export of rice from india: performance and determinants · determined by various factors and...

Agricultural Economics Research ReviewVol. 29 (No.1) January-June 2016 pp 135-150DOI: 10.5958/0974-0279.2016.00026.4

Export of Rice from India: Performance and Determinants§

Anup Adhikari, M.K. Sekhon* and Manjeet KaurDepartment of Economics and Sociology, Punjab Agricultural University, Ludhiana-141004, Punjab

Abstract

The study has examined the growth performance and identified determinants of rice exports from Indiawith special reference to basmati rice during the period 1980-81 to 2012-13. Compound growth rate,instability index and Markov chain analysis, were applied to estimate trend, and instability and theproject export to different countries. The time series data were made stationary before estimating thedeterminants of Indian rice export. The study has observed that rice contributed substantially to thenational income during the study period. The higher growth observed in value of basmati rice export(15.87%) was due to higher growth in unit value than quantity of export (7.55%) during 1980-81 to2012-13. The growth rate of unit value of rice export was higher in period I (13.48%) than period II(5.06%). The growth rates in export of non-basmati rice in terms of quantity, export earnings and unitvalue were 10.87 per cent, 17.74 per cent and 6.20 per cent, respectively during the study period. Theinstability index has been found highest for quantity (43.37 %) in case of basmati rice and value (141.36%)in case of total rice during the entire period. The UAE has been found to be a highly preferred market forIndian basmati rice and Nigeria for Indian non-basmati rice, as indicated by the probability of retentionof their previous shares. The study has projected that during 2013-14 the major markets for Indian basmatirice would be Iran and Saudi Arabia, whereas for Indian non- basmati rice, the major markets would beNigeria and South Africa. The estimated regression model has shown that export price, internationalprice, lagged production, domestic consumption, and exchange rate are the major determinants of riceexport from India. In order to sustain in the international market, Indian export price needs to be competitivebesides improvement in quality and sanitary standards.

Key words: Rice, export destinations, performance, instability index, Markov Chain Analysis, exportdeterminants

JEL Classification: F17, Q17

IntroductionInternational trade plays an important role in the

economic development of a country. The participationof India in international trade is largely confined toprimary products, especially of the agricultural sector.Indian trade policy for agricultural commodities isguided by the twin objectives of ensuring national food

security and building export markets for enhancing thefarmer’s income. In 2011, India was on the tenthposition in agriculture and food exports in the worldand had 2.07 per cent share in global export ofagricultural and food products. Rice is one of the mostimportant exportable agricultural commodities fromIndia. The steady increase in production, availabilityof buffer stock and rising demand for basmati rice inthe international market has helped India in increasingrice export. In 1990-91, India was able to export only505 thousand tonnes rice with a total values of ̀ 459.63crore and this amount increased to ̀ 33858.19 crore in

*Author for correspondenceEmail: [email protected]

§ This paper is drawn from the M.Sc. thesis of the first authorsubmitted to the Department of Economics and Sociology,Punjab Agricultural University, Ludhiana

136 Agricultural Economics Research Review Vol. 29 (No.1) January-June 2016

2012-13. The major markets for export of Indian ricewere: United Arab Emeritus, Saudi Arabia, Iran,Kuwait and Iraq (for basmati rice).

The new multilateral trade regime coupled withthe policy changes adopted by the most nations aimingtowards globalization, provided new opportunities aswell as posed several challenges for expanding tradein agricultural products. India too has been able toderive significant benefits from the changed globalenvironment. Since global trade environment is highlydynamic and export is influenced by both micro andmacro policies, it is important to understand theproduct-specific dynamics to improve exportperformance of agricultural commodities. The globalmarkets for Indian rice are highly dynamic and thebarriers to trade are being lowered gradually all aroundthe world (Singh, 2001). The export of rice is alsorelated with the buffer stock of rice held by thegovernment. Because of comfortable buffer stock, Indiabecame a major exporter of rice in 2012. There is astrong demand for Indian rice in the internationalmarkets. The increasing consumer demand for rice andIndia’s strength for production of basmati as well asnon-basmati rice, coupled with liberal export policy,and large public stock have created ample scope forrice export. In recent years, the African countries havealso shifted to Indian non-basmati rice because of pricecompetitiveness (Chandrashekhar, 2013).

The future performance of any product ininternational markets can be judged in the light of itspast performance. Therefore, evaluation of pastperformance of rice is necessary to work out its exportpotential, challenges and opportunities. The presentstudy has analysed major markets for Indian rice, interms of its future share. Rice export from India isdetermined by various factors and therefore, reliableestimates of determinants of export are essential forthe formulation of appropriate policies. The presentstudy has examined the growth performance andinstability of rice, important destinations, withprojections for export in future and has identified thefactors affecting export of rice.

Data and MethodologyThe study is based on the time series data on export

quantity, value and unit value which were compiledfrom various sources for a period of 33 years (1980-

81 to 2012-13). For analysis, the total period was sub-divided into two periods, viz. Period-I: Pre-WTOperiod (1980-81 to 1994-95); and Period-II: Post-WTOperiod (1995-96 to 2012-13). The division of periodwas based on the assumption that liberalization as wellas formation of WTO helped in boosting up rice export.The data on export quantity and value were collectedfrom Agricultural Statistical Compendium, andAPEDA. Similarly, data on production, import, stock,and international price of rice were collected fromvarious sources like indiastat.com website, hand bookof Indian Economy, pink sheet of World Bank, etc.The global data on exports and imports were collectedfrom various issues of Rice Year Book, USDA websites.To analyze trend, the compound growth rates for exportof basmati rice and total rice in terms of quantity, valueand unit value realizations were worked out for theboth sub-periods and the overall period. The InstabilityIndex was computed using Cuddy-Della Valle Index,I = CV *(1-R2)0.5(Singh and Byerlee, 1990), where,CV is the coefficient of variation and R2 is the correctedcoefficient of determination of the log linear function.

