Exploring the Roots of Small and Medium Enterprise Financing Issues in Myanmar
Bibliographic record
Abstract
Financial constraints are one of the top significant barriers for the growth and survivals of small and medium enterprises (SMEs). This study focuses on the causes of the SME financing issues in Myanmar before the covid-19 period from both demand side (SMEs) and supply side (banks). The study conducts a comparative analysis of SME financing between Myanmar and other ASEAN countries, as well as that of SMEs in comparison to large enterprises (LEs) within Myanmar. It utilizes firm-level data from the World Bank’s Business Environment and Enterprise Performance Survey (2014-2017) and country-level financial data from the Global Financial Development Database and Doing Business Survey (2010-2019) across eight ASEAN countries. The findings reveal that both Myanmar’s SMEs and its banking sector have internal weaknesses that hinder SME financing. Myanmar’s SMEs show weaknesses in key areas such as information and communication technology (ICT) skills, the use of audited financial statements, and export capabilities. Despite these shortcomings, Myanmar SMEs, particularly in the manufacturing sector, demonstrate growth potential in employment and innovation, like ASEAN SMEs. On the supply side, Myanmar’s banking sector shows inefficiencies, including high market concentration, low market stability, limited credit creation, and weak contract enforcement. These factors exacerbate collateral requirements for SMEs, further impeding their access to bank loans. As a results, Myanmar’s SMEs face more serious collateral challenges and lower financing opportunities than their ASEAN counterparts. To improve SME financing in Myanmar, policies must address the weaknesses on both the demand and supply sides.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".