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Record W4212897686 · doi:10.1093/wber/lhab024

The Intergenerational Effects of Economic Sanctions

2021· article· en· W4212897686 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

VenueThe World Bank Economic Review · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Sanctions and International Relations
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsSanctionsEconomic sanctionsEconomicsDemographic economicsPopulationControl (management)PoliticsDevelopment economicsEconomic growthPublic economicsPolitical scienceDemographySociologyLaw

Abstract

fetched live from OpenAlex

Abstract While economic sanctions are successful in achieving political goals, they can hurt the civilian population. These negative effects could be even more detrimental and long lasting for future generations. This study estimates the effects of economic sanctions on children’s education by exploiting the United Nations sanctions imposed on Iran in 2006. Using the variation in the strength of sanctions across industries and difference-in-differences with synthetic control analyses, this study finds that the sanctions decreased children’s total years of schooling by 0.1 years and the probability of attending college by 4.8 percentage points. Moreover, households reduced education spending by 58 percent—particularly on school tuition. These effects are larger for children who were exposed to the sanctions for longer. The results imply that sanctions have a larger effect on the income of children than their parents. Therefore, ignoring the effects of sanctions on future generations significantly understates their total economic costs.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score0.997

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.004

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 head0.023
GPT teacher head0.259
Teacher spread0.236 · 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