Structural adjustment programs and housing affordability in Accra, Ghana
Bibliographic record
Abstract
Due to the persistent socio‐economic problems that have beset African countries since the late 1970s, many of them have been forced to accept IMF and World Bank sponsored Structural Adjustment Programs (SAPs). Ghana came under one such program in 1983. While proponents of the program point to growth in GDP and other measures as evidence of successful adjustment in Ghana, critics have pointed to the negative impacts on the labor market, women, farmers and the like. This paper seeks to add to the debate by examining the impacts of SAPs on housing production, delivery and affordability from 1983–1998. It argues that since shelter is a very important basic need, what happens to its production, affordability and access under the SAPs should be considered among the criteria for judging their success or failure. The paper examines housing affordability in Accra, Ghana, using standard measurement criteria applied by lending institutions to determine affordability. It uses market data to compare and contrast housing prices and income ratios in Ghana from 1980 to 1998. The analysis is based on a combination of primary and secondary data from market surveys, the Ministry of Housing, the Ghana Statistical Services and a variety of other sources. It concludes that not all the dramatic increases in the price of both developed and undeveloped land over the past 16 years can be wholly attributed to the ongoing Structural Adjustment Programs (SAPs) per se. Nonetheless, SAP inspired policies such as currency devaluation and hikes in interest rates have contributed greatly to these changes. The end result is that real estate prices have been pushed beyond the affordability of a significant proportion of Ghana's population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".