Economic Shocks and the Growth of the Construction Industry in Ghana Over the 50-Year Period From 1968 to 2017
Why this work is in the frame
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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.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it