Browsing by Author "Georg, Co-Pierre"

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    A Blockchain-enabled System to enhance Food Traceability in Local Food Supply Chains (FSCs) suitable for Small Co-operatives in South Africa
    (2021) Kanjere, Julian; Georg, Co-Pierre
    Food is vital to human life. Therefore, ensuring its safety as it moves from producer to consumer in food supply chains (FSCs) is essential. This can be achieved through the use of food traceability technology which enables track and trace of produce within a FSC. Recently, blockchain technology (BCT) has shown great potential to enhance traceability in FSCs, owing to its ability to securely store data in a decentralised and tamper-evident manner. However, it appears that research on blockchain-enabled food traceability exists primarily within the context of large FSCs, whilst scarce for local FSCs in which traceability is often an inefficient and manual process. Given this background, this exploratory research is carried out, to investigate whether a blockchain-enabled system can be used to improve traceability in local FSCs. To do this, we (i) collaborate with Oranjezicht City Farm Market (OZCFM) - a farmers market in Cape Town, the smallholder farmers that supply OZCFM with fresh local produce and the OZCFM patrons that purchase the produce; (ii) map out the local FSC by conducting observations and running surveys with the aforementioned actors; (iii) design, develop and pilot FoodPrint - a web based and blockchain-enabled food traceability application. During the pilot within the OZCFM-related local FSC, FoodPrint is used to capture data on the harvest, transportation and storage of produce; and reveal produce provenance at destination by scanning of supplier-produce specific quick response (QR) codes. We find that FoodPrint provides tamper-evident traceability and authentic transparency of produce related data to the local FSC actors. Further, we note that scanning a FoodPrint QR code for produce provenance does not enhance the consumers trust of the local FSC, as it pre-exists. This implies that local FSCs with existing and functional trust mechanisms do not benefit from trust-enhancing mechanisms such as blockchain-enabled traceability. Future work may consider data privacy in FSCs and automating FSC data entry to reduce the risk of fraud.
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    A decentralised asset registry to expand access to finance for the agricultural sector in South Africa
    (2019) Mzuku, Kungela; Georg, Co-Pierre
    Over 61 percent of Africans are involved in agriculture; of this, only a few have access to financial services catered for their business. To get financial assistance, farmers have to provide sufficient collateral in the form of land, machinery and other large assets, many of which they do not own. Instead, farmers own mostly agricultural assets such as cattle, pigs and crop trees. The aim of this study is to make use of the agricultural resources available to farmers as collateral for financial loans. This was achieved through the development of a decentralised agricultural registry between farmers and the financial sector. Through an exploratory study, it was found many African countries introduced Movable Property laws to help increase acceptable collateral for financial loans. Unfortunately, many limitations were encountered which resulted in the adoption of the laws to be extremely low. As a result, this paper looks to blockchain technology as a solution as it would allow for transparency between farmers, government and financial sector. By creating a decentralised agricultural registry, farmers can register their biological assets and financiers can verify that the assets exists, are healthy and are currently not being used as collateral in another loan agreement. It is hoped that the registry can be used as a tool when financial agreements between farmers and banks are conducted.
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    A Feasibility Study on Using the Blockchain to Build a Credit Register for Individuals Who Do Not Have Access to Traditional Credit Scores
    (2019) Ortlepp, Bryony; Georg, Co-Pierre
    In South Africa and many other countries, credit registers and credit scores are used to determine how much credit a person can get access to, as well as the interest rate which they will be charged. In addition to this, some companies (such as insurance companies and rental agencies), use this data as part of the process to vet potential clients before allowing them to sign a contract. Part of the problem with this approach is that only certain records are stored on these credit registers. This excludes a large number of individuals, specifically those who are unbanked, those who have not got access to credit from formal institutions or those who do not own property and therefore pay their landlord for utilities. The purpose of this research is to determine the feasibility of using blockchain to store payment histories from small businesses to give their clients access to a credit record. The case study for this research will look specifically at a business which offers insurance to individuals living in informal settlements. This could be extended to many other businesses who work within informal settlements which allow cash payments on a regular basis for services offered. Shops in informal marketplaces which allow people to take products on credit and only pay later could also be included. By storing these transactions on the blockchain, individuals who would not usually have access to a credit history will have access to records of transactions that they have made and will be able to use these to show their ability and willingness to meet their financial obligations. This paper provides insight into existing credit registers and the process followed to build an informal credit register on the blockchain. The research covers an investigation into the feasibility of the project and it was found that this could is feasible and could add a lot of value, especially to those who do not have a credit history. There are many considerations, such as speed, security and costs which need to be taken into account, but these are outweighed by the benefits of the blockchain.
