The Effects of Tajikistan’s Accession to the Common Economic Space of Belarus, Kazakhstan, and Russia on Its Agricultural Sector Under Official and Depreciated Exchange Rates
Bibliographic record
Abstract
Tajikistan is planning to join the Common Economic Space (CES) of Belarus, Kazakhstan, and Russia in order to benefit from free movement of capital, goods, especially the labour force. Labour migrants abroad are equivalent to 15 percent of country’s population, 28 percent of the labour force, and 50 percent economically active population. Migrant remittances in Tajikistan are the main drivers of economic growth that induces currency appreciation. This paper, using a Partial Equilibrium Model, assesses the effects of Tajikistan’s accession to the CES on the country’s agricultural sector at official and depreciated (experimental) exchange rates. The latter enables testing the Marshall-Lerner Condition (MLC) and are calling to emphasize the cost of remittances-induced currency appreciation to producer, consumer, state budget, agricultural value added and balance of trade. The overall welfare effects of CES accession on the agricultural sector are positive under both exchange rates. Producer and overall gain at depreciated exchange rate exceeds the gain at official exchange rate by 34 percent, while consumers gain almost 2.9 times less under depreciated exchange rate. Budget losses are 15 percent less at depreciated exchange rate rather than the official one. Producer and consumer gains, under both exchange rates, prevail over budget losses, therefore an overall gain will be ensured. The value of agricultural production in scenario, ceteris paribus, almost two times less at official exchange rate (14 percent) than under the depreciated one (27 percent). Furthermore, the deterioration balance of trade is smaller at depreciated exchange rate, thus, the MLC is met.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".