Growth, Instability and Determinants in India’s ... · With economic liberalisation in India, one of the major shifts was the switch from a fixed exchange rate regime to flexible

Growth, Instability and Determinants in India’s Agricultural

Commodity Export: A Policy Period Analysis

Phool Chand

ABSTRACT

The economic liberalization in India resulted in a shift from a fixed exchange rate

regime to flexible exchange rate regime. Further, as a consequence of

liberalisation and globalization, there was a removal of tariffs and non-tariff

barriers, which led to a growth in exports. As agriculture forms the backbone of

the Indian economy, we analyse two aspects of agricultural commodity exports,

namely, growth and instability, over the period 1990-91 to 2010-11. In order to

see the impact of policy periods, the study is categorised into four different policy

periods, corresponding to liberalisation, WTO, world recovery and global crisis.

This is followed by an attempt to see the impact of macro-economic variables

such as exchange rate, GDP and money supply on the performance of agricultural

commodity exports. The study concludes that growth of agricultural commodities

exports largely depends upon GDP. Instability, on the other hand, depends upon

money supply. In fact, the behaviour of foreign exchange rate is anomalous, since

depreciation reduces export growth and it creates instability.

Keywords: Money supply, Foreign exchange rate, Liberalisation, Global crisis.

1.0 Introduction

With economic liberalisation in India, one of the major shifts was the

switch from a fixed exchange rate regime to flexible exchange rate regime. At the

same time, liberalisation and globalisation resulted in removal of tariffs and non-

tariff barriers, thereby leading to export growth. Agriculture forms the backbone

of the Indian economy and despite concerted industrialization in the last 40 years;

agriculture still occupies a place of pride. In the context of this, the present paper

is an attempt to study the performance of nine commodities of Indian agriculture

exports over the period 1990-91 to 2010-2011.

___________________________

Mr. Phool Chand, Assistant Professor, PGDAV College, University of Delhi,

Delhi

134 FOCUS: Journal of International Business

We analyse the two aspect of agricultural commodity export, namely,

growth and instability, over the period 1990-91 to 2010-11. In order to see the

impact of policy periods, the study is categorised into four different policy

periods corresponding to, liberalisation, WTO, world recovery and global crisis.

The remainder of the paper is organised as follows. Section 2 review the

extant literature related to balance of payment. Section 3 provides the rationale

for the commodities selected for the study. Section 4 outlines the data and

methodology explaining the measurement of growth and instability during four

policy periods. Section 5 consists of the analysis of growth and instability during

the policy periods. The determinants of growth and instability are explained

under section 6. Conclusion and implications of the study are discussed under

section 7.

2.0 Review of Literature

We went through the relevant published literature on agriculture trade and

found that most of the studies had dealt with some specific aspect of it. In most of

the literature, it has been found that the studies are related with the general trend

analysis of agriculture export. Hence, we felt the need for making a

comprehensive study of all aspects that affect the growth and stability in

agriculture export.

This section reviews some major studies in specific areas of agriculture

exports. Babcock et al. (2002) have analysed the impact of liberalizing

agricultural markets. They have used the Food and Agricultural Policy Research

Institute (FAPRI) modeling system to analyse the impact of trade and farm

policies on world flows, prices, and market equilibrium by considering two

scenarios. In the first scenario all distortions directly affecting agriculture

(domestic farm programs and border measures, e.g. TRQs and tariffs), are

removed; this is referred to as full liberalisation. In the second scenario, only

trade liberalisation (the elimination of border measures) is implemented; this is

referred to as trade- only liberalisation. For each scenario, policy parameters are

changed and a new baseline is computed for the outlook period (2002-2011). The

two trajectories are then compared. Results are reported as average annual

changes over the outlook period in deviation from the baseline. The world net

Growth, Instability and Determinants in India’s Agricultural Commodity Export 135

trade is estimated to increase by 7.9% following the removal of all distortions and

by 5% with trade liberalisation alone. On account of the removal of the export

subsidy, Indian exports are estimated to decrease under the full liberalisation

scenario. Under this scenario India is projected to become a net importer in

2003/04 and a year later with trade-only liberalisation. However, considering the

quality and price differences in Indian wheat and large transport costs from major

exporting countries such as the US, this projected import may not be realistic.

Alagh (1994) in his paper on “Macro Policies for Indian Agriculture” emphasizes

that significant structural changes are taking place in the Indian economy and that

the eighties have shown a remarkable diversification of India‟s agro-based

economy in response to substantial acceleration of demand. The analytical work

on the economy at a regionally disaggregated level shows that both prices and

infrastructural investments are important factors that determine variations in

agricultural productivity.

Agricultural Policy in India has so far been guided primarily by domestic

concerns, the most important among these being the need to provide food

security to the rapidly rising population through domestic production. A hallmark

of the earlier trade policy was that except for a few traditional commercial crops,

the entire agricultural sector was insulated from world agricultural markets

through controls of exports and imports. The structural adjustment programme

undertaken in India, including the reforms in trade policy and the multilateral

trading system following the signing of the Dunkel text would have serious

implications for the Indian agriculture. Nayyar & Sen (1994) has analyzed the

likely implications of trade liberalisation on the Indian agriculture by undertaking

a detailed comparison of domestic and international prices of 18 commodities.

