Economic Analyses of Livestock Marketing and Production in China
Notice bibliographique
Résumé
This dissertation comprises three interrelated essays on livestock marketing and production in China. In the first essay, the main objective was to propose and implement a procedure to estimate price’s budgetary and signaling effects on demand for goods in discrete choice experiments. A structural modeling procedure was developed and implemented to identify the double impact of price on utility and to guide the empirical approach. The application used data from an online survey of 622 Chinese consumers conducted in 2021. In addition to a choice experiment using beef brisket, the survey included questions about consumers’ perceived quality of the brisket. This study utilized a mixed logit model to explore consumers’ choice decisions and employed a panel linear regression model to estimate the price-quality relationship. Results show a positive and statistically significant effect of price on perceived product quality, suggesting that Chinese consumers use price as an indicator of quality, which might be related to uncertainty regarding beef brisket quality and distrust of the information presented on labels. However, this study found that the budgetary effect is greater than the signaling effect. Moreover, Chinese consumers are willing to pay premiums for overall quality improvements. These willingness to pay values would be smaller if consumers did not use price as a signal for quality. The second essay examined Chinese consumers’ preferences and willingness to pay for credence attributes of beef from the domestic market and beef from other large exporting countries. Three beef cuts were considered: steak, brisket, and tongue. Data was collected from an online survey incorporating choice experiments of 2,016 consumers from China in 2021. In the choice scenarios, each respondent was presented with three beef alternatives that differed in price, country of origin, food safety, and production certifications, and also included a “no purchase” option. Chinese consumers’ beef selections in the choice experiments were analyzed using a mixed logit model in willingness space. Results indicate the type of cut does not influence Chinese consumers’ evaluation of country of origin and credence attributes. Moreover, results show that Chinese consumers strongly prefer and are willing to pay more for domestic beef than imported beef products. Beef from New Zealand had the highest willingness to pay value among all the exporting countries included in the study, followed by Argentina, Australia, Canada, Uruguay, Brazil, and the United States. Also, enhanced food safety and Organic and Green Food certifications had positive willingness to pay values. Overall, the findings of this study offer evidence that Chinese consumers prefer safe and quality-assured beef products. The third and final essay examined the impacts of windfall income from picking and trading caterpillar fungus on pastoral households’ livestock production activities. Data was collected from a pastoral household survey (n=503) conducted in 2016 and 2017 in five Tibetan Autonomous Prefectures in China. This study employed propensity score matching procedures with the first nearest neighbor matching method to estimate the effect of participation in harvesting and trading the caterpillar fungus. The estimated results show that pastoral households with income from caterpillar fungus activities tend to maintain a smaller herd size, sell fewer animals for profit, slaughter more livestock for family consumption, and experience fewer livestock deaths compared to traditional pastoral households without caterpillar fungus income. Consequently, the new income source also decreases the grazing intensity. Moreover, the findings of this study provide evidence for promoting a more diversified and sustainable livelihood portfolio to decrease traditional pastoral households’ dependence on grasslands.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».