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    A Machine Learning Approach to Predicting the Employability of a Graduate
    (2019) Modibane, Masego; Georg, Co-Pierre
    For many credit-offering institutions, such as banks and retailers, credit scores play an important role in the decision-making process of credit applications. It becomes difficult to source the traditional information required to calculate these scores for applicants that do not have a credit history, such as recently graduated students. Thus, alternative credit scoring models are sought after to generate a score for these applicants. The aim for the dissertation is to build a machine learning classification model that can predict a students likelihood to become employed, based on their student data (for example, their GPA, degree/s held etc). The resulting model should be a feature that these institutions should use in their decision to approve a credit application from a recently graduated student.
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    A study on the effects of peer review in knowledge production
    (2023) Mwinyi, Sophia; Georg, Co-Pierre
    Collaborations in research takes place formally or informally. Formally, when researchers co-author scholarly work or through an editorial peer review and informally, when researcher peer review each others work or provide feedback or comments informally. While there has been a lot of research on the formal editorial peer review process in other fields, less is known about its effects in economics knowledge production. Economic decision-makers rely on data from government agencies, private firms, and peer-reviewed and published academic research to formulate well-informed policies and decisions. Therefore, peer review process is at the core of ensuring that policies and decisions are made from accurate information, not only in economics but also in other fields. Despite its extensive use, peer review process has been subjected to a number of biases that may affect the quality of the information generated. This paper evaluates various aspects affecting the peer review process using data from 661 manuscripts submitted to ERSA between 2013 and 2018. Aspects discussed include reviewer bias, recommendation biases resulting from conflict of interest, author prominence & institutional affiliation, gender composition of the authors, duration of manuscript review and quality of the manuscripts & ERSA's editorial process.
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    AI/Machine learning approach to identifying potential statistical arbitrage opportunities with FX and Bitcoin Markets
    (2019) Ntsaluba, Kuselo Ntsika; Georg, Co-Pierre
    In this study, a methodology is presented where a hybrid system combining an evolutionary algorithm with artificial neural networks (ANNs) is designed to make weekly directional change forecasts on the USD by inferring a prediction using closing spot rates of three currency pairs: EUR/USD, GBP/USD and CHF/USD. The forecasts made by the genetically trained ANN are compared to those made by a new variation of the simple moving average (MA) trading strategy, tailored to the methodology, as well as a random model. The same process is then repeated for the three major cryptocurrencies namely: BTC/USD, ETH/USD and XRP/USD. The overall prediction accuracy, uptrend and downtrend prediction accuracy is analyzed for all three methods within the fiat currency as well as the cryptocurrency contexts. The best models are then evaluated in terms of their ability to convert predictive accuracy to a profitable investment given an initial investment. The best model was found to be the hybrid model on the basis of overall prediction accuracy and accrued returns.
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    An Empirical Analysis and Evaluation of Internet Robustness
    (2023) Stampanoni, Michele; Georg, Co-Pierre
    The study of network robustness is a critical tool in the understanding of complex interconnected systems such as the Internet, which due to digitalization, gives rise to an increasing prevalence of cyberattacks. Robustness is when a network maintains its basic functionality even under failure of some of its components, in this instance being nodes or edges. Despite the importance of the Internet in the global economic system, it is rare to find empirical analyses of the global pattern of Internet traffic data established via backbone connections, which can be defined as an interconnected network of nodes and edges between which bandwidth flows. Hence in this thesis, I use metrics based on graph properties of network models to evaluate the robustness of the backbone network, which is further supported by international cybersecurity ratings. These cybersecurity ratings are adapted from the Global Cybersecurity Index which measures countries' commitments to cybersecurity and ranks countries based on their cybersecurity strategies. Ultimately this empirical analysis follows a three-step process of firstly mapping the Internet as a network of networks, followed by analysing the various networks and country profiles, and finally assessing each regional network's robustness. By using TeleGeography and ITU data, the results show that the regions with countries which have higher cybersecurity ratings in turn have more robust networks, when compared to regions with countries which have lower cybersecurity ratings.
