COMBINING REVEALED AND STATED PREFERENCE DATA TO ESTIMATE THE NONMARKET VALUE OF ECOLOGICAL SERVICES: AN ASSESSMENT OF THE STATE OF THE SCIENCE

Abstract This paper reviews the marketing, transportation and environmental economics literature on the joint estimation of revealed preference (RP) and stated preference (SP) data. The RP and SP approaches are first described with a focus on the strengths and weaknesses of each. Recognizing these strengths and weaknesses, the potential gains from combining data are described. A classification system for combined data that emphasizes the type of data combination and the econometric models used is proposed. A methodological review of the literature is pursued based on this classification system. Examples from the environmental economics literature are highlighted. A discussion of the advantages and disadvantages of each type of jointly estimated model is then presented. Suggestions for future research, in particular opportunities for application of these models to environmental quality valuation, are presented.     

A close look at second stage data envelopment analysis using compound error models and the Tobit model

In 2014 the Brazilian Electricity Regulator (ANEEL) evaluated the efficiency of power distribution utilities using Data Envelopment Analysis (DEA). Estimated efficiencies range from 22.46% to 100%. Although environmental information is available in the data set, corrected efficiencies were not investigated. Different second stage models can be applied to adjust for environmental heterogeneity. Although statistical correlation among efficiencies and environmental variables can be easily estimated, corrected efficiencies are subject to the underlying structure of the second stage model. Therefore, different second stage models may achieve different corrected efficiencies. We provide a detailed statistical analysis of the Tobit model and compound error models for second stage analysis. Limitations are described and the corrected efficiencies using these models are evaluated. Potentially, Brazilian power distribution utilities may achieve substantial changes in estimated efficiencies if second stage analysis is used.     

Variance components models for survival data

Extensions of the Cox proportional hazards model for survival data are studied where allowance is made for unobserved heterogeneity and for correlation between the life times of several individuals. The extended models are frailty models inspired by Y ashin et al. (1995). Estimation is carried out using the EM algorithm. Inference is discussed and potential applications are outlined, in particular to statistical research in human genetics using twin data or adoption data, aimed at separating the effects of genetic and environmental factors on mortality.     

The effects of virtualness on teamwork behavioral components: The role of shared mental models

Virtual teams are an essential part of work organizations. They help organizations utilize skills and expertise regardless of where they are located. The virtual team literature suggests that these teams often experience process losses compared to their face-to-face counterparts. In this paper we propose that as virtualness increases, a team's shared mental models become more complex; this limits the effectiveness of particular teamwork behaviors: mutual performance monitoring, backup behavior and adaptation. We identify specific factors that are reflected in increased mental model complexity of virtual teams. Further we examine the moderating role that the accuracy and similarity of these shared mental models as well as the level of virtualness has on the relation between complexity and teamwork behaviors. Finally, we examine the effects of training inventions on mental model accuracy. To this end, we review the existing literature to develop a model and specific propositions.     

Impact of selected policy and market factors upon student flows from high school to competing destinations

The impact of selected policy and market factors upon high-school leavers' decisions regarding three competing alternatives—labor-force entry, university education, non-university education—is examined using Ontario data on grade 13 students covering 1965–1978.The specific policy and market factors selected include tuition fees, student-aid, family income, graduates' wages, youth unemployment. Based upon economic-related theories of investor and consumer behaviour, predictions regarding how these factors influence students' choices are presented. Empirical tests of these predictions are then obtained by computing transition-rate elasticities estimated from a multinomial logistic model using maximum-likelihood techniques.The results suggest that both policy and market factors influence students' choices on the margin, and therefore exercise control over the flows from high-school to various competing destinations. The implications of these results are examined for policy-making and planning at the post-secondary level.     

Enrolment and Delayed Enrolment of Secondary School Age Children in Indonesia

We analyse the school participation decision of children between 13 and 18 years in Indonesia using the 1992 SUSENAS household survey. Our focus is on which household factors determine enrolment and delayed enrolment. We use the multinomial fixed effect model where the cluster-specific fixed effects correct for any regional-specific factors that may influence the demand for education. The model is estimated by conditional maximum likelihood. We find that parent's education has a positive effect on enrolment, where the effects are stronger for boys than for girls. On the other hand, literacy of parents has stronger effects on the girls education than on the boys'. The model without fixed effects is rejected against the model with fixed effects. Hence, omitting regional variation in the model would have led to biased estimates.     

Estimation and inference in two-stage,semi-parametric models of production processes

Many papers have regressed non-parametric estimates of productive efficiency on environmental variables in two-stage procedures to account for exogenous factors that might affect firms’ performance. None of these have described a coherent data-generating process (DGP). Moreover, conventional approaches to inference employed in these papers are invalid due to complicated, unknown serial correlation among the estimated efficiencies. We first describe a sensible DGP for such models. We propose single and double bootstrap procedures; both permit valid inference, and the double bootstrap procedure improves statistical efficiency in the second-stage regression. We examine the statistical performance of our estimators using Monte Carlo experiments.     

