Food Price Inflation and Relative Price Variability in India: Evidence from Decomposition Analysis

Abstract

The present study examines the decomposition of relative price variability into two components (i.e., inflationary and real factors) in India using monthly data on 105 commodities prices over the period January 2005 to March 2017. The analysis of the study revealed that under primary food articles, 53% of relative price variability is due to real factors, and the remaining 47% due to inflationary factors. Whereas for manufactured food products, 30% of relative price variability is due to real factors, and the rest of the 70% due to inflationary factors. Overall, the important conclusion that emerges from the analysis is that few commodity prices have a larger contribution to relative price variability in the food basket. The majority of commodity prices under primary food articles have a larger contribution to variability in relative price changes, whereas under manufactured food products have the least contribution. Moreover, this study also accounts for wholesale price index and consumer price index non-food commodities to identify to which extent relative price variability is determined by real and inflationary factors.

JEL Classifications: E30, E31, Q11, L66

Keywords

Food price inflation relative price variability decomposition analysis manufactured food

Introduction

Over the past few decades, a rapid increase in food inflation has created serious concern among the government authorities and policymakers across the world. It hampers the welfare of the country by increasing the infant and child mortality of the nation. Notably, the world has experienced food inflation shocks during 2007–2008, 2010–2011 and 2013 due to certain reasons. Walsh (2011) argued that food inflation is more explosive and higher than non-food inflation in lower-income countries, and persistent rise in food prices is transmitted to non-food inflation in many countries. One of the major problems in inflation targeting is high inflation pressure, which originated from food prices. In fact, food inflation is not only creating a problem for overall inflation, but also creating difficulties in the forecasting accuracy of the inflation targeting process (Šoškić, 2015). Food price inflation plays a significant role in the inflation dynamics and policy point of view of central banks, particularly for inflation-targeting countries. The rising importance of food price inflation contributed to a larger share in total household spending.

However, the impact of food prices on inflation varies across countries. Because it depends on economic development, the contribution of food in the aggregate consumer price index (CPI) basket, and the level of income of the country. Therefore, the rise in food prices may adversely affect poor consumers in developing countries, especially India, where most consumers spend their larger portion of income on food products. One of the major macroeconomic challenges that have been faced by India is to control food inflation pressures. The rising food price inflation is considered as one of the key contributors to the increase in overall wholesale price index (WPI) inflation in India (Anand et al., 2014). Because, the weight of food comprises nearly 40% and 25% in India’s CPI and WPI, respectively. The average WPI food price inflation was recorded at 8%, whereas non-food inflation was observed at 4% during January 2006 to March 2017 (Office of Economic Advisor, 2017). The inflation in primary food articles has started increasing in 2006 and peaked at 21.85% in May 2010 and finally reached 19.69% in December 2013. Similarly, inflation rate of manufactured food products was significantly high at 10.43% and 19.30% in July 2008 and March 2010, respectively, and again reached 12.60% in June 2016. The primary reason behind the high inflation rate (10.9%) of all commodities in May 2010 was high food inflation. The changes in the relative price of food items play a central character in the demand and supply. Therefore, the central bank should pay attention to food price inflation while targeting overall inflation for the country at large.

The rising relative price variability occurs due to both inflationary factors (supply side) and real factors (demand side). However, there has been a long debate on the effects of inflation on the relative price variability in the literature, which can be associated with market behaviour. The theory of inflation and relative price variability is based on the menu cost approach and rational expectation model. Inflation has an adverse effect on the real sector of the economy through its relative price variability.¹ Under the menu cost model, when a higher inflation level prevails, changes in prices become more scattered. Hence, larger variability in relative price changes (Sheshinski & Weiss, 1977). Ball and Mankiw (1995) argued that large shocks to a few commodities have a disproportionate effect on aggregate inflation due to the firms’ price adjustment. Hence, the distribution of relative price changes promotes aggregate inflation. Further, Ukoha (2007) suggests that high inflation will increase relative price variability among the agriculture commodities, in turn, inefficient and misallocation of agriculture resources. Whereas, under the rational expectation model, the rise in unexpected demand, which creates inflation, will tend to rise in relative prices (Barro, 1976; Lucas, 1973). However, along with inflationary factors (supply side), such as changes in technology, resource availability, and other factors of supply, variability in relative price is also originated from real factors, namely, real income, family composition, and various other factors of demand (Parks, 1978; Rather et al., 2014).

