Bibliographic record
Abstract
This study investigates the role of domestic and external shocks in business cycle fluctuations in Paraguay during 1991–2012. Time-series methods and a structural model-based approach are used to conduct an integrated analysis of business cycles. First, structural vector autoregression is used to assess the role played by external factors and domestic shocks in driving fluctuations in gross domestic product through impulse response functions and variance decompositions. The analysis finds that external shocks such as terms of trade, world interest rate and foreign demand account for over 50 percent of real gross domestic product fluctuations. Given Paraguay’s strong dependence on agriculture, an analysis is also done for the agricultural and non-agricultural sectors separately. The analysis finds that non-agricultural gross domestic product is to a large extent driven by external shocks, which account for over 50 percent of its volatility. In contrast, the volatility in agricultural gross domestic product is primarily due to shocks to domestic variables, mainly shocks to agricultural output. A further difference between the sectors is that shocks to government consumption are more important for agricultural gross domestic product, while shocks to the domestic real interest rate play a larger role in the volatility of non-agricultural gross domestic product. Second, the paper investigates the sources of business cycle fluctuations through the lens of a neoclassical growth model with an agricultural and non-agricultural sector. The analysis finds some signs of improvements, as labor market distortions have declined, firms’ access to credit improved, and agricultural efficiency rose over time. Nevertheless, challenges remain, as gaps in labor and capital returns between agriculture and non-agriculture remain large, efficiency in the non-agricultural sector shows no signs of improvement, and households’ access to finance has deteriorated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".