Trade and investment liberalization, food systems change and highly processed food consumption: a natural experiment contrasting the soft-drink markets of Peru and Bolivia
Bibliographic record
Abstract
BACKGROUND: Free trade agreements (FTAs) can affect food environments and non-communicable disease risks through altering the availability of highly-processed foods. Few studies have quantified such effects. Using a natural experiment this paper quantifies changes in Peru's soft-drink market before/after entry into the US-Peru FTA, compared with Bolivia, a county with no such agreement. METHODS: Difference-in-difference models were used to test for between country differences in the rate of per capita foreign direct investment (FDI) inflows, soft-drink imports, the volumes of various soft-drinks sold, and the volumes of sugar from soft-drinks before/after FTA ratification (2006) and enforcement (2009). RESULTS: In Peru average per capita FDI-inflows rose from US$103.11 in the pre-ratification period to US$269.79 post-ratification, with little change in Bolivia. This corresponded with a 122 % increase in Peruvian soft-drink production. There was a significant between-country difference in FDI-inflows pre-/post-ratification (DID:1.07, 95 % CI:0.19-1.96; p = 0.01). Despite little difference in total per capita soft-drink sales volumes there was a significant between-country difference in per capita sugar from soft-drinks pre-/post enforcement (DID:-0.99, 95 % CI: -1.91-0.06; p = 0.03) with stagnated growth in Peru and continued growth in Bolivia. This resulted from stagnated sugar sweetened carbonates growth and increased bottled water, juice and sports & energy drinks growth in Peru, with continued carbonates growth in Bolivia. There was a significant between-country difference in per capita carbonates (DID: -1.44, 95 % CI: -2.52-0.36, p = 0.01) and bottled water (DID:0.63; 95 % CI: -0.01-1.26; p = 0.04) sales volumes. CONCLUSIONS: The FTA may have resulted in increased FDI-inflows and soft-drink production and also contributed to the diversification of soft drinks produced and sold in Peru with some positive (stagnated carbonates and increased bottled water) and some negative (increased juice and sports & energy drinks) implications for nutrition. These changes were not evident in Bolivia. These results should be interpreted cautiously given the study design limitations.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".