The trade directions of export were analyzed usingthe first order Markov chain approach. Central to theMarkov chain analysis is the estimation of transitionalprobability matrix Pij. The elements Pij of the matrix Pindicates the probability that export will switch fromthe ith country to the jth country with passage of time(Dent, 1967; Lee et al., 1970; Gillet, 1976). Thediagonal elements of the matrix measure the probabilitythat the export share of a country will be retained.Hence, the examination of the diagonal elements willindicate the preference of an importing country for aparticular country’s exports.

In the context of the present study, the structuralchanges were treated as a random process with selectedimporting countries. The average export to a particularcountry was considered to be a random variable whichdepended only on the past exports to that country, andcan be denoted algebraically by Equation (1):

…(1)

where, Ejt denotes exports from India to the jth countryduring the year t, Eit-1 denotes exports from India tothe ith country during the period t-1, Pij is the probabilitythat exports will shift from the ith country to the jth

Adhikari et al. : Export of Rice from India: Performance and Determinants 137

country, ejt is the error- term which is statisticallyindependent of Eit-1, t is the number of years consideredfor the analysis, and r is the number of importingcountries. The transitional probabilities Pij which canbe arranged in a (c x r) matrix have the followingproperties:

O ≤ Pij ≤ 1

The minimum absolute deviation (MAD)estimation procedure was employed to estimate thetransitional probability, which minimizes the sum ofabsolute deviations (Fisher, 1967; Wagner, 1959). Theconventional linear programming technique was used,as this satisfies the properties of transitionalprobabilities of non-negativity restrictions and row sumconstraints in estimation. The linear programmingformulation is stated as:

Min OP* + Ie

Subject to, XP* + V = Y, GP* = 1, P*e ≥ 0

where,

0 = Vector of zeroes,P* = Vector in which probability Pij is arranged,I = Appropriate dimensioned column vector of unit,e = Vector of absolute error (|U|),Y = Vector of export to each country,X = Block diagonal matrix of lagged values of Y,V = Vector of errors, andG = Grouping matrix to add the row elements of P as

arranged in P* to unity.

After calculating the transitional probabilitymatrix, the expected shares of export were calculatedby Equation (2):

(j=1,2,3…, r) …(2)

where, Yjt is the predicted proportions of the jthcountry’s share at time ‘t’, Yt-1 is the observedproportion of the ith country’s share at time ‘t-1’, andPij is the estimated transitional probability matrix.

Thus, the expected export shares of each countryduring period ‘t’ were obtained by multiplying the

export to these countries in the previous period (t-1)with the transitional probability matrix. Multipleregression was carried out, using ordinary least square(OLS) estimation procedure, in the statistical softwareE-Views.

Determinants of Rice Exports

The factors affecting the export of rice wereidentified using log-log linear type of function:

lnQT = b0 +b1 lnEXP+b2 lnINT +b3 lnLGP+b4 lnDMC+b5lnEXC + µ …(3)

where, QT= Total rice export from India (’000 t), EXP= Export prices (`/t), INT= International price (Thai5% $/t), LGP= Lagged production of rice (milliontonnes), DMC = Domestic consumption of rice (’000tonnes), EXC = Exchange rate with dollar (`/$), µ =Error-term, and b1,……,b5 are the regressioncoefficients and b0 is a constant.

The export prices and international prices for ricehave been represented by their respective unit values.The monthly unit value of international price of ricewas derived from the international price given by pinksheet of World Bank and was converted according tothe fiscal year. The international price of rice isgenerally represented by the Thai rice, fob Bangkok.Due to non- availability of data on domesticconsumption, it was computed as per expression (4):

Domestic consumption = Production + Import +Stock change – Export

… (4)

While dealing with time series data, the first stepis to check whether the underlying time series isstationary or not. In a time series, the set of possiblevalues at a particular point of time’t’ is denoted by Yt,and a time series is denoted by {Y (t), t∈ T}. Forstationary, Yt must fulfill the following threecharacteristics:

(i) E(Yt) = µ ( i.e. Mean is constant)

(ii) Var(Yt)= E(Yt- µ)2 = σ2 (i.e. Variance is constant)

(iii) ρk =E[(Yt - µ) (Yt+k - µ] (i.e. Covariance isconstant)

To check unit root in the data, augmented Dickey-Fuller (ADF) test was used adopting the regressionEquation (5):


…(5)

where, εt is the white noise error, ∆Yt-1 = (Yt-1 – Yt-2),where ∆ represents the first difference, m representsthe number of lagged difference. These lags areincluded to make error- term white noise in aboveequation. β1 is the intercept and ‘t’ represents time trend.The null hypothesis of ADF is H0: δ=0; There is unitroot, and the alternative hypothesis is H1: δ<0; Thereis no unit root.

