The Supply and Demand of Ozempic in America: An Economic Analysis
Bibliographic record
Abstract
Under the direction of Dr. Pablo Hernandez This research project examines the supply and demand dynamics of Ozempic, a diabetes medication that has seen a dramatic increase in demand due to its off-label use for weight loss. This study provides a real-world application of key economic principles, particularly the law of supply and demand, price elasticity, and market equilibrium. Ozempic’s demand has surged due to increased consumer interest, largely driven by social media and weight loss trends. However, supply has not kept pace with changes in demand resulting in shortages and rising prices. This imbalance illustrates how changes in demand influence market conditions. These conditions demonstrate concepts such as inelastic supply, price adjustments, and government intervention in markets. In the U.S., demand plays a particularly strong role in determining price because the pharmaceutical market operates with less price regulation than in other countries. Unlike nations with government-imposed price caps or negotiation systems, the U.S. allows companies to set prices based on market conditions. In contrast, countries like Canada or those in the European Union have introduced price controls that limit how much demand can drive up costs. By analyzing Ozempic through the lens of supply and demand, this research connects classroom economic theories to real-world market behavior, showing how fundamental economic principles shape industries and consumer access to goods, particularly in the uniquely structured U.S. pharmaceutical market.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".