Technical Efficiency of Water Boards in South Africa: A Costing and Pricing Benchmarking Exercise

South Africa is a water-scarce country with deteriorating water resources quality, security, infrastructure investment and management. 89 per cent of households have access to water supply, but, only 64 per cent or 10.3 million households are estimated to have reliable water supply. The water sector in South Africa is cost-intensive with various monopolistic utilities. The sector is faced with weakening financial viability due to: inefficient operations coupled with inadequate investment, financing and under-pricing. As a result, cost recovery is not being achieved. In this paper, we use DEA to analyse the technical efficiency of the 9 water boards. We achieve this objective by using expenditure, water losses, sales volumes, and tariffs to model the industry’s efficiency frontiers. The study finds the mean technical efficiency scores of 73.2, 83.7, 85.8 and 92.3 per cent, in the four models respectively. The study also determined the average bulk water tariffs that should be charged in the sector, thereby establishing the basis for economic regulation

Production and Scale Efficiency of South African Water Utilities: The Case of Water Boards

South Africa is a water scarce country with deteriorating water resources. Faced with tight fiscal and water resource constraints, water utilities would have to adopt technically efficient water management technologies to meet developmental socio-economic objectives of universal coverage, aligned to the United Nation’s Sustainable Development Goal 6 (SDG 6). It is important to measure the technical efficiency of utilities as accurately as possible in order to inform policy. We do this by using a non-parametric method known as Data Envelopment Analysis (DEA) to determine, measure, analyse and benchmark the technical efficiency of all water boards in South Africa. Our contribution to the literature is twofold: This is the first paper to model technical efficiency of water boards as utility suppliers and guardians of water services in South Africa, and second, we address the over- and under estimation issues of technical efficiency measurement in the water sector. We do this by modelling one of the most pronounced negative externalities from water provision (water losses) as an undesirable output using the approach developed by You & Yan (2011). We find on average, technical efficiency of water boards is 49 per cent, with only three of the nine water boards technically efficient. Six of the smaller water boards showed high levels of inefficiency. Six water boards operate at increasing returns to scale (IRS) and two are scale efficient. Only Rand and Sedibeng water boards exhibited decreasing returns to scale (DRS). Therefore, redirecting potential efficiency savings to optimal uses could result in technical and scale efficiency for the sector. Scale efficiency results seems to support larger regional water boards as small to medium-sized water boards are scale inefficient with low technical efficiency. The ratio model with undesirable output outperforms previous methods to deal with undesirable (bad) outputs, which either provide an over- or underestimation of technical efficiency

The taxonomic and conservation status of Agrostis eriantha var. planifolia

Agrostis eriantha Hack. (1904) is a tufted, rhizomatous perennial that grows in wetlands of Swaziland, Lesotho, Limpopo, Mpumalanga, Gauteng, Free State, KwaZulu-Natal, and Eastern Cape. This relatively rare grass appears to be sensitive to disturbance and is mainly found in pristine habitats. In 1945, Goossen & Paperndorf described a form of the species collected by Pole-Evans on the farm Doornkloof, Irene, as A. eriantha Hack.var. planifolia Gooss. & Paperndorf. The main diagnostic character used to distinguish the two varieties was the length of the callus hairs as shown in Figures 1 and 2. In Agrostis eriantha var. eriantha, the callus hairs are up to one third the lemma length while in var. planifolia, the callus hairs are up to half the lemma length. Another suggested difference was in the leaf blades, which are said to be folded in var. eriantha and fl at in var. planifolia. Other possible differences are discussed in the results section.The Botanical Education Trust is gratefully acknowledged for the fi nancial award that was granted to the Taxonomic Problems in Plants of Conservation Importance Project. We would like to thank Mrs Lorraine Mills of the Department of Nature Conservation, Gauteng, for fi eldwork assistance, and Mrs Lyn Fish of SANBI for assistance and advice throughout this project.http://www.sanbi.org/products/publications/bothalia.htmam201

Efficiency of Healthcare Systems in the first wave of COVID-19 - a technical efficiency analysis

In this novel paper, we make use of a non-parametric method known as Data Envelopment Analysis (DEA) to analyse the 31 most infected countries during the first 100 days since the outbreak of the COVID-19 coronavirus for the efficiency in containing the spread of the virus – a question yet to be answered in the literature. Our model showed 12 of the 31 countries in our sample were efficient and 19 inefficient in the use of resources to manage the flattening of their COVID-19 contagion curves. Among the worst performers were some of the richest countries in the world, Germany, Canada, the USA and Austria, with efficiency between 50 and 60 per cent - more inefficient than Italy, France and Belgium, who were some of those hardest hit by the spread of the virus

The first 100 days of COVID-19 coronavirus – How efficient did country health systems perform to flatten the curve in the first wave?