CUSUM Test and CUSUM of Squares Test

The CUSUM test is based on the cumulative sumof the equation errors in regression. The E-Viewsrepresent graphically the cumulative sum of errorstogether with critical lines of 5 per cent. The equationparameters are not considered stable if the whole sumof recursive errors gets outside the two critical lines.CUSUM of Squares test is calculated similarly andinterpreted as CUSUM test, with the difference thatinstead of recursive errors, we use recursive doublederrors.

Results and Discussion

Scenario of World Rice Trade

India ranks second in the production of rice afterChina, and accounted for 22.12 per cent of global riceproduction in 2012-13. The international rice marketis characterized by high volatility with inelastic supplyand demand. The global trade of rice is very low incomparison to its production. Only seven per cent riceproduction is traded and global rice exports are highlyconcentrated with the top five exporters accounting for85 per cent of global net trade (Wailes and Chavez,2012).

The global export of rice increased from 11.70 Mtin 1990 to 38.66 Mt in 2013. Although, China and Indiatogether accounts for half of the world rice production,Thailand was a major player till 2011 on rice exportfront. When the Government of India removed ban onexport of non-basmati rice in September, 2011, Indiaemerged as a leading exporter of rice with market shareof 25.73 per cent, followed by Vietnam (19.38 %) andThailand (17.45 %) in 2012. India’s share in worldexport of rice has increased from 25.73 per cent in 2012to 27.16 per cent in 2013. The other exporters such as

Vietnam and Thailand shared 17.59 per cent and 17.33per cent, respectively in the world export of rice. Thecountry-wise rice export and imports, given inAnnexure I, show that India was a major exporter andChina was the largest importer of rice in the world,followed by Nigeria and Iran during the year 2013.

The international rice market is quite thin andoligopolistic in nature. The entry of a big player likeIndia can affect the world price significantly(Bhattacharyya and Pal, 2000). Protectionist measurestaken by the Indian government during 2008 like banon export of non-basmati rice and imposition of exporttariff on basmati rice to increase domestic supply andlower domestic price reduced the rice supply in worldmarkets. The period from mid-2007 to mid-2008 isreferred to as world rice crisis period, which resultedin rise in world price of rice. Deliberated trade policybased on scientific facts and figures will retain India’smarket share in the global rice trade.

India’s Production and Exports of Rice during1980-81 to 2012-13

India has been exporting substantial quantities ofrice to various parts of the world since 1980-81. In2012-13, the total production of rice was around 104Mt, of which around 10.1 Mt was exported (Figure 1).

In 1980-81, the total quantity of rice (basmati +non-basmati) exported from India to different countrieswas 7.27 lakh tonnes, which rose to 49.1 lakh tonnesin 1995-96. After a lot of fluctuations, the export oftotal rice started increasing again from 2009-10 and itreached 101.5 lakh tonnes in 2012-13. There was notmuch earning from exports of rice during 1980s and1990s but it increased gradually during the past fiveyears. In terms of value, the rice export was of ̀ 223.86crore in 1980-81 which increased to ` 1164.40 crorein 2008-09 ( Table 1). But in between 1980-81 and2008-09, it showed a fluctuating pattern in exportearnings which may be due to fluctuating domesticproduction, inconsistent export policies, frequent banson export of non-basmati rice, currency devaluation,and volatility in the international market. In the year2000-01, the earning from rice exports recorded thelowest value due to the fall in international prices ofrice. The exports of rice gradually increased from 2008-09 and reached ` 33,858.19 crore in 2012-13. The


Figure 1. India’s production and export of rice during 1980-81 to 2012-13

Table 1. Share of Indian rice export to total agricultural exports: 1980-81 to 2012-13

Year Total rice Total rice Unit value Agricultural Per cent shareexport export (‘/tonne) export of total rice

(Quantity, (value, (in crore `) export in’000 tonnes) in crore `) agricultural export

1980-81 727.35 223.86 3077.71 2375.70 9.421985-86 245.01 196.32 8012.61 3271.50 6.001990-91 505.11 459.63 9099.60 6012.76 7.641995-96 4914.01 4568.12 9296.11 20397.74 22.402000-01 1534.47 2943.39 19181.80 28657.37 10.272005-06 4088.16 6221.25 15217.73 49216.96 12.642006-07 4747.92 7035.88 14818.87 62411.42 11.272007-08 6469.28 11754.39 18169.55 79039.72 14.872008-09 2488.29 11164.4 44867.76 85951.67 12.992009-10 2156.32 11254.42 52192.72 89341.50 12.602010-11 2471.35 11585.92 46880.94 117483.60 9.862011-12 7175.89 24108.74 33596.86 187609.30 12.852012-13 10147.89 33858.19 33364.77 230141.13 14.71

Source: GoI (2013a); GoI (2013b); indiastat.com; Bansil (1992)

percentage share of total rice export in total agriculturalexport had increased from 9.42 per cent in 1980-81 to12.60 per cent in 2009-10, but decreased to 9.86 percent in 2010-11. The decrease in percentage terms wasdue to higher export of agricultural products, other thanrice. In the year 2012-13, the share of rice export intotal agricultural export was 14.71 per cent.

