Modeling Firm Dynamics to Identify the Cost of Financing
Bibliographic record
Abstract
Economic development requires the growth of productive …rms. However, …nancing constraints may limit …rms’investment abilities. This paper estimates the cost of …nancing constraints to …rms, for example in terms of idle investment opportunities, and their aggregate implications. To this end, I develop and estimate a dynamic model of …rm-level investment. The model allows me to deal with the main identi…cation problem faced by work that studies …nancing constraints, namely to identify the investment opportunities and the constraints of a …rm separately. The model also allows for other potential explanations of the observed phenomenon, in particular adjustment costs and uncertainty. I solve the model using dynamic programming methods and estimate it via simulation methods, using …rm level data from Ghana. Counterfactual analyses are then carried out to quantify the importance of …nancing constraints. These counterfactuals indicate that removing the constraints would imply economically signi…cant increases in investment that are associated with higher levels of consumption. I am grateful to Steve Berry, Chris Timmins, and Chris Udry for advice throughout this project. I also would like to thank Pat Bayer, Sigga Benediktsdottir, Hanming Fang, Garth Frazer, Nicola Fuchs-Schündeln, George Hall, Ethan Ligon, John Rust, and participants at several workshops for very helpful comments. I thank Cletus Kosiba
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".