Technical efficiency and firm heterogeneity in stochastic frontier models: application to smallholder maize farms in Ethiopia

This study estimates the technical efficiency measures of maize producing farm households in Ethiopia using stochastic frontier (SF) panel models that take different approaches to model firm heterogeneity. The efficiency measures are found to vary depending on how the estimation model treats both unobserved and observed firm heterogeneity. Estimates from the ‘true’ random effects (TRE) models that treat firm effects as heterogeneity are found to be identical to those from pooled SF models. Those results differ from the ones generated from the basic random effects (RE) models that treat firm effects as part of overall technical inefficiency. The more flexible generalised ‘true’ random effects (GTRE) model that splits the error term into firm effects, persistent inefficiency, transient inefficiency, and a random noise component indicates the presence of higher levels of persistent inefficiency than transient inefficiency. The basic truncated-normal RE model and heteroscedastic RE model yields similar efficiency estimates. The GTRE model predict persistent efficiency measures similar to those from the basic RE and flexible RE model with environmental variables incorporated in the variance function as well as in the deterministic production frontier. These results imply that the RE and GTRE panel models provide reliable efficiency estimates for our data compared to the TRE models. All the estimated SF models generate comparable production function parameters in terms of magnitude and sign. Overall, the results underscore the importance of scrutinising stochastic frontier models for their reliability of analytical results before drawing policy inferences.

     

Fixed Effects and Random Effects Estimation of Higher-order Spatial Autoregressive Models with Spatial Autoregressive and Heteroscedastic Disturbances

Abstract

This paper develops a unified framework for fixed effects (FE) and random effects (RE) estimation of higher-order spatial autoregressive panel data models with spatial autoregressive disturbances and heteroscedasticity of unknown form in the idiosyncratic error component. We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the disturbance process and define both an RE and an FE spatial generalized two-stage least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators and derive their joint asymptotic distribution, which is robust to heteroscedasticity of unknown form in the idiosyncratic error component. Finally, we derive a robust Hausman test of the spatial random against the spatial FE model.     

Fixed effects instrumental variables estimation in correlated random coefficient panel data models

We provide a set of conditions sufficient for consistency of a general class of fixed effects instrumental variables (FE-IV) estimators in the context of a correlated random coefficient panel data model, where one ignores the presence of individual-specific slopes. We discuss cases where the assumptions are met and violated. Monte Carlo simulations verify that the FE-IV estimator of the population averaged effect performs notably better than other standard estimators, provided a full set of period dummies is included. We also propose a simple test of selection bias in unbalanced panels when we suspect the slopes may vary by individual.     

A Tutorial on Multilevel Survival Analysis: Methods,Models and Applications

Data that have a multilevel structure occur frequently across a range of disciplines, including epidemiology, health services research, public health, education and sociology. We describe three families of regression models for the analysis of multilevel survival data. First, Cox proportional hazards models with mixed effects incorporate cluster‐specific random effects that modify the baseline hazard function. Second, piecewise exponential survival models partition the duration of follow‐up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. By incorporating cluster‐specific random effects, generalised linear mixed models can be used to analyse these data. Third, after partitioning the duration of follow‐up into mutually exclusive intervals, one can use discrete time survival models that use a complementary log–log generalised linear model to model the occurrence of the outcome of interest within each interval. Random effects can be incorporated to account for within‐cluster homogeneity in outcomes. We illustrate the application of these methods using data consisting of patients hospitalised with a heart attack. We illustrate the application of these methods using three statistical programming languages (R, SAS and Stata).     

On the Epidemiological Microfoundations of Sticky Information*

We estimate and compare two models in which households periodically update their expectations. The first model assumes that households update their expectations towards survey measures. In the second model, households update their expectations towards rational expectations (RE). While the literature has used these specifications indistinguishably, we argue that there are important differences. The two models imply different updating probabilities, and the data seem to prefer the second one. We then analyse the properties of both models in terms of mean expectations, median expectations, and a measure of disagreement among households. The model with periodical updates towards RE also seems to fit the data better along these dimensions.     