Technology Transfer with Capital Constraints and Environmental Protections: Models and Applications to the Philippines

An efficient technology transfer from advanced to developing countries is explored by extending dynamic input-output optimization models. We include capital investments for the transferred technologies that affect the structural change and the welfare streams of consumption and the environmental state in the developing country. This technology transfer model is then linearized to solve larger problems. The linearized model was estimated and applied to assess the optimal technology transfer schedule from Japan to the Philippines.     

Investigating effects of organisational culture and learning on environmental collaboration and performance of Korean exporting firms

This paper presents an empirical study of the effect of organisational culture and learning capability factors on environmental collaboration and performance in green supply chains. A conceptual model and variables were derived from organisational culture, learning, and collaboration theory and tested with Korean exporting firms. Learning capability was found to positively affect environmental collaboration as staff behaviour, attitudes and learning about environmental practices in a focal firm can be increased from suppliers and customers and then disseminated internally. Further, environmental collaboration was found to positively affect environmental performance due to a focal firm sharing these learned capabilities about the environment with other supply chain partners. However, organisational culture was not found to positively affect environmental collaboration. The findings suggest firms can improve environmental capabilities and performance through shared learning with supply chain partners and ensuring they are internally disseminated in the focal organisation.     

Integrating Strategic Carbon Management into Formal Evaluation of Environmental Supplier Development Programs

Increasing global environmental pressures from regulators, markets and communities have caused focal companies in supply chains to recognize the significance of environmentally conscious management. Greener supply chains are part of this recognition. Environmental supplier development is a valuable and viable strategy for greening supply chains. However, low carbon management is rarely explored in supplier development. Further, formal tools and models for focal companies to evaluate environmental supplier development programs (ESDPs) considering low carbon management and their effect on supplier performance improvement are limited. To help address these gaps in the literature, this paper proposes a portfolio evaluation model for ESDPs that consider three types of supplier performance: traditional operational factors, traditional environmental factors and low carbon management factors. This model applies the fuzzy scoring method to measure the effect of ESDPs on supplier performance, and uses fuzzy DEMATEL to examine the cause–effect interrelationships among the ESDPs. Subsequently, a real world example is used to demonstrate the application of the portfolio model and provide insights into environmental supplier development evaluation, followed by discussions of case application results. This paper concludes with directions for further research. Copyright © 2014 John Wiley & Sons, Ltd and ERP Environment     

Macroeconomic forecasting using structural factor analysis

The use of a small number of underlying factors to summarize the information from a much larger set of information variables is one of the new frontiers in forecasting. In prior work, the estimated factors have not usually had a structural interpretation and the factors have not been chosen on a theoretical basis. In this paper we propose several variants of a general structural factor forecasting model, and use these to forecast certain key macroeconomic variables. We make the choice of factors more structurally meaningful by estimating factors from subsets of information variables, where these variables can be assigned to subsets on the basis of economic theory. We compare the forecasting performance of the structural factor forecasting model with that of a univariate AR model, a standard VAR model, and some non-structural factor forecasting models. The results suggest that our structural factor forecasting model performs significantly better in forecasting real activity variables, especially at short horizons.     

Interpolation and backdating with a large information set

Existing methods for data interpolation or backdating are either univariate or based on a very limited number of series, due to data and computing constraints that were binding until the recent past. Nowadays large datasets are readily available, and models with hundreds of parameters are easily estimated. We model these large datasets with a factor model, and develop an interpolation method that exploits the estimated factors as an efficient summary of all available information. The method is compared with existing standard approaches from a theoretical point of view, by means of Monte Carlo simulations, and also when applied to actual macroeconomic series. The results indicate that our method is rather robust to model misspecification, although traditional multivariate methods also work well while univariate approaches are systematically outperformed. When interpolated series are subsequently used in econometric analyses, biases can emerge, but they are smaller with multivariate approaches, including factor-based ones.     

CONDITIONALLY HETEROSKEDASTIC FACTOR MODELS WITH SKEWNESS AND LEVERAGE EFFECTS

Conditional heteroskedasticity, skewness and leverage effects are well‐known features of financial returns. The literature on factor models has often made assumptions that preclude the three effects to occur simultaneously. In this paper I propose a conditionally heteroskedastic factor model that takes into account the presence of both the conditional skewness and leverage effects. This model is specified in terms of conditional moment restrictions and unconditional moment conditions are proposed allowing inference by the generalized method of moments (GMM). The model is also shown to be closed under temporal aggregation. An application to daily excess returns on sectorial indices from the UK stock market provides strong evidence for dynamic conditional skewness and leverage with a sharp efficiency gain resulting from accounting for both effects. The estimated volatilitypersistence from the proposed model is lower than that estimated from models that rule out such effects. I also find that the longer the returns' horizon, the fewer conditionally heteroskedastic factors may be required for suitable modeling and the less strong is the evidence for dynamic leverage. Some of these results are in line with the main findings of Harvey and Siddique ( 1999 ) and Jondeau and Rockinger ( 2003 ), namely that accounting for conditional skewness impacts the persistence in the conditional variance of the return process. Copyright © 2012 John Wiley & Sons, Ltd.     

