1
Food
Inflation in
India
Trends and
Causes
Gourav Kumar Vani
DIVISION OF AGRICULTURAL ECONOMICS,
INDIAN AGRICULTURAL RESEARCH INSTITUTE,
NEW DELHI-110012
ASSIGNMENT
ON
FOOD INFLATION IN INDIA: TRENDS AND CAUSES
SUBMITTED TO:
Dr. S.K. Srivastava,
NCAP, New Delhi
SUBMITTED BY:
GOURAV KUMAR VANI
ID NO. 10678
Ph.D. (Ag. ECON.)
SUBMITTED IN PARTIAL FULLFILMENT FOR COURSE WORK OF AG ECON 530.
DATE OF SUBMISSION: 06-11-2015
3
ACKNOWLEDGEMENT
I acknowledge the receipt of data on wholesale price index and a
excel work sheets from Dr. S.K. Srivastava for use in the preparation
of this assignment. The data and graphs provided in excel works
sheets helped me to arrive at the conclusion. I found suggestions by
Dr. S.K. Srivastava useful and have tried my best to incorporate in the
assignment. I thank the course teacher for proving the data set and
useful suggestions for my help and further improvement.
From
Gourav Kumar Vani
Contents
Introduction ___________________________________________________________________ 5
Trends in General and Food inflation ______________________________________________ 5
Analysis of Structural Breaks in various components of WPI series. ____________________ 5
References ___________________________________________________________________ 19
Table of Figures
Figure 1: Inflation in Food Articles and All Commodities ........................................................................................ 5
Figure 2: Breaks in WPI for all commodities ........................................................................................................... 7
Figure 3: Break points in WPI series for Primary Articles ........................................................................................ 8
Figure 4: Break points in WPI series for Manufactured Food Products .................................................................. 8
Figure 5: Break points in WPI series for Cereals ..................................................................................................... 9
Figure 6: Break points in WPI series for Bakery Products ....................................................................................... 9
Figure 7: Break points in WPI series for Grain Milled Products ............................................................................ 10
Figure 8: Break points in WPI series for Foodgrains ............................................................................................. 10
Figure 10: Break points in WPI series for Dairy Products ...................................................................................... 11
Figure 9: Break points in WPI series for Milk ........................................................................................................ 11
Figure 11: Break points in WPI series for Oilseeds ................................................................................................ 12
Figure 12: Break points in WPI series for Edible Oil .............................................................................................. 12
Figure 13: Weighted Contribution to Food Inflation ............................................................................................. 17
Figure 14: Wheat Stock held by FCI as percentage of Buffer Stock Norms ........................................................... 18
Table of Tables
Table 1:: Summary of Break dates for different commodities and commodity groups .......................................... 7
Table 2: Trends in Percentage Composition of Consumer Expenditure since 1993-94 for Rural India .............. 14
Table 3: Trends in Percentage Composition of Food Expenditure of the Rural Indian consumer ......................... 15
Table 4: Trends in Percentage Composition of Consumer Expenditure since 1993-94 for Urban India ............. 15
Table 5: Trends in Percentage Composition of Food Expenditure of the Urban Indian consumer ..................... 16
Table 6: Estimated Elasticities of food expenditure in India by commodities ...................................................... 16
5
Introduction
India being a developing country has to bear the necessary evil of inflation. Food
inflation or Agriflation has remained a major area of concern for the policy makers after
independence. Following the years of liberalization India started to change structurally but
still it had around 70 percent of the rural population depending on agriculture for its
livelihood in 1990’s. Households that were self employed in agriculture accounted for 28
percent of all the poor, while households that were primarily dependent on agriculture as
labour account for 47 percent of all the rural poor (Jain, Chaturvedi, Jharwal, & Parshad,
2007). In such a situation a high rate of food inflation was equal to taxing the poor people
who spend most of their income on food items. These facts made focus on food inflation even
more important to policy decisions. Currently population depending on agriculture has come
down to 52.8 percent but recent hyperinflation in food commodities has renewed the interest
of research scholars in Agriflation in India.