ESTIMATION OF CENSORED PANEL‐DATA MODELS WITH SLOPE HETEROGENEITY

This paper considers estimation of censored panel‐data models with individual‐specific slope heterogeneity. The slope heterogeneity may be random (random slopes model) or related to covariates (correlated random slopes model). Maximum likelihood and censored least‐absolute deviations estimators are proposed for both models. The estimators are simple to implement and, in the case of maximum likelihood, lead to straightforward estimation of partial effects. The rescaled bootstrap suggested by Andrews (Econometrica 2000; 68 : 399–405) is used to deal with the possibility of variance parameters being equal to zero. The methodology is applied to an empirical study of Dutch household portfolio choice, where the outcome variable (portfolio share in safe assets) has corner solutions at zero and one. As predicted by economic theory, there is strong evidence of correlated random slopes for the age profiles, indicating a heterogeneous age profile of portfolio adjustment that varies significantly with other household characteristics. Copyright © 2013 John Wiley & Sons, Ltd.     

The impact of role models on entrepreneurial intentions and behavior: a review of the literature

Despite the presence of the term ‘entrepreneurial role model’ (ERM) in the discourse on entrepreneurship, existing empirical evidence on the effects of role models is rather limited. By investigating 86 published journal articles, we provide a structured overview of the academic research on role models’ effects on entrepreneurial intentions and behavior. We reveal that prior research focuses particularly on different types of role models (by whom), at which stage of life (when) and in which context the exposure to role models occurs. We use these research areas to structure our review. By expanding the understanding of the current state of ERM research, we reveal research gaps and provide future research recommendations. Our work could help policy makers and educators consider the different types of role models, the sociocultural context and the life cycle stage of the participants in structuring their entrepreneurship education programs.

     

Beating the market? A mathematical puzzle for market efficiency

The efficient market hypothesis is highly discussed in economic literature. In its strongest form, it states that there are no price trends. When weakening the non-trending assumption to arbitrary short, small, and fully unknown trends, we mathematically prove for a specific class of control-based trading strategies positive expected gains. These strategies are model free, i.e., a trader neither has to think about predictable patterns nor has to estimate market parameters such as the trend’s sign like momentum traders have to do. That means, since the trader does not have to know any trend, even trends too small to find are enough to beat the market. Adjustments for risk and comparisons with buy-and-hold strategies do not satisfactorily solve the problem. In detail, we generalize results from the literature on control-based trading strategies to market settings without specific model assumptions, but with time-varying parameters in discrete and continuous time. We give closed-form formulae for the expected gain as well as the gain’s variance and generalize control-based trading rules to a setting where older information counts less. In addition, we perform an exemplary backtesting study taking transaction costs and bid-ask spreads into account and still observe—on average—positive gains.

     

A combined random effect and fixed effect forecast for panel data models

When some of the regressors in a panel data model are correlated with the random individual effects, the random effect (RE) estimator becomes inconsistent while the fixed effect (FE) estimator is consistent. Depending on the various degree of such correlation, we can combine the RE estimator and FE estimator to form a combined estimator which can be better than each of the FE and RE estimators. In this paper, we are interested in whether the combined estimator may be used to form a combined forecast to improve upon the RE forecast (forecast made using the RE estimator) and the FE forecast (forecast using the FE estimator) in out-of-sample forecasting. Our simulation experiment shows that the combined forecast does dominate the FE forecast for all degrees of endogeneity in terms of mean squared forecast errors (MSFE), demonstrating that the theoretical results of the risk dominance for the in-sample estimation carry over to the out-of-sample forecasting. It also shows that the combined forecast can reduce MSFE relative to the RE forecast for moderate to large degrees of endogeneity and for large degrees of heterogeneity in individual effects.     

When employee performance management affects individual innovation in public organizations: the role of consistency and LMX

Abstract

Public sector challenges translate in more complex job demands that require individual innovation. In order to deal with these demands, many public organizations have implemented employee performance management. In a multilevel study, we examine when employee performance management affects individual innovation. We contribute by focusing on consistent employee performance management and Leader–Member Exchange (LMX). Based on goal-setting theory, we first argue that employee performance management fosters individual innovation when it entails consistent subpractices. Subsequently, LMX is theorized to function as a moderator in this linkage. We use multilevel data from 68 elderly homes and 1095 caregivers in Flanders to test our hypotheses. The study reveals that individual innovation is related to consistent employee performance management, and that LMX functions as a moderator in this relationship. Our findings contribute to scholars’ understanding of effects from employee performance management in public organizations.     

