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Record W2310639459 · doi:10.1016/j.proenv.2016.03.039

An Exploratory Analysis of the Profitability of Small and Medium Firms Using Panel Data: The Case of the Greater Bucharest Metropolitan Area

2016· article· en· W2310639459 on OpenAlexaff
Călin Vâlsan, Elena Druică, Radu-Daniel Pintilii

Bibliographic record

VenueProcedia Environmental Sciences · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsBishop's University
Fundersnot available
KeywordsMetropolitan areaSample (material)Profitability indexPanel dataAggregate (composite)Financial crisisEconometricsPoint (geometry)BusinessRomanianEconomicsFinanceMacroeconomicsGeographyMathematics

Abstract

fetched live from OpenAlex

This study attempts to predict aggregate profits for small and medium Romanian firms using a relatively naïve model We use a dataset consisting of 4,519 observations spanning a period of eleven years, from 2001 to 2011 Each observation is obtained by aggregating the data associated with all small and medium firms that can be found for a given NACE and SIRUTA code in the greater Bucharest metropolitan area Our sample includes a number of more than 1,514 observations that correspond to firms with aggregate zero turnover and aggregate zero number of employees These are in fact shell companies, firms that are inactive, but somehow remained in the evidence of the Romanian Trade Register Office We split our sample into two distinct periods, using the 2008 financial crisis as the dividing point We fit a simple prediction model of aggregate total profits as a function of four variables, using the pre-financial crisis period We test the predictions of our model using the post-crisis period The results are imparting three important lessons First, by allowing shell companies in our sample, the prediction accuracy of our model appears to weaken Many surveys and economic policy studies conducted by the Romanian government take into account all companies in the evidence of the Trade Register Office, whether active or not We thus strongly recommend that policy initiatives be based solely on statistical surveys that include only firms in operation Second, we do not need very detailed information, a large number of explanatory variables, or a very sophisticated model in order to achieve a good prediction power Using only four variables, our naïve prediction model boasts an impressive out-of-sample R-square of almost 62% Third, the 2008 financial crisis that wreaked havoc in Western Europe and North America, represented a true tipping point for the economy of the greater Bucharest metropolitan area as well.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.080
GPT teacher head0.247
Teacher spread0.167 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

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