There has been a growing importance of decomposition of relative price changes over the period as inflation affects real sectors through the relative price changes. Suppose relative price changes occur due to real factors that lead to an efficient allocation of resources because these forces arise from market forces of the economy. On the other hand, if variability in relative prices changes arises due to inflation, it decreases the economic welfare benefits in the economy by an inefficient allocation of resources and agricultural sectors in particular. Therefore, it is essential to know at which magnitude relative price changes is determined by inflationary (supply side) and real factors (demand side) in the variability in relative price changes. This will provide useful insights for the policy makers to implement policy decisions regarding the responsible commodities of higher variability in relative price and forecast inflation accurately. The quantifying relative importance of both demand and supply-side factors would give an idea of specific policy suggestions regarding demand and supply to tame the high relative price of the food basket. This study can also offer some policy initiatives relating to disaggregated commodities on how changes in the relative price of a particular commodity can affect the economy’s stability, followed by the welfare of the people to a larger extent.

The study makes various significant input to the literature in several ways: first, numerous studies have conducted to understand, causes and cures of food price inflation in India (Bhattacharya et al., 2014; Gokarn, 2010; Gulati & Saini, 2013; Huria and Pathania, 2018; Rajan, 2014) and different researchers have given their different conclusions regarding trace the reasons for higher food price.² The common features of their studies have mainly restricted to only the nature of food inflation, and most of the studies are primarily confined to supply-side factors. However, the relative importance of demand and supply-side factors towards relative price changes of food items have not been considered in their studies. Second, the study identifies the each of commodity prices, which contributes to the variability of relative price. It also examines to what extent variability in relative price is caused by real factors (demand side) and inflationary factors (supply side). Third, a little study has recognized the commodities that led to variability in relative price changes in the food basket using different data periods and methodology. However, we have covered a larger number of commodities at the disaggregated level, longer time period, and Clements and Nguyen (1981, 1982) methodology for decomposition analysis in our study. Fourth, few studies have investigated the decomposition of relative price variability for the aggregate price level, namely, Clements and Nguyen (1981, 1982) for Australia, Ram (1990) for the USA and Rather et al. (2014) for India. However, none of the studies have explored the decomposition of relative price variability of food items in India. Fifth, this study also reflects on verifying to what extent non-food (both WPI and CPI) commodities are accountable in the variability of relative price changes of food items. Finally, to the best of our knowledge, this is the first study investigating the decomposition of relative price variability into two components due to real factors and inflationary factors. Therefore, this study accomplishes the gap in the existing literature.

The remaining of the article is organized as follows. The second section provides a brief review of the literature of the related studies. The third section outlines data and methodology. The fourth section presents primary analysis. The fifth section presents the empirical results and discussion. Finally, the sixth section provides conclusions and policy implications of the study.

Review of Literature

Inflation and Relative Price Variability

The relationship between inflation and relative price variability is not new, and a large number of studies have investigated the nexus between these two across the world. However, the effects of inflation on relative price variability for agricultural commodities are scarce. In particular, developing countries like India where a large section of people spends their larger portion of income on food and also contribute a leading share to overall inflation in WPI and CPI. The changes in the relative price of the food items may hamper the welfare of the country.

For instance, Parks (1978) examined the impact of unanticipated inflation and real income on the variance of relative price changes in the Netherlands and the USA during 1921–1963 and 1929–1975, respectively. His results showed that unanticipated inflation is positively impacted the variance of relative price changes. Clements and Nguyen (1981) investigated the effects of inflation on variance in relative price changes in Australia for seven commodity groups during 1959–1978. The results showed that past inflation in rent is identified as the variation in relative price changes. However, the current inflation drives relative price variability in cigarettes, rent and others. Further, the impact of inflation on relative price variability for food, cigarettes, etc., and motor vehicles is positive. Clements and Nguyen (1982) examined the relationship between inflation and relative price in Australia using decomposition analysis over the period 1959–1978. The author found that 76% of the variability in relative prices is determined by real factors. However, 24% of the variability in relative price is determined by inflationary factors. Further, they also revealed that 43% of overall relative price variability is contributed by clothing only. Ram (1990) demonstrated that real factors and inflationary factors are equally responsible for the variability of relative price in the USA. Ghauri et al. (2014) analysed the impact of unanticipated inflation (supply side) and real income (demand side) on relative price variability in Pakistan, covering the period from July 2001 to June 2011. Their findings of the study revealed that the supply-side factors drive relative price variability. However, the demand-side factor fails to determine relative price variability. However, Rather et al. (2014) revealed that inflation has a positive impact on relative price variability. Further, the authors also found that the relative price variability is determined by both real and inflationary factors. However, a higher proportion of relative price variability is derived from real factors.