Multilevel theory and the underspecification of multilevel models

This article emphasises the importance of the adequate specification of models of multilevel analysis in accordance with multilevel theories. Until recent times, multilevel theories tried only to explain the direct effect of group characteristics on an individual's characteristic. It seems to be more suited to adopt a more general theoretical approach, in which it is assumed that group characteristics affects individual processes. There a treshold effect and a process effect have to be distinguished. The propositions result in a model specification within the random coefficient model of multilevel analysis. The theory and model recommended are illustrated by means of data of Dar and Resh's (1986) study into social learning environment.     

Adjusted indicators of quality and equity for monitoring the education systems over time. Insights on EU15 countries from PISA surveys

In this work, we investigate how European countries belonging to EU15 are performing in terms of the quality and equity of their educational systems. To do so, we jointly analysed student competencies in mathematics and reading using data collected in four different waves (2006, 2009, 2012, 2015) by the Program for International Student Assessment (PISA) run by the Organisation for Economic Cooperation and Development (OECD). The aim of this analysis is twofold: (i) to assess the associations between students' competencies inmathematics and reading and their socioeconomic status and to investigate how this relationship varies across countries over time; (ii) to present a batch of adjusted indicators relevant to the investigation of educational performance over time in terms of quality (average student competencies) and equity (low association between student achievement and their socioeconomic backgrounds). We fitted a mixed-effect (multilevel) regression model with a bivariate latent structure and random intercepts and slopes to assess the effect of socioeconomic and cultural background on student competencies across countries over time and to assess the performance trajectories of EU15 countries with respect to European Commission benchmarks. We present and discuss our main findings and their implications in terms of the policies of EU15 countries.     

Current practice in the modelling of age,period and cohort effects with panel data: a commentary on Tawfik et al. (2012), Clarke et al. (2009), and McCulloch (2012)

This comment assesses how age, period and cohort (APC) effects are modelled with panel data in the social sciences. It considers variations on a 2-level multilevel model which has been used to show apparent evidence for simultaneous APC effects. We show that such an interpretation is often misleading, and that the formulation and interpretation of these models requires a better understanding of APC effects and the exact collinearity present between them. This interpretation must draw on theory to justify the claims that are made. By comparing two papers which over-interpret such a model, and another that in our view interprets it appropriately, we outline best practice for researchers aiming to use panel datasets to find APC effects, with an understanding that it is impossible for any statistical model to find and separate all three effects.     

Double serial correlation for multilevel growth curve models

Multilevel growth curve models for repeated measures data have become increasingly popular and stand as a flexible tool for investigating longitudinal change in students’ outcome variables. In addition, these models allow the estimation of school effects on students’ outcomes though making strong assumptions about the serial independence of level-1 residuals. This paper introduces a method which takes into account the serial correlation of level-1 residuals and also introduces such serial correlation at level-2 in a complex double serial correlation (DSC) multilevel growth curve model. The results of this study from both real and simulated data show a great improvement in school effects estimates compared to those that have previously been found using multilevel growth curve models without correcting for DSC for both the students’ status and growth criteria.     

Employee isolation and support for change in the public sector: a study of the internal audit profession

ABSTRACT

We investigate the preferences of public sector internal auditors for changes in their work practices. Informed by the literature, we posit that feelings of trust in the workplace and/or frustrations arising from perceived organizational ‘isolation’—a dimension of work alienation—motivate support and acceptance of change. We also argue that professional bodies directly and indirectly influence these relationships. These conjectures are tested using structural equation modelling and a survey of Canadian public sector internal auditors. The findings provide valuable insights into the process of adaptation and transformation in public sector settings across countries, jurisdictions, and accountability functions.     