In this novel paper, we make use of a non-parametric method known as Data Envelopment Analysis (DEA) to analyse the 31 most infected countries during the first 100 days since the outbreak of the COVID-19 coronavirus for the efficiency in containing the spread of the virus – a question yet to be answered in the literature. Our model showed 12 of the 31 countries in our sample were efficient and 19 inefficient in the use of resources to manage the flattening of their COVID-19 contagion curves. Among the worst performers were some of the richest countries in the world, Germany, Canada, the USA and Austria, with efficiency between 50 and 60 per cent - more inefficient than Italy, France and Belgium, who were some of those hardest hit by the spread of the virus

Global Healthcare Resource Efficiency in the Management of COVID-19 Death and Infection Prevalence Rates

In this paper, we use a novel DEA approach, developed by You and Yan (2011), which accounts for both desirable outputs (recovered cases) and undesirable outputs (infections and deaths), to analyse the technical efficiency of the health systems of 36 most infected countries during the first 11 months since the COVID-19 outbreak. The average technical efficiency scores across the 3 Models is 52%. Specifically, 6 of the 36 (17%) countries in our sample largely used tests, doctors and health spending efficiently in managing the COVID-19 case-mortality and prevalence rates. The remaining 30 DMUs used their available resources inefficiently. Developing countries performed better than developed nations who were inefficient. Therefore, most countries literally “threw” resources at fighting the pandemic, thereby probably raising inefficiency through wasted resource use. The study also showed that developed countries could also draw lessons from developing countries in the management of pandemics. The latter countries mostly face pandemics on a daily basis, therefore, have developed strategies to manage them

Global Healthcare Resource Efficiency in the Management of COVID-19 Death and Infection Prevalence Rates

In this paper, we use a novel DEA approach, developed by You and Yan (2011), which accounts for both desirable outputs (recovered cases) and undesirable outputs (infections and deaths), to analyse the technical efficiency of the health systems of 36 most infected countries during the first 11 months since the COVID-19 outbreak. The average technical efficiency scores across the 3 Models is 52%. Specifically, 6 of the 36 (17%) countries in our sample largely used tests, doctors and health spending efficiently in managing the COVID-19 case-mortality and prevalence rates. The remaining 30 DMUs used their available resources inefficiently. Developing countries performed better than developed nations who were inefficient. Therefore, most countries literally “threw” resources at fighting the pandemic, thereby probably raising inefficiency through wasted resource use. The study also showed that developed countries could also draw lessons from developing countries in the management of pandemics. The latter countries mostly face pandemics on a daily basis, therefore, have developed strategies to manage them

Efficiency of healthcare systems in the first wave of COVID-19 - a technical efficiency analysis

In this novel paper, we make use of a non-parametric method known as Data Envelopment Analysis (DEA) to analyse the 31 most infected countries during the first 100 days since the outbreak of the COVID-19 coronavirus for the efficiency in containing the spread of the virus – a question yet to be answered in the literature. Our model showed 12 of the 31 countries in our sample were efficient and 19 inefficient in the use of resources to manage the flattening of their COVID-19 contagion curves. Among the worst performers were some of the richest countries in the world, Germany, Canada, the USA and Austria, with efficiency between 50 and 60 per cent - more inefficient than Italy, France and Belgium, who were some of those hardest hit by the spread of the virus.https://www.iki.bas.bg/en/economic-studies-journal-0am2022Economic

Teachers' reasons for and against ICT integration into teaching and learning: a mixed methods approach

A research report submitted to the Wits School of Education, Faculty of Humanities Universtity of the Witwatersrand in fulfillment of the requirments for the Masters’ Degree Johannesburg 2017The purpose of this research was to use a “slightly adapted” instructional transformational model (also called Hooper and Rieber model), as a basis to explain teachers’ reasons to integrate technology (or not). The research relied on the use of mixed methods approach to collect data about technology integration from teachers who had recently attended Microsoft courses. In the first phase, surveys were used to explore how teachers were currently integrating technology, in relation to the knowledge and skills gained from their training. Teachers’ integration levels were linked to the literature on factors enabling technology integration (also called “enablers”) and factors hindering integration (also called “barriers”). This enabled the researcher to understand how different factors could have possibly influenced the teachers’ reasons. In the second phase, follow-up interviews were conducted with four teachers, who were part of the first phase, to provide an in-depth account of their levels of integration. The motivation theory was used to understand how teachers developed their reasons. In the end, the results from the two phases were mapped to the Hooper and Rieber model to give a rich, more balanced explanation of the teachers’ integration levels and the reasons for their levels. Amongst some of the reasons that motivated teachers to integrate technology include: to improve learner performance; to improve learners’ 21st Century skills, and to make learning authentic. However, the research also found that teachers face many barriers or challenges to technology integration, and the two most common include access to resources and lack of technological, pedagogical and content knowledge (also called TPACK). This research is valuable to anyone who wishes to understand how the interplay between internal (e.g. teacher skills level) and external factors (e.g. availability of resources) influence teachers’ reasons to integrate technology or not.MT 201

Assessing technical efficiency of provincial Health and education sectors in South Africa

The study used 12 Data Envelopment Analysis models with six 2017/18 analytical variables. The mean technical efficiency scores for the health sector ranged from 35.7 to 87.2 per cent and the education sector’s average technical efficiency scores from 45.9 to 97.9 per cent. At worst, Gauteng was efficient with eight provinces needing to save R46.4 billion. At best, only Mpumalanga, Western Cape and Free State were inefficient with potential to reduce 6 940 non-core health workers and save R61 million without compromising the prevailing service standards. These savings could also be invested in core healthcare infrastructure and personnel. Insofar as education is concerned, KwaZulu-Natal was the most efficient. At best, only KwaZulu-Natal, Limpopo and Northern Cape were efficient. The inefficient provinces could reduce education expenditure by R24.7 billion while maintaining the same educational outcomes with room to train existing and appoint additional 9 684 teachers.Thesis (PhD)--University of Pretoria, 2020.National Research FoundationUniversity of PretoriaNational TreasuryEconomicsPhD Tax PolicyUnrestricte