equity, and give rise to lawsuits.     

Specialists,Limit‐Order Traders,and the Components of the Bid‐Ask Spread

This study compares the components of the bid‐ask spread estimated from quotes that reflect the trading interest of specialists with those estimated from limit‐order quotes and all available quotes for a sample of New York Stock Exchange (NYSE) stocks. The results show that the adverse selection component of the spread estimated from specialist quotes is significantly smaller than the corresponding figures from limit‐order quotes and entire quotes. We interpret this as evidence that NYSE specialists transfer at least a part of adverse selection costs to outsiders through the discretionary use of limit orders. Our results show that the estimation/interpretation of the components of the spread using quote data that include both specialist and limit‐order interests is problematic.