Export Intensity and Total Factor Productivity in Kenya’s Manufacturing Sector
Bibliographic record
Abstract
Kenya has adopted an export-led manufacturing industrialization strategy as envisaged by many policy documents including the Kenya Vision 2030 which aimed at increasing the manufacturing share in Gross Domestic Product to 15 per cent by 2022. The share of manufactured exports in all exports was targeted at 60 per cent by 2022 as per the National Exports Development and Promotion Strategy. However, manufacturing sector’s productivity has been declining as demonstrated by its economic contribution which has averaged around 10 per cent from 2007 to 2022 and has persistently declined from 12.79 per cent in 2007 to 7.83 per cent in 2022 pointing towards premature deindustrialization. Besides, from 2007 to 2022, the share of total exports made up of manufactured goods averaged 33 per cent. The study aimed to estimate firms’ total factor productivity (TFP) and examine the impact of export activity on firms’ TFP in Kenya’s manufacturing industry. Firm TFP was computed utilizing the Levinsohn and Petrin (2003) technique. The study employed Propensity Score Matching and a dynamic panel model estimated using the generalized methods of moments technique, to analyze the effect of exporting on firms TFP. Export intensity, labor productivity and management experience had positive effects on firm’s TFP. However, firm size and capital intensity had negative effects on TFP. Based on the study findings, the government should emphasize on export promotion policies as well as adoption of labor intensive technologies in Kenya’s manufacturing sector.
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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 | 0.003 |
| 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) | 0.004 | 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".