Misallocation and Productivity: Micro Evidence from Bangladesh
Bibliographic record
Abstract
An important determinant of aggregate measured productivity is how resources are allocated across heterogeneous production units. Idiosyncratic distortions from institutional policies and factors can be a source for resource misallocation resulting in lower total factor productivity and aggregate output. Distortions create heterogeneity in production units: cause before-tax marginal revenue products to be higher in production units that face disincentives, and to be lower in production units that receive incentives. In the absence of distortions, production units equate marginal products with their corresponding factor prices, making resource allocations efficient because the more productive units proportionately use more resources. In the presence of distortions, they are equated with both factor prices and distortions, making equilibrium allocations dependent on both individual TFPs and distortions and resulting in aggregate output and TFP losses. \nUsing detailed household farm-level data from Bangladesh, I measure the observed gross TFP of Bangladesh's agriculture. I find that capital and intermediate inputs are misallocated in Bangladesh. If resources were hypothetically reallocated across farms, then aggregate TFP could increase by more than 120% relative to the observed TFP. Using firm-level manufacturing data from Bangladesh, I develop a model to measure industry-level and aggregate TFP. If allocations were efficient, then aggregate TFP could increase by 95%. Capital is more misallocated than labor in manufacturing. \nI develop a two-sector model of agriculture and non-agriculture, each with an endogenous distribution of production units. Sector-wise, the distribution of active production units depends on the productivity of the unit operation and idiosyncratic distortions that the unit faces in that sector. I capture idiosyncratic distortions as a producer-sector-specific output tax that stands in as a catch-all for the policies and institutions that alter the relative prices faced by producers within each sector. I use micro-level data on manufacturing plants and farms and a quantitative framework to measure the distortions. I calibrate my model to the micro data from Bangladesh with observed distortions. I find that eliminating distortions in each sector raises productivity in that sector, but at the aggregate level it is only improvements in agricultural labor productivity that generate substantial structural change in the economy.
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 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) | 0.147 | 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".