Bayesian model selection using encompassing priors

This paper deals with Bayesian selection of models that can be specified using inequality constraints among the model parameters. The concept of encompassing priors is introduced, that is, a prior distribution for an unconstrained model from which the prior distributions of the constrained models can be derived. It is shown that the Bayes factor for the encompassing and a constrained model has a very nice interpretation: it is the ratio of the proportion of the prior and posterior distribution of the encompassing model in agreement with the constrained model. It is also shown that, for a specific class of models, selection based on encompassing priors will render a virtually objective selection procedure. The paper concludes with three illustrative examples: an analysis of variance with ordered means; a contingency table analysis with ordered odds-ratios; and a multilevel model with ordered slopes.     

Identification of rational expectations models

This paper deals with the problem of the identification of simultaneous Rational Expectations (RE) models. In the case of RE models with current expectations of the endogenous variables, the necessary and sufficient conditions for the global identification are derived explicitly in terms of the structural parameters and the linear homogenous identifying restrictions. It is shown that in the absence of a priori restrictions on the processes generating the exogenous variables and the disturbances, RE models and general distributed lag models are ‘observationally equivalent’. In the case of RE models with future expectations of the endogenous variables, a general solution that highlights the ‘non-uniqueness’ problem and from which other solutions such as forward or backward solutions can be obtained, is derived. It is shown that untestable and often quite arbitrary restrictions are needed if RE models with future expectations are to be identifiable. Certain order conditions similar to those obtained for the identification of RE models with current expectations are also derived for this case.     

Behavior of the firm under rate-of-return regulation with two capital inputs

Traditional electric utility companies face a trade-off between building generation facilities that utilize renewable energy (RE) and non-renewable energy (non-RE). The firm's input decision to build capacity for either source depends on several constraining factors, including input prices, policies that promote or discourage RE use, and the type of regulation faced by the firm. This paper models the utility company's decision between RE and non-RE capital inputs. From the model, we derive the result that rate-of-return (ROR) regulation decreases the investment in RE capital relative to the unregulated firm. These findings suggest restructuring electricity generation markets, which removes the ROR on generating assets, can increase the relative use of RE. A second result of the model shows that the renewable portfolio standard (RPS) increases the investment in capital that requires RE as a source of electricity, as expected. This paper contributes to the literature on the substitution between renewable and non-renewable resources, by examining the policies that affect the investment in the two types of technologies. The model can also be applied to other regulated utilities, such as water or natural gas companies, with outputs that are produced from different types of capital.     

The relationship between perceived training and development and employee retention: the mediating role of work attitudes

Abstract

This paper considers how utilizing a model of job-related affect can be used to explain the processes through which perceived training and development influence employee retention. We applied Russell’s model of core affect to categorize four different forms of work attitude, and positioned these as mediators of the relationship between perceived training and development and intention to stay. Using data from 1191 employees across seven organizations, multilevel analyses found that job satisfaction, employee engagement and change-related anxiety were significantly associated with intention to stay, and fully mediated the relationship between perceived training and development and intention to stay. Contrary to our hypotheses, emotional exhaustion was not significantly associated with intention to stay nor acted as a mediator when the other attitudes were included. These findings show the usefulness of Russell’s model of core affect in explaining the link between training and development and employee retention. Moreover, the findings collectively suggest that studies examining employee retention should include a wider range of work attitudes that highlight pleasant forms of affect.     

Hyperdeterminantal point processes

As well as arising naturally in the study of non-intersecting random paths, random spanning trees, and eigenvalues of random matrices, determinantal point processes (sometimes also called fermionic point processes) are relatively easy to simulate and provide a quite broad class of models that exhibit repulsion between points. The fundamental ingredient used to construct a determinantal point process is a kernel giving the pairwise interactions between points: the joint distribution of any number of points then has a simple expression in terms of determinants of certain matrices defined from this kernel. In this paper we initiate the study of an analogous class of point processes that are defined in terms of a kernel giving the interaction between 2M points for some integer M. The role of matrices is now played by 2M-dimensional “hypercubic” arrays, and the determinant is replaced by a suitable generalization of it to such arrays—Cayley’s first hyperdeterminant. We show that some of the desirable features of determinantal point processes continue to be exhibited by this generalization.