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    An investigation of the impact of NFTs on the Modern Art Market
    (2023) Mwesigwa, Timothy; Georg, Co-Pierre
    The adoption of Non-Fungible Tokens (NFTs) and blockchain technology by the art industry, creates a digital art market. NFTs also change the way artists earn a living from their creations and enable digital asset ownership which impacts any potential digital art owners. This thesis reviews the available academic literature on the impact of NFTs on three elements of the art industry; it's structure, it's creators and it's consumers. I find that the history of the art market determined its modern structure, which is divided between primary and secondary markets, each market dealing directly and indirectly for art with original artists, respectively. NFTs foster disintermediation in the secondary art market, made of traditional intermediaries like art dealers, by enabling a more efficient copyright management mechanism on the blockchain. Additionally, because NFTs enable unique digital identification of digital art, they create a new market for digital assets which extend the structure of the art market to encompass the digital realm. However, the current legislation concerning digital property is structured in a way which favours licencing models which do not enable full ownership of digital files. The hybrid nature of NFTs transactions, which have elements of both sales and licensing agreements, creates the main challenge policy-makers face to craft regulation applicable to NFT transactions. Common law plays a crucial role in this process by balancing digital contracting and property rights which ultimately affects investment in digital property. NFTs also enable the implementation of novel funding models for artists on the blockchain. These include fractional equity in art and the efficient payment resale royalties, which are previously put forward through the Artist's contract, but only practically achieved using NFTs, smart contracts and tokenisation on the blockchain. This thesis lays the foundation for subsequent studies to explore other substantial segments of the NFT market, especially the collectables and gaming segments
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    Auctions and mechanism design for decentralized marketplaces
    (2021) Maree, Christopher; Georg, Co-Pierre
    Those that come up with commercially viable ideas are often not the best suited to implement them. This can lead to allocational inefficacy in the deployment of good ideas. The transfer or licensing of patents is a means of commercializing ideas. However, in the current patent market, the idea seller and the idea buyer often don't match which results in the proliferation of adverse selection. This thesis examines the existing patent market and finds many examples of opacity. Pitfalls abound for both sellers and buyers which result in inefficiencies when attempting to find the best fit for seller and buyer. Improvements in allocating ideas to the best implementers would help inventors and companies alike. Brilliant ideas are frequently generated from universities. This thesis presents a means to commercialize these ideas by issuing licenses on the blockchain in an innovative marketplace for ideas. This commercialization of ideas generates funds that support the institution that originally conceived the ideas and indirectly supports foundational research. The marketplace for ideas is based on sealed bid auctions which ensure that the company that values the idea the most is allocated the license. An optional Harberger Tax system is included to generate constant revenue for the universities from the licensed ideas. This mechanism decreases information asymmetries, increases market liquidity and provides representative license pricing. Smart contracts deployed on the Ethereum blockchain are used to eliminate auction corruption through trustless sealed bid auctions. Smart contracts also automate license issuance, payments and act as a public ledger of license ownership and provenance. A full front-end web application is presented to interface with the marketplace for all users.