Growth of Rice Export from India

The annual compound growth rates (ACGR) forexport of basmati rice and total rice in terms of quantity,value and unit value during the period 1980-81 to 2012-13 are presented in Table 2. During period 1980-1994,the quantity of rice export recorded a positive but non-


significant compound growth due to lower domesticproduction and restriction of minimum export pricetill 1991. In contrast, the export value and unit valueregistered a positive growth rate of 13.74 per cent and13.48 per cent per annum. The export of basmati ricein quantity grew at the rate of 12.11 per cent per annumduring the period 1995-2012. The increase in quantityof rice export was due to a strong demand for basmatirice in the international markets and comfortableproduction in the country. The value of export ofbasmati rice also increased at a compound growth rateof 17.74 per cent per annum. The export performanceof basmati rice in terms of quantity and value was betterin period II.

During the overall period 1980-2012, the total riceexport in terms of quantity, value, and unit value grewat a positive and significant growth rate of 10.87 percent, 17.74 per cent, and 6.20 per cent per annum,respectively, which may be due to higher demand ofbasmati rice in the international market, consistentpolicies for export of basmati rice, higher internationalprice, and increased domestic production makingcomfortable stock of rice in the central pool (Gangwarand Rai, 1995; Shende et al., 1998).

Instability in Rice Export from India

The higher export instability in terms of quantityof rice during period I may be due to more variabilityin quantum exported than during period II. Theconsistent export policy, international demand for and

domestic production of basmati rice during period IImay be the other factors. Similarly, variability inquantum of export and unit price were the dominantfactors for the variability in export earnings. The exportearnings showed a higher stability during period I thanperiod II. The unit value realization exhibited moreinstability during period II than period I and overallperiod, may be due to the impact of devaluation ofIndian rupee after 1991.

The instability index of basmati rice and total riceexports in terms of quantity, value and unit value ispresented in Table 3. The quantity exported registeredinstability of 46.23 per cent, 53.23 per cent and 50.68per cent during period I, period II and overall period.The export value recorded instability of 41.22 per cent,43.53 per cent, and 37.40 per cent during period I,period II and overall period, respectively. The quantityexported has been more stability during period I thanperiod II and overall period. However, the export valuewas found more unstable during period II than periodI and overall period, while unit value realization wasmore stable during period I as compared to period IIand overall period.

The variability in rice exports was due to thechanges in the composition of rice varieties. The exportinstability of rice in terms of quantity as well as valuemay be due to increasing domestic and internationalrice demand, volatility in world prices, changes inexport policy of India and political situation ininternational markets.

Table 2. Compound growth rates of quantity, value and unit value of basmati rice and total rice export from India,1980-81 to 2012-13

(in per cent)

Period Basmati rice Total rice (basmati and non-basmati)Quantity Value Unit value Quantity Value Unit value

I (1980-81 to 1994-95) 0.22ns 13.74** 13.48** 4.07ns 14.96** 10.46**(2.06) (1.93) (0.84) (3.17) (2.84) (1.05)

II (1995-96 to 2012-13) 12.11** 17.78** 5.06** 3.72ns 12.34** 8.31**(0.93) (1.72) (0.99) (2.34) (1.85) (1.65)

Overall (1980-81 to 2012-13) 7.55** 15.87** 7.74** 10.87** 17.74** 6.20**(0.73) (0.69) (0.49) (1.25) (0.94) (0.60)

Note: Figures within the parentheses indicate standard error in respective values** and * denote significance at 1 per cent, and 5 per cent levels, respectively.


Major Export Destinations of Indian Basmati Rice

The major export destinations of Indian rice varyaccording to variety of rice. Basmati rice is mainlyexported to Iran, Saudi Arabia, Kuwait, Iran, UK, USA,UAE and Iraq. The major destinations of Indian basmatirice and their shares illustrated in Table 4 show thatnearly 75 per cent of the total export of basmati ricefrom India was concentrated in countries like SaudiArabia, UK, Kuwait, UAE, Iran and Iraq. Among themajor importing countries, Iran accounted for thehighest share (33.3%) in total export of basmati ricefrom India, followed by Saudi Arabia (18.85 %), UAE(6.76 %), Kuwait (5.46 %) and Iraq (5.55 %) in theyear 2012-13. Since 2008, when a new rice variety,‘Pusa 1121’ was notified as basmati rice, Iran has beena major buyer of this rice variety (Sidhu et al., 2014).The preference of Iran for this variety of basmati ricewas due to its lower cost vis-à-vis of other varietiesmaking it affordable for every class of consumer inIran.

Rice is not produced in Saudi Arabia and thecountry imports a substantial quantity of basmati ricefrom India to meet its strong consumer demand. Also,the Government of Saudi Arabia does not impose anytariff on rice import providing Saudi traders theopportunity to re-export the imported rice to the nearbycountries. There has been a shift in export destinationsof Indian basmati rice over the period of six years(2007-2012).

Trade Directions of Basmati Rice Export fromIndia

The directions of trade of basmati rice export todifferent destinations was examined by estimating thetransitional probability matrix using Markov chainanalysis and the values of transitional probabilitymatrix (TPM) are presented in Table 5. There are sixmajor countries that imported Indian basmati rice inlarge quantities and rest of the countries were pooledunder ‘others’ category. The diagonal elements in theTPM provide information on the probability ofretention of trade, while row elements indicate theprobability of loss in trade on account of competingcountries. The column elements indicate the probabilityof gain in trade from the competing countries.

