Reevaluating the prudence of economic forecasts in the EU: The role of instrument persistence

Christodoulakis and Mamatzakis (2009, Journal of Applied Econometrics 24, pp. 583–606) estimate the EU Commission loss preferences for selected economic forecasts of 12 EU Member States. They employ the generalized method of moments (GMM) estimation procedure proposed by Elliott et al. (2005, Review of Economic Studies 72, pp. 1107–1125) and find the forecasts to be somewhat optimistic on average. However, this note shows the GMM estimator to possess nonstandard limiting distributions when some of the instruments are highly persistent, which is the case with one of the instruments employed by Christodoulakis and Mamatzakis. Standard distributions are recovered in some interesting particular cases which are relevant in practice. A reexamination of the EU Commission loss preferences using methods robust to persistence and a dataset extended to 2017 reveals that, while the conclusions of the original study are, by and large, still justified, the EU Commission loss preferences have become more symmetric over the whole studied period.     

Reference points for measuring social performance: Case study of a social business venture

We present a longitudinal qualitative case study to elaborate on how a social venture forms reference points for social performance. Although organizations increasingly use various social performance targets to direct their operations, the scholarly knowledge on social performance reference points remains limited. We make use of the prior accounting literature and draw on the idea of compromising accounts to discuss how provisional and performative metrics can have a significant role in how organizations develop new ways to evaluate their social performance. Given that the social performance reference point criteria are ambiguous and the corresponding referents malleable, performative accounts are helpful as they can intervene in the organizational life by making particular things visible, providing space for interpretations, and facilitating discussion, thus creating temporary settlements and enabling opportunities for productive compromises between different organizational groups and evaluative principles. The recursive feedback loops between reference point referents, criteria and accounting artefacts help the organization to make sense of its own social performance and interpret the associated performance feedback, and thereby provide ground for organizational decisions on further action. Moreover, we discuss how imperfect accounts can be useful for social businesses in their pursuit of developing their activities and achieving social impact.     

Does educational diversity of managers matter for the performance of team‐managed funds?

We study the effect of the educational diversity of managers on the performance of team‐managed mutual funds using a large sample of U.S. equity funds from 1994 to 2013. We consider diversity in terms of both final educational degree and field of educational specialisation. We find that, in general, both types of diversity have a positive impact on fund performance, and our results are robust over a wide range of performance metrics and changes in market conditions.     

Blockchain in Additive Manufacturing and its Impact on Supply Chains

Additive manufacturing (AM) appears to be a particularly attractive use case for blockchain. This research combines inductive in‐depth interviews with the Delphi method to explore what potentials blockchain technology in AM creates, which adoption barriers firms need to overcome, and how supply chains will be affected by the integration of these two potentially disruptive technologies. The results suggest opportunities that are related to intellectual property (IP) rights management, the monitoring of printed parts throughout their lifecycle, process improvements, and data security. The most important barriers for blockchain adoption in AM are an absence of blockchain‐skilled specialists on the labor market, missing governance mechanisms, and a lack of firm‐internal technical expertise. By addressing important limitations of AM, blockchain is expected to improve the competitiveness of AM in parts’ production, catalyzing the trend toward more decentralized manufacturing resulting in more agile, resilient, and flexible supply chains and reduced logistics costs. Beyond that, blockchain‐based AM platforms are expected to enhance supply chain visibility, drive supply chain digitalization, support supply chain finance, and contribute to the emergence of shared factory systems.     

Nonfinancial Considerations in Eco-Innovation Decisions: The Role of Family Ownership and Reputation Concerns

Societal pressures for greater sustainability can encourage firms to target part of their innovation activities at ecological initiatives (i.e., eco-innovation). Yet, depending on their value function, firms can respond differently to such pressures and exhibit variance in their eco-innovation activities. In this paper, we investigate the idea that a firm’s ownership structure may play a significant role in determining its engagement in eco-innovation. Specifically, we propose that ownership by family blockholders increases the value attached to the company’s reputation and that this, in turn, stimulates higher levels of eco-innovation. In other words, we model the company reputation motive as a key mediator in the relationship between family ownership and firm-level eco-innovation. To account for family firm heterogeneity, we also model the moderating role of owners’ intention to pass the business on to the next family generation (transgenerational intentions) and of the extent to which these owners reside in the firm’s local community (local embeddedness). As theoretical backdrop, our study builds on institutional theory and the mixed gamble logic. To test our hypotheses, we use a large sample of German firms and nonlinear moderated mediation regression analysis. Results reveal that family ownership is positively related to the introduction of eco-innovations by firms, in part because of the stronger emphasis being placed on the company’s reputation. We find that this effect is strongest when the owning-family has transgenerational intentions. As such, this study advances our understanding of firm-level drivers of eco-innovation. In view of the prevalence of family-owned firms and the mounting importance of ecological sustainability, it is valuable to extend knowledge on the contingent and indirect effect of family ownership on eco-innovation.     

Missing the Popular Vote: Pitfalls in US Democracy and Reform Proposals

Intereconomics - Only a few years ago, it was a widespread belief that globalisation would trigger processes of democratisation worldwide. However, even old and established democracies such as the...     

Contagion of network products in small-world networks

We formulate a model in which agents embedded in an exogenous social network decide whether to adopt a new network product or not. In the theoretical part of the paper, we characterize the stochastically stable equilibria for complete networks and cycles. For an arbitrary network structure, we develop a novel graph decomposition method to characterize the set of recurrent communication states, which is a superset of stochastically stable equilibria of the adoption game presented in our model. In the simulation part, we study the contagion process of a network product in small-world networks that systematically represent social networks. We simulate a generalization of the Morris (Rev Econ Stud 67(1):57–78, 2000) Contagion model that can explain the chasm between early adopters and early majority. Our numerical analysis shows that the failure of a new network product is less likely in a highly cliquish network. In addition, the contagion process reaches to steady state faster in random networks than in highly cliquish networks. It turns out that marketers should work with mixed marketing strategies, which will result in a full contagion of a network product and faster contagion rates with a higher probability.

     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

Testing the Empirical Relevance of the ‘Saving for a Rainy Day’ Hypothesis in US Metro Areas

The joint implication of the consumption Euler equation and cointegration between income and consumption is that savings predict future income declines, the ‘saving for a rainy day’ hypothesis. The empirical relevance of this hypothesis plays a key role in discussions of fiscal policy multipliers, and it holds under the null that the permanent income hypothesis is true. We find little support for this hypothesis using time series data for the 100 largest US Metropolitan Statistical Areas for the period 1980q1–2015q4. Our approach is to test for cointegration and weak exogeneity between income and consumption, and by exploring the direction of Granger causality between the two time series. We find that income more often predicts consumption and saving than the converse. We also give evidence that house price changes played a role in US income and consumption dynamics, before, during and after the Great Recession.