Assessing the efficiency of secondary schools: Evidence from OECD countries participating in PISA 2015

One of the most important issues accompanying the publication of the main results of the well-known Programme for International Student Assessment (PISA) is the classification of countries according to student attainment. However, this ranking does not take into account some highly relevant factors, such as the different resource endowments of each education system or the heterogeneous context in which schools operate. This study aims to provide a fuller picture of education system operation worldwide by assessing the managerial efficiency of secondary schools in a cross-country framework. To do this, we use data from OECD countries participating in PISA 2015 and apply a robust nonparametric approach that accounts for the fact that schools were operating under heterogeneous conditions before the efficiency measures of performance were estimated. Our results suggest that the consideration of both school resources and environmental factors significantly modifies the country ranking based solely on student results.     

DETERMINANTS OF FOREIGN DIRECT INVESTMENT – A TALE OF NINE THEORETICAL MODELS

Abstract.  This paper presents a review of nine theoretical models of foreign direct investment (FDI). Discussed are early studies of determinants of FDI (1) as well as determinants of FDI based on the neoclassical trade theory (2), ownership advantages (3), aggregate variables (4), the ownership, location and internalization advantage framework (5), horizontal and vertical FDI models (6), the knowledge-capital model (7), diversified FDI and risk diversification models (8) and policy variables (9). From each of the nine theories, the relevant determinants of FDI are derived. Empirical studies indicate the importance of these determinants in the real world. The paper shows that there is not one single theory of FDI, but a variety of theoretical models attempting to explain FDI and the location decision of multinational firms. Therefore, any analysis of determinants of FDI should not be based on a single theoretical model. Instead, FDI should be explained more broadly by a combination of factors from a variety of theoretical models such as ownership advantages or agglomeration economics, market size and characteristics, cost factors, transport costs, protection, risk factors and policy variables.     

ECONOMETRIC ESTIMATION OF ARMINGTON IMPORT ELASTICITIES FOR A REGIONAL CGE MODEL OF THE ILLINOIS ECONOMY

One of the main concerns associated with the development and use of regional CGE models is the determination of key parameter values, particularly substitution and other price elasticities. A common problem is the lack of appropriate regional data for econometric estimation. Consequently, it is important to identify key parameters that are likely to be important in determining quantitative results and then to prioritize these for estimation where appropriate data are available. In this paper, the focus is on the estimation of the regional trade (import) substitution parameters, which tend to be important in analysis for regional economies (given their openness to trade). Here, commodity import elasticities for the Illinois economy are estimated and tested in a single region CGE model of the Illinois economy. In our econometric estimation, we apply a model that takes account of market size and distance in estimating the substitutability between commodities produced in Illinois and other US states.     

Pooling data for the analysis of dynamic marketing systems

Vector autoregressive (VAR) models have become popular in marketing literature for analyzing the behavior of competitive marketing systems. One drawback of these models is that the number of parameters can become very large, potentially leading to estimation problems. Pooling data for multiple cross-sectional units (stores) can partly alleviate these problems. An important issue in such models is how heterogeneity among cross-sectional units is accounted for. We investigate the performance of several pooling approaches that accommodate different levels of cross-sectional heterogeneity in a simulation study and in an empirical application. Our results show that the random coefficients modeling approach is an overall good choice when the estimated VAR model is used for out-of-sample forecasting only. When the estimated model is used to compute Impulse Response Functions, we conclude that one should select a modeling approach that matches the level of heterogeneity in the data.     

MEASURING RISK IN ENVIRONMENTAL FINANCE

Abstract.  Environmental sustainability indices, such as the Dow Jones Sustainability Indexes and the Ethibel Sustainability Index, quantify the development and promotion of sustainable social, ethical and environmental values in the community. Moreover, such indices provide a benchmark for managing sustainability portfolios, and developing financial products and services that are linked to sustainable economic, environmental, social and ethical criteria. This paper reviews the existing data and risk indices in environmental finance. The main purpose of the paper is to analyse existing sustainability and ethical indices in environmental finance, and evaluate empirical environmental risk by estimating conditional volatility clustering that is inherent in these indices. Financial volatility models are estimated to analyse the underlying conditional volatility or time-varying risk that is inherent in alternative environmental sustainability indices. Volatility clustering is observed for most series, but some extreme observations are also evident. The log- and second-moment conditions suggest that valid inferences can be drawn for purposes of sensible empirical analysis.