A close look at Table 5 indicated that UAE wasthe most stable market among the major importers ofIndian basmati rice, as exhibited by the highestprobability of retention at 0.8226, which means thatUAE had retained its original export share of 82.26per cent during the period 2000-01 to 2012-13. Iranand Saudi Arabia imported larger quantities of basmatirice than UAE; but UAE was the most stable market.Saudi Arabia had the probability of retention 0.7517,and it retained its original export share of 75 per cent.Similarly, Iran had retained its original export share of72 per cent. This implies that Saudi Arabia and Iranwere also the stable importers of Indian basmati rice.Kuwait, UK and other countries have shown lower

Table 3. Instability index of rice export from India: 1980-81 to 2012-13

Period Basmati rice Total riceCV(%) Instability index CV(%) Instability index

QuantityI (1980-81 to 1994-95) 33.16 33.16 46.23 46.23II (1995-96 to 2012-13) 69.77 19.49 53.23 53.23Overall (1980-81 to 2012-13) 95.91 43.37 97.71 50.68

ValueI (1980-81 to 1994-95) 71.92 34.49 76.41 41.22II (1995-96 to 2012-13) 101.98 34.43 88.40 43.53Overall (1980-81 to 2012-13) 152.98 33.52 141.36 37.40

Unit ValueI (1980-81 to 1994-95) 55.09 11.42 42.61 13.87II (1995-96 to 2012-13) 35.60 21.60 60.52 36.56Overall (1980-81 to 2012-13) 63.00 20.41 74.91 34.57


Table 4. Share of basmati rice exported from India to major countries: 2007-08 to 2012-13 (Value in crore `)

Year Saudi Kuwait UK USA UAE Iran Iraq Others TotalArabia

2007-08 2038.35 401.68 291.08 142.10 689.83 20.33 16.81 744.39 4344.57(46.92) (9.25) (6.70) (3.27) (15.88) (0.47) (0.39) (17.13) (100.00)

2008-09 3102.81 733.94 431.04 272.60 2786.20 980.76 34.48 1135.22 9477.05(32.74) (7.74) (4.55) (2.88) (29.40) (10.35) (0.36) (11.98) (100.00)

2009-10 3295.47 1030.14 195.89 153.50 3094.65 2053.09 35.90 1030.46 10889.10(30.26) (9.46) (1.80) (1.41) (28.42) (18.85) (0.33) (9.46) (100.00)

2010-11 3132.94 1091.64 351.77 250.32 2839.76 2033.96 165.31 1488.93 11354.63(27.59) (9.61) (3.10) (2.20) (25.01) (17.91) (1.46) (13.11) (100.00)

2011-12 3380.88 1362.92 629.46 503.88 3432.79 2843.21 672.87 2623.60 15449.61(21.88) (8.82) (4.07) (3.26) (22.22) (18.40) (4.36) (16.98) (100.00)

2012-13 3659.08 1059.68 849.98 561.69 1311.20 6463.50 1076.67 4427.58 19409.38(18.85) (5.46) (4.38) (2.89) (6.76) (33.30) (5.55) (22.81) (100.00)

Note: Figures within the parentheses indicate per cent share of each importing country.Source: GoI (2013b)

probability of retention, viz. 0.3831, 0.4673 and 0.5400,respectively. On the contrary, Iraq has shown ‘zero’probability of retention, indicating that Iraq was anunstable importer of Indian Basmati rice. The majorgainer among the importers of Indian basmati rice overthe study period was Iran which had a transferprobability of 1.000 from Iraq and 0.1603 from UAE.In a similar manner, Saudi Arabia gained 53.27 percent market share from UK and 35.70 per cent fromothers.

In addition to having high probability of retention,UAE was also likely to gain 11.56 per cent marketshare from Kuwait. On the other hand, UAE was likelyto lose 16.03 per cent market share to Iran and 1.71per cent to Kuwait. Iraq, which is an unstable market

for Indian basmati rice, is likely to gain 7.66 per centmarket share from Iran.

Actual and Estimated Shares of Indian BasmatiRice Export to Importing Countries

The actual and estimated shares of basmati riceexported from India to different countries (inpercentage term) were shown in Table 6. A comparisonof this proportion during the study period revealed thatthe observed proportions of export shares wereconsistent with the estimated shares of export, whichwere derived from the Markov chain process. However,differences have been observed in some years, whichcould be due to limitation of the model that the presentestimates depend only on the previous year

Table 5. Transitional probability matrix of Indian basmati rice export, 2000-01 to 2012-13

Country Saudi Arabia Kuwait UK UAE Iran Iraq Others

Saudi Arabia 0.7517 0.1055 0.0498 0.0000 0.0000 0.0000 0.0930Kuwait 0.0000 0.3831 0.0015 0.1156 0.0000 0.0018 0.4980UK 0.5327 0.0000 0.4673 0.0000 0.0000 0.0000 0.0000UAE 0.0000 0.0171 0.0000 0.8226 0.1603 0.0000 0.0000Iran 0.0000 0.1005 0.0000 0.0000 0.7209 0.0766 0.1021Iraq 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000Others 0.3570 0.0000 0.0680 0.0350 0.0000 0.0000 0.5400


Tabl

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nd e

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ated

shar

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f Ind

ian

basm

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ice

expo

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dia,

200

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13 (

in c

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Year

Saud

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bia

Kuw

ait

UK

UA

EIr

anIr

aqO

ther

sTo

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Act

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Estim

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Act

ual

Estim

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Act

ual

Estim

ated

Act

ual

Estim

ated

Act

ual

Estim

ated

Act

ual

Estim

ated

Act

ual

Estim

ated

Act

ual

2000

-01

1098

.78

227.

