Productive efficiency has far-reaching implications on the direction of economic growth and welfare. While this has led to an expansive literature on the drivers of productive efficiency, this literature has proceeded without considering the role of energy poverty. Yet, energy poverty affects productive efficiency on several fronts. This paper fills this knowledge gap, u. sing a sample comprising 100 developing countries for the period 2000–2017. W e found robust evidence suggesting that energy poverty negatively affects productive efficiency—i.e., energy-poor countries become productively inefficient. Further analysis in the paper revealed that this negative effect persists largely across regions and is not sensitive to cross-country differences in income level. We also found that the negative effect of energy poverty on productive efficiency becomes more pronounced at a higher level of productive efficiency. We discuss the policy implications of our findings.@en

The conventional portfolio design approach assumes Gaussian return distributions, but this is not accurate in practice. Asymmetric and heavy-tailed return distributions necessitate consideration of higher-order moments such as skewness and kurtosis, in addition to mean and variance. This study proposes a multi-objective approach using a mean-variance-skewness-kurtosis model to construct a diversified portfolio. A parametrized polynomial goal programming (PGP) method is used to optimize the portfolio by maximizing returns and skewness while minimizing variance and kurtosis. Empirical data from the S &P ESG index family is used, and PGP generates multiple portfolios reflecting investors’ preferences for the four moments. To compare between the obtained portfolios, we represent the empirical cumulative distribution of the portfolio returns for all studied values of weights and show how this can be used to assist the inverstor in selecting the best set of weights.@en