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Record W4232648162 · doi:10.18374/ijsm-13-1.8

PRODUCTIVITY AND EFFICIENCY OF MICRO ENTERPRISES IN BANGLADESH: RELATIVE IMPORTANCE OF THE CONSTRAINTS

2013· article· en· W4232648162 on OpenAlexaff
Belayet Hossain

Bibliographic record

VenueInternational Journal of Strategic Management · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsProductivityConstraint (computer-aided design)EconomicsInvestment (military)Production (economics)EfficiencyProduction–possibility frontierAgricultural economicsLabour economicsEconometricsMicroeconomicsMacroeconomicsStatisticsMathematics

Abstract

fetched live from OpenAlex

The study attempts to analyze the effects of primary constraints on the productivity and efficiency of the microenterprises in Bangladesh to identify the relative importance of each constraint. An expanded form of stochastic production frontier model has been developed to address both productivity and efficiency issues simultaneously. Household Income and Expenditure Survey (2005) data, collected by Bangladesh Bureau of Statistics are used in the estimation. The results clearly reveal that of the four primary constraints faced by microenterprises in Bangladesh, credit and utility have the highest detrimental effects on both productivity and efficiency. Between credit and utility, the adverse effect of utility constraint is found to be more than that of credit suggesting that policy makers need to address the utility constraint first to improve the productivity and efficiency of the microenterprises in Bangladesh. The output elasticity is estimated to be the highest for capital, which indicates that there is under-investment in microenterprises. Firm's specific efficiency score vary significantly from 0.34 to 0.94 with a mean of 0.69. The study demonstrates clear policy suggestions about what to be done to improve productivity and efficiency of the microenterprises in Bangladesh.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.221

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 communication0.0000.000
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 head0.031
GPT teacher head0.232
Teacher spread0.201 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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