Reforming the Power Sector in Transition: Do Institutions Matter?

This paper quantitatively explores high-level links between power sector reforms and wider institutional reforms in the economy for a set of 27 diverse countries in rapid political and economic transition since 1990. Panel-data econometrics based on bias corrected dynamic fixed effect analysis (LSDVC) is performed to assess the impact of reforms on macroeconomic and power sector outcomes. The results indicate that power sector reform is indeed a more complicated process than initially perceived. The results also show that power sector reform is greatly inter-dependent with reforms in other sectors in the economy. We conclude that the success of power sector reforms on outcomes in developing countries will largely depend on the extent in which countries are able to synchronize inter-sector reforms in the economy

Reforming Small Power Systems under Political Volatility: The Case of Nepal

This paper assesses the electricity sector reforms across small power systems while citing Nepal as an example. The on-going political instability and increasing electricity demand make power sector reform in Nepal and similar small systems a more complex process. As international reform experiences provide plenty of lessons to learn; raising electricity tariffs and adjusting subsidies in the presence of an effective regulation body are important in the short and medium term. The creation of an effective regulatory commission is also more urgent than unbundling the sector in smaller systems though accounting separation may sometimes be desirable as in the present context in Nepal. In the long run as the system grows, vertical separation and competitive privatisation may be pursued together with the creation of a functioning wholesale market by horizontally splitting the generation segments

Market Integration, Efficiency, and Interconnectors: The Irish Single Electricity Market

Interconnections can be an effective way to increase competition in wholesale electricity markets in particular for smaller markets with few actors. This paper quantitatively examines the potentials for interconnections in the Irish Single Electricity Market (SEM). We use a time-varying Kalman filter technique to assess the degree of market integration between SEM and other large, mature and interconnected wholesale electricity markets in Europe. The results indicate a low degree of market integration between SEM and other European markets and thereby raising the possibility to benefit from increased electricity trade. As wholesale prices in SEM remain relatively high and volatile; a larger interconnector capacity can promote competition, close the gap with the European wholesale prices, improve security of supply, and mitigate price volatility. The results indicate that wholesale spot trading of renewable may not increase market integration. The results suggest that an interconnector capacity amounting to about 21% of generation capacity in SEM is likely to achieve an integration coefficient of 0.86 similar to what currently exists between the markets in Austria and the Netherlands

Investigation of timetabling in tertiary institutions in Southern Africa

This paper deals with approaches to the timetabling problem, focusing on tertiary institutions in Southern Africa. A questionnaire which dealt with, inter alia, student population, number of class groups, methods used for timetabling and local constraints, was distributed to tertiary institutions in Southern Africa. The response rate was over 80%. Analysis of the responses yielded a number of interesting results, chief among these being that there is little consensus on any one method, and that the timetabling process is not fully automated in any institution. The analysis further indicated that a great deal of time and effort is involved in the process, up to 200 person-hours in some institutions. This paper details previous work in the field and outlines results from the questionnaire. Future research will be directed towards either finding a more efficient approach to the problem, or detemining which of the current methods is in fact most effective

Economic Reforms and Human Development: Evidence from Transition Economies

Do market-oriented economic reforms result in higher levels of human well-being? This article studies the impact of macro-level institutional and infrastructure reforms on the economic, educational and health dimensions of human well-being among 25 transition economies. We use panel data econometrics based on the LSDVC technique to analyse the effects of market-oriented reforms on the human development index (HDI), as a measure of human well-being, from 1992 to 2007. The results show the complexity of reform impacts in transition countries. They show that institutional and economic reforms led to positive economic effect and significant impacts on other dimensions of human development. We find some positive economic impacts from infrastructure sectors reforms. However, not every reform measure appears to generate positive impacts. Large-scale privatizations show negative effects in health and economic outcomes. The overall results show the importance of the interaction among different reform measures and the combined effect of these on human development

Market-Related Reforms and Increased Energy Efficiency in Transition Countries: Empirical Evidence

Energy efficiency improvement is a desirable response to growing climate change and security of energy supply concerns. This article studies the impacts of a varied set of macro-level market-oriented reforms as well as structural change on economy-wide measure of energy efficiency across a group of the transition countries. These countries experienced a rapid marketization process, which, since the early 1990s, transformed their economies from central planning towards market-driven models. We use a bias-corrected fixed-effect analysis technique to estimate this effect for the period 1990 to 2010. The results suggest that reforms aimed at market liberalization, financial sector and most infrastructure industries drove energy efficiency improvements. We find significant differences in improvements in energy efficiency between transitional Central European and Baltic States, South East Europe ones and the Commonwealth of Independent States. The reasons for these differences are also discussed

