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........................................................................................5Figure 2: Breaks in WPI for all commodities..........................................................................................................7Figure 3: Break points in WPI series for Primary Articles.......................................................................................8Figure 4: Break points in WPI series for Manufactured Food Products..................................................................8Figure 5: Break points in WPI series for Cereals.....................................................................................................9Figure 6: Break points in WPI series for Bakery Products.......................................................................................9Figure 7: Break points in WPI series for Grain Milled Products............................................................................10Figure 8: Break points in WPI series for Foodgrains.............................................................................................10Figure 10: Break points in WPI series for Dairy Products.....................................................................................11Figure 9: Break points in WPI series for Milk.......................................................................................................11Figure 11: Break points in WPI series for Oilseeds...............................................................................................12Figure 12: Break points in WPI series for Edible Oil.............................................................................................12Figure 13: Weighted Contribution to Food Inflation............................................................................................17Figure 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..........................................7Table 2: Trends in Percentage Composition of Consumer Expenditure since 1993-94 for Rural India..............14Table 3: Trends in Percentage Composition of Food Expenditure of the Rural Indian consumer.........................15Table 4: Trends in Percentage Composition of Consumer Expenditure since 1993-94 for Urban India.............15Table 5: Trends in Percentage Composition of Food Expenditure of the Urban Indian consumer.....................16Table 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.

Trends in General and Food inflation

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

y i=x iT β+ui

2005M4 2006M7 2007M10 2009M1 2010M4 2011M7 2012M10 2014M1 2015M4-10

-505

10152025

Food Articles All Commodities

%

Figure 1: Inflation in Food Articles and All Commodities

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 y i=x i

T β j+ui (i=i j−1+1 ,…, i j , j=1 , …,m+1)where j denotes the segment index. In practice the breakpoints i j 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∧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 January-2008. Similarly the breaks were detected for other commodity WPI series also and are summarized in the table 1.

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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)

Figures in parenthesis indicate the month.

Figure 2: Breaks in WPI for all commodities

Time

WP

I for

All

com

mod

ities

1985 1990 1995 2000 2005 2010

5010

015

0

9

Time

WP

I for

Prim

ary

artic

les

1985 1990 1995 2000 2005 2010

5010

015

0

Figure 4: Break points in WPI series for Manufactured Food Products

Time

WP

I for

Man

ufac

ture

d Fo

od P

rodu

cts

1985 1990 1995 2000 2005 2010

5010

015

0

Figure 3: Break points in WPI series for Primary Articles

Figure 6: Break points in WPI series for Bakery Products

Time

WP

I for

Bak

ery

Pro

duct

s

1985 1990 1995 2000 2005 2010

2040

6080

100

120

140

Figure 5: Break points in WPI series for Cereals

Time

WP

I for

Cer

eals

1985 1990 1995 2000 2005 2010

5010

015

020

0

Time

WP

I for

Gra

in M

illed

Pro

duct

s

1985 1990 1995 2000 2005 2010

5010

015

0

11

Figure 9: Break points in WPI series for Milk

Figure 10: Break points in WPI series for Dairy Products

Figure 8: Break points in WPI series for Foodgrains

Figure 7: Break points in WPI series for Grain Milled Products

Time

WP

I for

Foo

dgra

ins

1985 1990 1995 2000 2005 2010

5010

015

020

0

Time

WP

I for

Milk

1985 1990 1995 2000 2005 2010

5010

015

020

0

Time

WP

I for

Dai

ry P

rodu

cts

1985 1990 1995 2000 2005 2010

5010

015

0

Figure 12: Break points in WPI series for Edible Oil

Figure 11: Break points in WPI series for Oilseeds

Time

WP

I for

Oils

eeds

1985 1990 1995 2000 2005 2010

5010

015

020

0

Time

WP

I for

Edi

ble

Oil

1985 1990 1995 2000 2005 2010

4060

8010

012

014

0

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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 1993-94

1999-2000 20004-05 2009-10

2011-12

Cereal 24.2 22.2 18.0 15.6 12.0

Pulse and their products

3.8 3.8 3.1 3.7 3.1

Milk and milk products

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 51.4Source:-NSSO, Ministry of Statistics and Programme Implementation, GOI, New

Delhi, June 2013

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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 38.29% 37.37% 32.73% 29.10% 24.69%Pulse and their products

6.01% 6.40% 5.64% 6.90% 6.38%

Milk and milk products

15.03% 14.81% 15.45% 16.04% 18.72%

Edible oil 6.96% 6.23% 8.36% 6.90% 7.82%Egg, fish, meat 5.22% 5.56% 6.00% 6.53% 7.41%Vegetables 9.49% 10.44% 11.09% 11.57% 9.88%Fruits and nuts 2.69% 2.86% 3.45% 2.99% 3.91%

Food total 100.00% 100.00% 100.00% 100.00% 100.00% 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

Source:- NSSO, Ministry of Statistics and Programme Implementation, GOI, New Delhi, June 2013

Item group 1993-94 1999-2000 20004-05 2009-10 2011-12

Cereal 14.0 12.4 10.1 9.1 7.3

Pulse and their products

3.0 2.8 2.1 2.7 2.1

Milk and milk products

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

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%

Food total 100.00% 100.00% 100.00% 100.00% 100.00%

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

Source: Prepared by Dr. S.K. Srivastava.

Figure 13: Weighted Contribution to Food Inflation

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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 2004-05. 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 ofIndia, 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 Corportationof 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 2009-2010 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.

Jan/1

1

Mar/11

May/11

Jul/11

Sep/11

Nov/11

Jan/1

2

Mar/12

May/12

Jul/12

Sep/12

Nov/12

Jan/1

3

Mar/13

May/13

Jul/13

Sep/13

Nov/13

-20%

-10%

0%

10%

20%

30%

40%

50%

60%Food Grains (Cereals and Pulses) Vegetables Fruits EGGS,MEAT & FISH CONDIMENTS & SPICES

OTHER FOOD ARTICLES MILK

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.

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)

4. Effect of Minimum Support Prices: Sonna, et al. (2014) found strong effect of increase in average MSP in short run on food inflation.

Jan/0

0Jun/0

0

Nov/00

Apr/01

Sep/01

Feb/02

Jul/02

Dec/02

May/03

Oct/03

Mar/04

Aug/04Jan

/05Jun/0

5

Nov/05

Apr/06

Sep/06

Feb/07

Jul/07

Dec/07

May/08

Oct/08

Mar/09

Aug/09Jan

/10Jun/1

0

Nov/10

0%

100%

200%

300%

400%

500%

600%

700%Figure 14: Wheat Stock held by FCI as percentage of Buffer Stock Norms

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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 short-run.

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.

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from Food Corportaion of India: http://www.fci.gov.in/storages/view/6

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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.

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