They have also analyzed the likely impact of multilateral trade liberalisation,

including that of the signing of the Dunkel text, on Indian agriculture. The

implication of the Dunkel text, which envisages withdrawal of subsidies on

exports, removal of all non- price based restrictions, increase in market access,

withdrawal of domestic support to agricultural except in green areas, etc, would

profoundly influence world agricultural trade and prices and have far – reaching

implication for the agricultural sector in India. They conclude that because of the

deterioration in India‟s terms of trade, structural rigidities inhibiting supply

response, and important deleterious impact of TRIPS on Indian agriculture, on


balance, the costs of multilateral trade liberalisation may be higher than the

benefits. They argue that firstly, tariffs on imports and taxes on exports should be

higher than the usual optimum levels; secondly, quantitative restrictions on trade

should continue in some commodities like food grains along with retaining buffer

stocks for food security reasons; and thirdly, considerations of India‟s

comparative advantage should be made an integral part of domestic agricultural

policies both for investment planning in infrastructure and in setting „support

price‟.

Agricultural price policy is another important area, which is affected by

the new economic policy directly. This is because the „market- friendly‟ approach

of the new policy, lifting internal controls on trade in food grains, integration

with the world economy consequent on the signing of the Dunkel text etc are

likely to weaken considerably the role and importance of the administered price

structure prevalent in India. Vyas (1994) argues that the price policy, which had

evolved in the context of the shortages in India, constituted a part of a food

management system, that was primarily concerned with providing food security

to the population through augmenting domestic production of food grains and

protecting the interests of the consumers. While the price policy aimed at keeping

the food prices low, it was also supposed to play a positive role in augmenting

production by ensuring remunerative prices to the farmers along with the

assurance of minimum support prices. It was, of course, fully recognized that

non–price factors like irrigation and rural infrastructure played a much greater

role in increasing production.

It is generally claimed that trade policy reforms including devaluation of

their currencies by the developing countries and withdrawal of subsidies to their

agriculture by the developed countries would give special advantages to the

agricultural and agro- industrial exports from developing countries. Hence, agro –

industries, besides catering to a burgeoning domestic market, have great export

potential. This underlines the need to give high priority to their development in

India in the context of the new economic policy. Goyal (1994) in his paper,

„policies towards development of agro- industries in India‟ has discussed the

various issues relating to the prospects of agro- industries in India. Goyal points

out that in the context of traditional agriculture, the technology of production in

agro- industries and post- harvest processing remained primitive and stagnant for


a long time. He also outlines the important factors that were responsible for the

growth of cottage, small and agro – based industries on modern lines in post-

independent India. He suggests a policy mix for the development of agro-

industries keeping in view the crucial role of agro- industries.

The role of exports as an engine of economic growth has long been a

focus in the trade and development literature. Agriculture‟s contribution to total

exports is often substantial in developing countries, and it is surprising that there

have been few empirical studies on the impact of agricultural exports on GDP.

Dawson (2005) has examined the contribution of agricultural exports to

economic growth in LDCs by developing sources- of – growth equation from a

dual economy model by using panel data for 62 LDCs for 1974- 1995 and has

shown that there are significant structural differences in economic growth

between low, lower–middle, and upper income LDCs. Investment in the

agricultural export subsector has a statistically identical impact on economic

growth as investment in the non- agricultural export subsector. Suggesting that

export- promotion policies should be balanced.

Lopez & Dawson (2010) show that long run a relationship exists between

GDP and agricultural and non- agricultural exports of developing countries. The

relationship between GDP and agricultural and non- agricultural exports were

estimated for 42 countries using panel co-integration method. Structural

differences exist in the relationship by broad income group. Balanced export –

promotion policies are implied for the poorest countries, but for those with higher

incomes, higher economic growth is achieved from non- agricultural exports.

Johnston & Mellor (1961) argue that increasing agricultural exports leads

to increasing economic growth, and that export-led growth from agriculture may

represent optimal resource allocation for those countries which have a

comparative advantage in agricultural production.

Regional disparities and instability in agriculture has remained the subject

of deep concern in the area of agricultural economics in India. Instability in

agricultural production raises the risk involved in farm production and affects

farmers‟ income and decisions to adopt high paying technologies and make

investments in farming. It also affects price stability and the consumers, and

increases vulnerability of low-income households to market. Instability in

agricultural and food production is also important for food management and


macroeconomic stability (Chand and Raju, 2009). Besides instability, Indian

agriculture is also known for sharp variations in agricultural productivity across

space which results in various types of disparities in resource endowments,

climate and topography and also due to historical, institutional and socio-

economic factors.

Potential of green revolution technologies in increasing productivity and

production of various crops in India was recognized in the very early stages of

adoption of this technology. Along with this, a concern arose whether increase in

production, brought about by crop technology, was accompanied by rise in year-

to-year variability in production. The first serious attempt to examine the effect

of new seed-fertilizer technology, known as green revolution technology, on

year-to-year fluctuations in crop output was made by Mehra (1981). The study

has compared variability‟s in production, across crops and regions in India,

during the period 1949-50 to 1964-65 and 1964-65 to 1978-79, to find changes in

instability in the period before and after introduction of high yielding

technologies. The analysis shows that during the ten-year period since the

adoption of innovative technologies, the standard deviation and coefficient of

variation of production of all the crops aggregates increased as compared with the

period 1949-50 to 1964-65.

