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Record W2006604352 · doi:10.1016/j.polsoc.2014.03.002

The political economy of foreign direct investment—Evidence from the Philippines

2014· article· en· W2006604352 on OpenAlexaff
Jeffrey Drope, Jenina Joy Chavez, Raphael Lencucha, Benn McGrady

Bibliographic record

VenuePolicy and Society · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsMcGill University
FundersJohns Hopkins Bloomberg School of Public HealthBloomberg PhilanthropiesJohns Hopkins University
KeywordsExciseTax policyMultinational corporationForeign direct investmentPoliticsEconomicsLegislatureEconomic policyCorporate governanceTax reformInvestment (military)Order (exchange)Corporate taxMarket economyBusinessTax avoidancePolitical scienceFinanceMacroeconomics

Abstract

fetched live from OpenAlex

Abstract Much of the conventional wisdom about the political economy of foreign direct investment suggests that many developing country governments lower regulatory and/or legislative standards in order to woo potential investors. Using the case of the tobacco industry's efforts to influence excise tax policy reforms in the Philippines, we find a much more complex reality. Over a period of more than 15 years of concerted efforts and significant financial investment, a large multinational tobacco firm was consistently unable to realize its tax policy goals with serious, negative implications for the firm. In the most recent major policy confrontation over excise tax reform that led to one of the largest tax increases on tobacco products ever in a developing country, a number of major variables mitigated the powerful firm's influence. These variables included strong support for tax reform from a number of influential political actors and a well-organized civil society movement, which led to broader public support for both public health and fiscal reasons. Global governance around economic policy and the effects of domestic institutional structures also had marked effects on the outcomes.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.058
GPT teacher head0.263
Teacher spread0.204 · 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 designTheoretical or conceptual
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

Citations12
Published2014
Admission routes1
Has abstractyes

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