Electricity Market Integration, Decarbonisation and Security of Supply: Dynamic Volatility Connectedness in the Irish and Great Britain Markets

This study investigates the volatility connectedness between the Irish and Great Britain electricity markets and how it is driven by changes in energy policy, institutional structures and political ideologies. We assess various aspects of this volatility connectedness including static (unconditional) vs dynamic (conditional), symmetric vs asymmetric characteristics between 2009 and 2018. We find that volatility connectedness is time varying and is significantly affected by important events, policy reforms or market re-designs such as Brexit, oil price slump, increasing share of renewables, and fluctuations in the exchange rates. Our asymmetric analysis shows that the magnitude of the good volatility connectedness is marginally larger than that of the bad volatility connectedness. Our result suggests that good volatility levels would be even higher once the Irish market adopts the carbon price floor. Therefore, supporting renewable generation by setting an appropriate carbon price in interconnected wholesale electricity markets will improve market integration

Electrification and Socio-Economic Empowerment of Women in India

This study examines the effect of quality of electrification on empowerment of women in terms of economic autonomy, agency, mobility, decision-making abilities, and time allocation in fuel collection in India. It moves beyond the consensus of counting electried households as a measure of progress in gender parity, and analyzes how the quality of electrification affects women's intra-household bargaining power, labor supply decision and fuel collection time. We develop a set of indices using principal component analysis from a large cross-section of gender-disaggregated survey. We use two stage least squares instrumental variables regression to assess the causal effect of access and hours of electricity on women's empowerment using geographic instrumental variables along with district and caste fixed effects. The results show that quality of electrication has significant positive effects on all empowerment indices. However, the effect differs at the margin of defficiency, location, living standards and education. The study recommends revisiting the paradigm of access to electrification and women empowerment by focusing on the quality of not only extensive but also intensive electrification to enhance life and economic opportunities for women and their households

Classification of Product Images in Different Color Models with Customized Kernel for Support Vector Machine

Support Vector Machine (SVM) is widely recognized as a potent data mining technique for solving supervised learning problems. The technique has practical applications in many domains such as e-commerce product classification. However, data sets of large sizes in this application domain often present a negative repercussion for SVM coverage because its training complexity is highly dependent on input size. Moreover, a single kernel may not adequately produce an optimal division between product classes, thereby inhibiting its performance. The literature recommends using multiple kernels to achieve flexibility in the applications of SVM. In addition, color features of product images have been found to improve classification performance of a learning technique, but choosing the right color model is particularly challenging because different color models have varying properties. In this paper, we propose color image classification framework that integrates linear and radial basis function (LaRBF) kernels for SVM. Experiments were performed in five different color models to validate the performance of SVM based LaRBF in classifying 100 classes of e-commerce product images obtained from the PI 100 Microsoft corpus. Classification accuracy of 83.5% was realized with the LaRBF in RGB color model, which is an improvement over an existing method

Using Mobile Data and Deep Models to Assess Auditory Verbal Hallucinations

Hallucination is an apparent perception in the absence of real external sensory stimuli. An auditory hallucination is a perception of hearing sounds that are not real. A common form of auditory hallucination is hearing voices in the absence of any speakers which is known as Auditory Verbal Hallucination (AVH). AVH is fragments of the mind's creation that mostly occur in people diagnosed with mental illnesses such as bipolar disorder and schizophrenia. Assessing the valence of hallucinated voices (i.e., how negative or positive voices are) can help measure the severity of a mental illness. We study N=435 individuals, who experience hearing voices, to assess auditory verbal hallucination. Participants report the valence of voices they hear four times a day for a month through ecological momentary assessments with questions that have four answering scales from ``not at all'' to ``extremely''. We collect these self-reports as the valence supervision of AVH events via a mobile application. Using the application, participants also record audio diaries to describe the content of hallucinated voices verbally. In addition, we passively collect mobile sensing data as contextual signals. We then experiment with how predictive these linguistic and contextual cues from the audio diary and mobile sensing data are of an auditory verbal hallucination event. Finally, using transfer learning and data fusion techniques, we train a neural net model that predicts the valance of AVH with a performance of 54\% top-1 and 72\% top-2 F1 score