Economic Shocks and the Growth of the Construction Industry in Ghana Over the 50-Year Period From 1968 to 2017
Bibliographic record
Abstract
The study analyses the relationship between the growth of the construction industry and economic shocks in Ghana over the 50-year period from 1968 to 2017 using an autoregressive modelling scheme that incorporates several economic shocks as separate independent variables. The independent variables used in the model included one positive economic shock and five negative shock variables. The positive shock variable was the sharply increased government expenditures on construction activities in selected years that allowed the government to host international events in Ghana within a period of two years. The five adverse economic shocks included in the model were political instability related to military coups, exchange rate depreciation of the local currency, Ghana cedi, with respect to the United States dollar, the average yearly temperature, aggregate electricity energy production shortfall related to a severe El Nino weather phenomenon, and incidence of extreme rainfall. The results of the analysis indicated that the most important factor influencing the growth of the construction industry in Ghana over the 50-year study period was political instability. Beyond political instability, the next most important factor was the purposely-driven sharp increases in government expenditures on construction activities for selected years that allowed the country to host international events in the country. The other significant economic shocks were the exchange rate depreciation, average temperatures, and electricity energy production shortfall; all three factors adversely affected the growth of the construction industry. The results of our study are generally consistent with those obtained from the literature concerning the positive and negative effects of economic shocks on the construction industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".