However, few studies have focussed on the relationship between inflation and relative price variability of food items. For instance, Lach and Tsiddon (1992) analysed the effects of inflation on the dispersion of prices utilizing disaggregated data on 26 foodstuffs in Israel, spanning, January 1978 to September 1984. The study results found that intra-market relative price variability has resulted from the expected inflation rather than unexpected inflation. Lapp and Smith (1992) investigated the effects of macroeconomic instability on relative price variability for 47 agricultural commodities in the USA over the period 1962–1987. Their finding revealed that unanticipated inflation was found to be positive with the variation in relative price changes. However, contrary to this, Smith and Lapp (1993) found that the actual inflation positively impacts relative price variability in agriculture rather than unexpected inflation in the UK over the period 1956–1988. Loy and Weaver (1998) examined the relationship between anticipated and unanticipated inflation and relative agricultural price volatility in Russian food markets during January 1993 to December 1995. The results of the study showed that the anticipated inflation rate drove variability in relative prices. Rezit (2005) studied the change in relative price variability is positively induced by inflation rate changes and economic activity among 53 agricultural commodities. Ukoha (2007) investigated the impacts of inflation on relative price variability among agricultural commodities in Nigeria during 1970–2003. He found that the effects of inflation on relative price variability of agricultural commodities are positive and significant in both the short- and the long run. Baek (2010) also argued that changes in inflation have a positive impact on relative price variability across US food products.

From the above literature review, we conclude that numerous studies have focussed on factors responsible for high food price inflation in worldwide. The basic idea of these studies is mainly constrained to only the nature of food inflation, and mostly confined to supply-side factors. However, the relative importance of demand and supply-side factors towards relative price variability has not been considered. Hence, it is essential to inspect the extent to which relative price variability is caused due to real (demand side) and inflationary (supply side) factors. To the best of our knowledge, none of the studies have investigated the decomposition of relative price variability for the basket in terms of food inflation into two components due to real factors and inflationary factors. Therefore, this study aims to inspect the decomposition of relative price variability in India.

Data and Methodology

Data

The present article uses of monthly data on 105 commodities prices, which represents 23% of WPI in India. There are 112 numbers of commodities included in primary food articles and manufactured food products (2004–2005 base year).³ Due to the lack of historically comparable data on the all-India CPI commodity-wise food items over the period, we have used the WPI disaggregated data during January 2005 to March 2017. Further, in order to check robustness on CPI disaggregated data, we have used the available data during January 2014 to February 2020.⁴ Further, to verify to what extent non-food items contribute variability of relative price changes of food items, we included WPI and CPI non-food commodities.⁵ The data period selection has been considered based on the availability of uniform and consistent data on the price of commodities over the period of time. We have also constructed an index for food inflation using the weighted least square of both primary food articles and manufactured food products using WPI indices. The data on WPI food and non-food price indices and their weights, and CPI food and non-food indices and their weights are retrieved from the Office of the Economic Adviser, published by the Ministry of Commerce and Industry and Consumer Price Indices Warehouses, Ministry of Statistics and Programmes Implementations, respectively. However, the data on consumer price index for industrial workers’ food (CPI-IW) are collected from the Labour Bureau.

Methodology

In order to decompose relative price variability into two components, that is, due to real factors and inflationary factors, we have used the technique developed by Clements and Nguyen (1981, 1982). The essence of present technique is to identify which commodity share has higher relative price variability of food basket. It also helps figure out the relative influence of individual commodity in the variability of relative price and identify in which magnitudes it is affected by inflationary factors and real factors.

