Jibonayan Raychaudhuri profile photo

Jibonayan Raychaudhuri

Associate Professor

Norwich, UNITED KINGDOM

His research covers trade policies, as well as how the spending habits of consumers change towards the purchase of eco & organic groceries.

Biography

Jibonayan Raychaudhuri is Associate Professor in the School of Economics, UEA. His research covers trade theory, economic valuation, the effectiveness of eco labels and the spending habits of consumers on eco-labeled products.

His work on product valuation and labelling is conducted with the Environmental and Resource Economics Group at the University of Manchester, where Jibonayan is a research affiliate with the Sustainable Consumption Institute.

Areas of Expertise

Environmental EconomicsInternational EconomicsDevelopment EconomicsFinancial Economics

Accomplishments

University wide Transforming Teaching Award (nomination)

2015

Economic Society Award (nomination)

2015

Award from the Innovations for Poverty Action, Small and medium Enterprises (SME) Initiative

2015

Emerald Literati Network Outstanding Paper Award for Excellence

2012

Phi Beta Kappa Alumni International Graduate Scholarship Award for Academic Excellence

2008

Education

University of California, Irvine

Ph.D., Economics

2009

University of California, Irvine

M.A., Mathematical Behavioral Sciences

2009

University of California, Irvine

M.A., Economics

2008

I.G.I.D.R., Mumbai

M.Phil. (Coursework completed), Economics

2004

Jadavpur University

M.A., Economics

2000

Jadavpur University

B.A., Economics

1998

Media Appearances

Research investigates impact of carbon footprint label

Science Daily  online

2015-10-26

Co-author Dr Jibonayan Raychaudhuri, of UEA's School of Economics, said if consumers are willing to pay more for carbon labelled, or low carbon footprint, goods -- and by doing so contribute to the public good -- there is an incentive for firms to lower the carbon footprint of their products, label them accordingly and charge a higher price.

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Articles

Ecolabels and The EconomicRecession | The School of Economics Discussion Paper Series

2018


We consider products that vary in socio-economic quality, reflected in different eco-labels, like carbon, organic and fair trade. We find that in violation of traditional price theory, the expenditure shares on organic products declined while the expenditure shares of fair trade products increased.

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The impact of credit constraints on exporting firms: Evidence from the provision and subsequent removal of subsidised credit | The World Economy

2017

We study the causal impact of credit constraints on exporters using a natural experiment provided by two policy changes in India, first in 1998 which made small‐scale firms eligible for subsidised direct credit, and a subsequent reversal in policy in 2000 wherein some of these firms lost their eligibility.

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The “new-new” trade theory: a review of the literature | International Trade and International Finance

2016

We review the literature on the so-called “new-new” trade theory models starting with the pioneering work by Melitz (Econometrica, 71(6):1695–1725, 2003). We review some of the empirical work that motivated the development of these “new-new” trade theory models.

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Effects of Carbon Reduction Labels: Evidence from Scanner Data | Economic Inquiry

2015

We investigate the effects of carbon reduction labels using a detailed scanner data set. Using a difference‐in‐differences estimation strategy, we find that having a carbon label has no impact on detergent prices or demand. We also investigate possible heterogeneous effects of carbon labels using the synthetic control method.

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Economic assessment of the recreational value of ecosystems: methodological development and national and local application | Environmental and Resource Economics

2014

We present a novel methodology for spatially sensitive prediction of outdoor recreation visits and values for different ecosystems. Data on outset and destination characteristics and locations are combined with survey information from over 40,000 households to yield a trip generation function (TGF) predicting visit numbers.

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