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Impact of religious participation, social interactions and globalisation on meat consumption: evidence from India

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Abstract

From both health and environmental policy perspectives, it is advisable to ensure that individuals maximise 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 is rapidly changing. In this paper, we hypothesise that social interactions and globalisation are two factors that explain this shift in consumption behaviour, especially amongst Hindu households. These hypotheses are based on the theoretical findings of Levy and Razin (2012). The empirical results show that Hindus that are members of religious groups are less likely to eat meat than non-member Hindus, whereas Hindus that are members of non-religious types of groups are more likely to eat meat than non-members. 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 globalisation on social and religious norms, consumption behaviour, and ultimately, on climate change.

Suggested Citation

  • Massimo Filippini & Suchita Srinivasan, 2018. "Impact of religious participation, social interactions and globalisation on meat consumption: evidence from India," CER-ETH Economics working paper series 18/304, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
  • Handle: RePEc:eth:wpswif:18-304
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    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. 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).
    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).
    6. Bajan, Bartłomiej & Piechocka, Magdalena, . "Economic Development and Diet Composition: Cross-Continental Insights into Bennett's Law," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 17(2).

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    Keywords

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    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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