Determinants of Pollution Abatement and Control Expenditure in Romania: A Multilevel Analysis

The transition process in Central and Eastern Europe was associated with growing environmental awareness. This paper analyses the determinants of Pollution Abatement and Control Expenditure (PACE) at plant level in the case of Romania using survey data and a Multilevel Regression Model (MRM). Our findings suggest that, although Romania has improved its environmental performance, formal and informal regulation are still only partially developed due to the difficulties of economic transition, and heterogeneity across regions remains considerable.Pollution Abatement and Control Expenditure, transition economy, Multilevel Regression Model (MRM)

POLLUTION ABATEMENT AND CONTROL EXPENDITURE IN ROMANIA: A MULTILEVEL ANALYSIS

The transition process in Central and Eastern Europe was associated with growing environmental awareness. This paper analyses the determinants of Pollution Abatement and Control Expenditure (PACE) at plant level in the case of Romania using survey data and a Multilevel Regression Model (MRM). Our findings suggest that, although Romania has improved its environmental performance, formal and informal regulation are still only partially developed due to the difficulties of economic transition, and heterogeneity across regions remains considerable.Pollution Abatement and Control Expenditure, Transition Economy, Multilevel Regression Model (MRM)

Pollution Abatement and Control Expenditure in Romania: A Multilevel Analysis

The transition process in Central and Eastern Europe was associated with growing environmental awareness. This paper analyses the determinants of Pollution Abatement and Control Expenditure (PACE) at plant level in the case of Romania using survey data and a Multilevel Regression Model (MRM). Our findings suggest that, although Romania has improved its environmental performance, formal and informal regulation are still only partially developed due to the difficulties of economic transition, and heterogeneity across regions remains considerable.pollution abatement and control expenditure, transition economy, Multilevel Regression Model (MRM)

Chapter 20: What do interviewers learn? Changes in interview length and interviewer behaviors over the field period. Appendix 20

Appendix 20A Full Model Coefficients and Standard Errors Predicting Count of Questions with Individual Interviewer Behaviors, Two-level Multilevel Poisson Models with Number of Questions Asked as Exposure Variable, WLT1 and WLT2 Analytic strategyTable A20A.1 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Exact Question Reading with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.2 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Nondirective Probes with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.3 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Adequate Verification with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.4 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Appropriate Clarification with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.5 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Appropriate Feedback with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.6 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Stuttering During Question Reading with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.7 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Disfluencies with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.8 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Pleasant Talk with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.9 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Any Task-Related Feedback with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.10 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Laughter with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.11 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Minor Changes in Question Reading with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.12 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Major Changes in Question Reading with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.13 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Directive Probes with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.14 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Inadequate Verification with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.15 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Interruptions with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Appendix 20B Full Model Coefficients and Standard Errors Predicting Interview Length with Sets of Interviewer Behaviors, Two-level Multilevel Linear Models, WLT1 and WLT2 Table A20B.1 Coefficients and Standard Errors from Multilevel Linear Regression Models Predicting Total Duration, No Interviewer Behaviors, WLT1 and WLT2 Table A20B.2 Coefficients and Standard Errors from Multilevel Linear Regression Models Predicting Total Duration, Including Standardized Interviewer Behaviors, WLT1 and WLT2 Table A20B.3 Coefficients and Standard Errors from Multilevel Linear Regression Models Predicting Total Duration, Including Inefficiency Interviewer Behaviors, WLT1 and WLT2 Table A20B.4 Coefficients and Standard Errors from Multilevel Linear Regression Models Predicting Total Duration, Including Nonstandardized Interviewer Behaviors, WLT1 and WLT2 Table A20B.5 Coefficients and Standard Errors from Multilevel Linear Regression Models Predicting Total Duration, Including All Interviewer Behaviors, WLT1 and WLT2 Appendix 20C Mediation Models for Each Individual Interviewer Behavior Table A20C.1 Indirect, Direct And Total Effect of each Interviewer Behavior on Interview Length through Interview Order, Work and Leisure Today 1 Table A20C.2 Indirect, Direct And Total Effect of each Interviewer Behavior on Interview Length through Interview Order, Work and Leisure Today

Bayesian measures of explained variance and pooling in multilevel (hierarchical) models