25
20
15
10
5
0
-5
-10
2005M4
2005M9
2006M2
2006M7
2006M12
2007M5
2007M10
2008M3
2008M8
2009M1
2009M6
2009M11
2010M4
2010M9
2011M2
2011M7
2011M12
2012M5
2012M10
2013M3
2013M8
2014M1
2014M6
2014M11
2015M4
2015M9
%
Trends in General and Food inflation
Food Articles
All Commodities
Figure 1: Inflation in Food Articles and All Commodities
From the Figure2, it can be observed that food inflation has remained above the general
inflation for much of the period including even in the recent past. It can further be noted that
food inflation reached all times high of more than 20 percent in December-2009. Again in
October-2013, it reached a peak. In recent past, food inflation has cooled down considerably.
Analysis of Structural Breaks in various components of WPI series.
To analyse the structural breaks in series breakpoint analysis was performed on the
wholesale price index series for various commodities and commodity groups. The analysis
was performed using “strucchange” package in R version software. Here, breaks are detected
not for the break in the continuity of the series but in the trend accompanying the series. The
function used for detection of break in trend was “breakpoints”.
All procedures in the”strucchange” package are concerned with testing or assessing
deviations from stability in the classical linear regression model
yi = xiT β + ui
In many applications it is reasonable to assume that there are 𝑚 breakpoints, where the
coefficients shift from one stable regression relationship to a different one. Thus, there are
𝑚 + 1 segments in which the regression coefficients are constant, and the model can be
rewritten as
yi = xiT βj + ui (𝑖 = 𝑖𝑗−1 + 1, … , 𝑖𝑗 , 𝑗 = 1, … , 𝑚 + 1)
where 𝑗 denotes the segment index. In practice the breakpoints 𝑖𝑗 are rarely given
exogenously, but have to be estimated. breakpoints estimates these breakpoints by
minimizing the residual sum of squares (RSS) of the equation above. The foundation for
estimating breaks in time series regression models was given by Bai (1994) and was extended
to multiple breaks by Bai (1997ab) and Bai & Perron (1998). Breakpoints implements the
algorithm described in Bai & Perron (2003) for simultaneous estimation of multiple
breakpoints. The distribution function used for the confidence intervals for the breakpoints is
given in Bai (1997b). The ideas behind this implementation are described in Zeileis et al.
(2003).
The algorithm for computing the optimal breakpoints given the number of breaks is
based on a dynamic programming approach. The underlying idea is that of the Bellman
principle. The main computational effort is to compute a triangular RSS matrix, which gives
the residual sum of squares for a segment starting at observation i and ending at 𝑖 ′ with 𝑖 <
𝑖 ′ .The regression model used in the present study is
𝑌 = 𝑓(𝑡) = 𝛼 + 𝛽𝑡
Where, Y is the WPI series for a given commodity is the time component and
𝛼 𝑎𝑛𝑑 𝛽 are parameters of regression equation.
From the Figure 2, it can be observed that the WPI series for all commodities had
breaks at points June-1989, November-1994, Februrary-2004, and October-2008. Break at
mid-1989 was result of growing uncertainty prevailing regarding LGP (Liberalization,
Globalization Privatization) model while break at 1994 end was result of reforms which took
place during early 1990’s. Break in series at early 2004 was result of drought of 2003-04. The
last break identified was result of onset of drought in 2008-09.
From the Figure 2, it can be observed that breaks in the trend for the series of primary
articles were May-1990, May-1998, Februray-2004, and October-2008. The first break was
result of devaluation of the currency while the latter break in May-1998 was result of
deficient rains in many parts of the country. The third break in February-2004 can be
attributed to drought in 2003-04. The fourth and the last break can be attributed again to
drought of 2008-09.
From analysis, it was found that breaks in trend in the whole price index series for
manufactured product were found at March-1989, June-1998, Februray-2003, and January2008. Similarly the breaks were detected for other commodity WPI series also and are
summarized in the table 1.
7
Table 1:: Summary of Break dates for different commodities and commodity groups
Particulars
Break Dates
All commodities
1989(6) 1994(11) 2004(2) 2008(10)
Primary Article
1990(5) 1998(5) 2004(2) 2008(10)
Cereals
1990(12) 1999(1) 2005(5)
Food grains
1990(12) 1999(1) 2005(3)
Milled Grain Products
1986(11) 1996(7) 2001(3) 2005(12)
Bakery Products
1991(5) 1996(11) 2003(2) 2007(12)
Oilseeds
1990(6) 2002(6) 2007(2)
Edible Oils
1990(9) 1999(12) 2004(11)
Milk
1986(11) 1996(6) 2001(5) 2008(1)
Dairy Products
1991(7) 2000(2) 2006(9)
Manufactured food products
1989(3) 1998(6) 2003(2) 2008(1)
100
50
WPI for All commodities
150
Figures in parenthesis indicate the month.
