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Is natural resource abundance a curse or an opportunity? Economic complexity, FDI, and industrial policies in Mozambique

2024· article· en· W4403067849 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResources Policy · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Technological Innovation
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
FundersFundação para a Ciência e a TecnologiaInstituto de Sistemas Complejos de IngenieríaConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsResource curseNatural resourceCurseNatural resource economicsForeign direct investmentEconomicsNatural (archaeology)Abundance (ecology)Resource (disambiguation)BusinessInternational tradeEcologyMacroeconomicsGeographyBiologyComputer science

Abstract

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Recent research has shown that a lack of structural transformation predicates the onset of the resource curse, that is, the notion that resource-rich countries paradoxically have lower growth prospects in the long run. Such structural transformations can be mapped through economic complexity indicators, which have been shown to predict the long-term economic underperformance of countries before it is manifested in lower economic growth rates. Economic complexity thus provides countries with an early warning before the onset of the resource curse. FDI and effective industrial policy have been proposed as potential tools to facilitate diversification and counter the resource curse. Emerging insights from economic complexity can further unpack how FDI and industrial policy impact the resource curse. To illustrate this, we critically evaluate the role that FDI and industrial policy have played in the case of Mozambique. We investigate whether these tools have contributed to circumventing or accelerating the onset of the resource curse in the country. Our empirical results cover a period between 1996 and 2019, showing that FDI mainly focussed on natural resource products in the periphery of the product space with a low to average product complexity index. Moreover, industrial policies have also promoted diversification into some related mining goods and relatively simple activities, such as textiles and agriculture, that would only slightly improve the country's overall complexity but not lead to structural realignment. Neither FDI nor industrial policies have exploited the most promising new industrial opportunities associated with mining activities, which can help master new technological and productive knowledge. Where industrial policy has targeted more complex goods, these have often been unrelated to existing capabilities and consequently been unsuccessful. Hence, despite the economic growth that Mozambique has experienced, it has not been able to improve its industrial structure, which points towards the eventual onset of the resource curse. Based on these observations, we make recommendations on how FDI and industrial policies could be refocused in a smart diversification direction to improve Mozambique's industrial structure in a promising and achievable direction. • Lack of structural transformation predicates the onset of the resource curse, impacting long-term economic performance. • Economic Complexity offers a framework to map the long-term impacts of the resource curse and identify better development opportunities. • We use Economic Complexity to evaluate the impacts of past Industrial Policies and FDI on Mozambique's structural transformation. • Despite the short-term economic growth experienced by Mozambique, FDI and Industrial Policy failed to support significant structural transformation. • Mozambique can benefit from adopting a Smart Diversification framework for long-term structural transformations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.190
GPT teacher head0.326
Teacher spread0.137 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it