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    Blockchain from farm to fork - a new business model for tracing local South African market food supply chains using blockchain technology
    (2022) Oates, Natasha Lee; Georg, Co-Pierre
    One of the most prominent applications of blockchain technology is in supply chain traceability. This paper focuses on a developed case study involving a business model that makes use of blockchain technology to track the produce supply chain of a local South African farmer's market, the Oranjezicht City Farm Market. The research analyses existing literature surrounding food supply chain management approaches that leverage distributed ledger technologies, as well as the case study business model and collected data concerning the market. From this analysis, a financial model forecasting future cashflows is produced. The food supply chain for the local Cape Town Oranjezicht market is explored to determine if a blockchain-based traceability system is a viable option. Aspects of monetization and business plans are the focus and viable implementation strategies are assessed. The case study is presented using a Business Model Canvas and analysed using a SWOT analysis and Porter's Five Forces model to determine the strengths and weaknesses of the inherent business model. A financial model forecasting the future cashflows of said business model is constructed to determine the financial viability of the monetization strategy. The case study business model shows the ability to successfully provide value and solve the problems of each of the local farmer's market stakeholders, namely the farmers, the market and the customers. Analysis of the developed financial projections indicates that only the high road scenario produces a breakeven point at month 36 of the projections. This high road scenario is seen as a highly optimistic view for a financial model. Overall, it is determined that a subscription-based business model for a supply chain traceability application will contribute value and solve problems for the main environment stakeholders, but is not considered financially viable in this local, Cape Town context.
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    Collaboration networks in economic science
    (2018) Rose, Michael E; Georg, Co-Pierre; Taylor, David
    When preparing a research article, Economists receive feedback from other academics, present on conference and give talks in seminars. This form of collaboration is termed informal because informal collaborators have, unlike authors, no formal property rights associated with their contribution. However, informal collaboration is so widespread that it appears to be part of the academic production function. Yet, it has received little attention in academia, least in Economics where patterns of informal collaboration differ from that of natural sciences. Social informal collaboration, the provision of direct feedback, gives rise to a social network. This thesis examines this network. The analysis focuses on the role of individual scientists in the network, which is estimated by different network centralities. Data originate from about 6000 published research articles from six Financial Economics journals between 1997 and 2011. A theoretical model describes how network centrality proxies the effort informal collaborators exert informally in a project, and how this improves the citation count of the research paper. We then investigate how observable characteristics of authors determine this and other centrality measures and find that common metrics such as productivity and number of citations correlate little with network centrality. As information transmission is an important aspect of social networks we study how network centrality of Economists relates to placement outcomes of their students in the academic job market. These findings suggest that even informal networks matter in the production of academic research; that these networks contain information above currently used measures of scholarly influence in the profession; and that these networks are used to decrease information asymmetry in the academic labor market.
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    Data Capture Automation in the South African Deeds Registry using Optical Character Recognition (OCR)
    (2019) Favish, Ashleigh; Georg, Co-Pierre
    The impact of apartheid on land registration is still evident within South Africa. The Deeds Registry is facing a current backlog in registering an estimated 900,000 title deeds. Providing formal ownership, through title, is seen as necessary for unlocking the 'dead capital’ of unregistered property, fostering access to capital markets and poverty alleviation. Within the current legislative framework, the Deeds Registry only accepts paper documents, which introduces inefficiencies. To increase the number of deeds processed per day, automation of manual data capture is tested using an OCR pipeline. To adapt to the linguistics used in title deeds, text analysis and parsing is done using Regex. Uploading the scanned title deeds onto IPFS is as an additional security measure included in the pipeline. Previous research has failed to apply these techniques to formal land registration or other South African government institutions. The preliminary results show that this pipeline has an overall accuracy of 89.6%. This represents the comparison of the expected output to the output extracted using OCR. The results are significantly less accurate when classifying handwritten and stamped information. Thus, further measures are required to increase accuracy for these fields. The OCR accuracy was 98.3% for the fields extracted from typed text characters. This is within the accuracy range of manual data capture. A secondary quality check, which is currently done on manual data capture, would still be necessary to ensure accuracy of inputs. Overall it appears that this application would be appropriate for incorporation into the Deeds Registry to streamline their processes while ensuring title deed validity.