3530

3.70

90.5

30.

000.

0044

5.58

2165

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(50.

73)

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(14.

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-(4

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-(0

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-(0

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-(2

0.57

)-

(100

.00)

2001

-02

1058

.81

1146

.86

196.

1120

4.55

193.

2622

7.23

49.9

711

6.35

12.3

214

.51

0.00

0.40

331.

2945

6.04

1841

.76

(57.

49)

(52.

95)

(10.

65)

(9.4

4)(1

0.49

)(1

0.49

)(2

.71)

(5.3

7)(0

.67)

(0.6

7)(0

.00)

(0.0

2)(1

7.99

)(2

1.06

)(1

00.0

0)20

02-0

310

52.2

110

17.1

817

2.79

188.

9120

5.16

165.

8184

.33

75.3

70.

1116

.89

1.51

1.29

546.

0037

6.31

2062

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(51.

03)

(55.

23)

(8.3

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0.26

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(9.0

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9)(0

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(0.9

2)(0

.07)

(0.0

7)(2

6.48

)(2

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)10

0.00

2003

-04

1067

.96

1095

.22

137.

6917

8.64

219.

3918

5.61

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observations and the exports also depend on suddenpolicy changes, leading to abrupt increase or decreasein exports to a country.

Projection of Indian Basmati Rice Export to MajorImporting Countries

Table 7 shows the export of Indian basmati rice todifferent countries which was computed using thetransitional probability matrix. It was projected thatduring 2013-14, the major markets for Indian basmatirice would be Iran (30.64 %), Saudi Arabia (25.68 %)and others (21.75 %). The projected exports to SaudiArabia have exhibited an increasing trend in bothabsolute value and percentage to total export. Thereason behind increasing projected exports to SaudiArabia is that it is likely to gain 53.27 per cent marketshare from UK and 35.70 per cent from ‘others’.Similarly, the projected value of basmati export toKuwait has shown an increasing trend. The projectedmarket share is likely to increase marginally from 7.54per cent to 9.30 per cent from 2013-14 to 2017-18. Inthe case of Iran and Iraq, the projected value has showna decreasing trend, in both absolute and relative to totalexport from India. The reason for showing a decreasingtrend by Iraq was that it had totally lost its market shareto Iran, although it had gained small market shares fromIran and Kuwait. The projected exports to Iran haveshown a decline from ` 5946 crore in 2013-14 to` 3278 crore in 2017-18, i.e. 30.64 per cent to 16.89per cent during the foresaid period.

Determinants of Rice Export from India

Difference in comparative advantage, geographicaland political proximity and degree of trade barriers arethe major determinants of destinations for exports ofany food commodity (Kumar et al., 2008). The factorsdetermining rice export from India were studied byusing multiple regression of log-log form wherequantity exported was regressed against export price,exchange rate, international price, domesticconsumption and lagged production. The data used forregression analysis were for 33 years (1980-81 to 2012-13). Since all variables are stationary at I (1), as shownin Annexure II, OLS estimation was employed andcoefficients are given in Table 8.

Table 7. Projected exports of Indian basmati rice to major importing countries: 2013-14 to 2017-18 (Value in crore `)

Year Saudi Arabia Kuwait UK UAE Iran Iraq Other countsries

2013-14 4984.81 1463.86 920.09 1375.72 5946.15 496.79 4221.97(25.68) (7.54) (4.74) (7.09) (30.64) (2.56) (21.75)

2014-15 5744.79 1707.68 967.24 1448.66 5003.67 457.88 4079.46(29.60) (8.80) (4.98) (7.46) (25.78) (2.36) (21.02)

2015-16 6290.35 1787.80 1017.77 1531.84 4297.05 386.14 4098.43(32.41) (9.21) (5.24) (7.89) (22.14) (1.99) (21.12)

2016-17 6734.15 1806.46 1069.93 1610.20 3729.27 332.17 4127.20(34.70) (9.31) (5.51) (8.30) (19.21) (1.71) (21.26)

2017-18 7105.83 1804.72 1118.37 1677.81 3278.57 288.73 4135.36(36.61) (9.30) (5.76) (8.64) (16.89) (1.49) (21.30)

Note: Figures within the parentheses indicate per cent to respective valuesSource: GoI (2013b)

Table 8. Estimates of determinants of rice export fromIndia

Variable Coefficient Standarderror

Constant 0.15** 0.06D (Export price) -2.00*** 0.21D (Exchange rate) 1.08ns 0.64D (International price) 1.20*** 0.25D (Domestic consumption) -0.95ns 0.64D (Lagged production) 0.53ns 0.64

R-squared=0.8153, Adjusted R-squared=0.7798, D-W=2.44, F-statistics=22.958, Prob(F-statistics)=0.0000Note: D=Difference; ** and *** indicate significance at 5per cent and 1 per cent levels, respectively. ns= Non-significant


Table 8 shows that export price and internationalprice are significant at 1 per cent level of significance.However, other three variables, viz. exchange rate,domestic production, and lagged production, are non-significant. So, domestic consumption and laggedproduction were treated separately and two modelswere formulated.

In model I, quantity exported was the dependentvariable and export price, exchange rate, internationalprice and domestic consumption were independentvariables. In model II, export price, exchange rate,international price and lagged production wereindependent variables and the results are shown inTable 9.