Hazell (1982) came out with another study which made use of the same

data as used by Mehra (1981), but adopted improved analytical framework to

analyse variability. Hazell (1982) confirmed the finding of Mehra (1981), and

went a step further in concluding that increase in production instability was an

inevitable consequence of rapid agricultural growth and there is little that can be

done about it. Both these studies attributed the increase in instability to the new

seed-fertilizer technology. Another paper by Ray (1983a) went a little deeper to

probe causes of instability in Indian agriculture during the period 1950 to 1980.

The paper adopted a very simple but highly robust indicator of fluctuations in

output. This was given by standard deviation in annual output growth rates over a

specified period. The study found that instability in production increased in the

1960s and rose further during the 1970s for most of the crops and crop

aggregates. An interesting finding in this paper was that instability in wheat

production, which was experiencing highest coverage under HYV‟s among all


crops, also increased markedly during the 1960s, but its production increased at a

fairly stable rate during the 1970s.

Based on the detailed analysis of various factors affecting growth and

instability, Ray (1983a) strongly refuted the assertion made by Hazell (1982) that

“production instability is an inevitable consequence of rapid agricultural growth

and there is little that can be affectively done about it”. According to Ray

(1983a), the magnitude of production instability is essentially a function of the

environment which can be considerably moulded through human efforts. The

author suggested that causes for increase in production instability after adoption

of green revolution technology were (i) increase in the variability of rainfall and

prices and (ii) increase in sensitivity of production to variation in rainfall, and not

the growth in production.

In another similar but more detailed study by Rao et.al. (1988), it was

found that amplitude of fluctuations in output for all categories of crops, except

wheat, have increased significantly in the post-green revolution period, 1966-

1985 or 1968-1985. The study concluded that since wheat benefited to the

greatest extent from green revolution technology, the observed increase in

variability in foodgrains and all crops output cannot be attributed to green

revolution technology as such. Like Ray (1983a), this study has also attributed

rising vulnerability of agricultural output to increase in sensitivity of output to

variations in rainfall traceable to the high complementarily of new seed- fertilizer

technology with water. Both, Ray (1983a) and Rao et al. (1988) on one hand

refute the impact of green revolution technology on variation in output for some

crops, and, on the other hand, ascribe it to the increase in sensitivity of output and

complementarity of new technology with irrigation- which are indeed a part of

the new technology. However, in conclusion, the authors clearly state that the

instability in agricultural production has increased in post-green revolution period

(Rao et al.)

Larsen et al. (2004) have examined instability in area, yield and

production for the major crops in India by dividing the period 1950-51 to 2000-

02 into pre-green revolution and post-green revolution periods. The paper has

reported that production instability for foodgrains increased by 153 per cent and

yield instability increased by 244 per cent between the two sub-periods. Based on

this, the authors have concluded that widespread adoption of green revolution


technology increased instability in yield and production of foodgrains. There was

a serious inconsistency in the results on instability in food grain production

reported in this paper. While instability in production of cereals and pulses was

reported to decline between pre and post green revolution periods by 10 and 5 per

cent, respectively, the instability in the production of food grains, which is sum of

cereals and pulses, was reported to have increased by 153 per cent in the same

period. Further, this study did not divide post 1968 period into sub-periods.

In contrast to the choices by Larsen to keen entire post green revolution

period as one set, Sharma et al. (2006) have estimated variability in production

and yield by choosing smaller set of years. The results of the two studies on

instability are somewhat contradictory in the sense that Larson has reported a rise

in the instability over time, whereas Sharma have reported a decline in instability

over time.

Ray (1983b) developed a very simple measure of instability given by the

standard deviation in annual growth rates. This method satisfies the properties

like instability based on de-trended data and comparability. Moreover, the

methodology does not involve actual estimation of the trend, computation of

residuals and de-trending, but all these are taken care in the standard deviation of

annual growth rates.

Kumar & Rai (2007) have analysed the performance and competitiveness

of export of tomato and its products from India to find (i) production and export

performance of tomatoes in India, (ii) impact of trade liberalisation on export of

tomato and its products, (iii) major destinations of Indian tomato and tomato

products, and (iv) determinants of tomato export. The export performance ratio

(EPR) has been estimated to examine the export competitiveness of India in

tomato and tomato products. Annual compound growth rate and coefficient of

variation for two periods, before (1985-1994) and after (1995- 2004) the

commencement of WTO have been estimated to study the impact of trade

liberalisation on the export performance of India in tomato and its products.

Export demand function has been estimated using OLS technique and the factors

affecting the export of tomato and its products from India have been identified.

The study reveals that the existence of high instability in export of tomato and its

products require the attention of policymakers to retain hold on the international

market.