1985
1990
1995
2000
Time
Figure 2: Breaks in WPI for all commodities
2005
2010
150
100
50
WPI for Primary articles
1985
1990
1995
2000
2005
Time
150
100
50
WPI for Manufactured Food Products
Figure 3: Break points in WPI series for Primary Articles
1985
1990
1995
2000
2005
2010
Time
Figure 4: Break points in WPI series for Manufactured Food Products
2010
150
100
50
WPI for Cereals
200
9
1985
1990
1995
2000
2005
2010
Time
100
80
60
20
40
WPI for Bakery Products
120
140
Figure 5: Break points in WPI series for Cereals
1985
1990
1995
2000
Time
Figure 6: Break points in WPI series for Bakery Products
2005
2010
150
100
50
WPI for Grain Milled Products
1985
1990
1995
2000
2005
2010
Time
150
100
50
WPI for Foodgrains
200
Figure 7: Break points in WPI series for Grain Milled Products
1985
1990
1995
2000
Time
Figure 8: Break points in WPI series for Foodgrains
2005
2010
150
100
50
WPI for Milk
200
11
1985
1990
1995
2000
2005
2010
Time
100
50
WPI for Dairy Products
150
Figure 10: Break points in WPI series for Milk
1985
1990
1995
2000
Time
Figure 9: Break points in WPI series for Dairy Products
2005
2010
200
150
100
50
WPI for Oilseeds
1985
1990
1995
2000
2005
2010
Time
100
80
40
60
WPI for Edible Oil
120
140
Figure 11: Break points in WPI series for Oilseeds
1985
1990
1995
2000
Time
Figure 12: Break points in WPI series for Edible Oil
2005
2010
13
Causes of food inflation India
1. Supply shortfall: Imbalance between demand and supply is cited as one of the major
source of food inflation in India. This imbalance is due to two major factors as
following

Inelastic supply of agricultural commodities: It is well known fact that supply of
agricultural commodities cannot respond very well to the price incentive
generated by the market. This is because agricultural production takes place only
during a specified season and all operations are time bound. Each agricultural
operation is so time bound and these timings cannot be altered in short run.
According to research work done by Kumar, et al. (2010) own price elasticities
for rice, wheat, sugarcane, pulse grains and oilseeds were 0.24, 0.22, 0.12, 0.17
and 0.51 percent respectively. This shows that supply of these crops is price
inelastic.

Shift in consumption pattern: It is natural to expect the fall in consumer
expenditure on food commodities as per Engle law of consumption expenditure.
So with growth in per capita income the above expectation remains a valid one.
This can be observed in considerable shift in consumption pattern of the
agricultural commodities in last five decades after independence. Table 2 and 4
shows the changing pattern of spending on different food items groups out of total
income in rural and urban areas following liberalization. From the Table 2, it can
be observed that consumer spending in rural areas has shifted from cereals and
pulse based diet to high value items like milk and vegetable.
From Table 4, it can be observed that percentage expenditure on all
groups of food items has come down drastically but fall in share of cereals and
pulses is more than fruits and vegetables, milk and egg, meat & fish . Thus, total
spending in urban areas on these luxury food items of has increased. Though
absolute as well as real expenditure on food items has increased considerably both
in rural and urban areas but in terms of percent expenditure, it has come down
drastically on most of the item groups both. This shift is in partial conformity with
Engel’s law of consumer expenditure (Vani, 2013). While the whole picture
becomes very clear if we see the percentage composition of food expenditure in
rural and urban areas. This is evident from Table 3 and 5,wherein it is clearly
visible that out of total food expenditure the percentage expenditure has
substantially increased on high value food products (luxury food items) both in
urban as well as rural areas of India. This trend is again confirmed by the
estimates of expenditure elasticities estimated by Ganeshkumar, et al. (2012).
Results are provided in Table 3.