CUSUM Test and CUSUM of Square Test

The CUSUM test is based on the cumulative sumof the recursive residuals. The test finds parameterinstability, if the cumulative sum goes outside the areabetween the two critical lines. Both these tests havesuggested stability of parameters, as revealed by Figure2 (a-d).

Finally, both the functions were tested by variouseconometric tests to achieve best, linear and unbiasedcoefficient. The results of various tests are depicted inTable 10.

Both the models have shown that export of ricefrom India is highly price sensitive. The negative andsignificant coefficient of Indian export price indicates

that it has a negative impact on exports of rice. In orderto grab international markets, Indian export price hasto be competitive. Increase in export price means thatexports become expensive in the world market. Otherempirical studies drawing similar results were ofAbolagba et al. (2010), Bilal and Rizvi, (2013) andShende et al. (1999). However, Kumar et al. (2008)have found a positive relationship between Indianexport price on export of cucumber and gherkinproducts from India, but the result was non-significant,indicating that export price has not played anysignificant role in its export.

The international price of rice is significant withhigh positive elasticity, which indicates thatinternational price has a positive impact on exports ofrice from India. This may be due to the fact that whenprice of Thai rice increases, the importer shifts towardsIndian rice due to price advantage, indicating the roleof government to set price just below Thai 5%, in sucha way that price becomes competitive in theinternational markets. The exchange rate has a positiveimpact on rice export from India and is significant at10 per cent level (model I). When the exchange rate ofrupee with dollar increases (devaluation of rupeeoccurs), then exports from India become cheaper forthe importing countries, and hence export increases.Thus, a positive impact of exchange rate is assumedon rice exports from India. Haleem et al. (2005), Kumaret al. (2008) and Shende et al. (1999) have also revealedthe positive and significant effect of exchange rate.

Table 9. Estimates of determinants of rice export from India by model I and model II

Dependent variable: D (Quantity) Model I Model IICoefficient Std. error Coefficient Std. error

Constant 0.16** 0.06 0.13** 0.06D (Export price) -2.02*** 0.20 -1.98*** 0.21D (Exchange rate) 1.21* 0.62 1.02ns 0.66D (International price) 1.23*** 0.24 1.15*** 0.25D (Domestic consumption) -1.30** 0.47 - -D (Lagged production) - - 1.16** 0.49

R-squared=0.8104 R-squared=0.7994Adjusted R-squared=0.7823 Adjusted R-squared=0.7697D-W=2.37 D-W=2.55F-statistics=28.854 F-statistics=26.9033

Prob (F-statistics) = 0.0000

Note: D=Difference ;*, **, and *** indicate level of significance at 10 per cent, 5 per cent and 1 per cent level respectively.


Figure 2. CUSUM test and CUSUM of square test, (a) CUSUM 5% significance model I , (b) CUSUM 10%significance Model II, (c) CUSUM 5% significance, and (d) CUSUM 10% significance

(a) (b)

(c) (d)

The domestic consumption of rice shows a negativeand significant effect on its export from India. Whendomestic consumption increases, smaller surpluses areavailable for exports, and ultimately its exportdecreases. Abolagba et al. (2010) and Bilal and Rizvi(2013) have endorsed the same view. But, in recentyears, a drastic change has been seen in the food habitsof people. The consumption of cereals is decreasing inIndia because of increasing preferences for processedfoods, vegetables and high protein food items (Chopraet al., 2014). The coefficient of lagged production hasbeen found positive and statistically significant at 5per cent level of significance. This means that rice

export is more influenced by the previous year’sproduction. The positive and significant influence oflagged production has been seen not only on export ofrice (Gangwar and Rai, 1995 ; Sekhar, 2003) but alsoon export of onion, banana and black pepper ( Hemaand Kumar, 2007), indicating increase in exportearnings with increase in production.

ConclusionsRice contributes substantially to the national

income through exports of its basmati as well as non-basmati rice varieties. The study has revealed thatIndian rice exports had a fabulous performance during

CUSUM 5% significance

CUSUM 5% significance CUSUM 10% significance

CUSUM 10% significance


Table 10. Econometric test for assumptions of Model I and Model II

Hypothesis Name of test F- Observed Prob. Decisionstatistics R- squared chi-square

R-square for observed

Model IHo= The variance of error-terms are constant Breusch- 0.3733ns 1.677 0.7948 The variance ofH1= The variance of error terms are not constant Pagan- error-terms

Godfrey are constant

Ho= There is no serial correlation Breusch- 1.538ns 3.5066 0.1732 No serialH1= There is a serial correlation Godfrey Serial correlation

CorrelationLM Test

Ho= The error-term are normally distributed. Jarque- 1.5847ns 0.457 Error-terms areH1= The error-terms are not normally distributed. Bera normally

distributedModel II

Ho= The variance of error-terms are constant Breusch- 0.2439ns 1.116 0.8917 The variance ofH1= The variance of error-terms are not constant Pagan- error-terms

Godfrey is constant

Ho= There is no serial correlation Breusch- 1.657ns 3.746 0.1536 No serialH1= There is a serial correlation Godfrey Serial correlation

CorrelationLM Test

Ho= The error-term are normally distributed. Jarque- 1.038ns 0.595 Error-terms areH1= The error-terms are not normally distributed. Bera normally

distributed

Note: NS=Non-significant

the study period 1980-81 to 2012-13. It has registeredpositive growth rates in terms of quantity, value andunit value for basmati rice and total rice. The instabilityindex has been found higher for export quantity (43.37%) of basmati rice than its value (33.52 %) and unitvalue (20.41 %). India faces competition in export ofnon-basmati rice from Thailand and Vietnam ininternational market due to better quality of rice fromthese countries, and from Pakistan for basmati rice.The study has also revealed that export of rice fromIndia is highly price sensitive. Export price,international price, lagged production, domesticconsumption, and exchange rate are the majordeterminants of rice export from India. In order tosustain in the international market, Indian export priceneeds to be competitive besides meeting quality andsanitary and phyto-sanitary standards.