Ramane et al. (2003) conducts an empirical analysis of India‟s

Agricultural trade performance in an inter-temporal framework. The study uses

annual compound growth rates and instability index for the eight-year period

starting from 1991-92. An exponential regression function has been used for

computing growth rate and instability index. The study points to the decline in

share of agriculture and allied exports in India‟s overall export basket. The

growth trend of agriculture and allied exports follow an uneven trend. High

instability of agricultural exports was confined by a large value of the instability

index for the period under study.

Tandon (2005) attempts to take a closer look at the performance of India‟s

agricultural exports and imports during pre and post reform periods and to assess

the changing growth pattern of trade. The author uses log transformed equation

for finding out the annual growth rates.

Log Yt = α1 + α 2 * time + e,

Where, Yt = aggregate export earnings of the agricultural sector

Time = time variable

α 1 = intercept

α 2 = slope coefficient

e = random error

For structural break, the author uses dummy variable by assigning value 0 for

pre-reform period and 1 for post reform period. For this purpose the author used

log transformed function form, indicating by

Log Yt = α1 + α 2 * time + α 3

* dummy + α 4

* dummy

*time + e,

Besides, the instability index (II) is also computed. The author concludes

that the trend of agriculture exports recorded a combination of two types of shifts

during the post-reform period. While the agriculture exports increased due to

relatively open agriculture economy (effect of intercept dummy), the exports

decelerated due to relatively strong emphasis on manufactured exports (effect of

slope dummy).

Further, instability of agriculture exports has gone down during the post-

reform period. It has been observed that lower rate of growth of aggregate

agriculture exports is associated with lesser uncertainty revealed by a decline in

instability index for aggregate agriculture exports.


3.0 Commodities selected for Study

This study analyses the performance of nine commodities of Indian

agricultural exports over the period 1990-91 to 2010-11. The selection of nine

commodities is done on the basis of their significant contribution towards the

total agriculture and allied products exports. The commodity that we have

selected includes tea, cashew nuts, Cake of Soybeans, Coffee, Oil Castor,

Tobacco, Onions, Sesame Seed and Cake of Rapeseed. The following figures

(Figure 1, 2 and 3) show the general trend of agriculture and allied products in

relation to merchandise export and principal commodities export on one hand and

the performance of 9 commodities in relation to agriculture and allied export.

From Figure 1 and Figure 2, we can say that the proportions of

agricultural commodities exports are declining over a period of time. It indicates

that principal commodities are not playing a significant role towards the overall

improvement of merchandise export. However, the performance of 9

commodities, are increasing throughout the time span of the study. This gives a

justification for the selection of such 9 commodities for carrying the present

study of growth and instability on one hand and determinants of growth and

instability on the other hands.

4.0 Data and Research Methodology

4.1 Data Sources

The requirement of the study included two sets of variables. One set

relates to agriculture export (in term of dollar) of select commodities, while the

other set relates to macroeconomic variables. The first set consists of data in term

of on price, quantity and value of tea, cashew nuts shelled, cake of soybeans, oil

of castor beans, tobacco unmanufactured, onions, sesame seed and cake of

rapeseed. The other set comprises of gross domestic product (GDP), nominal

exchange rate, and broad money supply (BM). Both sets of data collected from

Handbook of Statistics on Indian Economy, released by Reserve Bank of India

(2011-2012).


Figure 1: Share of agriculture export out of merchandise export

Figure 2: Share of agriculture export out of principal commodities export

Figure 3: Share of 9 commodities out of agriculture export

0

0.05

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Pro

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YEAR

Proportion of Agriculture & Allied Products out of total Merchandise Export

AAPX/TX

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Proportion of Agriculture & Allied Products out of Principal Commodities Export

AAPX/…

0.0

5000.0

10000.0

15000.0

20000.0

25000.0

30000.0

1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

Pro

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YEAR

Share of 9 Commodities out of Agriculture Export

Total…Total Value of…


4.2 Methodology

In order to analyse the trends in India‟s Agriculture export, we have

computed the overall as well as policy period‟s growth rates. The time span of

the study covers a period from 1990-91 to 2010-11.

For analytical purposes to divide the time span of the study into four policy

period

1. Policy period I (1990-91 to 1993-94) period of liberalisation

2. Policy period II (1994-95 to 2000-01) period of globalisation

3. Policy period III (2001-02 to 2006-07) period of world recovery

4. Policy period IV (2007-08 to 2008-09) period of global crisis

In the current study there are two type of analysis we need to explore, one of

them is to find out the growth rate and another is to find out the instability index.

First we show how the growth rate of the concern analysis is calculated and

then we will talk about how to develop instability index. We have used semi-log

regression equation and for that we have regress the log of variable with respect

the time. Therefore, regression equation can be written as follows:

Y = ……. (1)

Taking log of both sides and adding an error term;

Log Y = α + βt + µt ……. (2)

Where Log Y = natural log of variable Y

α = intercept term

β = slope of the regression equation, which basically tell us about the

growth rate.

t =time (1990-091 to 2010-11)

µt = error term.