From the Table 6, it can be observed that expenditure elasticities are
negative for cereals and pulses while for milk, fruits and vegetables, it is in the
positive but less than one. Thus these three groups of food items are necessary
goods compared to cereals and pulses which have turned out to be inferior goods.
High protein food items such as egg, fish, chicken and meat are the luxury items
of food consumption (elasticity greater than one). Hence it can be concluded that
with increase in income Indian consumers have shifted the consumption pattern
but production has not shifted sufficiently to ward off inflation concerns
(Mohanty, 2014). From Figure 13, it can be observed that vegetables and egg, fish
& meat contribution to food inflation has increased in 2013 compared to2011
while contribution of cereals has gone down substantially. Similar trend in
contribution to food inflation were observed by Mohanty (2014) for period 2005
to 2013. However, this made many researchers to wrongly understand that
inflation in cereals affect less to the consumer. But the fact is that the total
expenditure of the consumer is highest on the cereals and pulses put together,
more than the total combined expenditure on vegetables and egg, fish & meat.
Thus, if even if cereals contribute less in the weighted contribution to inflation, it
should not be wrongly concluded that cereal inflation would affect less to the
consumer. So also, it should not to be concluded that greater stock of cereals by
FCI would hurt the Indian consumers less than it is widely considered.
Table 2: Trends in Percentage Composition of Consumer Expenditure since
1993-94 for Rural India
Item group
Cereal
1993-94
1999-2000
20004-05
2009-10
2011-12
24.2
22.2
18.0
15.6
12.0
Pulse and
products
their
3.8
3.8
3.1
3.7
3.1
Milk and
products
milk
9.5
8.8
8.5
8.6
9.1
Edible oil
4.4
3.7
4.6
3.7
3.8
Egg, fish, meat
3.3
3.3
3.3
3.5
3.6
Vegetables
6.0
6.2
6.1
6.2
4.8
Fruits and nuts
1.7
1.7
1.9
1.6
1.9
Food total
63.2
59.4
55.0
53.6
48.6
Non food total
36.8
40.6
45.0
46.4
51.4
Source:-NSSO, Ministry of Statistics and Programme Implementation, GOI, New
Delhi, June 2013
15
Table 3: Trends in Percentage Composition of Food Expenditure of the Rural Indian
consumer
Item group
1993-94
1999-2000
20004-05
2009-10
2011-12
Cereal
Pulse and
products
Milk and
products
Edible oil
their
38.29%
6.01%
37.37%
6.40%
32.73%
5.64%
29.10%
6.90%
24.69%
6.38%
milk
15.03%
14.81%
15.45%
16.04%
18.72%
6.96%
6.23%
8.36%
6.90%
7.82%
Egg, fish, meat
5.22%
5.56%
6.00%
6.53%
7.41%
Vegetables
Fruits and nuts
9.49%
2.69%
10.44%
2.86%
11.09%
3.45%
11.57%
2.99%
9.88%
3.91%
100.00%
100.00%
100.00%
100.00%
100.00%
Food total
Source: Calculated by author based on data available in Table 2.
Table 4: Trends in Percentage Composition of Consumer Expenditure since
1993-94 for Urban India
Item group
Cereal
1993-94
1999-2000
20004-05
2009-10
2011-12
14.0
12.4
10.1
9.1
7.3
Pulse and
products
their
3.0
2.8
2.1
2.7
2.1
Milk and
products
milk
9.8
8.7
7.9
7.8
7.8
Edible oil
4.4
3.1
3.5
2.6
2.7
Egg, fish, meat
3.4
3.1
2.7
2.7
2.8
Vegetables
5.5
5.1
4.5
4.3
3.4
Fruits and nuts
2.7
2.4
2.2
2.1
2.3
Food total
54.7
48.1
42.5
40.7
38.5
Non food total
45.3
51.9
57.5
59.3
61.5
Source:- NSSO, Ministry of Statistics and Programme Implementation, GOI, New
Delhi, June 2013
Table 5: Trends in Percentage Composition of Food Expenditure of the Urban
Indian consumer
Item group
1993-94
1999-2000
20004-05
2009-10
2011-12
Cereal
25.59%
25.78%
23.76%
22.36%
18.96%
Pulse and their products
5.48%
5.82%
4.94%
6.63%
5.45%
Milk and milk products
17.92%
18.09%
18.59%
19.16%
20.26%
Edible oil
8.04%
6.44%
8.24%
6.39%
7.01%
Egg, fish, meat
6.22%
6.44%
6.35%
6.63%
7.27%
Vegetables
10.05%
10.60%
10.59%
10.57%
8.83%
Fruits and nuts
4.94%
4.99%
5.18%
5.16%
5.97%
100.00%
100.00%
100.00%
100.00%
100.00%
Food total
Source: calculated by authors based on data available in Table 4.
