Trust and adaptive learning in implicit contracts

Trust is a phenomenon that still is quite rarely investigated in agency theory. According to a common intuitive reasoning, trust should develop over time and it should evolve even in finite implicit-contract relationships. However, if the contracting parties are fully rational, theory cannot explain this. We therefore extend the standard model and develop a model of a finite relationship where the principal promises to pay a voluntary period-by-period bonus if the agent has worked according to the implicit agreement. The agent is boundedly rational and unable to foresee the principal’s future bonus decisions. The principal is, with some probability, honest and pays a promised bonus even in situations where ex-post cheating would be optimal. Based on the agent’s adaptive learning process, we show how trust evolves depending on the principal’s bonus-payment strategy. Depending on different levels of the agent’s bounded rationality, we derive the principal’s optimal pure strategy as part of a unique equilibrium. In an extension we show that the results are robust if the agent has bounded recall. The optimal strategy pattern mirrors a subset of trigger strategies which is exogenous in the standard model. Our findings imply that subjective incentives are more effective with increasing tenure of employees, or, that the optimal level of trust depends on how fast work environments change.     

Set Identified Linear Models

We analyze the identification and estimation of parameters β satisfying the incomplete linear moment restrictions E(z^⊤(xβ−y)) = E(z^⊤u(z)), where z is a set of instruments and u(z) an unknown bounded scalar function. We first provide empirically relevant examples of such a setup. Second, we show that these conditions set identify β where the identified set B is bounded and convex. We provide a sharp characterization of the identified set not only when the number of moment conditions is equal to the number of parameters of interest, but also in the case in which the number of conditions is strictly larger than the number of parameters. We derive a necessary and sufficient condition of the validity of supernumerary restrictions which generalizes the familiar Sargan condition. Third, we provide new results on the asymptotics of analog estimates constructed from the identification results. When B is a strictly convex set, we also construct a test of the null hypothesis, β₀∈B, whose size is asymptotically correct and which relies on the minimization of the support function of the set B− {β₀}. Results of some Monte Carlo experiments are presented.     

Internationaler Joint Venture Erfolg: Eine Meta-Analyse

To analyze the success factors of international joint ventures (IJVs), this study meta-analytically integrates 106 studies of 32.318 firms. The results show that the success factors from the partner level (e.g., operational and strategic fit, partner experience) and IJV level (e.g., commitment, trust, control, interdependence, conflicts, conflict management, and organizational learning) determine IJV performance. Further, results reveal that IJV type, industry, and geographical distance moderate the relationships between success factors and IJV performance. Thus, this study contributes to extant knowledge by synthesizing and contextualizing quantitative empirical IJV research.     

Practical implications of the resource-based view

Managers use many decision making tools when developing a firm’s strategic direction. Ideally, such tools yield a good solution for an acceptable amount of application effort. This paper presents the results of an experimental research project that compares the effectiveness of a theory-based strategic decision making tool, theVRIO-framework, with two alternative decision making heuristics for predicting the stock-market performance of different companies. First, we assess how the predictions of theVRIO-framework contrast with decisions based on “gut feeling” using the forecasts derived from a recognition-based decision making tool, theRecognition Heuristic. Secondly, theVRIO-framework’spredictive power is compared to predictions derived fromAnalyst Ratings. Our results suggest that the predictions of theVRIO-framework are superior to those of theRecognition Heuristic and theAnalyst Ratings, supporting the practical usefulness of resource-based theory. We conclude that resource analysis is important to strategic decision making and discuss the implications of our findings for future research and management practice.     

Why Health Care Reform Now? Strategic Framing and the Passage of Obamacare

Economic reform and health care reform were both focal points outlined in President Obama's policy agenda, with increasing pressure to address economic and social insecurity given that President Obama entered office during the Great Recession (2007–09). The Patient Protection and Affordable Care Act (commonly known as ‘Obamacare’) successfully passed in March 2010 in the context of the economic crisis. We argue that the strategic framing of the economic crisis, through reasoning and arguments linking health care reform with economic downfall, is important in understanding the successful passage of Obamacare, and that this is reflected through strategic frames in speeches delivered by the President on health care reform. Health care reform has been successful not in spite of but rather because of the economic crisis of 2008, that allowed President Obama to use a strategic frame focusing on costs and economic problems. The two main frames identified are the ‘market’ and ‘rights’ frames. President Obama's strategic frames specifically surrounding the economic and cost‐containment priority of health care reform are categorized as a ‘market’ frame in this article. He used this frame until the passage of the law in 2010, when the frame shifted to ‘rights’ frames, largely portrayed through anecdotes and focused on the concept of ‘access’ to care rather than the ‘cost’ of care. This is observable through tracking speeches and statements made in support of health care reform between 2009 and 2013.     

The Clustering of Categorical Data: A Comparison of a Model-based and a Distance-based Approach

For clustering multivariate categorical data, a latent class model-based approach (LCC) with local independence is compared with a distance-based approach, namely partitioning around medoids (PAM). A comprehensive simulation study was evaluated by both a model-based as well as a distance-based criterion. LCC was better according to the model-based criterion and PAM was sometimes better according to the distance-based criterion. However, LCC had an overall good and sometimes better distance-based performance as PAM, although this was not the case in a real data set on tribal art items.     

Parameter estimation for binomial AR(1) models with applications in finance and industry

Methods for analyzing and modeling count data time series are used in various fields of practice, and they are particularly relevant for applications in finance and economy. We consider the binomial autoregressive (AR(1)) model for count data processes with a first-order AR dependence structure and a binomial marginal distribution. We present four approaches for estimating its model parameters based on given time series data, and we derive expressions for the asymptotic distribution of these estimators. Then we investigate the finite-sample performance of the estimators and of the respective asymptotic approximations in a simulation study, including a discussion of the 2-block jackknife. We illustrate our methods and findings with a real-data example about transactions at the Korea stock market. We conclude with an application of our results for obtaining reliable estimates for process capability indices.     

Estimation of odds of concordance based on the Aalen additive model

The Cox regression model is often used when analyzing survival data as it provides a convenient way of summarizing covariate effects in terms of relative risks. The proportional hazards assumption may not hold, however. A typical violation of the assumption is time-changing covariate effects. Under such scenarios one may use more flexible models but the results from such models may be complicated to communicate and it is desirable to have simple measures of a treatment effect, say. In this paper we focus on the odds-of-concordance measure that was recently studied by Schemper et al. (Stat Med 28:2473?C2489, 2009). They suggested to estimate this measure using weighted Cox regression (WCR). Although WCR may work in many scenarios no formal proof can be established. We suggest an alternative estimator of the odds-of-concordance measure based on the Aalen additive hazards model. In contrast to the WCR, one may derive the large sample properties for this estimator making formal inference possible. The estimator also allows for additional covariate effects.     

An EM-Based Viterbi Approximation Algorithm for Mixed-State Latent Factor Models

In this article, a state-space model based on an underlying hidden Markov chain model (HMM) with factor analysis observation process is introduced. The HMM generates a piece-wise constant state evolution process and the observations are produced from the state vectors by a conditionally heteroscedastic factor analysis observation process. More specifically, we concentrate on situations where the factor variances are modeled by univariate Generalized Quadratic Autoregressive Conditionally Heteroscedastic processes (GQARCH). An expectation maximization (EM) algorithm combined with a mixed-state version of the Viterbi algorithm is derived for maximum likelihood estimation. The various regimes, common factors, and their volatilities are supposed unobservable and the inference must be carried out from the observable process. Extensive Monte Carlo simulations show promising results of the algorithms, especially for segmentation and tracking tasks.