Macroeconomic Variables and Value Creation in the Nigerian Quoted Companies
Bibliographic record
Abstract
This study uses 1,425 observations, relating to firm level and time series data sets, to examine the effect of macroeconomic variables on the economic value created by the Nigerian quoted companies. The data described macroeconomic variables such as inflation (INF), interest rates (INT), capital expenditure ratio of government (CAR) foreign exchange rates (FRXG), gross domestic product (GDPG) and the developments in the capital (CMKG) and labour market (LBMG) and the economic value added (EVA) by 186 purposively selected quoted companies for the years 2001-2012. To allow for comparison, the companies were categorized into two sub-sectors: manufacturing (715 observations) and services (710 observations). The study uses descriptive and inferential statistical tools such as mean, standard deviation, correlation, pooled ordinary least square (OLS) regression and generalized method of moments (GMM) techniques to analyze data. The study found that EVA followed an autoregressive function after one period and lagged EVA was included in model. Due to the problem of heteroskedasticity, Generalized Method of Moment results were relied upon and significant (positive and negative) impact of CAR (β = -0.0173, p<0.05), FRXG (β = 0.00857, p < 0.01), INF (β = -0.00896, p < 0.05), INT (β = 0.0262, p < 0.1) and LBMG (β = 0.00158, p < 0.01) on EVA was found, for all the companies. We concluded that value creation, measured by EVA, is a function of prior year EVA and that inflation rate, interest rate, foreign exchange rate, capital expenditure ratio and the development in labour market were important macroeconomic factors that should be improved upon if quoted companies were to optimally create economic value in Nigeria.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".