Initial resource heterogeneity differences between family and non-family firms: Implications for resource acquisition and resource generation

A fundamental, but overlooked stream of resource-based theory (RBT) is the analysis of combinations of initial heterogeneous resource endowments with homogeneous resources that are acquired in the market. These combinations can generate heterogeneous, specific non-tradable resources, which are a potential source of superior competitive advantage and, hence, performance. In order to operationalize this idea empirically, we analyse the development of internationalization resources (considered a specific category of non-tradable resources) within family and non-family firms. Compared to non-family firms, we argue that family firms are able to combine a particular type of heterogeneous initial resource (i.e. familiness) with homogeneous tradable resources acquired in the market. This question is tested using a panel of family and non-family Spanish manufacturing firms for the period 1990 to 2010. As a result, this study contributes to the literature on RBT, extending previous theoretical and empirical research in this stream.     

Heterogeneity and varying effect in hazards regression

In the analysis of survival data, when nonproportional hazards are encountered, the Cox model is often extended to allow for a time-dependent effect by accommodating a varying coefficient. This extension, however, cannot resolve the nonproportionality caused by heterogeneity. In contrast, the heteroscedastic hazards regression (HHR) model is capable of modeling heterogeneity and thus can be applied when dealing with nonproportional hazards. In this paper, we study the application of the HHR model possibly equipped with varying coefficients. An LRR (logarithm of relative risk) plot is suggested when investigating the need to impose varying coefficients. Constancy and degeneration in the plot are used as diagnostic criteria. For the HHR model, a ‘piecewise effect’ (PE) analysis and an ‘average effect’ (AE) analysis are introduced. For the PE setting, we propose a score-type test for covariate-specific varying coefficients. The Stanford Heart Transplant data are analyzed for illustration. In the case of degeneration being destroyed by a polynomial covariate, piecewise constancy and/or monotonicity of the LRRs is considered as an alternative criterion based on the PE analysis. Finally, under the framework of the varying-coefficient HHR model, the meanings of the PE and AE analyses, along with their dynamic interpretation, are discussed.     

Bias modelling in evidence synthesis

Summary.  Policy decisions often require synthesis of evidence from multiple sources, and the source studies typically vary in rigour and in relevance to the target question. We present simple methods of allowing for differences in rigour (or lack of internal bias) and relevance (or lack of external bias) in evidence synthesis. The methods are developed in the context of reanalysing a UK National Institute for Clinical Excellence technology appraisal in antenatal care, which includes eight comparative studies. Many were historically controlled, only one was a randomized trial and doses, populations and outcomes varied between studies and differed from the target UK setting. Using elicited opinion, we construct prior distributions to represent the biases in each study and perform a bias-adjusted meta-analysis. Adjustment had the effect of shifting the combined estimate away from the null by approximately 10%, and the variance of the combined estimate was almost tripled. Our generic bias modelling approach allows decisions to be based on all available evidence, with less rigorous or less relevant studies downweighted by using computationally simple methods.     

从二级包干看高校岗位绩效工资分配制度改革

二级包干由于扩大了学院(部、处)收入分配自主权,建立了考核机制,能有效地强化成本核算和成本管理的意识,调动教职工的工作积极性和主动性,增强学校办学活力,是高校进行岗位绩效工资分配制度改革的一种行之有效的分配形式。绩效工资核定的两个重要原则是贡献异质性和边际效用。而一个完备的绩效评价机制是绩效工资机制正常运作的支撑和保证。高校实施绩效工资要在岗位明确、竟聘上岗的前提下,建立起有效的绩效评价机制,并在绩效和工资间建立正相关,发挥其激励作用。     

Large-Scale Replication Projects in Contemporary Psychological Research

ABSTRACT

Replication is complicated in psychological research because studies of a given psychological phenomenon can never be direct or exact replications of one another, and thus effect sizes vary from one study of the phenomenon to the next—an issue of clear importance for replication. Current large-scale replication projects represent an important step forward for assessing replicability, but provide only limited information because they have thus far been designed in a manner such that heterogeneity either cannot be assessed or is intended to be eliminated. Consequently, the nontrivial degree of heterogeneity found in these projects represents a lower bound on the true degree of heterogeneity. We recommend enriching large-scale replication projects going forward by embracing heterogeneity. We argue this is the key for assessing replicability: if effect sizes are sufficiently heterogeneous—even if the sign of the effect is consistent—the phenomenon in question does not seem particularly replicable and the theory underlying it seems poorly constructed and in need of enrichment. Uncovering why and revising theory in light of it will lead to improved theory that explains heterogeneity and increases replicability. Given this, large-scale replication projects can play an important role not only in assessing replicability but also in advancing theory.     

