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The presentation is from the one day workshop on ‘Pulses for Nutrition in India: Changing Patterns from Farm-to-Fork’ organized on Jan 14, 2014. The workshop is based on a few studies conducted by the International Food Policy Research Institute under the CGIAR’s Research Program on Agriculture for Nutrition and Health. These studies covered the entire domain of pulse sector in India from production to consumption, prices to trade, processing to value addition, and from innovations to the role of private sector in strengthening the entire pulse value chain. These studies were designed to better understand the drivers of changing dynamics of pulses in the value chain from farm-to-fork, and explore opportunities for meeting their availability through increased production, enhanced trade and improved efficiency.
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COOLING EFFECT OF PULSE IMPORTS
ON PRICES: THE CASE OF PIGEON PEA
IN INDIA
January 14, 2014
New Delhi
Devesh Roy
Akanksha Negi
India -largest producer and consumer of pulses in the world (FAO,
2008).
Yet, India’s pulse production- consistently inadequate in meeting
the rising demand.
▫ Subpar pace of increase in production : production fluctuating
between 11 and 14 million tons annually.
▫ Evolving demographic dietary patterns.
▫ Pulses, nutritionally important crop: Dominant sources of plant
based proteins in traditional vegetarian Indian diet.
▫ Heterogeneity in pulses intake: function of tastes and
preferences (amongst other things).
▫ Persistently high prices: result of demand outpacing supply both
on an aggregate level and across variety.
Scenario of Pulses in India
Research Question
Price evolution of pulses
11:00 AM
3
MSP over time (recent data- bit
slower growth in MSP)
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URAD
Moong
Tur
Unit value of Import vs MSP Unit value (import) Rs/T MSP Rs/T MSP as % of import value
2009-10 2010-11 2011-12 2009-10 2010-11
2011-12 2009-10 2010-11 2011-12
Peas 15663 15033 20265 0 0 0 0 0 0
Chickpeas 25102 25049 37094 17600 21000 28000 70 84 75
Moong/Urad 44795 53450 41941 27600 31700 44000 62 59 105 Lentils 37566 37754 30861 18700 22500 28000 50 60 91
Tur (Arhar) 42551 33177 30709 23000 30000 38500 54 90 125 Others 38533 48004 36885 - - -
Total pulses 28343 27044 27028 - - -
Import of Pulses Qty in Lakh MT
2009-10 2010-11 2011-12
Peas 16.56 15.05 20.23
Moong, Urad, Massor,
others
13.66 8.26 8.38
Tur 3.89 3.46 4.26
Chick peas 3.38 1.01 2.03
Total pulses 37.50 27.78 34.91
Primer on pulse prices
Low weightage to Pulses vis-à-vis cereals and animal source foods e.g. Milk in Wholesale Price Index.
Experiencing high prices post 2005.
Hypothesis: Under rising prices, Imports cool down domestic prices.
Examples:
Massive wheat Imports- to the tune of 6 million tons.
Liberalization of Edible Oil Imports
Stop gap allowance for Imports -Milk, Sugar and Onions.
Steady growth in Pulses Imports since 2000: Growing up by as much as 36 percent
Evolution of Pulses Imports coinciding with persistent increase in prices: To what extent have imports cooled domestic markets?
This paper is an attempt to fill in this gap in the literature.
Pulses in price index
Year High Inflation Low inflation
High weight Low weight High weight Low weight
1998 7 types of edible oil
Masur (11.6,0.3)
Rice, wheat Other coarse cereals
Arhar (29.5, 0.6)
Moong (11.1, 0.4)
Sugar Urad (4,0.5)
Milk Some fruits and vegetables
Gram (1.9,1.6)
Green pea (-21.4,0.5)
Onions Gur/Khandsari
Bajra
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Pulses in price index
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Pulses in price index
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Pulse in price index
High inflation Low inflation
High weight Low weight High weight Low weight
2010 Fish Moong Rice Masoor
Milk Urad wheat
Sugar Gram
Arhar
Green Peas
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Why Pigeon Pea –Home Scenario
• One of the major pulses in India alongside Chick Peas, Black Gram,
Green Gram and lentils.
• India accounting for 90% of total global area and 93% of world
production (FAO, 2009).
• What happened to Pigeon Pea prices?
▫ The retail price of pigeon pea reached as high as Rs 120 per kg and other
pulses remained above Rs 70 per kg for more than six months (Reddy,
2009).
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Data and Variables
Time Period: 2002-2012
Data Sources ▫ Imports: Customs Dataset
▫ Wholesale Price Index (WPI): Office of the Economic Advisor, Ministry of Commerce an Industry.
http://www.eaindustry.nic.in/
Variables: ▫ Weekly Imports (in constant million USD)
▫ Weekly Wholesale price index (WPI)
We use variables in their log transformations for technical reasons.
Data
Presentation of variables: 2002-2012
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Pigeon Pea
New long run
Equilibrium?
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Some Other Cases: Black Matpe, Chick Pea
Lentils, Peas
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Some other cases of persistent import
penetration
• Edible oils
• In other cases more stop gap role of food imports in
India – case of sugar, milk, vegetables
• how does it differentiate the cooling effects of
imports on prices?
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Generically, what do we do?
