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Record W2949448504

Essays on Access to External Finance, Acquisitions and Productivity

2013· article· en· W2949448504 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueASEP · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
FundersDivision of Graduate EducationSocial Sciences and Humanities Research Council of CanadaAkademie Věd České RepublikyGrantová Agentura České RepublikyUniverzita Karlova v Praze
KeywordsProductivityBusinessFinanceEconomicsMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

This thesis consists of three chapters that are empirical investigations of classical questions in the financial and industrial economics literature on the influence of institutions and industry conditions on the firm's access to finance, the propensity to merge, and productivity. In the first chapter, coauthored with Jan Bena, we examine whether financial markets development facilitates the efficient allocation of resources. Using European micro-level data for 1996-2005, we show that firms in industries with high growth opportunities use more external finance in financially more developed countries. This result is particularly strong for firms that are more likely to be financially constrained and dependent on domestic financial markets, such as small and young firms. Our findings are robust to controlling for technological determinants of external finance needs and to using different proxies for growth opportunities. In the second chapter, I investigate the role of productivity in the selection of firms into acquisitions and whether acquisitions lead to productivity gains. Using matching methodology and a large dataset of domestic acquisitions among public and private firms in Europe over the period 1998-2008, I find that first, targets are under-performing before engaging in horizontal...

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.998

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.059
GPT teacher head0.235
Teacher spread0.176 · 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