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Record W4407876071 · doi:10.7202/1116491ar

Expanding Economic Base Theory to informal and non-monetary income: Evidence from the State of Bahia, Brazil

2025· article· en· W4407876071 on OpenAlexvenueno aff
Ludovica Milano, Magali Talandier

Bibliographic record

VenueCanadian Journal of Regional Science · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsState (computer science)Base (topology)Computer science

Abstract

fetched live from OpenAlex

In this paper, we discuss an operational framework to quantify and analyze regional economic development through the application of economic base theory. This theory asserts that regional development hinges not only on capturing income from outside the region (the basic sector), but also on the region’s ability to retain this income and circulate it locally for community benefit (in the non-basic sector). Our study focuses on Bahia, the largest state in Brazil’s Northeast Region, with a particular emphasis on its regiões geográficas imediatas (commuting zones). The study’s original contribution is to apply the economic base model in a Global South context using income data rather than employment data as a proxy. The available Brazilian data allow us to include in our analysis income flows that are generally overlooked in mainstream economics and entirely omitted from applications of this theory in the Global North. The findings underscore the pivotal role of informality, non-monetary income, and transfers, prompting further examination of regional income and its diverse sources in economic development analysis. Based on our results, we argue for a broader understanding of territorial economies as complex systems of socially embedded practices. This perspective calls for regional studies to shift away from the prevailing productivist viewpoint and toward a more holistic approach that embraces the diversity and richness of territorial economies.

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)0.003
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 score0.162
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.243
Teacher spread0.223 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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