Jibonayan Raychaudhuri 
Associate Professor | University of East Anglia
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 Department of Economics, UEA. His research covers trade policies, as well as how the spending habits of consumers change towards the purchase of eco and organic groceries in times of recession. He uses an integrated approach that combines recent developments in political economy, financial economics and form theory with the existing literature in trade theory in order to explain the formation of trade policies and the key factors in trade flows.
His work in environmental economics – especially 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 (5)
Trade Policy
Spending Habits of Consumers
Economics
Product Valuation
Financial Economics
Accomplishments (3)
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
2013
Education (6)
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., Economics 2004
Jadavpur University: M.A., Economics 2000
Jadavpur University: B.A., Economics 1998
Links (3)
Media Appearances (1)
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.
Articles (5)
Ecolabels and The EconomicRecession
The School of Economics Discussion Paper Series2018 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.
The impact of credit constraints on exporting firms: Evidence from the provision and subsequent removal of subsidised credit
The World Economy2017 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.
The “new-new” trade theory: a review of the literature
International Trade and International Finance2016 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.
Effects of Carbon Reduction Labels: Evidence from Scanner Data
Economic Inquiry2015 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.
Economic assessment of the recreational value of ecosystems: methodological development and national and local application
Environmental and Resource Economics2014 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.
