Location, Ownership, Origin and the Spillover-Productivity Nexus. Evidence from Uganda Manufacturing Firms
Bibliographic record
Abstract
The main purpose of this article is to investigate the drivers of labor productivity in the firms at the intra-industry level with focus on the spillover effects of FDI. Using a fixed effects approach, we estimate an expanded Cobb-Douglas production function in its intensive form to isolate the effects of increased capital intensity on labor productivity as well as the spillovers, using annual Private Sector Investment Survey data collected on the Ugandan manufacturing firms over the period 2007- 2010. Over all, there are significant negative horizontal spillovers for the domestic firms in Uganda, with OECD-originating FDI appearing to be the main source of such effects. By location, these are most adverse in the western and eastern regions and better spillovers can be traced in the central region. Additional findings point to firm size, labor quality and profit as positive contributors to labor productivity, whereas technology gap exhibits a detrimental impact just as we document no significant effect of capital intensity. Larger domestic firms appear to benefit significantly from spillovers in industries where foreign firms have a larger presence. The aforementioned findings reflect the need for well-designed policies to improve the competitiveness of local firms particularly via an incentive-equal opportunity-policy that captures both domestic and foreign investors and to improve infrastructure and other investor-friendly environment in the East and Western parts of Uganda. Similarly, our results suggest that the promotion of joint ventures (foreign) is likely to generate unequivocal benefits to the manufacturing sector in Uganda not only in terms of less negative horizontal spillovers but also from the labor quality, firm size and profit spillovers perspective. Finally, the finding of learning difficulties of domestic firms from foreign firms calls for programs in line with skill acquisition through job training and the review of the curriculum to focus on labor quality.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".