AcknowledgementsThe authors are grateful to the anonymous referee

for the valuable suggestions. The views expressed arethose of the authors only.

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Received: March, 2015; Accepted: February, 2016


Annexure 1. World rice trade showing major exporters and importers: 1990-2013 (’000 tonnes)

Country 1990 1995 2000 2005 2010 2011 2012 2013

ExportersIndia 0.51 4.18 1.45 4.69 2.23 4.64 10.25 10.50 (4.36) (20.10) (6.37) (16.20) (7.01) (12.70) (25.73) (27.16)Vietnam 1.67 2.32 3.37 5.17 6.73 7.00 7.72 6.80 (14.27) (11.15) (14.79) (17.86) (21.17) (19.16) (19.38) (17.59)Thailand 3.94 5.89 6.55 7.27 9.05 10.65 6.95 6.70 (33.68) (28.32) (28.75) (25.11) (28.47) (29.15) (17.45) (17.33)Pakistan 0.90 1.59 2.03 3.03 4.00 3.41 3.50 3.50 (7.69) (7.64) (8.91) (10.47) (12.58) (9.33) (8.79) (9.05)USA 2.42 2.99 2.85 3.86 3.87 3.25 3.33 3.27

(20.68) (14.38) (12.51) (13.33) (12.17) (8.89) (8.36) (8.46)Others 2.25 3.83 6.54 4.92 5.91 7.59 8.09 7.89

(19.23) (18.41) (28.71) (16.99) (18.59) (20.77) (20.31) (20.41)World Total 11.70 20.80 22.78 28.95 31.79 36.54 39.83 38.66

(100.00) (100.00) (100.00) (100.00) (100.00) (100.00) (100.00) (100.00)Importers

China 0.06 1.96 0.28 0.61 0.37 0.58 2.90 3.20 (0.51) (9.42) (1.23) (2.11) (1.16) (1.59) (7.28) (8.28)Nigeria 0.22 0.45 1.25 1.78 2.00 2.55 3.40 2.60 (1.88) (2.16) (5.49) (6.15) (6.29) (6.98) (8.54) (6.73)Iran 0.87 1.58 1.10 1.20 1.52 1.87 1.75 2.15 (7.44) (7.60) (4.83) (4.15) (4.78) (5.12) (4.39) (5.56)Phillipines 0.54 0.28 0.90 1.89 2.40 1.20 1.50 1.10 (4.62) (1.35) (3.95) (6.53) (7.55) (3.28) (3.77) (2.85)Indonesia 0.08 3.01 1.50 0.50 1.15 3.10 1.96 0.65 (0.68) (14.47) (6.58) (1.73) (3.62) (8.48) (4.92) (1.68)Others 9.94 13.52 17.75 22.98 24.35 27.25 28.32 28.96 (84.96) (65.00) (77.92) (79.38) (76.60) (74.58) (71.10) (74.91)World Total 11.70 20.80 22.78 28.95 31.79 36.54 39.83 38.66

(100.00) (100.00) (100.00) (100.00) (100.00) (100.00) (100.00) (100.00)

Note: Figures within the parentheses are percentage of the total.Source: USDA (2013)


Annexure II. Stationary (unit root) test for variables

At level At first differenceVariable Linear graph Test statistic Critical values Test statistic Critical values Decision

(99%) (95%) (99%) (95%)

QT Intercept -0.91 -3.65 -2.95 -6.20** -3.66 -2.96 I(1) at 1%Trend and intercept -3.21 -4.28 -3.56 -6.24** -4.28 -3.56 I(1) at 1%

EXC Intercept -2.44 -3.65 -2.96 -3.94** -3.66 -2.96 I(1) at 1%Trend and intercept -0.93 -4.27 -3.56 -4.20** -4.28 -3.56 I(1) at 1%

EXP Intercept -1.80 -3.66 -2.96 -4.91** -3.67 -2.96 I(1) at 1%Trend and intercept -3.36 -4.28 -3.56 -4.83** -4.29 -3.57 I(1) at 1%

INT Intercept -1.27 -3.65 -2.96 -4.75** -3.66 -2.96 I(1) at 1%Trend and intercept -2.01 -4.27 -3.56 -5.12** -4.28 -3.56 I(1) at 1%

DC Intercept -3.91** -3.67 -2.96 -6.85** -3.67 -2.96 I(1) at 1%Trend and intercept -3.30 -4.27 -3.56 -5.12** -4.29 -3.57 I(1) at 1%

LGP Intercept -1.53 -3.66 -2.96 -9.92** -3.66 -2.96 I(1) at 1%Trend and intercept -4.85** -4.27 -3.55 -9.81** 4.28 3.56 I(1) at 1%

Notes: Critical values have been taken from Mackinnon (1996)All variables are in log formOptimum lag selection is 8 on basis of Schwartz Information Criterion (SIC) default set by E-Views.** denotes significance at 1 per cent level.

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