The above written regression equation help us to find out the rate at which the

current account value grew with the passage of time. The “β” give us the rate of

growth i.e. annual compounding growth rate (ACGR); however, that rate of

growth is for the complete number of years taken into consideration i.e. from

1990-91 to 2010-11.

te


4.3 Policy Periods Growth Rates

In the above equation the purpose of finding out different growth rate for

different policy period will not work since we have not taken into account policy

change variable indicator. So on account of this requirement we need to change

our regression equation. In order to capture the policy change indicator variable,

we need to introduce dummies in the semi-log regression equation. After doing

this we would be able to find out different growth rates for different policy

periods as well as different intercept term for different policy period. So after

introducing dummies variable, the regression equation will be changed.

New regression equation can be written as follows:

Log Y = a1 +b1t + b2D2 + b3D3 + b4D4 + b5D2t + b6D3t + b7D4t + µt …… (3)

Where in

Log Y = natural log of variable Y

a1 = intercept at the I policy period

b1 = growth rate in the I policy period

b2 = difference in the intercept of II and I policy period

b3 = difference in the intercept of III and I policy period

b4 = difference in the intercept of IV and I policy period

b5 = difference in the slope of II and I policy period

b6 = difference in the slope of III and I policy period

b7 = difference in the slope of IV and I policy period

µt = error term.

D2 = 0 for 1990-91 to 1993-94,

1 for 1994-95 to 2000-01,

0 for 2001-02 to 2006-07.

0 for 2007-08 to 2010-11

D3 = 0 for 1990-91 to 1993-94,

0 for 1994-95 to 2000-01,

1 for 2001-02 to 2006-07.

0 for 2007-08 to 2010-11

D4 = 0 for 1990-91 to 1993-94,

0 for 1994-95 to 2000-01,

0 for 2001-02 to 2006-07.

1 for 2007-08 to 2010-11


Therefore, with the help of above written regression equation we can easily find

out the intercept as well as growth rate (ACGR) for different policy period as

follows:

Policy Period Intercept ACGR

I (Liberalisation) a1 b1

II (Globalisation) a1 + b2 b1 + b5

III (World Recovery) a1 + b3 b1 + b6

IV (Global Crisis) a1+ b4 b1+ b7

In this way by introducing dummies, we can find out annual compounding

growth rate and intercept for different policy periods.

4.4 Measurement of growth rates and instability indices

Extant Studies

For the measurement of instability index, we have seen number of articles

indicating different methods of such measurement depending upon requirement

of concern research. Coppock (1977) argued that instability means “excessive

departure from some normal level”. When production, exports, imports and

employment in a certain line of activity show irregular change during a year or

over a period of years that are considered as a form of economic instability

(Lundberg, 1968).

Kaundal (2005)in his article on “Impact of Economic Reforms on External

Sector”, considered only random (residual) variation in the measurement of

instability index which resulted into low instability index of exports earning by

expressing the instability index as

iteXit ….. (4)

Where the detrended export variable is:

iteXitei

ˆˆ ….. (5)

Export Instability Index = …..(6)

Kaundal defines „Export Instability Index‟ as the standard deviation of the

observed deviations from the „estimated exponential time trend‟. So, this study

1

2 2

1

100*n

i

i

en k X


provides us the scope to consider overall variations in the measurement of the

index of instability.

Criticism of extant approaches

If we consider all components of time series which constitute the overall

pattern of exports they can be expressed as:

Xt= Tt× Ct × St × Rt …… (7)

Where

Tt represents Trend variation

Ct represents cyclical variations

St represents Seasonal variations

Rt represents random variations

This model is used where it is assumed that the forces giving rise to the four

types of variations are interdependent, so that overall pattern of variations in the

time series is the combined result of the interaction of all the forces operating on

the time series. Most business and economic time series data are the result of

interaction of a large number of forces which, individually, cannot be treated

responsible for generating any particular type of variations. In other words, forces

responsible for one type of variations are also responsible for the other type of

variations.

In annual time series data seasonal variation does not exist. Random variation

cancels out in the long-run. Exponential time trend takes into account moving

average trend. Therefore, in the above approach actually only cyclical variation

has been considered.

We are using an instability index which captures all kind of variation occurring in

a time series data.

We made this research work more interesting by including the impact of

liberalisation, globalisation, world recovery and global crisis on the issue of

instability index measurement.

4.5 Our Approach -Measuring Growth and Instability

Consider: )( jTeXjt

……. (8)


where:

Xjt = Exports of „jth‟ head (merchandise).

Such that:

utjTLogXjt ……. (9)

jTtLogXj ˆˆ)ˆ( ……. (10)

T = Time variable (1991-2011).

ut= error term

wthRateompoundGrojthAnnualCj ̂

Index of Instability of „jth‟ head of export (like tea):

100*)ˆ/(exp()ˆ(exp(( tLogXjtLogXjXjtIjx ……. (11)

4.6 Determinants of Agricultural Export

Equations

Two equations were used for analysing the determinants of growth and

instability.

Growth:

(VAX)t = e a1+d1T

(NER)tb1

*(GDP)tc1

* (BM)t

d1 .........(12)

Instability:

(MSDVAX)t = e a1+d1T

(NER)tb1

*(GDP)tc1

* (BM)t

d1 .................(13)

Where,

VAX = Value of agriculture exports

MSDVAX = 3-Year Standard Deviation of value of agriculture export during

1991-2011.