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    Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques
    (2019) Riedl, Anna Teresa; Georg, Co-Pierre
    The International Token Classification (ITC) Framework by the Blockchain Center in Frankfurt classifies 795 cryptocurrency tokens based on their economic, technological, legal and industry categorization. This work analyzes cryptocurrency data to evaluate the categorization with real-world market data. The feature space includes price, volume and market capitalization data. Additional metrics such as the moving average and the relative strengh index are added to get a more in-depth understanding of market movements. The data set is used to build supervised and unsupervised machine learning models. The prediction accuracies varied amongst labels and all remained below 90%. The technological label had the highest prediction accuracy at 88.9% using Random Forests. The economic label could be predicted with an accuracy of 81.7% using K-Nearest Neighbors. The classification using machine learning techniques is not yet accurate enough to automate the classification process. But it can be improved by adding additional features. The unsupervised clustering shows that there are more layers to the data that can be added to the ITC. The additional categories are built upon a combination of token mining, maximal supply, volume and market capitalization data. As a result we suggest that a data-driven extension of the categorization in to a token profile would allow investors and regulators to gain a deeper understanding of token performance, maturity and usage.
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    Essays on the housing market
    (2023) Davids, Allan; Georg, Co-Pierre; Taylor David
    Housing represents the single most important asset, accounting for around 50% of global household wealth. At the household level, a house typically represents the largest asset most households will own through their lifetime. This is even more pronounced in emerging economies where household participation rates in real estate across the wealth distribution are higher than participation rates in financial assets. Most homes are purchased by means of a mortgage which typically constitutes the largest liability most households will have through their lifetime. Mortgages also make up a sizeable share of the asset base of banks. Moreover, the housing market is pivotal to the economy and the financial system, as illustrated by its central role in the Great Financial Crisis of 2007-2009. Despite this, the empirical housing literature in emerging markets is relatively underdeveloped relative to the literature in advanced economies. A major reason for this relates to the lack of detailed and reliable housing transaction data in emerging market economies. Leveraging a number of novel datasets, I explore two aspects of the housing market in Cape Town, South Africa, that are of academic and policy interest: foreign investment in the housing market and the discounts associated with home foreclosures. I document sizeable foreign investment in the housing market in Cape Town between 2011 and 2018, showing that these investors sort into the wealthier suburbs in the city that have had historically large communities of foreign inhabitants. Despite these sizeable net inflows, foreign ownership, has in fact, decreased throughout the sample period, highlighting that foreign buyers are not crowding out local buyers. Turning to purchase and investment outcomes, I find that foreign buyers and sellers realize worse outcomes than local buyers and sellers, purchasing otherwise identical properties for a premium, leading to lower returns upon resale. This result can partly be explained by wealth effects and information asymmetries. I also highlight an important feature of foreign demand, so far overlooked by the existing literature: the tendency of foreign buyers to be cash buyers. I show that failing to control for the financing choices of the buyer leads to a sizeable underestimate of the foreign buyer premium driven by the fact that buyers who purchase properties using cash as opposed to a mortgage typically attract a sizeable discount. In the following chapter, I examine the relationship between exchange rate depreciations and foreign non-resident investment in the housing market. I show that foreign non-resident transactions increase following exchange rate depreciations and are also increasing in the size of the depreciation. I find no evidence of similar effects for foreign born permanent residents in South Africa, highlighting that the exchange rate effect is linked to purchasing property in foreign currency and not to whether or not the buyer is a foreigner. I then use large and sudden exchange rate depreciations as a positive exogenous shock to foreign non-resident demand to study the effect of foreign non-resident investment on house prices, leveraging an identification strategy that compares quality adjusted prices in geographically close suburbs that differ in their ex-ante attractiveness to foreign non-residents, and find a positive causal effect of foreign non-resident demand. While this may raise concerns about the impact of foreign non-resident demand on the affordability of homes for local buyers, the fact that foreign non-resident investment in housing appears to be counter-cyclical, increasing following large depreciations, suggests that these inflows may have important stabilizing effects on house prices. Finally, I document the extent of home foreclosures in Cape Town and estimate the discounts that these properties sell for. Leveraging features of the institutional setting I study and the data I employ, I am able to provide a more complete characterization of the dynamics affecting foreclosure discounts by disentangling the foreclosure discounts when foreclosures sell at a foreclosure auction from the discounts that arise when these foreclosures sell in the private market outside of the auction. I find evidence that foreclosure discounts are substantial both in the private non-auction market and when sold at an auction. In the former, the discounts can be rationalized as a classic firesale discount driven by the financial distress of the seller. In the latter, I present evidence that limited competition at auctions driven by costs to participation could rationalize the foreclosure discounts at auctions, consistent with housing search theory. I also conduct an extensive robustness exercise to account for potential factors which could lead to an upward bias in the estimated foreclosure discount showing how various factors confound the true foreclosure discount.
