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Record W2100691081 · doi:10.1177/0020715213519458

Peripheral accumulation in the world economy: A cross-national analysis of the informal economy

2013· article· en· W2100691081 on OpenAlexvenueno aff
Anthony Roberts

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWorld economyInformal sectorEconomicsMainstreamDeveloping countryGlobalizationEconomyPost-industrial economyInformation economyForeign direct investmentStructural changeEconomic systemMarket economyEconomic growthMacroeconomicsPolitical science

Abstract

fetched live from OpenAlex

The persistence and growth of the informal economy have puzzled researchers and challenged mainstream explanations of the development of the informal economy. This study utilizes a world-systems approach for explaining cross-national variation in the size of the informal economy for a sample of 74 developing and developed countries observed over a recent 8-year period (1999–2007). According to this approach, the informal economy is a characteristic of peripheral accumulation in the world economy and its development is driven by unequal exchange in international trade and foreign capital penetration. Based on estimates from random and fixed-effects regression models using multiple measures of world-system position, countries in the periphery and semi-periphery of the world economy have larger informal economies than core countries. More importantly, this difference in the development of the informal economy between the core, semi-periphery, and periphery is partially explained by the effects of international trade and foreign direct investment. Overall, the findings indicate that the development and persistence of the informal economy are driven by the structure of the world economy and processes of economic globalization.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.106
GPT teacher head0.373
Teacher spread0.267 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations42
Published2013
Admission routes1
Has abstractyes

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