Credit and Debt in an Unequal Society: Establishing a Consumer Credit Market in South Africa, Jürgen Schraten
Bibliographic record
Abstract
Reviewed by: Credit and Debt in an Unequal Society: Establishing a Consumer Credit Market in South Africa by Jürgen Schraten Rinki Dahiya BOOK REVIEW of Schraten, Jürgen. 2020. Credit and Debt in an Unequal Society: Establishing a Consumer Credit Market in South Africa. New York: Berghahn Books. 219 pp. $135 (hardcover). Consumer credit has become a feature of contemporary capitalism, as it involves illiquid citizens in the economy. Consumer loans, formerly emergency tools, have become common components of commodity supply and finally commodities themselves. People in financialized economies now normally tap into their future income; at the same time, consumer credit markets are increasing the number of debtors and preventing them from improving their standard of living, or excluding them from social life altogether (1). This book explains how the lack of liquidity has been a major source of distress for millions of people. Schraten organizes the text into five chapters in addition to an introduction and conclusion. The first chapter puts the consumer credit market in a perspective missing from earlier works. It analyzes the market as a whole, as a circuit of supply and demand, consisting of legal relations and the exchange of money. Its overview of the credit supply in South Africa shows how easily accessible but expensive consumer loans have become. Customers are attracted by clever advertisements, which depict borrowing as a responsible act, but it becomes dangerous to customers after they default on repayment. Details of future consequences are hidden in the proverbial small print, distributed over several different documents (32). The result is a growing consumer credit market with a rising number of customers having impaired records. The second chapter, “Raising the Storm of a Free Consumer Credit Market,” proceeds with a historical overview of how a monetized economy developed in South Africa. It emphasizes that the long-standing tradition of tight monetary supply still influences the political and economic notions of money. The third chapter begins with a detailed analysis of the realm of legality as developed by the National Credit Act (NCA), which has affected consumers’ economic behavior regarding credit. The chapter discusses the National Credit Regulator (NCR), the National Consumer Tribunal (NCT), debt counselors, credit bureaus, and other institutional actors; it emphasizes that the NCR and NCT were governmental bodies superior to market actors, but with restricted powers of enforcement. After this overview, it discusses the payment system, often considered an overlooked component. [End Page 134] The fourth chapter shows how the National Credit Amendment Act was an important step toward preventing irresponsible lending to those with the lowest income. It highlights a dramatic discrepancy between the acknowledgement of existing problems with the NCA and suggestions for remedies. It reviews legislative amendments and suggests a completely new procedure, called debt intervention, arguing that the powers of the NCR and NCT should be outlined more clearly to prevent irresponsible lending. It concludes by looking at international consumer-credit-market frameworks, revealing that the South African NCA was mostly influenced and shaped by unique traits of Australian, Canadian, English, French, New Zealand, and Welsh laws. The fifth chapter, “The Model of Rational Action in the South African Consumer Credit Market,” analyzes the roots of power and the legitimacy of societal settings. It splits into two sections: the first analyzes the South African consumer credit market from a reflexive level of observation, revealing that the credit market has been shaped by the idea of self-governing markets and economic rationality; the second section recounts the arguments of Georg Simmel, Adam Smith, Max Weber, and other classical theorists to grasp the paradigmatic rationality established in industrialized market economies. The final chapter concludes the book by reviewing how economic settings produce a complex network revolving around distributed agency, in effect proving that the NCA cannot prevent basic free-market problems. This book differs from other scholarly works in its concentration on the economic effects a legal framework has on market participants’ equality. According to Schraten, “the public legitimation of the South African Consumer credit market and the idea of naturally and freely interacting economic actors are revealed as deceptions” (181). The book—not an examination of credit and debt, but an account of the...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".