NER = Nominal exchange rate

GDP = Gross domestic product

BM = Broad money

T = Time

Taking log and adding error term:

Ln (VAX)t = a1 + b1Ln(NER) +c1Ln(GDP) + d1Ln(BM) +d1T + u1t …..

(14)


Ln (MSDVAX)t = a1 + b1Ln(NER) +c1Ln(GDP) + d1Ln(BM) +d1T + u1t

.....(15)

5.0 Results and Analysis

5.1 Growth and instability

The basic objective of this paper is to analyse the trends in growth and

instability of Agriculture Commodities Exports. For capturing these trends we

have studied that same at two levels: first, at the level of individual commodities;

and second, at the level of the general macro-economic determinants that

influence the trends in these principal commodity exports. The former are once

again studied in terms of the three aspects, that is, price, quantity and value and

the latter in terms of growth and instability. Finally, we shall be relating these

trends to macroeconomic variables in terms of growth and instability. Table 1

gives the overall growth rates of the 9 agricultural commodities exports.

Table 1: Overall Growth Rates of Agriculture Commodities Export

Tea

Ca

shew

Nu

ts

Ca

ke

of

So

yb

ean

s

Co

ffee

,

Gre

en

Oil

Ca

sto

r

Bea

ns

To

ba

cco

On

ion

s

Ses

am

e

See

d

Ca

ke

of

Ra

pes

eed

Value 2.77* 4.26* 8.08* 3.94* 9.58* 9.18* 9.25* 14.1* 7.75*

Quantity 1.66* 4.19* 4.67* 2.34* 5.43* 6.19* 12.06* 9.89* 1.80NS

Price 1.11* 0.06NS

3.41* 1.60* 4.15* 2.99* 2.82* 4.30* 5.96*

*Implies that ACGR is significant at 5 % level

NS Implies that ACGR is not significant

Another sub-theme in the analysis is to study the trends in growth and

instability during the four policy periods, namely, liberalisation, WTO, world

recovery and global crisis. This has been done with the help of the dummy

variable exercises performed on all 9 principal agricultural. We now consider the

trends of each of the commodities in terms of growth and instability of these

exports during the four policy periods, in terms of value, price and quantity.


Individual commodities

Tea

Tea, as one of the principal commodities exported from India has done

fairly well. Although its started out poorly during the first phase of liberalisation,

in value terms, since it declined by around 15% per annum, it has shown a

constant rise from single digit growth to double digit growth, period after period

(Table 2). With the exception of the globalisation period, when the instability

rose, it has remained between 3 to 6 per cent. Even during the crisis tea

performed very well since it grew at 13.28% and the instability was half of that

during globalization.

Table 2: Growth and Instability of Tea

Value Quantity Price

Policy Periods GR II GR II GR II

Liberalisation -14.75*** 6.04 -11.47*** 6.145 -3.28NS

1.04

Globalisation 2.54*** 14.76 3.62*** 8.73 -1.08NS

7.56

World Recovery 6.86*** 3.46 0.977*** 4.05 5.88*** 3.51

Global Crisis 13.28*** 5.68 15.26*** 7.94 -1.99NS

2.36

Note: - GR stands for growth rate, II stands for instability index,

***Implies that GR is significant at 5 % level and NS Implies that GR are not significant

In terms of quantity Tea, as one of the principal commodities exported from

India has followed a similar pattern. Although its start was very poor during the

first phase of liberalisation, in value terms, since it declined by around 12% per

annum, it has shown a constant rise from single digit growth to double digit

growth, period after period. With the exception of the world recovery period,

when the instability fell, it has remained between 6 to 8 per cent. Even during the

crisis tea performed very well since it grew at 15.26% and the instability was

almost constant. But the best period, in this case was world recovery since the

growth was 6.86% in value terms and just 0.97% in quantity terms.

In terms of price the trend is very apparent. Although the volatility is not

very much, since the instability lies between 1- 3.5, with the exception of


globalisation, there is no appreciation in the price, save world recovery period.

That too is like recovering the lost ground because in all other periods the price

has fallen (though not significantly).

Cashew Nuts

Cashew nut is another principal commodity exported from India. Its

performance is very good because in value terms it has been consistently been

growing at 12.74%, across all periods. Although the instability has been very

high it started at a low level. During crisis the volatility increased to 15.56%

(Table 3).

In terms of quantity the trend is encouraging because it has to be read

with the trend in price. The trend in quantity declined from almost 16% to (-)

0.54%. On the other hand, the trend in price rose from (-) 3.24% to 8.96%. The

recovery in price during crisis was phenomenal. This shows a strong independent

trend, irrespective of the international policy regime and period.

