Asymmetric information as a barrier to knowledge spillovers in expert markets

This paper investigates barriers to effective knowledge spillovers for markets in which the product can be characterized as a credence good, i.e. its complexity impedes the evaluation of quality by customers both ex-ante and ex-post. We focus on the German market for energy efficiency consultants, as an emerging and subsidized sector in which the service offered has strong credence good properties. Based upon in-depth interviews with stakeholders, we analyze the determinants and barriers to knowledge spillovers. We find that the incentive to foster spillovers to increase suppliers’ knowledge is limited by the difficult commercialization of additional capabilities. The implementation of a public certification scheme has failed to increase the sectoral knowledge spillovers. By contrast, the participation in formal knowledge networks has been more effective in prompting companies to foster knowledge spillovers, which has also led to a higher degree of specialization. We conclude that access to certification schemes should be further restricted to increase market transparency and private networks should be supported to achieve the aim of increasing knowledge spillovers.     

Detecting nonlinearity in time series by model selection criteria

This article analyzes the use of model selection criteria for detecting nonlinearity in the residuals of a linear model. Model selection criteria are applied for finding the order of the best autoregressive model fitted to the squared residuals of the linear model. If the order selected is not zero, this is considered as an indication of nonlinear behavior. The BIC and AIC criteria are compared to some popular nonlinearity tests in three Monte Carlo experiments. We conclude that the BIC model selection criterion seems to offer a promising tool for detecting nonlinearity in time series. An example is shown to illustrate the performance of the tests considered and the relationship between nonlinearity and structural changes in time series.     

A novel approach to legacy donations with long-term benefits supported by numerical illustrations

Philanthropic donors face challenges in matching the causes to which they donate, the time horizon—and thus impact—of their donations, and the charitable vehicles they choose for making contributions. Wealthier donors may elect to create their own foundations and customize their charitable support. Less wealthy donors have limited choices: they may contribute to a nonprofit's current operations or to existing nonprofit endowments. We present a novel approach for making charitable donations, blending aspects of each of these strategies. Our approach has potential long-term financial benefits, allows donors to control their charitable donations in a convenient and easy-to-implement manner, can be established through an existing nonprofit organization, expands opportunities for more donors because it requires a smaller corpus contribution with lower management costs than creating a foundation, provides tax savings in the United States and other countries (e.g., the UK, Canada, and Australia) comparable to other planned giving vehicles, and may be implemented during one's lifetime using donor advised funds or as part of a legacy plan through the donor's estate documents, which is when the long-term benefits accrue.     

The people behind the scenes: Episodic volunteering at fundraising events

Nonprofit organizations (NPOs) have become increasingly dependent on episodic volunteers (EVs), those that would help carry out an event with little training and expectation to commit to future events. Despite its importance to the survival of NPOs, the use of EVs with respect to fundraising has received little research focus. Furthermore, none of the existing studies identified examined how fundraising EVs differ from other EVs. This study seeks to contribute to our understanding of fundraising EVs using a global perspective. Data were generated using surveys distributed in seven countries, tallying more than 4,000 respondents. Variables included demographic characteristics, previous donation and volunteer history, motives, mode of volunteering, and overall experience. The data were analyzed by applying between-group (logistic regression on participation) and within-group analysis (ordered logistic regression on willingness to participate in the future). Data were further analyzed by examining differences by country. We found that country, gender, religion, income, employment, history of donations, mode of volunteering, and several motives, both intrinsic and extrinsic, were significant in the logistic regression analysis of participation in fundraising episodic volunteering. The ordered logistic analysis unexpectedly found that the only predictors to foster a willingness to engage again were the responsiveness of the event team and a desire to fulfill spiritual satisfaction. In recruiting and selecting EVs for fundraising events, NPOs should consider previous or current donors and those with regular volunteering experience and they should market volunteer opportunities towards those in search of spiritual fulfillment and meaning. Moreover, NPOs should prioritize quick and clear communication with fundraising EVs in order to foster a willingness to volunteer again. Lastly, NPOs should regularly assess for country-specific factors and contexts that may affect episodic volunteering in fundraising events. Our paper illustrates who fundraising EVs are, their motives, how they choose to volunteer, and what contributes to their willingness to volunteer again. Given the limited research on fundraising EVs, this study serves to help lay the foundation of research for this unique subgroup. Our aim was to not only address the dearth of literature but serve as a springboard for future research on fundraising EVs.     

Technical efficiency analysis correcting for biases from observed and unobserved variables: an application to a natural resource management project

This article brings together the stochastic frontier framework with impact evaluation methodology to compare technical efficiency (TE) across treatment and control groups using cross-sectional data associated with the MARENA Program in Honduras. A matched group of beneficiaries and control farmers is determined using propensity score matching techniques to mitigate biases stemming from observed variables. In addition, possible self-selection arising from unobserved variables is addressed using a selectivity correction model for stochastic frontiers recently introduced by Greene (J Prod Anal 34:15?C24, 2010). The results reveal that average TE is consistently higher for beneficiary farmers than the control group while the presence of selectivity bias cannot be rejected. TE ranges from 0.67 to 0.75 for beneficiaries and from 0.40 to 0.65 for the control depending on whether biases were controlled or not. The TE gap between beneficiaries and control farmers decreases by implementing the matching technique and the sample selection framework decreases this gap even further. The analysis also suggests that beneficiaries do not only exhibit higher TE but also higher frontier output.     

Uncertainty and central bank transparency: A non-Bayesian approach

We use a non-Bayesian approach to uncertainty, where “ambiguity” is taken into account, in order to analyze the issue of central bank transparency, and we underline that the use of such an approach may greatly change the results. We reconsider a specific argument against transparency found in the literature. We show that, in the presence of ambiguity, the argument can become a case in favor of transparency, which seems more in accordance with some stylized facts. Reduced Knightian uncertainty associated with increased transparency can contribute to making transparency beneficial.     

Econometric analysis of copyrights

This paper explores the impact of copyrights on firm value and on the demand for firm output. Using panel data on franchise value and ticket sales from the National Football League over the 1991–2000 period, we analyze the effect of copyrights (in this case, team logos) using several parametric estimators, the Arellano and Bond [1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277–297] dynamic panel data estimator, and a semi-non-parametric method based on difference-in-differences propensity score matching. We find a negative effect of logo changes on franchise value that is robust across multiple specifications. In addition, logo changes also appear to have a moderate positive, albeit not particularly robust, impact on ticket sales.