Preeti laddha weather and macroeconomics
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1 International Conference on Agribusiness and Food Industry in Developing Countries: Opportunities and Challenges WEATHER RISK, AGRO COMMODITY PRICES AND MACRO ECONOMIC LINKAGES: EVIDENCE FROM INDIAN SCENARIO USING CO-INTEGRATION MODEL Presented By: Ms.Preeti Laddha Ms.Surabhi Agarwal
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Rainfall has been an unpredictable element in agricultural production for long, the farmer has to depend on the vagaries of nature to sustain his crops. The Indian subcontinent receives its rainfall from the south-west (summer) monsoon during June-September with very little rainfall in winter. June and July thus become crucial months for sowing the summer crop, which accounts for 50% of total agriculture input. Any deficit here will affect all the summer crops like groundnuts, cotton, sugarcane, kharif rice and soybeans. Experience and theory suggest that commodity prices and weather indices do not correlate well in a local area. This makes it virtually impossible to manage weather risk with a price hedge. There are no physical markets in weather. Moreover weather risk is localized and beyond human control Weather insurance often has failed because of inherent defects in its planning. We study the impact of weather i.e. rainfall on agricultural production and thus by extension the prices, and the GDP. Our results through ADF co-integration analysis indicate a short term relationship between the factors but there seems to be no convergence among these factors in the long term.
Transcript of Preeti laddha weather and macroeconomics
1
International Conference onAgribusiness and Food Industry in
Developing Countries: Opportunities and Challenges
WEATHER RISK, AGRO COMMODITY PRICES
AND MACRO ECONOMIC LINKAGES:
EVIDENCE FROM INDIAN SCENARIO USING
CO-INTEGRATION MODEL
Presented By:Ms.Preeti LaddhaMs.Surabhi Agarwal
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AGRICULTURE IN INDIA
• India sustains 16% of the world’s population on 2.4% of land resource
• Agriculture contributes 24% of the Indian GDP• Employment to 57% of work force• Single largest private sector occupation• Raw material source to large number of industries
like (textiles, silk, sugar, rice, flour mills, milk products)
Objectives of Study
• To measure and analyze the impact of weather on commodity prices.
• Measure the degree of weather risk inherent on commodity prices and consequent linkages to inflation, exchange rates and GDP
• Our recommendations
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Methodology• Five major crops were selected namely
rice , wheat , cotton ,sugar and oilseeds.• Index numbers of prices and production
has been taken for these commodities as proxy for commodity prices and production
• Actual Rainfall as % of Normal Rainfall has been taken
• Co-Integration Model has been used to examine cause-effect relationship.
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Growth Rate in Agriculture GDP in various Year
6.5 2001-02
6.87 1998-99
0.3 1999-2000
-0.1 2000-01
-2.82 1997-98
-5.2 2002-03
10.1 1996-97
-1.13 1995-96
5.08 1994-95
4.1 1993-94
6.22 1992-93
-1.85 1991-92
4.43 1990-91
GDP growth rate in agriculture (%)Year
Graph between Growth in Agricultural GDP and Years
GDP Agriculture for various year
-6-4-202468
1012
1990
-91
1992
-92
1994
-95
1996
-97
1998
-99
2000
-01
2002
-03
year
agri
cultu
re G
DP
GDP Agriculture
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Risk in Agriculture
Dependence on Weather
• Up to 80% of variability in crop yields is attributed to weather
• Less than 40% of net sown area is irrigated• Most irrigation from non-perennial sources• Extreme Weather Events in India (cold wave,
drought, fog, heat wave, tropical cyclones, floods)• Dwindling ground water resources
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Actual Rainfall as % of Normal in various year
922001-02
1061998-99
961999-2000
922000-01
1021997-98
812002-03
1031996-97
1001995-96
110 1994-95
1001993-94
931992-93
911991-92
1191990-91
Actual Rainfall as % of Normal RainfallYear
Graph for Rainfall in various Year
rainfall in various year
020406080
100120140
1990
-91
1992
-92
1994
-95
1996
-97
1998
-99
2000
-01
2002
-03
year
actu
al r
ainf
all a
s %
of
norm
al actual rainfall as %ofnormal rainfall
