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Record W4416783258 · doi:10.3390/jrfm18120676

The Feldstein–Horioka Puzzle, a Global Glut of Savings, and Omitted Variable Bias: 1970–2023

2025· article· en· W4416783258 on OpenAlexvenueno aff
Jonathan E. Leightner

Bibliographic record

VenueJournal of risk and financial management · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Technological Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsVariable (mathematics)Capital (architecture)Focus (optics)Falling (accident)Developed countryOmitted-variable bias

Abstract

fetched live from OpenAlex

Feldstein and Horioka in 1980 estimated (I/GDP)i = α + β(S/GDP)i where “i” is for a given country over time, “I” is domestic investment, and “S” is domestic savings. Feldstein and Horioka found βs that were insignificantly different from one and significantly different from zero. According to Feldstein and Horioka, these results conflict with an assumption of perfect capital mobility because, if capital were perfectly mobile, then β should be zero. We estimated (I/GDP)it = α + β(S/GDP)it using data from 22 countries from 1970 to 2023, where i denotes the country and t denotes the year. We found βs to be significantly less than 1 but greater than 0. We then used Reiterative Truncated Projected Least Squares, which was designed to solve the omitted variable problem (and helps a researcher visualize the effects of heteroscedasticity), to estimate a βit for every observation. We find that βit decreases for countries that export capital and increases for countries that import capital. We argue that the Feldstein-Horioka “puzzle” is based on a confusion—when the effect of both exporting and importing capital is considered, β should equal approximately one. Feldstein and Horioka focus on single countries, but when pairs of savings exporters and importers are considered, their “puzzle” disappears. However, the fact that βit is now much less than 1 and falling over time suggests that a global glut of savings is worsening.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.324

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)0.0000.000

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 head0.014
GPT teacher head0.203
Teacher spread0.189 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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