Cited by 41Global trade and economicsMerger and Competition AnalysisInternational Business and FDI

Bayesian learning in an infant industry model

Hoff, Karla·1997·Journal of International Economics

Integrity signals

Registry

Crossref · 10.1016/s0022-1996(97)00007-x

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Because Hoff's model is clearly more realistic, her results weaken the case for subsidizing an infant industry.

    “Because Hoff's model is clearly more realistic, it is notable that her results weaken the case for subsidizing an infant industry.”

    From Is There a Case for Industrial Policy? A Critical Survey (2006) · cites this paper as Hoff (1997)

Cited by

Sorted by citations

Authors
Hoff, K.
Affiliations
University of Maryland, College Park (US)
Corresponding author
Hoff, Karla
Venue
Journal of International Economics
Publisher
Elsevier BV
Published
1997-11
Volume
43
Issue
3-4
Pages
409-436
ISSN
0022-1996 (ISSN-L)
OpenAlex
W1993622991
Cited by
41
References
0
Citations by year
2023: 2 · 2021: 2 · 2018: 1 · 2017: 6 · 2015: 1 · 2014: 2 · 2013: 3 · 2012: 6
FWCI
1.11
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of International Economics (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Economics, Econometrics and Finance › General Economics, Econometrics and Finance › Global trade and economics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Merger and Competition Analysis
  • Social Sciences › Business, Management and Accounting › Strategy and Management › International Business and FDI
Keywords
infant industry argument, protectionism, imperfect information, barriers to entry, Bayesian learning, uncertainty, optimal subsidies, information spillovers

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review