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Production of five selected Commodities in various year
Cotton Oilseeds Rice Sugarcane Wheat
116.2 152.2 146.1 185.42 184.92002-03
132.9 194.8 187.5 190.2 206.72001-02
126.6 176.5 170.9 189.4 1982000-01
153.3 193.3 180.3 191.6 2171999-2000
163.4 224.9 173 184.8 202.51998-99
144.3 198.2 166 178.9 188.5 1997-98
189.2 231.3 164.4 177.6 1971996-97
171 212.1 154.8 179.9 176.41995-96
158.1 208.4 164.5 176.3 186.81994-95
142.8 203.4 161.5 147 1701993-94
151.6 193.6 146.5 145.9 162.51992-93
129.2 181.5 150.2 162.6 158.21991-92
130.9 179.5 149.4 154.3 156.61990-91
Production (Index number)Year
Graph of selected five commodity for various year
Production of commodity for various year
0
50
100
150
200
250
1990
-91
1991
-92
1992
-92
1993
-94
1994
-95
1995
-96
1996
-97
1997
-98
1998
-99
1999
-200
0
2000
-01
2001
-02
2002
-03
year
prod
uctio
n(in
dex
num
ber)
wheatricecottonoilseedssugarcane
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Prices of Five Selected commodities
445.28319.95441.56350.52349.312002-03
442.75346.02444.22287.02366.542001-02
447.81362.61446.88261.62386.222000-01
442.75369.72454.86309.88361.621999-2000
383.56364.98388.36353.06410.821998-99
349.14317.58356.44289.56381.31997-98
346.61282.03343.14292.1327.181996-97
283.36267.81311.22297.18391.141995-96
275.77282.03295.26281.94378.841994-95
2532372662542461993-94
2271802492652181992-93
2041602172662381991-92
1721521782231461990-91
WheatSugarcaneRiceOilseedsCotton
PricesYear
Graph of Prices of The Commodities in various year
Price of commodity in various year
0
100
200
300
400
500
1990
-91
1992
-92
1994
-95
1996
-97
1998
-99
2000
-01
2002
-03
Year
Pri
ces
wheatricecottonoilseedssugar
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AnalysisOLS Model Result
0.386853 0.62197 0.91476 0.956431 Wheat
0.311734 0.55833 0.770684 0.87788 Sugarcane
0.37137 0.60940.71533 0.845772 Rice
0.52286 0.72309 0.267377 0.51708 Oilseeds
0.448288 0.669543 0.208537 0.456658 Cotton
R SquareMultiple RR SquareMultiple R
GDP, Price, production, rainfall
Prices, Rainfall, productionCOMMODITY
OLS MODEL SIGNIFICANCE RESULTS
Significant Significant Sugarcane
Significance LevelSignificance Level
Significant Significant Wheat
Significant Significant Rice
Significant Significant Oilseeds
Significant Significant Cotton
GDP, Production, Prices, Rainfall
Prices, Production, Rainfall
COMMODITY
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CO-INTEGRATION RESULTS
Significant -2.261 -3.009 Not significant
-2.261 -1.1487 Wheat
Significant -2.261 -4.005Not significant
-2.261 -1.93812 Sugarcane
Significant -2.261 -3.989 Not significant
-2.261 -1.26638 Rice
Not significant
-2.261 -1.317 Significant -2.261 -2.56052 Oilseeds
Not significant
-2.261 -1.987 Not significant
-2.261 -1.92241 Cotton
SignificantCritical Value
T statSignificantCritical Value
T stat
GDP, rainfall, production, prices
Price, rainfall, productionCOMMODITY
OLS Model & Co-Integration Model Results
OLS MODEL shows that there is significant relationship in both cases for the sample of all five commodity.
Co-integration Model show that there does not exists a equilibrium between price, rainfall and production.
Co-integration Model show that there exists a equilibrium between GDP, price, rainfall and production in case of Rice, sugarcane and wheat.
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Inference• High degree of co-relation between
commodity prices, rainfall and production of commodity. – In the case of cotton (45.66) and
oilseeds(51.7) it is less• In the case of Price, Rainfall and
production only oilseeds is co-integrated. This indicates a convergence in spite of a low co-relation among them.
• The reduced dependence on the rainfall aided by bumper production probably results in the lag effects on the macroeconomic factors.
• The development of instruments (insurance, weather derivative) which reduced the risk which will influence the prices.
• Need to design weather risk insurance models and strengthened the weather derivative to trickle down the risk.
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• In the case of GDP, Price, Rainfall and production Sugar and cotton is co-integrated. This indicates a convergence in spite of a low co-relation among them.
• Study shows that there is high impact of rainfall over production and Prices.
• Lack/excess Rainfall can create a supply shock.
THANKYOU !!!!
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