Table 3: Growth and Instability of Cashew Nuts



Liberalisation 12.74** 3.54 15.98*** 2.62 -3.24NS

6.23

Globalisation 12.74** 13.48 5.64** 6.73 -2.60NS

7.48

World Recovery 12.74** 9.05 0.75* 7.27 7.40*** 6.98

Global Crisis 12.74** 15.56 -0.54* 10.92 8.96** 4.91


*** and ** imply that GR is significant at 5 % level and 10 % level respectively

NS Implies that GR are not significant

The index of instability shows an increasing trend. This implies that

balanced growth in terms of price as well as quantity would be a better pattern of

export growth rather than growth only through price. Therefore, competitiveness

should be better interpreted in terms of value rather than price alone. Stablility is

equally important as growth. There is a need to emphasize both price and value

terms. Ideally, quantity demanded should be elastic and revenue should grow

accordingly.


Cake of Soybeans

Soyabean shows an extremely uncertain trend. There are violent shifts in

terms of growth. The growth rate varies from (-) 8.71% to 31% (Table 4). This

creates great volatility in cropping patterns, as well as, income patterns. In

addition, the instability is very high in all periods.

Table 4: Growth and Instability of Cake of Soybeans



Liberalisation 13.77NS

20.9 12.96NS

16.93 0.811NS

5.63

Globalisation -8.71** 19.01 -3.53*** 6.66 -5.178NS

15.87

World Recovery 30.99NS

13.75 24.04NS

17.09 6.95NS

12.77

Global Crisis .049NS

18.11 4.96NS

18.62 -0.091NS

1.98




Globalisation has been a very bad period in terms of all three variables:

value, quantity and price. Similarly, global crisis has been a very bad period in all

three terms. An extremely low volatility in crisis period does not augur well. It

only shows a very poor trade prospect. Thus, low instability by itself is not so

desirable. A situation like world recovery actually is preferable because it shows

the lowest volatility and the highest growth, although not significant.

Coffee, Green

Coffee is another major primary export. In growth terms it shows a very

high rate. Once again world recovery is the best period because the growth rate is

very high and the instability is very low. In quantity terms, the instability is low

but growing (Table 5). Globalisation has been a poor period for coffee. A

continuous variation in quantity and price terms damages the trend of coffee

exports.


Table 5: Growth and Instability of Coffee, Green



Liberalisation 28.83*** 19.96 9.49** 2.86 19.34*** 22.28

Globalisation -14.54*** 15.24 2.16NS

6.7 -16.65*** 12.88

World Recovery 28.83*** 8.66 0.22*** 7.41 18.99NS

5.48

Global Crisis 28.83*** 22.65 16.46NS

9.92 5.21NS

12.62 *** and ** imply that GR is significant at 5 % level and 10 % level respectively


Castor oil

Castor oil also shows a very unstable trend, as it ranges from (-) 2.02 to

24.85%. A similar trend happens in the case of quantity. The instability in

quantity is much lower. The price trend shows increasing instability. Although,

the price recovery was very high in the crisis period, the instability was also very

high (Table 6).

Table 6: Growth and Instability of Oil Castor



Liberalisation 24.85*** 13.8 16.58*** 8.52 8.27NS

6.88

Globalisation -2.022*** 19.24 -1.55*** 11.97 -0.47NS

7.66


8.26 14.16NS

11.26 5.40NS

8.57

Global Crisis 21.98NS

10.84 7.5NS

3.87 14.48NS

15.09 *** and ** imply that GR is significant at 5 % level and 10 % level respectively ; NS implies that

GR are not significant

Tobacco, Unmanufactured

Tobacco shows the natural pattern of rejection of tobacco as an

undesirable commodity. With the exception of the world recovery period when

tobacco grew in value and quantity terms, in all other periods there has been a

decline. In term of price, the recovery was very high in the world recovery

period. However, the period of liberalisation has been historical in term of high

instability of tobacco price (Table 7)


Table 7: Growth and Instability of Tobacco



Liberalisation -24.83*** 17.98 -11.66NS

19.25 -13.17*** 8.31

Globalisation -1.54*** 21.44 -0.18NS

18.04 -1.36*** 6.78

World Recovery 16.19*** 2.76 10.17*** 2.18 6.02*** 4.19

Global Crisis -3.84* 9.46 -3.57NS

6.36 -0.27*** 3.09



NS Implies that GR is not significant

Onions

The pattern of onion export shows the pull and push of domestic vs.

international pressures of demand and supply. World recovery was a period of

plenty. Therefore, in all three terms oinions show growth. The growth in terms

price is very significant. But this also points out to the main source of instability

which itself is price (Table 8).

Table 8: Growth and Instability of Onions



Liberalisation 4.51 9.99 5.09NS

10.39 -0.58NS

0.6

Globalisation -1.53 18.65 0.006NS

18.64 -1.53NS

8.31

World Recovery 22.24* 13.72 12.16* 13.01 10.07*** 15.22

Global Crisis -4.25 8.83 -14.32* 5.5 10.08** 45.47

*** and *imply that GR are significant at 5 % and 15 % level respectively

NS Implies that GR is not significant

Sesame Seed

In the case of sesame seeds except in certain periods such as world

recovery in value terms and global crisis in quantity terms there is no period

when there has been consistent growth. Although instability has been declining

the very patterns are so varied that it leads uncertainity and inconsistency (Table

9).