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    Feasibility study of using blockchain to improve transparency and trust in the charity industry
    (2021) Pahl, Julika; Georg, Co-Pierre
    In 2012, the UN Secretary stated that corruption prevented 30 percent of all development assistance from reaching its destination (UNSG, 2012). This thesis discusses the importance of trust and transparency in the charity sector, and how technology, specifically blockchain, could address these two factors. This paper aims to demonstrate this by developing a minimum viable product on the Ethereum blockchain, called the LoveEconomy, for a local South African non-profit organization, the Secret Love Project. The LoveEconomy is designed as a circular economy, whereby local businesses and users of the platform benefit from each other, whilst also supporting the charity, which takes care of homeless people in Cape Town. Blockchain has many features that could potentially transform charitable giving and aid distribution by enhancing transparency, reducing costs through disintermediation, and enabling new mechanisms for monitoring and tracking charities' impact. Trust and transparency are closely linked in the charity industry, as transparency about the distribution of the funds and the end impact are critical for the trust of the public (Populus and Charity Commission For England & Wales, 2018).
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    Heterogeneous agent models to determine spillover effects in the context of quantitative easing
    (2019) Koziol, Tina; Georg, Co-Pierre
    We develop heterogeneous agent models to investigate financial spillover effects in the context of Quantitative Easing (QE). We consider these spillover effects from two perspectives. The first perspective studies spillovers within a network of financial institutions. The aim is to understand where amplification effects occur in the event of a shock. For this purpose, we calibrate a model of fire-sale contagion to the South African banking sector. We use cross-sectional balance sheet data for 29 South African banking institutions. Fire-sale externalities are pecuniary externalities that operate through prices. They pose a threat to the financial system because they amplify price shocks across assets and thus lead to liquidation spirals. In the first step, we investigate general shock propagation scenarios to an unsecured lending portfolio of a large bank and to a marketable asset held by all banks, i.e. South African government bonds. We rank individual banks according to their contribution to systemic risk and show the importance of cash liquidity buffers in reducing risk of fire-sale occurrences. Further, we find a critical threshold parameter which, if exceeded, makes the banking system highly unstable. In the second step, we build on findings presented by Cecchetti et al. (2017) that determine a relationship between Quantitative Easing and risk-taking behavior of financial institutions in emerging markets. Assuming that QE increases banks’ leverage, we show that the fire-sale contagion channel becomes much more pronounced. The same shock to the government bond asset class leads to higher banking sector instability. The risk to banking sector losses is not linear, but rather increases exponentially with higher leverage ratios. The second perspective of the dissertation considers spillovers between financial markets in the context of QE. We contribute to the literature that investigates the portfolio balance effect associated with QE. In essence, the portfolio balance channel is the consequence of an assumed imperfect substitutability of assets. To account for this, we develop a dynamic agent-based model to study international asset price spillover. Our two-country model features heterogeneity in assets and in investor preferences. Both are crucial for a meaningful model-based impact assessment of QE because preferences for asset maturity, asset class (bonds, equities and currencies) and whether an asset is issued at home or abroad can influence the substitutability of assets, and hence the portfolio balance effect of central bank asset purchases. We implement a novel pricing mechanism that allows us to approach market clearing prices. This allows us to take advantage of the flexibility of the agent-based methodology, while keeping the model comparable to more standard equilibrium-based portfolio balance models. We calibrate the two countries in our model to the Eurozone (EZ) and a representative sample of rest-of-the-world (ROW) countries in order to estimate the international impact of the ECB’s asset purchase program announced in January 2015. For this purpose, we compile data on asset holdings of 15 374 EZ and 25 930 ROW open-end investment funds from the Morning Star Database, as well as data on investment portfolios of EZ and ROW banks from the ECB’s Statistical Warehouse and Bankscope. When simulating our model, we find a negative impact of central bank asset purchases on both domestic and foreign returns. While the effects of QE on domestic bond yields and the exchange rate are rather modest and smaller than commonly assumed in the literature, they can cause domestic stock prices increase substantially. Somewhat surprisingly, however, we find that spillovers from portfolio balancing to the rest of the world are negligible.