Explained variance (R^2) is a familiar summary of the fit of a linear regression and has been generalized in various ways to multilevel (hierarchical) models. The multilevel models we consider in this paper are characterized by hierarchical data structures in which individuals are grouped into units (which themselves might be further grouped into larger units), and there are variables measured on individuals and each grouping unit. The models are based on regression relationships at different levels, with the first level corresponding to the individual data, and subsequent levels corresponding to between-group regressions of individual predictor effects on grouping unit variables. We present an approach to defining R^2 at each level of the multilevel model, rather than attempting to create a single summary measure of fit. Our method is based on comparing variances in a single fitted model rather than comparing to a null model. In simple regression, our measure generalizes the classical adjusted R^2. We also discuss a related variance comparison to summarize the degree to which estimates at each level of the model are pooled together based on the level-specific regression relationship, rather than estimated separately. This pooling factor is related to the concept of shrinkage in simple hierarchical models. We illustrate the methods on a dataset of radon in houses within counties using a series of models ranging from a simple linear regression model to a multilevel varying-intercept, varying-slope model.adjusted R-squared, Bayesian inference, hierarchical model, multilevel regression, partial pooling, shrinkage

Determinants of Pollution Abatement and Control Expenditure: Evidence from Romania

The aim of the present study is to shed some light on the factors affecting Pollution Abatement and Control Expenditure (PACE) in the context of a transition economy such as Romania, in contrast to the existing literature which mostly focuses on developed economies. Specifically, we use survey data of the Romanian National Institute of Statistics and estimate Multilevel Regression Model (MRM) to investigate the determinants of environmental behaviour at plant level. Our results reveal some important differences vis-à-vis the developed countries, such as a less significant role for collective action and environmental taxes, which suggests some possible policy changes to achieve better environmental outcomes.transition economy, pollution abatement and control expenditure, Multilevel Regression Model (MRM)

Nonresponse in the Belgian fertility and family survey.

Combining response data from the Belgian Fertility and Family Survey with individual level and municipality level data from the 1991 Census for both nonrespondents and respondents, multilevel logistic regression models for contact and cooperation propensity are estimated. The covariates introduced are a selection of indirect features out of the researcher control only. Contrary to previous research, Socio Economic Status is found to be positively related to cooperation. Another unexpected result is the absence of any considerable impact of ecological correlates such as urbanicity.Cooperation; Data; Design; Fertility and family survey; Logistic regression; Mixture experiment; Model; Models; Multilevel analysis; Nonrespons; Optimal; Optimal design; Process variables; Processes; Qualitative variables; Selection; Split-plot experiment; Variables;

Performance of likelihood-based estimation methods for multilevel binary regression models.

By means of a fractional factorial simulation experiment, we. compare the performance of penalised quasi-likelihood (PQL), non-adaptive Gaussian quadrature and adaptive Gaussian quadrature in estimating parameters for multilevel logistic regression models. The comparison is done in terms of bias, mean-squared error (MSE), numerical convergence and computational efficiency. It turns out that in terms of MSE, standard versions of the quadrature methods per-form relatively poorly in comparison with PQL.Bias; Binary regression; Convergence; Efficiency; Factorial; Fractional factorial experiment; Gaussian quadrature; Logistic regression; Methods; Model; Models; Monte Carlo simulation; Multilevel analysis; Parameters; Penalised quasi-likelihood; Performance; Regression; Simulation;

Bayesian measures of explained variance and pooling in multilevel (hierarchical) models

Explained variance (R^2) is a familiar summary of the fit of a linear regression and has been generalized in various ways to multilevel (hierarchical) models. The multilevel models we consider in this paper are characterized by hierarchical data structures in which individuals are grouped into units (which themselves might be further grouped into larger units), and there are variables measured on individuals and each grouping unit. The models are based on regression relationships at different levels, with the first level corresponding to the individual data, and subsequent levels corresponding to between-group regressions of individual predictor effects on grouping unit variables. We present an approach to defining R^2 at each level of the multilevel model, rather than attempting to create a single summary measure of fit. Our method is based on comparing variances in a single fitted model rather than comparing to a null model. In simple regression, our measure generalizes the classical adjusted R^2. We also discuss a related variance comparison to summarize the degree to which estimates at each level of the model are pooled together based on the level-specific regression relationship, rather than estimated separately. This pooling factor is related to the concept of shrinkage in simple hierarchical models. We illustrate the methods on a dataset of radon in houses within counties using a series of models ranging from a simple linear regression model to a multilevel varying-intercept, varying-slope model

DETERMINANTS OF POLLUTION ABATEMENT AND CONTROL EXPENDITURE: EVIDENCE FROM ROMANIA

The aim of the present study is to shed some light on the factors affecting Pollution Abatement and Control Expenditure (PACE) in the context of a transition economy such as Romania, in contrast to the existing literature which mostly focuses on developed economies. Specifically, we use survey data of the Romanian National Institute of Statistics and estimate Multilevel Regression Model (MRM) to investigate the determinants of environmental behaviour at plant level. Our results reveal some important differences vis-à-vis the developed countries, such as a less significant role for collective action and environmental taxes, which suggests some possible policy changes to achieve better environmental outcomes.Pollution Abatement and Control Expenditure, Transition Economy, Multilevel Regression Model (MRM)