Table 9: Growth and Instability of Sesame Seed




13.5 4.29NS

5.83 0.28NS

17.49

Globalisation 9.57NS

12.092 15.77NS

18.58 -6.2NS

10.41

World Recovery 26.58*** 16.8 16.56NS

9.77 10.02NS

9.67


8.12 26.29** 4.33 -7.66NS

4.94


***Implies that GR are significant at 5 % level, and NS Implies that GR are not significant

Cake of Rapeseed

In the case of rapeseed the main change is that prices have been falling.

The terms of trade has been falling. This has adversely affected all other

variables. On the whole the trend is not very encouraging. Although instability

has been falling it does not augur well for the future of the commodity (Table

10).

Table 10: Growth and Instability of Cake of Rapeseed




6.86 12.09NS

7.18 15.44*** 6.94

Globalisation -30.35*** 53.75 -33.08*** 35.36 2.73** 15.29


16.37 34.6NS

20.25 8.96NS

9.67


11.91 10.16NS

17.72 -0.61** 5.66


*** and ** implies that GR are significant at 5 % and 10 % level respectively


Macroeconomic determinants

Finally, we examine the trends in the macro-economic determinants.

Exchange rate has been depreciating but at a diminishing rate. Particularly,

during world recovery, the Rupee was appreciating. The instability of all three

macro variables has been rather low. With the exception of broad money and


GDP in the beginning, the macroeconomic variables‟ growth is not significant.

The peculiarity is that while macro variables show a conservative trend the trends

in primary commodity exports are much more vibrant. This shows that they have

an independent set of factors that drive these exports (Table 11).

Table 11: Growth and Instability in Macro-Economic Variables

EXCHANGE RATE

BROAD MONEY

SUPPLY

GROSS DOMESTIC

PRODUCT

Policy Periods Growth

Rates

Instability

Index

Growth

Rates

Instability

Index

Growth

Rates

Instability

Index

Liberalisation 19* 7.35 15.88* 0.65

13.95* 0.023

Globalisation 6.47* 1.17 15.49NS

0.82

12.17* 2.10

World

Recovery -1.57* 1.60 15.58NS

1.92

13.01NS

1.69

Global Crisis 4.05* 4.28 15.98NS

0.69 15.31NS

1.75

NS Implies that ACGR are not significant; *implies that ACGR are significant at 5 % level

6.0 Determinants of growth and instability

As mentioned before, to study the impact of macro-economic variables on

agricultural commodity exports we have estimated double log equations

(equations (12) and (13) specified earlier). The results of estimation are given

below in tables 12 and 13.

The results of the first equation show the following:

1. Exchange rate depreciating (of Rupee) negatively and significantly affects

growth of commodity exports. If exchange rises by 1% export growth will fall by

2%.

2. GDP significantly and largely influences growth of exports. If GDP rises by

1% then export growth will rise by 2.5%.

3. Although broad money represents availablitity credit, it seems that money

supply significantly and negatively influences growth of exports.


Table 12: Determinants of Growth

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.969

R Square 0.937

Adjusted R

Square 0.925

Standard Error 0.117

Observations 19

ANOVA

Df SS MS F

Significance

F

Regression 3 3.086 1.029 75.085 2.9113E-09

Residual 15 0.206

0.013

7

Total 18 3.291

Coefficient

s

Standard

Error t Stat

P-

value

Intercept 1.963 2.652 0.74 0.47

LNER -1.912 0.260 -7.34 0.0021

LGDP 2.540 0.732 3.47 0.003

LBM -1.217 0.561 -2.17 0.046

The second equation (Table 13) explains the impact of macro-economic variables

on exports.

1. It shows that exchange rate depreciation does not significantly influence

instability of exports.

2. GDP does influence negatively. This implies that if GDP grows then export

instability falls.

3. On the other hand, broad money leads to a growth in standard deviation of the

exports.


Table 13: Determinants of Instability

Regression Statistics

Multiple R 0.565

R Square 0.320

Adjusted R

Square 0.183

Standard Error 0.0517

Observations 19

ANOVA

Df SS MS F

Significance

F

Regression 3 0.0188

0.006

2 2.3475 0.1138

Residual 15 0.0400

0.002

6

Total 18 0.0588

Coefficient

s

Standard

Error t Stat

P-

value

Intercept 1.84 1.171 1.57 0.137

LNER 0.098 0.115 0.852 0.407

LGDP -0.60 0.323

-

1.849 0.084

LBM 0.47 0.247 1.901 0.076

7.0 Conclusion

The study analyses the growth and instability aspects of principal

agricultural commodity exports over the period 1990-91 to 2010-11, segregating

it by four policy periods. Further, it looks at the impact of macro-economic

variables such as exchange rate, GDP and money supply on the performance of

these exports. We find that growth of agricultural commodities exports largely


depends upon GDP. Further, exchange rate depreciation is not helpful in raising

the level of exports. The results also show that the elasticity of exports is less

than one so that revenue falls. Instability is found to depend on money supply.

Exchange rate does not affect instability of exports. The absorption approach

operates through GDP and it clearly raises the level by 2.5 times. Instability is

affected only by broad money and GDP.

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Growth, Instability and Determinants in India’s ... · With economic liberalisation in India, one of the major shifts was the switch from a fixed exchange rate regime to flexible