Examining land reform in South Africa: evidence from survey data

Land and land reform have long been contentious and highly charged topics in South Africa, with land performing the dual functions of redress for the past and development for the future. This research explores both these aspects of land, with the focus being on the impact of land receipt on household welfare and food insecurity, and social preferences for fairness and redistribution more generally. One of the main aims is to contribute to the land reform debate by providing previously-lacking quantitative evidence on the aggregate welfare outcomes of land redistribution, as well as the extent of social preferences for redistribution in the land restitution framework. In exploring these issues, the welfare outcomes of land are first explored using the National Income Dynamics Study (NIDS) data and unconditional quantile regression analysis. The focus is then narrowed to the food insecurity impact of land receipt, beginning with a methodological chapter outlining the development of a new food insecurity index applying the Alkire-Foster method of multidimensional poverty measurement (2009; 2011). This is followed by the presentation and discussion of food insecurity profiles of land beneficiary and non-beneficiary households. The new index is also used as an outcome measure in exploring the determinants of household food insecurity. These two sections again use the NIDS data. The final section shifts the emphasis from the economic welfare benefits of land redistribution to notions of fairness and social justice encapsulated by land restitution. A behavioural laboratory experiment is used to investigate social preferences for fairness, and the factors that influence redistributive inclinations, by exploring the relative weights placed on fairness considerations and self-interest, as well as the fairness ideal. The findings indicate that beneficiaries do not use the land received for productive purposes, a possible explanation for the limited economic welfare impacts of land reform that are observed. Despite this limited developmental impact, the laboratory experiment makes it clear that land reform plays an important role in addressing other needs and wants in society, particularly in respect of preferences for fairness and addressing historical injustices

About Voice: A Longitudinal Study of Speaker Recognition Dataset Dynamics

Like face recognition, speaker recognition is widely used for voice-based biometric identification in a broad range of industries, including banking, education, recruitment, immigration, law enforcement, healthcare, and well-being. However, while dataset evaluations and audits have improved data practices in computer vision and face recognition, the data practices in speaker recognition have gone largely unquestioned. Our research aims to address this gap by exploring how dataset usage has evolved over time and what implications this has on bias and fairness in speaker recognition systems. Previous studies have demonstrated the presence of historical, representation, and measurement biases in popular speaker recognition benchmarks. In this paper, we present a longitudinal study of speaker recognition datasets used for training and evaluation from 2012 to 2021. We survey close to 700 papers to investigate community adoption of datasets and changes in usage over a crucial time period where speaker recognition approaches transitioned to the widespread adoption of deep neural networks. Our study identifies the most commonly used datasets in the field, examines their usage patterns, and assesses their attributes that affect bias, fairness, and other ethical concerns. Our findings suggest areas for further research on the ethics and fairness of speaker recognition technology.Comment: 14 pages (23 with References and Appendix

Fair value and cost accounting, depreciation methods, recognition and measurement for fixed assets

In accounting and finance, fair value is a rational and unbiased estimate of the potential market price of a good, service or asset. On the other hand, cost accounting policy is more conservative and prudence. Accounting fairness refers mostly to the fair presentation, the initial recognition and measurement or valuation of an element. Therefore, adopting different accounting policies results in the assets being presented in the entity’s financial statements with different values. With the application of cost or fair value accounting policies across firms or countries, the financial statements are being incomparable. Another issue arises from depreciation methods applied. With the application of different depreciation accounting methods across firms or countries, the financial statements are being incomparable. Both accounting policies for recognition and measurement and depreciation methods, determine the net value of fixed assets in financial statements’ presentations. Thus, a decision-making procedure exists for recognition and measurement of property assets using the above components. The research objects of the paper are to explore in detail the relationship between cost and fair value accounting policies with depreciation methods, by enabling decision-making options. The financial method of discounted cash flow (DCF) technique is used for fair value accounting as well as for impairment test and the depreciation accounting methods are used for cost accounting policy, in order to explore the decision options for a property asset recognition and measurement. Following the above procedure, a fair value accounting model is correlated with the deprecation methods and an analysis of the impact of each decision-making alternative in financial statements’ figures is producedpeer-reviewe

Transport Protocol Throughput Fairness

Interest continues to grow in alternative transport protocols to the Transmission Control Protocol (TCP). These alternatives include protocols designed to give greater efficiency in high-speed, high-delay environments (so-called high-speed TCP variants), and protocols that provide congestion control without reliability. For the former category, along with the deployed base of ‘vanilla’ TCP – TCP NewReno – the TCP variants BIC and CUBIC are widely used within Linux: for the latter category, the Datagram Congestion Control Protocol (DCCP) is currently on the IETF Standards Track. It is clear that future traffic patterns will consist of a mix of flows from these protocols (and others). So, it is important for users and network operators to be aware of the impact that these protocols may have on users. We show the measurement of fairness in throughput performance of DCCP Congestion Control ID 2 (CCID2) relative to TCP NewReno, and variants Binary Increase Congestion control (BIC), CUBIC and Compound, all in “out-of-the box” configurations. We use a testbed and endto- end measurements to assess overall throughput, and also to assess fairness – how well these protocols might respond to each other when operating over the same end-to-end network path. We find that, in our testbed, DCCP CCID2 shows good fairness with NewReno, while BIC, CUBIC and Compound show unfairness above round-trip times of 25ms

50 Years of Test (Un)fairness: Lessons for Machine Learning

Quantitative definitions of what is unfair and what is fair have been introduced in multiple disciplines for well over 50 years, including in education, hiring, and machine learning. We trace how the notion of fairness has been defined within the testing communities of education and hiring over the past half century, exploring the cultural and social context in which different fairness definitions have emerged. In some cases, earlier definitions of fairness are similar or identical to definitions of fairness in current machine learning research, and foreshadow current formal work. In other cases, insights into what fairness means and how to measure it have largely gone overlooked. We compare past and current notions of fairness along several dimensions, including the fairness criteria, the focus of the criteria (e.g., a test, a model, or its use), the relationship of fairness to individuals, groups, and subgroups, and the mathematical method for measuring fairness (e.g., classification, regression). This work points the way towards future research and measurement of (un)fairness that builds from our modern understanding of fairness while incorporating insights from the past.Comment: FAT* '19: Conference on Fairness, Accountability, and Transparency (FAT* '19), January 29--31, 2019, Atlanta, GA, US

Gender Discriminatory Taxes, Fairness Perception, and Labor Supply

In this paper, we examine the gender specific impact of discriminatory taxation on fairness perception and individual labor supply decisions. Using the controlled environment of an experimental laboratory, we manipulate both distributional as well as procedural justice of taxation between subjects. We violate distributional fairness through the random application of tax rates, while procedural justice is broken by levying discriminatory tax rates based on taxpayer gender. For both inequality in outcome as well as discrimination, we find strong differences in reactions between male and female participants. Male participants perceived gender discriminatory taxation as unfair in and of itself. Female participants perceived random taxation as well as gender discriminatory taxation to be unfair, as long as they ended up with the higher tax rate. The perceived fairness strongly drove (did not affect) male (female) participants’ labor supply. Taken both subgroups together, while mere outcome inequality did not influence labor supply decisions significantly, we find evidence of a negative effect of gender-based discrimination on labor supply