A Semi-Nonparametric Approach to Model Panel Count Data

In count data models, overdispersion of the dependent variable can be incorporated into the model if a heterogeneity term is added into the mean parameter of the Poisson distribution. We use a nonparametric estimation for the heterogeneity density based on a squared Kth-order polynomial expansion, that we generalize for panel data. A numerical illustration using an insurance dataset is discussed. Even if some statistical analyses showed no clear differences between these new models and the standard Poisson with gamma random effects, we show that the choice of the random effects distribution has a significant influence for interpreting our results.     

Some partially variance balanced structurally incomplete row-column designs

Earlier results by the authors are used to provide the intrablock analysis for row-column designs that have observations at nodes of the row-column lattice, the design being structurally incomplete when some nodes are empty. Construction, properties, and intrablock analyses of some special b× b row-column designs with b empty nodes taken along the principal diagonal of the lattice are developed. The designs discussed have m > 1 associate classes and are said to be partially variance balanced. The special designs fall in two classes and are shown to be nearly optimal in a specified class of designs. A small catalog of designs constructed is provided and they should be useful when empty nodes do not represent wasted experimental units, perhaps because the row and column assignments of treatments are sequenced.     

Nonparametric Discrete Choice Models With Unobserved Heterogeneity

In this research, we provide a new method to estimate discrete choice models with unobserved heterogeneity that can be used with either cross-sectional or panel data. The method imposes nonparametric assumptions on the systematic subutility functions and on the distributions of the unobservable random vectors and the heterogeneity parameter. The estimators are computationally feasible and strongly consistent. We provide an empirical application of the estimator to a model of store format choice. The key insights from the empirical application are: (1) consumer response to cost and distance contains interactions and nonlinear effects, which implies that a model without these effects tends to bias the estimated elasticities and heterogeneity distribution, and (2) the increase in likelihood for adding nonlinearities is similar to the increase in likelihood for adding heterogeneity, and this increase persists as heterogeneity is included in the model.     

Identifying Structural Models of Committee Decisions With Heterogeneous Tastes and Ideological Bias

In practice, members of a committee often make different recommendations despite a common goal and shared sources of information. We study the nonparametric identification and estimation of a structural model, where such discrepancies are rationalized by the members’ unobserved types, which consist of ideological bias while weighing different sources of information, and tastes for multiple objectives announced in the policy target. We consider models with and without strategic incentives for members to make recommendations that conform to the final committee decision. We show that pure-strategy Bayesian Nash equilibria exist in both cases, and that the variation in common information recorded in the data helps us to recover the distribution of private types from the members’ choices. Building on the identification result, we estimate a structural model of interest rate decisions by the Monetary Policy Committee (MPC) at the Bank of England. We find some evidence that the external committee members are less affected by strategic incentives for conformity in their recommendations than the internal members. We also find that the difference in ideological bias between external and internal members is statistically insignificant. Supplementary materials for this article are available online.     

Increasing heterogeneity in the economic returns to higher education in urban China

This study investigates individual heterogeneity in the economic returns to higher education in urban China following large-scale higher education expansion. We draw on data from the urban section of two waves of the Chinese General Social Survey, analyzing a sample of 1022 individuals in total who (1) were aged between 25 and 32; (2) completed high school education; and (3) were currently employed. Individual-level estimates of the distributions of the returns to higher education are obtained using a nonparametric kernel approach. While the average rate of returns to higher education increased for the 2003–2010 period, the extent of heterogeneity in these returns increased as well. Analysis of the heterogeneous returns to higher education across the distribution of income shows that the effects of college education are greatest at the upper end of the income distribution. Moreover, effect heterogeneity across the income distribution increased from 2003 to 2010.