Good Consumer Information: the Information Paradigm at its (Dead) End?

The traditional information paradigm postulates that increasing the amount of information and establishing full transparency help consumers with their decisions. We challenge this assumption and address criteria that good consumer information needs to fulfil. Based on the findings from research in behavioural economics and finance, necessary conditions for good consumer information include transparency, comprehensibleness, and comparability, whereas quality—in terms of clarity, fit to personal needs, and verifiability—represents the sufficient condition for good consumer information. Information that consumers currently receive hardly fulfils these conditions which, in turn, considerably hampers the trustworthiness and usability of this information. To mitigate consumers’ information problem and to recover the idea of the information paradigm, we suggest to extend the information model and to integrate the idea of collective consumers, to establish product testing principles, and to implement controlled minimum standard for (financial) products.     

Aufschwung in Deutschland weiter kräftig — Anspannungen nehmen zu

The business cycle upswing in Germany has gained strength and breadth. In addition to private consumption, the expansion is now also supported by investment and foreign trade. The latter benefits from growing exports to the euro area, where the economy is gaining momentum. Since the euro area upswing rests on solid fundamentals, the ECB should be able to start tapering without putting the economy at risk.     

Collective Choices Under Ambiguity

We investigate experimentally whether collective choice environments matter for individual attitudes to ambiguity. In a simple two-urn Ellsberg experiment, one urn offers a 45 % chance of winning a fixed monetary prize while the other offers an ambiguous chance. Participants choose either individually or in groups of three. Group decision rules vary in the level of individual responsibility for the others’ payoffs: the collective choice is taken by majority, randomly delegated to two group members, or randomly delegated to a single group member. Although most participants display consistent ambiguity attitudes across their decisions, taking responsibility for the others tends to foster ambiguity aversion.     

Alumni Loyalty: Systematic Literature Review

The term alumni loyalty (AL) is widely used by scientists in various disciplines and particularly often in discussions of the key factors influencing alumni contributions. However, research in this field varies considerably in its assumptions and methods and contains contradictory findings. Additionally, there is no consistent definition of AL. This situation makes it difficult for the university administration to see the overall picture or to evaluate which results are most reliable and should be used as the basis for practice and policy decisions. A systematic literature review (SLR) would address these problems by identifying, meticulously evaluating, and integrating findings of the relevant studies. To date, no SLR has been undertaken in the field of alumni loyalty. The purpose of this study is twofold. First, the paper conducts an SLR in this research area. Second, the study provides a more comprehensive definition of alumni loyalty. The research identified 102 relevant articles and classified them according to seven theoretical approaches: microeconomics, charitable giving, management, relationship marketing, educational science, services marketing, and the integrative approach, which combines the above disciplines. Having determined four primary aspects of AL (behavioral, attitudinal, material and nonmaterial), the paper derives four quadrants of AL definitions and assigns each publication to one or two of them. Following this step, the overall picture and definition of AL were derived. The findings can be used by academics as a framework to position new research activities appropriately and by practitioners as a roadmap to enhance AL rate.     

The influence of farm input subsidies on the adoption of natural resource management technologies

Farm input subsidies are often criticised on economic and ecological grounds. The promotion of natural resource management (NRM) technologies is widely seen as more sustainable to increase agricultural productivity and food security. Relatively little is known about how input subsidies affect farmers’ decisions to adopt NRM technologies. There are concerns of incompatibility, because NRM technologies are one strategy to reduce the use of external inputs in intensive production systems. However, in smallholder systems of Africa, where the average use of external inputs is low, there may possibly be interesting complementarities. Here, we analyse the situation of Malawi's Farm Input Subsidy Program (FISP). Using panel data from smallholder farm households, we develop a multivariate probit model and examine how FISP participation affects farmers’ decisions to adopt various NRM technologies, such as intercropping of maize with legumes, use of organic manure, water conservation practices and vegetative strips. As expected, FISP increases the use of inorganic fertilizer and improved maize seeds. Yet, we also observe a positive association between FISP and the adoption of certain NRM technologies. For other NRM technologies, we find no significant effect. We conclude that input subsidies and the promotion of NRM technologies can be compatible strategies.     