• Conduct an analysis of the cooling effect of imports on prices of pulses.
• need a mapping between the imported item and its counterpart in the domestic price data.
• Pigeon pea one of the pulses where can be direct one to one mapping.
• Mapping realized using a novel approach of employing the customs data disaggregated at 8 digit level.
• Several details in customs data but importantly allows date of imports. Only upon dating, we can match high frequency imports with similar frequency of price indices.
• Cooling effect is an issue that extends to several other commodities with import penetration
11:00 AM Research Question
Vector error correction model
∆𝑙𝑛𝑀𝑡 = 𝑣𝑀 +∝𝑀 𝑧𝑡−1 + 𝜏𝑖∆𝑙𝑛𝑀𝑡−𝑖 + 𝜑𝑖∆𝑙𝑛𝑃𝑡−𝑖 + 𝜖𝑀𝑡
3
𝑖=1
3
𝑖=1
∆𝑙𝑛𝑃𝑡 = 𝑣𝑃+∝𝑃 𝑧𝑡−1 + 𝛿𝑖∆𝑙𝑛𝑀𝑡−𝑖 + 𝜋𝑖∆𝑙𝑛𝑃𝑡−𝑖 + 𝜖𝑃𝑡
3
𝑖=1
3
𝑖=1
with 𝛽′ = (1, −0.365), ∝ = −0.357, 0.021 , and 𝑣 = (0.0001, 0.0013)
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The long run dynamics between Import and Prices are captured by the cointegrating equation:
𝑙𝑛𝑀𝑡 = −0.566 − 0.365𝑙𝑛𝑃𝑡
Stationarity
• Both the series are non-stationary • stationary time series-never wanders too far from the mean.
The effect of errors decay and disappear over time. Things that happened recently relatively more important than things that happened a long time ago.
• non-stationary time series- time series that eventually explodes. Things that occurred a long time ago have large impact compared to things that occurred more recently
• (random walk if moves up and down)
11:00 AM
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Impulse response function • Rather than seeing serial correlation as technical violation of an
OLS assumption, modern view is:
▫ serial correlation as a potential sign of improper theoretical specification
• leads to ‘dynamic’ regression models i.e. inclusion of lagged (dependent and independent) variables.
• Greene (2003)- Impulse and unit response functions in time series models- counterpart - marginal effects in cross-sectional setting.
• Imagine models in equilibrium. An IRF is when the independent variable goes up 1 unit in one period and back to 0 next period.
• unit response function - when the independent variable goes up 1 unit and remains up one unit for all remaining periods.
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Salient findings
• coefficient of prices in the equation is statistically significant implying
imports and prices are related in a negative way.
• The error adjustment by imports in the current period=-0.36- Imports fall
rapidly when away from equilibrium.
• Prices display a somewhat lukewarm adjustment mechanism imports are
flexible whereas prices tend to be sticky in the short run.
• Second since data on imports and prices at a very high frequency, imports
behaviourally tend to be more volatile compared to prices.
• Both the adjustment coefficients significant at 1% level.
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Impulse: Prices
Response: Imports
Impulse: Imports
Response: Prices
Impulse Response Functions
Take away from impulse responses
• A unitary shock in Ms is associated with a sustained
increase in prices until 20 weeks after which it
stabilizes to a constant impulse
• Is it imports heating markets?
• Not really look at the rate of price change – it
decelerates
• Imports might be cooling by suppressing rate of price
escalation
• There is catch up at half yearly level
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Take away from impulse responses:
Continued
• Imports bear on levels of prices with a lag
• Imports bear on price growth comparatively
• Imports have not super-cooled the pigeon pea
markets i.e. have not immediately brought clam in
markets
• Could it be the dominance of intensive margin in
pigeon pea imports?
• Action on extensive margin would it make the
outcome better?
11:00 AM
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Are Prices causing Imports or Imports
causing Prices?
• Granger Causality: In a bivariate setup, X is said to granger
cause Y if controlling for the past values of Y, lagged values of
X are instrumental in predicting current Y realizations.
• Not a real causality but one leading to another
• No prior basis to assume whether imports granger cause prices
or vice versa.
• In the present bivariate setup, its hard to say conclusively
whether imports are granger causing prices or vice versa.
• This ambiguity has information
• Prices and imports seem to be intertwined as theory and
observations would suggest
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Bi-directional causality has important
message
• Prices may have induced imports
• Imports itself might bear on prices through different
channels such as consumer response, agent’s
expectations
• Economic phenomenon often show such bi-
directional relationships
• E.g. stock prices and exchange rates in India (Kumar
2009)
• Implication- realize the intertwined nature of imports and
domestic prices
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The other side
• Imports do respond to price shocks
• Is it timely and is it enough
• May be not
• On either side five to six months span seems focal
• Imports take time and will be so unless more
consistent trade relationships become norm i.e. are
bootstrapped
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Take home messages for policy • Imports and prices are intertwined need to be
considered in conjunction
• Clear evidence of imports and prices co-movement
• Roughly imports’ role in cooling pulse markets
reflect the reactionary stance rather than a pro active
disposition
• With status quo in trade policy it can only be
protracted cooling of markets
• Expansion on the extensive margin might speed
things up
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