An environmental degradation index based on stochastic dominance.

We employ a stochastic dominance (SD) approach to derive a relative environmental degradation index across countries. The variables that are considered include countries' greenhouse gas (GHG) emissions, water pollution and the net forest depletion, as from the dataset of the World Bank. A worst-case scenario index to measure environmental degradation across different countries and at different times is constructed applying a methodology that is based on multi-variate comparisons of country panel data over various years and consistent tests for SD efficiency. The test statistics and the estimators are computed using mixed integer programming methods. It is found that in the worst-case scenario index GHG emissions contribute the most (with a weight around 68%), net forest depletion contributes with around 30%, and water pollution contributes the least (with a weight around 2%). Our index can be a useful tool for policy making in conveying information on the environmental quality and a quick assessment of sustainable performance across countries and over time

Growth and Pollution Convergence: Theory and Evidence

Stabilizing pollution levels in the long run is a pre-requisite for sustainable growth. We develop a neoclassical growth model with endogenous emission reduction predicting that, along optimal sustainable paths, pollution growth rates are (i) positively related to output growth (scale effect) and (ii) negatively related to emission levels (defensive effect). This dynamic law reduces to a convergence equation that is empirically tested for two major and regulated air pollutants - sulfur oxides and nitrogen oxides - with a panel of 25 European countries spanning the years 1980-2005. Traditional parametric models are rejected by the data. More flexible regression techniques confirm the existence of both the scale and the defensive effect, supporting the model predictions.Air pollution, convergence, economic growth, nonparametric regressions

A sovereign risk index for the Eurozone based on stochastic dominance

We propose a new method to assess sovereign risk in Eurozone countries using an approach that relies on consistent tests for stochastic dominance e\ua2 ciency. The test statistics and the estimators are computed using mixed integer programming methods. This paper\u92s analysis is based on macroeconomic fundamentals and their importance in accounting for sovereign risk. The results suggest that the net international investment position/GDP and public debt/GDP are the main contributors to country risk in the Eurozone. We also conduct ranking analysis of countries for \u85scal and external trade risk. We \u85nd a positive correlation between our rankings of the most vulnerable countries and the S&P\u92s ratings, whereas the correlation for other countries is weaker

Measuring Human Development in the MENA Region

We aim to assess welfare improvements in the Middle East and North Africa (MENA) region using the Human Development Index (HDI). We obtain weighting schemes that yield the best- and worst-case scenarios for measured human development, relying on consistent tests for stochastic dominance efficiency (SDE), with the official equally weighted HDI taken as a benchmark. In the best-case scenario index, life expectancy and GDP indexes receive the highest weights for the 1975–2005 period, while the education index is the dominant contributor to the worst-case scenario in the same period. In addition, we observe a relative change in the best- and worst-case scenarios between two fifteen-year periods. The GDP index is the main contributor to the best-case scenario between 1975 and 1990, whereas the education index is the main contributor to the worst-case scenario during that period. Life expectancy is the main contributor to the best-case scenario in the 1990–2005 period, while the GDP and education indexes are the primary contributors to the worst-case scenario during that period

Growth and the pollution convergence hypothesis: a nonparametric approach

Abstract The pollution-convergence hypothesis is formalized in a neoclassical growth model with optimal emissions reduction: pollution growth rates are positively correlated with output growth (scale effect) but negatively correlated with emission levels (defensive effect). This dynamic law is empirically tested for two major and regulated air pollutants -nitrogen oxides (NOX) and sulfur oxides (SOX) -with a panel of 25 European countries spanning over years . Traditional parametric models are rejected by the data. However, more flexible regression techniques -semiparametric additive specifications and fully nonparametric regressions with discrete and continuous factors -confirm the existence of the predicted positive and defensive effects. By analyzing the spatial distributions of per capita emissions, we also show that cross-country pollution gaps have decreased over the period for both pollutants and within the Eastern as well as the Western European areas. A Markov modeling approach predicts further cross-country absolute convergence, in particular for SOX. The latter results hold in the presence of spatial non-convergence in per capita income levels within both regions. JEL Classification numbers: C14, C23, Q5

Robust Tests for Convergence Clubs

In many applications common in testing for convergence the number of cross-sectional units is large and the number of time periods are few. In these situations asymptotic tests based on an omnibus null hypothesis are characterised by a number of problems. In this paper we propose a multiple pairwise comparisons method based on an a recursive bootstrap to test for convergence with no prior information on the composition of convergence clubs. Monte Carlo simulations suggest that our bootstrap-based test performs well to correctly identify convergence clubs when compared with other similar tests that rely on asymptotic arguments. Across a potentially large number of regions, using both cross-country and regional data for the European Union we find that the size distortion which afflicts standard tests and results in a bias towards finnding less convergence, is ameliorated when we utilise our bootstrap test

Air and water pollution over time and industries with stochastic dominance

We employ a stochastic dominance (SD) approach to analyze the components that contribute to environmental degradation over time. The variables include countries\u2019 greenhouse gas (GHG) emissions and water pollution. Our approach is based on pair-wise SD tests. First, we study the dynamic progress of each separate variable over time, from 1990 to 2005, within 5-year horizons. Then, pair-wise SD tests are used to study the major industry contributors to the overall GHG emissions and water pollution at any given time, to uncover the industry which contributes the most to total emissions and water pollution. While CO2 emissions increased in the first order SD sense over 15 years, water pollution increased in a second-order SD sense. Electricity and heat production were the major contributors to the CO2 emissions, while the food industry gradually became the major water polluting industry over time. SD sense over 15 years, water pollution increased in a second-order SD sense. Electricity and heat production were the major contributors to the CO2 emissions, while the food industry gradually

Quantile forecast combination using stochastic dominance

This paper derives optimal forecast combinations based on stochastic dominance efficiency (SDE) analysis with differential forecast weights for different quantiles of forecast error distribution. For the optimal forecast combination, SDE will minimize the cumulative density functions of the levels of loss at different quantiles of the forecast error distribution by combining different time-series model-based forecasts. Using two exchange rate series on weekly data for the Japanese yen/US dollar and US dollar/Great Britain pound, we find that the optimal forecast combinations with SDE weights perform better than different forecast selection and combination methods for the majority of the cases at different quantiles of the error distribution. However, there are also some very few cases where some other forecast selection and combination model performs equally well at some quantiles of the forecast error distribution. Different forecasting period and quadratic loss function are used to obtain optimal forecast combinations, and results are robust to these choices. The out-of sample performance of the SDE forecast combinations is also better than that of the other forecast selection and combination models we considered

Advertising expenses and operational performance: Evidence from the global hotel industry

We investigate the impact of advertising on hotel management companies’ (HMC) operational efficiencies on a sample of 90 international HMCs over the period 2010–2019. We employ time-dependent conditional frontier estimators to investigate the effect on HMCs’ technological change and technological catch-up levels. Our findings support the existence of a non-monotonic convex relationship between HMCs’ advertising expenses and their operational performance. Lastly, we also utilize the double bootstrap approach in a truncated regression setting to validate the overall effect of advertising expenses on HMCs’ operational performance levels. The findings suggest that advertising expenses decrease operational inefficiencies. © 2020 Elsevier B.V