From dogmatic views on conservation agriculture adoption in Zambia towards adapting to context

Conservation Agriculture (CA) has been widely promoted in sub-Saharan Africa (SSA) as a sustainable agricultural practice, yet with debatable success. Most authors assume successful adoption, only if all three principles of CA are implemented: (1) minimum or zero tillage, (2) maintenance of a permanent soil cover, and (3) integration of crop rotations. Based on this strict definition, adoption has declined or remained stagnant. Presently, not much attention has been given to context-suited adaptation possibilities, and partial adoption has not been recognized as an entry point to full adoption. Furthermore, isolated success cases have not been analysed sufficiently. By applying the QAToCA approach based on focus group discussions complemented by semi-structured qualitative expert and farmer interviews, we assessed the reasons behind positive CA adaptation and adoption trends in Zambia. Main reasons behind Zambia’s emerging success are (1) a positive institutional influence, (2) a systematic approach towards CA promotion – encouraging a stepwise adaptation and adoption, and (3) mobilization of strong marketing dynamics around CA. These findings could help to eventually adjust or redesign CA promotion activities. We argue for a careful shift from the ‘dogmatic view’ on adoption of CA as a packaged technology, towards adapting its principles to the small-scale farming context of SSA.     

Evaluating irrigation investments in Malawi: economy‐wide impacts under uncertainty and labor constraints

Irrigation expansion is critical to increase crop yields and mitigate effects from climate change in Sub‐Saharan Africa, but the low profitability has led to little irrigation investments in the region so far. Using an integrated modeling framework, we simultaneously evaluate the returns to irrigation arising from both economic and biophysical impact channels to understand what determines the profitability of irrigation in Malawi. Our results confirm that the returns to irrigation cannot cover the costs in Malawi. While labor‐intensive irrigation expansion leads to unfavorable structural change in the short‐run, the profitability hinges on low irrigated yields that fall far from expectations due to insufficient input use and crop management techniques. On the other hand, we find that the nonmonetary benefits of irrigation regarding higher food security, lower poverty, and reduced vulnerability to climate change make investments in irrigation worthwhile to improve the livelihoods of smallholders.     

Rural Poverty: Old Challenges in New Contexts

Poverty is still a predominantly rural phenomenon. However,the context of rural poverty has been changing across the world,with high growth in some economies and stagnation in others.Furthermore, increased openness in many economies has affectedthe specific role of agricultural growth for rural poverty reduction.This paper revisits an ‘old’ question: how doesgrowth and poverty reduction come about if most of the poorlive in rural areas and are dependent on agriculture? What isthe role of agricultural and rural development in this respect?Focusing on Sub-Saharan Africa, and using economic theory andthe available evidence, the author comes to the conclusion thatchanging contexts has meant that agricultural growth is onlycrucial as an engine for growth in particular settings, morespecifically in landlocked, resource-poor countries, which areoften also characterized by relatively low potential for agriculture.However, extensive market failures in key factor markets andlikely spatial effects give a remaining crucial role for ruraldevelopment policies, including focusing on agriculture, toassist the inclusion of the rural poor in growth and development.How to overcome these market failures remains a key issue forfurther research. JEL codes: O41, Q10, O55     

Selecting credit rating models: a cross-validation-based comparison of discriminatory power

In commercial banking, various statistical models for corporate credit rating have been theoretically promoted and applied to bank-specific credit portfolios. In this paper, we empirically compare and test the performance of a wide range of parametric and nonparametric credit rating model approaches in a statistically coherent way, based on a ‘real-world’ data set. We repetitively (k times) split a large sample of industrial firms’ default data into disjoint training and validation subsamples. For all model types, we estimate k out-of-sample discriminatory power measures, allowing us to compare the models coherently. We observe that more complex and nonparametric approaches, such as random forest, neural networks, and generalized additive models, perform best in-sample. However, comparing k out-of-sample cross-validation results, these models overfit and lose some of their predictive power. Rather than improving discriminatory power, we perceive their major contribution to be their usefulness as diagnostic tools for the selection of rating factors and the development of simpler, parametric models.