Table 6: Estimated Elasticities of food expenditure in India by commodities
Commodity
Expenditure Elasticity
Rice
-0.21
Wheat
-0.13
Pulse
-0.24
Edible Oil
0.90
Milk
0.55
Vegetables
0.64
Sugar
0.83
Eggs
1.31
Fish, Chicken and Meat
1.17
Source: - Ganeshkumar, et al., 2012
17
Food Grains (Cereals and Pulses)
Vegetables
Fruits
EGGS,MEAT & FISH
CONDIMENTS & SPICES
OTHER FOOD ARTICLES
MILK
60%
40%
20%
Nov/13
Sep/13
Jul/13
May/13
Mar/13
Jan/13
Nov/12
Sep/12
Jul/12
May/12
Mar/12
Jan/12
Nov/11
Sep/11
Jul/11
May/11
Mar/11
-20%
Jan/11
0%
Figure 13: Weighted Contribution to Food Inflation
Source: Prepared by Dr. S.K. Srivastava.
Note: The weighted contribution is the multiplication of weight of the item/commodity in the
index and increase in the component index compared to same month in previous year.
2. Effect of ill distribution of rainfall: Agricultural sector depends on monsoon for
because only 39 percent of net sown area was irrigated in 2001. Mohanty (2014) had
found two structural breaks in food inflation series at April-1999 and April-2007. Of
these two, the later break was attributed to the deficient rains during 2002-03 and 200405. It had been reported that due to drought and floods in various parts states during
2008-09 caused considerable loss of output and food grain, sugarcane and oilseed output
growth were 1.34 per cent, -22 per cent and -5.38 per cent respectively. As a
consequence, average WPI inflation rates between March 2008 and July 2010 were
double digit number for milk (15.24 %), fruits (11.22 %), foodgrains (12.34 %) and egg,
fish & meat (17.66 %) (Chand, 2007; Nair & Eapen, 2012). However, it remains a major
fact that good rains after drought in 2008-09 failed to cool down the effect drought on
food prices.
3. Government’s Apathy: Central Government has failed in keeping the prices under
check. On one side Government has failed to release the sufficient quantity of foodgrains
from Food Corporation of India’s (FCI) godown to market while on other hand it could
not prevent loss of extra grains stored for the period. Also, when country was facing
drought situation then Union Government prescribed the strategic reserves of wheat and
rice of 3 and 2 million tonnes to be maintained in addition to the minimum stock of wheat
and rice to be held at 11 and 5.2 million tonnes during October (Food Corportation of
India, 2015). During 1st-July-2012, FCI held stock of 80 million tonnes of food grains
against capacity of 64 million tonnes including CAP storage capacity ( Food Corportation
of India, 2015). The Food Corporation of India (FCI) has admitted in data accessed
through RTI that the amount of damaged wheat has increased from 2,010 tonnes in 20092010 to 2,401.61 tonnes (2011-2012). (Vani, 2013). Wheat stock held by FCI were higher
than the specified buffer stock norms for most of the period between January 2000 and
January 2011 except for three years period beginning from January 2005 to January 2008.
This can be observed from the figure 3.The below norm stock held by FCI for wheat for
these three was on account of the normal rainfall during these years.
700%
600%
500%
400%
300%
200%
100%
Jan/11
Jul/10
Jan/10
Jul/09
Jan/09
Jul/08
Jan/08
Jul/07
Jan/07
Jul/06
Jan/06
Jul/05
Jan/05
Jul/04
Jan/04
Jul/03
Jan/03
Jul/02
Jan/02
Jul/01
Jan/01
Jul/00
Jan/00
0%
Figure 14: Wheat Stock held by FCI as percentage of Buffer Stock Norms
Source: Calculated by the authour based on data available provided by Dasgupta, Dubey, &
Sathish, 2011.
