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Impact of religious participation, social interactions and globalization on meat consumption: Evidence from India

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  • Filippini, Massimo
  • Srinivasan, Suchita

Abstract

From both health and environmental policy perspectives, it is advisable to ensure that individuals maximize the nutritional gains from eating meat, without having a significantly adverse environmental impact, i.e. sustainable meat consumption pathways are imperative. This is especially true for developing countries, where rising incomes and growing populations have meant that meat consumption has also risen. India is an example of a country where a large share of the population has been vegetarian due to religious and cultural factors, although this has changed in recent times. In this paper, we mainly focus on Hindu households, who are the religious majority in India, and we hypothesize that social interactions and globalization could be two factors that explain this shift in consumption behavior for them. These hypotheses are based on the theoretical findings of Levy and Razin (2012). The empirical results show that membership in religious groups among Hindu households is likely to lead to a lower likelihood of them eating meat. On the other hand, membership in groups of a non-religious nature for Hindu households implies a greater likelihood of them eating meat. We also find that Hindu households that frequently use sources of media such as newspapers, the radio or television are more likely to consume meat compared to Hindus that do not. This paper provides important policy implications, both in terms of the formulation of Nationally Recommended Diets in developing countries, and in terms of identifying the channel of influence of both social networks and globalization on social and religious norms, consumption behavior, and ultimately, on climate change.

Suggested Citation

  • Filippini, Massimo & Srinivasan, Suchita, 2019. "Impact of religious participation, social interactions and globalization on meat consumption: Evidence from India," Energy Economics, Elsevier, vol. 84(S1).
  • Handle: RePEc:eee:eneeco:v:84:y:2019:i:s1:s0140988319303457
    DOI: 10.1016/j.eneco.2019.104550
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    Cited by:

    1. de Miguel, Carlos & Filippini, Massimo & Labandeira, Xavier & Labeaga, José M. & Löschel, Andreas, 2019. "Low-carbon Transitions: Economics and Policy," Energy Economics, Elsevier, vol. 84(S1).
    2. Lavinia Scudiero & Mehroosh Tak & Pablo Alarcón & Bhavani Shankar, 2023. "Understanding household and food system determinants of chicken and egg consumption in India," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 15(5), pages 1231-1254, October.
    3. Tan-Soo, Jie-Sheng & Li, Jun & Qin, Ping, 2023. "Individuals' and households' climate adaptation and mitigation behaviors: A systematic review," China Economic Review, Elsevier, vol. 77(C).
    4. Yugang He & Jingnan Wang & Baek-Ryul Choi, 2021. "Religious Participation: Does It Matter for Sustainable Culture and Entertainment Consumption?," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    5. Srinivasan, Suchita & Carattini, Stefano, 2020. "Adding fuel to fire? Social spillovers in the adoption of LPG in India," Ecological Economics, Elsevier, vol. 167(C).

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    More about this item

    Keywords

    Meat consumption; Religious norms; Social interactions; Globalization; India;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy; Animal Welfare Policy
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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