More Than Good Intentions: How a New Economics is Helping to Solve Global Poverty (2011) by Dean Karlan and Jacob Appel
Bibliographic record
Abstract
Karlan and Appel present a number of case studies carried out in economically poor countries such as Ghana, India, Kenya, and Bangladesh to examine the effectiveness of developmental projects launched to reduce poverty.This book is also about how individual philanthropists of the developed world can help people of low-income countries improve their socioeconomic conditions.The authors use the Randomised Controlled Trial (RCT) as a method for assessing problems connected to poverty.They examine change in the lives of individuals with access to development programs such as microcredit, which involves "provision of small loans to the poor" (p.9).Karlan and Appel go on to compare these peoples' lives with those who did not have access to microcredit.In all of the case studies, the authors show how small details could be a tool for assessing the effectiveness of development projects."Getting poor people to borrow money has become one of the best hopes for alleviating poverty" (p.57), but the problem with this is that poor people in the developing world hesitate to take loans.In their case studies, Karlan and Appel explain why poor people are reluctant to take loans.Firstly, many poor people do not perceive the provision of loans as a miracle cure for their problems; rather, they perceive it as a debt with a great challenge to repay.Secondly, many poor people are excluded from obtaining loans because of lenders' "restrictions on the use of borrowed money" (p.74).For example, in the case of Sri Lanka, Karlan and Appel find that borrowers were allowed to take loans only for financing business activities.But the reality was that everybody was not willing to and capable of conducting business.The book has 308 pages and 12 chapters with short and simple headings such as To Buy, To Borrow, and To Save.The first half of the book takes a close look at microcredit, whereas in the second half, the authors try to convince donors to examine individual projects in terms of their potential impact on reducing poverty before making donations.Full of illustrations and metaphorical expressions, this is an easily readable book that could be persuasive to donors who would like to donate to projects focused on the reduction of poverty.The authors are successful in persuading potential donors that "good intentions" (p. 3) expressed in the provision of loans to the poor are not enough to solve the problem of poverty.Karlan and Appel claim that small development actions such as the provision of microcredit should be scaled up so as to bring larger spillover effects in the endeavour to reduce poverty on a
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".