During 2009,International Food Price Index was 20 per cent lower than it was in 2008 but
in food prices were rocketing in domestic market. At this point of time also Government
did not import sufficient quantity to cool down the prices. Government knew that drought
was setting in India and there would be crisis in not adequate measures are taken. Instead
of planning for import of foodgrains, Government of India allowed export of foodgrains.
Consequently, share of foodgrains exproted in total production went up from 6.2 per cent
to 10 percent between 2003 to2009 This happened despite the fact that India had frequent
droughts for three periods starting from 2002-03, 2004-05 and 2008-09. This means that
addditional qunatity of foodgrains produced between 2002 to 2009, went to foreign
countries when Indian markets and people were hungry for food (Chand, 2007; Mohanty,
2014).
This apathy of public agencies was also due to Sugar barons lobbying
Governmenet for not imposing ban on the export of Sugar. Early in 2009, Sugar export
was allowed when sugar prices were hovering around $290 per tonne and later on when
sugar shortage started in domestic market then taking a cue from Indian sugar shortage,
world market price went up to $470 per tonne. Later on Government of India imported
sugar at double the price it was exported. Thus india lost more than it could gain by
exporting while at the same time Suagar barons gained not only be exporting but also by
making money in domestic market in later period. Same was the case for Onion as well
(Chand, 2007)
19
4. Effect of Minimum Support Prices: Sonna, et al. (2014) found strong effect of
increase in average MSP in short run on food inflation.
5. Rural Wages and MGNREGA: Rural wages matters most to the farming because
farming is still an area dominated by high degree of manual operations and machanization
was hindered due to small size of farm holdings (Mohanty, 2014). Thus every increase in
rural wages would lead to many times effect on food prices. Sonna, et al. (2014) found
rural wages as strong influencer on food inflation in short-run as well as in long-run.
Effect of rural wage increase would last longer than hike in average MSP. Since food
inflation onslaught in 2009 coincided with expansion of MGNREGA to all 615 districts
having rural areas, led many scholars to think that MGNREGA is the cause for labour
scarcity and rural wage hike. But according to Sonna, et al. (2014), effect of MGNREGA
on food inflation was very weak and would not persist for long time. Also, Mohanty
(2014) found that rural wages, which had remained stagnant for quite some time, started
to rise in early-2000 (much before start of MGNERGA) to catch up with fast pace of
growth of economy. Hence, It would be uncesserary to put onus for rural wage hike and
food infaltion entirely on MGNREGA.
6. Cost of agricultural inputs: Both Mohanty (2014) and Sonna, et al. (2014) agrees to
the fact that agricultural input cost had strong positive impact on food inflation in shortrun.
7. Effect of Exogenous Variables: There are many variables which are out of the reach
of economy and therefore, the government cannot exercise any control on such variables
at least in short run. To list some of the variables relevant to the context of food inflation
are oil prices, exchange rate, global prices of food and agricultural inputs. Oil prices have
very much influence on cost of cultivation because energy and fertilizer cost are the two
major costs in cost of cultivation that are directly linked with oil price. Effect of oil prices
becomes even worse when high global prices combined with depreciating currency. In
this context, inflation induced by oil-price shocks of 1970’s and ensuing events must be
remembered (Mohanty, 2014). It has been observed that whenever India faces or is about
to face shortage of critical food supplies then global food prices shoot up to very high
levels. Because India is the major consumer of the most of the staple food grown across
the world. Thus whenever Indian Government procured from international markets, it had
to pay higher prices.
References
Food Corportation of India. (2015). Storage and Contract detail section. Retrieved October 10, 2015,
from Food Corportaion of India: http://www.fci.gov.in/storages/view/6
Bai, J. (1994). Least Square Estimation of a Shift in Linear Processes. Journal of Time Series
Analysis(15), 453-472.
Bai, J. (1997a). Estimating Multiple Breaks One at a Time. Econometric Theory(13), 315-352.
Bai, J. (199b). Estimation of a Change Point in Multiple Regression Models. Review of Economics and
Statisitcs(79), 551-563.
Bai, J., & Perron, P. (1998). Estimating and Testing Linear Models With Multiple Structural Changes.
