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.
M.A., Mathematical Behavioral Sciences
M.Phil. (Coursework completed), Economics
Science Daily online
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.view more
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.
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.
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.
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.
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.