Ist der Fahrplan der Energiewende noch einzuhalten?

The German energy transition represents a policy-driven, sustainability-oriented restructuring of both supply- and demand-side components of the entire energy system by 2050. Whereas the development of renewable energies in the electricity sector is right on track, due to the feed-in tariffs of the German Renewable Energy Sources Act, many other crucial requirements for a successful transition are not, amongst others the improvement of energy effi ciency and the decarbonisation of the transport sector. Contrary to the public discussion, the primary future challenges do not consist in limiting electricity prices or abandoning feed-in support schemes, but rather in coordinating the variety of actors as well as appropriately matching the different system elements (grids, technologies, energy sectors, demand and supply side, etc.). Much remains to be done. By highlighting some examples like the need to take into account future implications of climate change for the energy sector, the consequences of the current crisis in the European Union’s emissions trading scheme and the need for a cautious adjustment of the EEG, the paper argues that the major challenges regarding the German energy transition mainly go beyond the current policy-driven and short-term discussion of energy prices. Germany’s pioneering attempt to integrate steadily increasing share of non-dispatchable electricity from renewable sources is challenging the stability of the system. Several characteristics in the current selfregulating system are identifi ed and analysed, which reveal themselves as potential weaknesses or shortcomings in the upcoming system.     

INPUT–OUTPUT ANALYSIS: THE NEXT 25 YEARS

This year marks the 25th anniversary of the International Input–Output Association and the 25th volume of Economic Systems Research. To celebrate this anniversary, a group of eight experts provide their views on the future of input–output. Looking forward, they foresee progress in terms of data collections, methods, theory testing, and focus and scope.     

Service innovation as a political process

Service innovation processes are driven by stakeholders in interaction and are understood and sketched as a value negotiation process that consists of an iterative process of securing potential value in service. While previous research has focused on service innovation as a harmonious closed system, our study explores service innovation as a political process in which stakeholders negotiate to create and secure future value. Data are collected through interviews and participant observations in four different case studies. Our study contributes to the field by illuminating service innovation as a political process and explaining how this is operationalized. The findings also contribute to an understanding of how stakeholder resources impact a chosen strategy; the resulting strategy’s impact on the service concept vis-à-vis its potential value; and how several involved stakeholders formulate, negotiate, and secure future potential value, which are the activities that drive a service innovation process.     

Did the Federal Crop Insurance Reform Act Alter Farm Enterprise Diversification?

We estimate how much United States farms changed enterprise diversification in response to a marked increase in crop insurance coverage brought about by the 1994 Federal Crop Insurance Reform Act, which substantially increased insurance subsidies. The analysis exploits farm‐level panel census data to compare farm‐specific changes in enterprise diversification over time. By examining diversification decisions of the same farms over time, we control for time‐invariant unobserved individual heterogeneity. We then use pooled cross‐sectional data from the United States Department of Agriculture (USDA) Agricultural Resource Management Survey to estimate the relationship between farm diversification and average returns. We find that the insurance subsidies caused a modest increase in enterprise specialisation and production efficiency. Estimated efficiency gains are far less than the subsidies.     

THE BIAS OF THE MULTIPLIER MATRIX WHEN SUPPLY AND USE TABLES ARE STOCHASTIC

The literature on stochastic input–output (I–O) analysis has paid considerable attention to the bias in the Leontief inverse. This paper extends previous studies by assuming supply and use tables (SUTs rather than I–O tables or input coefficients matrices) to be stochastic. This is a natural starting point because SUTs have become the basic data sources for I–O applications. In a Monte Carlo simulation experiment, a given SUT is randomized in two different ways and the effects are determined for eight different multiplier matrices. The analysis is carried out for Spain, Italy, the Netherlands, Germany and Finland, using their SUTs for 2006. The findings indicate that, in general, biases are statistically significant but negligibly small. This corroborates earlier findings obtained for stochastic I–O tables.     

Heuristic learning and the discovery of specialization and exchange

I develop and calibrate an agent-based model of boundedly rational, adaptive agents in a two-good production and exchange economy to replicate human-subject outcomes in the same eight-person experimental economy. To test agents’ ability to capture human behavior, I extend the model and use its output to make predictions about a second experimental environment in which the group of eight agents is slowly constructed by merging smaller groups. This environment improves human-subject performance in the specialization and exchange task, and commensurate improvement emerges for some parameterizations of the agent-based model. This iterative process yields incremental improvement of decision-level theories about economic discovery.     

On Conformity Testing and the Use of two Stage Procedures

Conformity testing is a systematic examination of the extent to which an entity conforms to specified requirements. Such testing is performed in industry as well as in regulatory agencies in a variety of fields. In this paper we discuss conformity testing under measurement or sampling uncertainty. Although the situation has many analogies to statistical testing of a hypothesis concerning the unknown value of the measurand there are no generally accepted rules for handling measurement uncertainty when testing for conformity. Usually the objective of a test for conformity is to provide assurance of conformity. We therefore suggest that an appropriate statistical test for conformity should be devised such that there is only a small probability of declaring conformity when in fact the entity does not conform. An operational way of formulating this principle is to require that whenever an entity has been declared to be conforming, it should not be possible to alter that declaration, even if the entity was investigated with better (more precise) measuring instruments, or measurement procedures. Some industries and agencies designate specification limits under consideration of the measurement uncertainty. This practice is not invariant under changes of measurement procedure. We therefore suggest that conformity testing should be based upon a comparison of a confidence interval for the value of the measurand with some limiting values that have been designated without regard to the measurement uncertainty. Such a procedure is in line with the recently established practice of reporting measurement uncertainty as “an interval of values that could reasonably be attributed to the measurand”. The price to be paid for a reliable assurance of conformity is a relatively large risk that the procedure will fail to establish conformity for entities that only marginally conform. We suggest a two‐stage procedure that may improve this situation and provide a better discriminatory ability. In an example we illustrate the determination of the power function of such a two‐stage procedure.     

Trader Anonymity, Price Formation and Liquidity

Using data from the Frankfurt Stock Exchange we analyze price formationand liquidity in a non-anonymous environment with similarities to thefloor of the NYSE. Our main hypothesis is that the non-anonymity allows the specialist to assess the probability that atrader trades on the basis of private information. He uses this knowledgeto price discriminate. This can be achieved by quoting a large spread and granting price improvement to traders deemed uninformed.Consistent with our hypothesis we find that price improvement reflects loweradverse selection costs but does not lead to a reduction in the specialist's profit. Further, the quote adjustmentfollowing transactions at the quoted bid or ask price is more pronounced than the quote adjustment aftertransactions at prices inside the spread. Our results indicate that anonymity comes at the cost ofhigher adverse selection risk.     

Sponsorship effect metric: assessing the financial value of sponsoring by comparisons to television advertising

Since sponsoring is an increasingly important marketing communication medium, it is important for managers to know whether their sponsorships are giving a good financial return relative to other communication alternatives. Although several “rules of thumb” are commonly used in the industry for converting sponsorship exposure time into television advertising equivalent values, this research is the first to empirically test them by means of two experiments employing realistic stimuli and representative samples. Across 6 stimuli brands, study 1 finds that an average of 127 s of sponsor logo exposure achieves the same effects as a 30 s TV ad, but variance around this figure is high and depends on a variety of factors such as communication goal, sponsor fit, type of exposure, and whether the sponsor is a known brand. Study 2 finds that combining sponsoring with television advertising does increase the effects of sponsoring, although effects vary widely depending on the context, but even in the best case the increase is not supportive of the high levels of advertising suggested by many industry experts. Overall, the findings will allow managers to more confidently assess the financial value of their sponsorship investments.