Econometrica(66), 47-78.
Bai, J., & Perron, P. (2003). Comuptation and Analaysis of Multiple Structural Change Models. Journal
of Applied Econometrics(18), 1-22.
Bhattacharya, R., & Gupta, A. S. (2015, June). Food Inflation in India: Causes and Consequences
(Working Paper No. 2015-151). National Institute of Public Finance and Policy,New Delhi.
Chand, R. (2007, February 27). Understanding the Nature and Ccauses of Food Inflation. Economic &
Political Weekly, 55(9), pp. 9-13.
Chand, R., Gulati, A., Parappurathu, S., & Ganguly, K. (2001). Policy Brief 35:Managing Food Inflation
in India: Reforms and Policy Options. National Centre for Agricultural Economics and Policy
Research. New Delhi: National Centre for Agricultural Economics and Policy Research.
Dasgupta, D., Dubey, R. N., & Sathish, R. (2011). Domestic Wheat Price Formation and Food Inflation
in India:International Prices, Domestic Drivers (Stocks, Weather, Public Policy), and the
Efficacy of Public Policy Interventions in Wheat Markets. Working Paper Series:2/2011-DEA,
Ministry of Finance, Government of India, Department of Economic Affairs, New Delhi.
Food Corportation of India. (2015). Buffer Stock Norms. Retrieved October 10, 2015, from Food
Corportation of India: http://www.fci.gov.in/upload/Stock/4.pdf
Ganeshkumar, A., Mehta, R., Pullabhotla, H., Prasad, S. K., Ganguly, K., & Gulati, A. (2012, January).
Demand and Supply of Cereals in India 2010-2025. (01158), 1-17. New Delhi, India:
IFPRI,Environment and Production Technology, New Delhi Office. Retrieved September 23,
2015,
from
http://www.researchgate.net/publication/254416821_Demand_and_supply_of_cereals_in_
India_2010-2025
Jain, R., Chaturvedi, G., Jharwal, S., & Parshad, M. (2007, October 10). Report of the working group
on risk management in agriculture for the eleventh five year plan(2007-12). Retrieved
October
06,
2015,
from
planningcommission.nic.in:
http://planningcommission.nic.in/aboutus/committee/wrkgrp11/wg11_risk.pdf
Kumar, P., Shinoj, P., Raju, S., Kumar, A., Rich, K. M., & Siwa, M. (2010, January-June,). Factor
Demand, Output Supply Elasticities and Supply Projections for Major Crops of India.
Agricultural Economics Research Review, 23, 1-14. Retrieved September 23, 2015, from
http://ageconsearch.umn.edu/bitstream/92090/1/1-P-Kumar.pdf
Mohanty, D. (2014, January 13). Why is recent food inflation in India so persistent? Mumbai,
Maharashtra, India: Speech delivered at the annual Lalit Doshi Memorial Lecture, Xavier’s
Chapter, delivered at the St. Xavier’s College.
21
Nair, S. R., & Eapen, L. M. (2012, May 19). Food Price Inflation in India (2008 to 2010) A Commoditywise Analysis of the Causal Factors. Economic & Political Weekly, 47(20), pp. 46-54.
NSSO, Ministry of Statistics and Programme Implementation, GOI, New Delhi. (June 2013). Key
Indicators of Household Consumer Expenditure in India, NSS report 68th round Survey (July
2011-2012). New Delhi: NSSO, Ministry of Statistics and Programme Implementation, GOI,
New Delhi.
R Core Team. (2014). R: A language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. Retrieved from http://www.R-project.org/
Sonna, T., Joshi, H., Sebastian, A., & Sharma, U. (2014). Analytics of Food Inflation in India. Mumbai:
Reserve Bank of India.
Vani, G. K. (2013). Food Security in India: Myths and Realities. Seminar Report, University of
Agricultural Sciences, GKVK, Bengaluru-65, Department of Agricultural Economics,
Bengaluru-560065.
Retrieved
October
10,
2015,
from
http://www.slideshare.net/AgriGouravvani
Zeileis, A., Kleiber, C., & Kramer, W. (2003). Testing and Dating of Structural Changes in Practice.
Computational Statistics and Data Analysis(44), 109-123. doi:10.1016/S01679473(03)00030-6