The Causal Relationship of Microfinance and Economic Development: Evidence from Transnational Data
Bibliographic record
Abstract
The purpose of this study is to investigate the so far underexamined statistical causality of the relationship between microfinance and economic development. For a representative transnational dataset covering the period 1995 - 2012 we instrumentalize pairwise vector autoregressive (VAR) estimation models and the Granger approach. We utilize prevalent microfinance institutions’ (MFI) performance indicators as measures of microfinance as well as relevant economic development indicators that not only measure economic and capital growth but also poverty, income inequality and labor participation. We find bidirectional causal interactions between both MFIs’ social and financial performance and economic development. Based on our results important implications for microfinance theory, research and practice can be derived. Future empirical research should account for the statistical causality between microfinance and economic development. In practice, purposeful and progressive action that considers the directions of causality between microfinance and economic development verified within our study should be taken to promote economic growth and poverty alleviation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".