Decoding Global Tea Trade: Unveiling Market Dynamics in India
D. Murugananthi
Directorate of Agribusiness Development, Tamil Nadu Agricultural University, Coimbatore-641 003, India.
M. Priyadharshini
Department of Agricultural and Rural Management, Tamil Nadu Agricultural University, Coimbatore- 641 003, India.
S. Aruna Prabha *
Department of Agricultural and Rural Management, Tamil Nadu Agricultural University, Coimbatore- 641 003, India.
*Author to whom correspondence should be addressed.
Abstract
Tea is the second important beverage consumed Worldwide after water. Domestic consumption of tea in India had increased from 346 M kg in 1980 to 1135 M kg in 2020. The price of tea in global market keeps wavering due to demand and supply dynamics, labour problem, market competition, pandemic and so on. Tea is one of the Internationally traded commodity and price is the key variable which links the markets globally. If countries are linked by trade in a free market regime, global demand and supply shocks will have an equal impact on the domestic and international prices. Hence, the present study analysed the price integration of tea among the three different markets globally. The major tea markets taken for the study are Kenya (Mombasa), North India (Kolkata) and South India (Coonoor - CTTR) markets. To study the price variation seasonal decomposition was used. ADF test was used to assess the stationarity, Johansen cointegration and VECM model was used to analyse the long term cointegration among the markets. Granger causality was used to assess the direction of flow of information across markets. All the price series are first difference stationary. The study revealed that the Kolkata market played a lead role in determining the prices. The presence of one co- integration equation among auction prices of tea markets and indicated the presence of long run equilibrium relationship among the auction markets.
Keywords: Tea, price integration, seasonal decomposition, augmented dickey fuller test, johansen cointegration, granger causality, vector error correction model (VECM)