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    How to attribute credit if you must
    (2021) Meiklejohn, Luke S; Georg, Co-Pierre
    Data ownership is of fundamental importance in the digital economy of today. Commercializing academic research, whilst maintaining ownership of it, is a task that can now be accomplished due to the strengths of blockchain technology, which allows data to be registered, made unique, and traced to its origins. We propose a blockchain use-case for licencing academic research, based off an academic project named UniCoin. In this thesis, we discuss how to fairly attribute credit between all sources of knowledge that contribute to new pieces of academic research, using citation network analysis and centrality measures. Katz centrality, in-degree centrality, and PageRank are three potentially useful centrality measures, with varying results: these are compared using case studies based on three papers co-authored by Andrei Shleifer. We use these centrality measures to guide how to fairly attribute credit, and thus how to distribute licencing revenues generated through UniCoin.
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    How to build a self-sovereign identity system that is beneficial to both the individual and business
    (2019) Moodley, Jothi; Georg, Co-Pierre
    Self-sovereign identity defines a system in which an entity can generate and maintain their own proof of identity. There are several solutions aimed at providing this service and storing the relevant information on a blockchain. We describe how to develop such a system using Ethereum’s smart contract platform and a browser-based application, and we demonstrate its use in a corporate that sells more than one funeral insurance product. Individuals and organizations should be able to create claims on their identities, however, only reputable organizations can verify these claims. These operations are executed by functions contained in the smart contracts and the transactions can be stored on a blockchain. A major benefit of this innovation is that an identity can be easily re-used and we show how an insurance department can do this using credentials already requested by another department. This method allows for much needed efficiency over the current system.
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    The Industrial Development Corporation's Distressed Funding: Effectiveness in rescuing distressed companies following the global financial crisis
    (2015) Matlaila, Mamoekeng; Georg, Co-Pierre
    The recent global financial crisis which began in the United States of America in 2007, spread to almost all economies in the world and evolved into a world economic downturn. Governments around the world introduced different rescue interventions to avoid the collapse of the financial and banking system and to stimulate economic growth. In addition to large scale economic stimulus packages, other forms of Government interventions were introduced in direct support of non-financial firms including Small and Medium Sized Enterprises (SMEs). These Government interventions have attracted little empirical attention with recent studies pointing out to the need for more evaluation of the impact of direct support interventions. This study attempts to contribute to the literature which focuses on the impact of interventions introduced by governments in developing countries, to resolve market failure in non-financial corporate companies as well as SMEs. This study is focused on assessing the effectiveness of the IDC distressed funding scheme in rescuing distressed companies in South Africa following the recent global financial crisis. We investigate the effects of the scheme on the financial performance of beneficiary companies. Our results show that overall the funding had a positive impact on beneficiary companies. The impact was greatest on the solvency, capital structure and leverage of the awarded companies. The funding was most effective in the first year following the injection of the capital into the business. The profitability and liquidity of the beneficiary company did not change significantly following accessing of the funding.
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    A Model For Collaborative Creation And Ownership Of Digital Products
    (2023) Chirema, Takunda; Georg, Co-Pierre
    This thesis presents Axone, a system that enables decentralized collaborative creation of digital products through interconnected digital content blocks. Axone provides the provenance of a digital product by storing its history since creation in an immutable Directed Acyclic Graph (DAG) data structure. This history comprises digital content blocks used in its creation, including how they referenced each other in the development of the final digital product. Through referencing, credit attribution is achieved and royalty fees due to the referenced content block are recorded and enforced. Content creators can concurrently work on a succeeding content block to produce various versions of unique digital products from the same original content block. Axone focuses on written work enabling different authors to contribute to a book (the digital product) in the form of chapters (digital content blocks), until its completion. Axone uses blockchain technology and web monetization to provide provenance for each chapter and to stream payments to authors.