Environmental inspections and emissions of the pulp and paper industry : the case of Quebec

Since the early 1970s, industrial countries have enacted (or amended) many environmental laws and regulations to control and improve air and water quality. Developing countries are increasingly enacting similar legislation. But imposing a ceiling on a plant's emissions does not guarantee reduced emissions or an improved environment. Ensuring the attainment of the regulation's objectives requires monitoring the behavior of the regulated facility and enforcing environmental standards. Most of the literature in environmental economics is theoretical and simply assumes that polluters comply with regulations. Although monitoring and enforcement problems are clearly a pitfall of environmental regulation, little empirical work has been done about the effect of current monitoring strategies on pollution emissions. The authors supply an empirical framework for measuring the impact of environmental inspections on plant emissions. They apply it to pulp and paper plants in Quebec for which reliable data were available. The results suggest that both inspection and the threat of inspections reduce pollution emissions. They also show that a plant's decision whether to report its emissions levels to the regulator is not random. Inspections improve the frequency of reporting.Sanitation and Sewerage,Water and Industry,Environmental Economics&Policies,Water Conservation,Wetlands,Insurance&Risk Mitigation,Water and Industry,Environmental Economics&Policies,Sanitation and Sewerage,TF030632-DANISH CTF - FY05 (DAC PART COUNTRIES GNP PER CAPITA BELOW USD 2,500/AL

Nonparametric Identification of Latent Competing Risks and Roy Duration Models

This paper considers nonparametric identification of ``latent'' competing risks and Roy duration models in which one does not know which process has been observed. It is shown that these models are identifiable without the usual conditional independence and exclusion restrictions

Carbohydrate restriction for glycemic control in Type 2 diabetes : a systematic review and meta-analysis

Aim To conduct a systematic review and meta‐analysis to evaluate the effect of carbohydrate restriction on glycaemic control in Type 2 diabetes. Methods We searched Medline, EMBASE and CINAHL for the period between 1976 and April 2018. We included randomized controlled trials comparing carbohydrate restriction with a control diet which aimed to maintain or increase carbohydrate intake, and that reported HbA1c as an outcome and reported the amount of carbohydrate consumed during or at the end of the study, with outcomes reported at ≥3 months. Results We identified 1402 randomized controlled trials, 25 of which met the inclusion criteria, incorporating 2132 participants for the main outcome. Definitions of low carbohydrate varied among the studies. The pooled effect estimate from meta‐analysis was a weighted mean difference of –0.09% [95% CI –0.27, 0.08 (P = 0.30); I2 72% (P <0.001)], suggesting no effect on HbA1c of restricting the quantity of carbohydrate. A subgroup analysis of diets containing 50–130 g carbohydrate resulted in a pooled effect estimate of –0.49% [95% CI –0.75, –0.23 (P <0.001); I2 0% (P = 0.56)], suggesting a clinically and statistically significant effect on HbA1c in favour of low‐carbohydrate diets in studies of ≤6 months’ duration. Conclusions There was no overall pooled effect on HbA1c in favour of restricting carbohydrate; however, restriction of carbohydrate to 50–130 g per day had beneficial effects on HbA1c in trials up to 6 months. Future randomized controlled trials should be of >12 months’ duration, assess pre‐study carbohydrate intake, use recognized definitions of low‐carbohydrate diets and examine reasons for non‐concordance in greater detail

Using Bootstrapped Confidence Intervals For Improved Inferences With Seemingly Unrelated Regression Equations

The usual standard errors for the regression coefficients in a Seemingly Unrelated Regression model have a substantial downward bias. Bootstrapping the standard errors does not seem to improve inferences. In this paper Monte Carlo evidence is reported which indicates that bootstrapping can result in substantially better inferences when applied to t-ratios rather than to standard errors. 1. Introduction Systems of Seemingly Unrelated Regression (SUR) equations have received extensive attention in the econometrics and statistics literature. A well documented result in this context is that the usual standard errors for the regression coefficients have a substantial downward bias. The central implication of this is that the usual hypothesis tests have a tendency to overreject. A natural suggestion is that better standard errors might be obtained via the bootstrap. However, research in this direction has suggested that bootstrapping may not be the panacea. Marais (1986) reported Monte Car..

Nonparametric Hypothesis Testing with Parametric Rates of Convergence.

Nonparametric estimators are frequently criticized for their poor performance in small samples. In this paper, the author considers using kernel methods for the estimation of the expected derivatives of a regression function. The proposed estimators are shown to be asymptotically normal and n-consistent. As a consequence, their standard errors are comparable to parametric estimates. An empirical example demonstrates the facility of the approach. Copyright 1991 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Semiparametric IV Estimation with Parameter Dependent Instruments

A well-known result in the method of moments literature is that the efficient instruments for the estimation of a model are functions of the conditional expectation of its gradient. Some recent studies have suggested the nonparametric estimation of these instruments when they are of unknown functional form. When these instruments in turn depend on the unknown parameters it has been suggested that these be replaced by preliminary consistent estimates. It is shown here that solving the sample moment equations simultaneously over the instruments and the residuals of the model will generally produce the same asymptotic efficiency and avoid the disadvantages inherent with the use of preliminary estimates.