Missed Opportunities: Innovation and Resource-Based Growth in Latin America
Bibliographic record
Abstract
No AccessPolicy Research Working Papers25 Jun 2013Missed Opportunities: Innovation and Resource-Based Growth in Latin AmericaAuthors/Editors: William F. MahoneyWilliam F. Mahoneyhttps://doi.org/10.1596/1813-9450-2935SectionsAboutPDF (0.2 MB) ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked In Abstract:Latin America missed opportunities for rapid resource-based growth that similarly endowed countries—Australia, Canada, Scandinavia—were able to take advantage of. Fundamental to this poor performance was deficient technological adoption driven by two factors. First, deficient national "learning" or "innovative" capacity, arising from low investment in human capital and scientific infrastructure, led to weak ability to innovate or even take advantage of technological advances abroad. Second, the period of inward-looking industrialization discouraged innovation and created a sector whose growth depended on artificial monopoly rents rather than the quasi-rents arising from technological adoption, and at the same time undermined resource-intensive sectors that had the potential for dynamic growth. This paper—a product of the Office of the Chief Economist, Latin America and the Caribbean Region—was prepared as a background paper for the region's flagship report, From Natural Resources to the Knowledge Economy (2001). Previous bookNext book FiguresReferencesRecommendedDetailsCited ByEnvironmental Collapse and Institutional Restructuring: The Sanitary Crisis in the Chilean Salmon Industry16 April 2016Mushrooms and Yeast: The Implications of Technological Progress for Canada's Economic GrowthSSRN Electronic JournalExport diversification and structural changes in South AfricaJournal of Governance and Regulation, Vol.4, No.31 January 2015Engineers, Innovative Capacity and Development in the AmericasSSRN Electronic JournalCatching Up in the 21st Century: Globalization, Knowledge and Capabilities in Latin America, a Case for Natural Resource Based ActivitiesCost-Reducing R&D in the Presence of an Appropriation Alternative: An Application to the Natural Resource CurseSSRN Electronic JournalUnderstanding the development of technology-intensive suppliers in resource-based developing economiesResearch Policy, Vol.39, No.2Firm‐specific and economy wide determinants of firm profitabilityManagerial Finance, Vol.35, No.11The Role and Development of Technology-Intensive Suppliers in Resource-Based Economies: A Literature ReviewSSRN Electronic JournalUnderstanding the dynamics and competitiveness of the South African minerals inputs clusterResources Policy, Vol.31, No.1 View Published: December 2002 Copyright & Permissions Related RegionsLatin America & CaribbeanRelated CountriesArgentinaAustraliaCanadaChileUnited StatesRelated TopicsAgricultureEducationFinance and Financial Sector DevelopmentHealth Nutrition and PopulationIndustryMacroeconomics and Economic GrowthPrivate Sector DevelopmentRural DevelopmentSocial Protections and Labor KeywordsAGRICULTURECOMPETITIVENESSDEBTDEVELOPMENTDEVELOPMENT POLICIESHUMAN CAPITALINCENTIVESINDUSTRIALIZATIONINVESTMENTKNOWLEDGE ECONOMYLAGSMONOPOLYMONOPOLY RENTSNATURAL RESOURCESNET EXPORTSPOLITICAL ECONOMYPRODUCTIVITY GROWTHTECHNICAL ASSISTANCETOTAL FACTOR PRODUCTIVITYTRADE PDF DownloadLoading ...
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".