1. To study the comparative advantage in export of basmati and non-basmati rice from India
2. To analyse the change in direction of trade in export of basmati and non basmati rice from India
3. To workout the export projections of Indian basmati and non basmati rice to different destinations

Data: Study is based on secondary data on export of Indian Basmati and non-basmati rice over the period of 15 years (2008-09 to 2022-23).

Analytical techniques:

1. Revealed Symmetric Comparative Advantage (RSCA)

The Revealed Symmetric Comparative Advantage (RSCA) (Laursen, 2015 and Islam et al., 2021) index will be used to obtain the competitiveness of Indian rice export. It is an efficient and symmetric form of Balassa’s Revealed Comparative Advantage (RCA) index (Balassa, 1965). RSCA index is the best tool for analyzing comparative advantage and widely used (Laursen, 2015). The mathematical depiction of RSCA is as follows:                            

RSCA=(RCA-1) / (RCA+1)

The index lies between -1 to 1. If RSCA > 0 indicates that a country relishes a comparative advantage in the product that it exports, whereas RSCA < 0 indicates otherwise.

2. Markov chain model

            Annual export data of Indian basmati and non-basmati rice will be used for analysing the direction of trade and changing pattern of export. The trade directions will be analysed using the first order Markov chain approach. The lingo software will be adopted to study the transition probability matrix. Central to Markov chain analysis is the estimation of the transitional probability matrix ‘P’ whose elements, Pij indicate the probability of exports switching from country ‘i’ to country ’j’ over time. The diagonal element Pij where i=j, measures the probability of a country retaining its market share or in other words, the loyalty of an importing country to a particular country’s exports.

            Annual export data for the period 2008-09 to 2022-23 for the period of 15 years will be used to analyze the direction of trade and changing pattern of export of Indian basmati and non-basmati rice to different countries. The average exports to a particular country will be considered to be a random variable which depends only on the past exports to that country, which can be denoted algebraically as

                                  Ejt= …………………………(6)

Where,

Ejt= exports from India to the jth country in the year t

Eit-1 = exports of ith country during the year t-1

Pi j = the probability that exports will shift from ith country to jthcountry

ejt = the error term which is statistically independent of Eit-1

n = the number of importing countries

The transitional probabilities Pij, which can be arranged in a (c x n) matrix, have the following properties.

 And 0 £ P I j £ 1 ...................(7)

Thus, the expected export share of each country during period ‘t’ will be obtained by multiplying the exports to these countries in the previous period (t-1) with the transitional probability matrix. The probability matrix will be estimated for the period 2008-09 to 2022-23. Thus, transitional probability matrix (T) will be estimated using linear programming (LP) framework by a method referred to as minimization of Mean Absolute Deviation (MAD).

Min. OP* + I e

Subject to

X P* + V = Y

GP* = 1

P* ³ 0

Where

P* is a vector of the probabilities P I j

O is the vector of zeros

I is an appropriately dimensioned vector of export.

e is the vector of absolute errors

Y is the proportion of exports to each country.

X is a block diagonal matrix of lagged values of Y

V is the vector of errors

G is a grouping matrix to add the row elements of P arranged in P* to unity.

Using the estimated transitional probabilities, the exports of Indian basmati and non-basmati rice to various destinations will be predicted by multiplying the same with the respective shares of base year. The export shares to different countries will be predicted for the years 2023-24 to 2026-27 by using 2 step, 3 step, 4 step and 5 step transitional probabilities.