The study has used the ordinary least square method to estimate the α and β for 105 commodities using the equation $(π_{i t} - π_{t}) = α_{i} + β_{i} π_{i} + ε_{t},$

Measurement of Relative Price Variability

Suppose, $π_{i t}$ is the rate of change in price of ith commodity, which is defined as:

π_{i t} = \ln (\frac{p_{i t}}{p_{i t - 1}})

(1)

where $p_{i t}$ is the ith commodity in period t and ln is natural logarithm. The food inflation can be interpreted as:

π_{t} = \sum_{i = 1}^{n} ω_{i} π_{i t}

(2)

where $ω_{i}$ refers to weight of ith commodity. The relative price variability can be measured as:

λ_{t} = \sum_{i = 1}^{n} ω_{i} {(π_{i t} - π_{t})}^{2}

(3)

When, prices of all the commodities are proportional, then $π_{i t} = π_{t} and λ_{t} = 0$ . The relative price change of commodity i with food inflation can be denoted as:

ω_{i} (π_{i t} - π_{t}) = α_{i} + β_{i} π_{t} + ε_{t}

(4)

where $α_{i}$ and $β_{i}$ are commodity-wise coefficients and $ε_{t}$ is the random error term with $E (ε_{t}) = 0;^{α_{i}} / ω_{i}$ is the autonomous trend in the relative price of commodity i, reflecting real change in the economy and $β_{i} / ω_{i}$ is the elasticity of the relative price of commodity i w.r.t inflation. If we substitute Equation (4) into Equation (3), it gives us measurement of relative price variability as:

λ_{t} = \sum_{i = 1}^{n} ω_{i} {[(\frac{α_{i}}{ω_{i}}) + (\frac{β_{i}}{ω_{i}}) π_{t}]}^{2}

(5)

where $ε_{t}$ is random error term set equal to its expected value.

Decomposition of Relative Price Variability of Food Items

The share of commodity i in $λ_{t}$ for time t is:

θ_{i t} = ω_{i} \frac{{[(\frac{α_{i}}{ω_{i}}) + (\frac{β_{i}}{ω_{i}}) π_{t}]}^{2}}{λ_{t}}

(6)

where $θ_{i t}$ is non-negative and its sum is equal to one. Thus, the effect of real changes and inflationary changes on the variability of relative price of ith item are shown in α and β in Equation (4), respectively. Hence, real and inflationary component of $θ_{i t}$ are expressed as:

θ_{i t}^{R} = \frac{(\frac{α_{i}^{2}}{ω_{i}})}{λ_{t}}

(7)

and θ_{i t}^{I} = \frac{(\frac{β_{i}^{2}}{ω_{i}})}{λ_{t}} π_{t}^{2}

(8)

Both $θ_{i t}^{I} and θ_{i t}^{R}$ and are non-negative, and interaction component due to both real and inflation factors can be represented as:

θ_{i t}^{RI} = 2 α_{i} \frac{(\frac{β_{i}}{ω_{i}})}{λ_{t}} π_{t}

(9)

which can be either positive or negative. The total share of real changes in $λ_{t}$ can be interpreted as:

λ_{t}^{R} = \sum_{i = 1}^{n} \frac{(\frac{α_{i}^{2}}{ω_{i}})}{λ_{t}} = \sum_{i = 1}^{n} θ_{i t}^{R}

(10)

and the share of due to inflationary factors in $λ_{t}$ is:

λ_{t}^{I} = \sum_{i = 1}^{n} \frac{(\frac{β_{i}^{2}}{ω_{i}})}{λ_{t}} π_{t}^{2} = \sum_{i = 1}^{n} θ_{i t}^{I}

(11)

and interaction component of both real and inflationary share in $λ_{t}$ is:

θ_{i t}^{RI} = \sum_{i = 1}^{n} 2 α_{i} \frac{(\frac{β_{i}}{ω_{i}})}{λ_{t}} π_{t} = \sum_{i = 1}^{n} θ_{i t}^{RI}

(12)

Hence,

λ_{t}^{R} + λ_{t}^{I} + λ_{t}^{IR} = 1

(13)

The average share of real, inflation and interaction component in relative price variability over the period can be defined, respectively, as:

{\bar{λ}}^{R} = \frac{1}{T} \sum_{t = 1}^{T} λ_{t}^{R}; {\bar{λ}}^{1} = \frac{1}{T} \sum_{t = 1}^{T} λ_{t}^{I} and {\bar{λ}}^{RI} = \frac{1}{T} \sum_{t = 1}^{T} λ_{t}^{RI}

(14)

where T is the time. The average share of ith commodity in relative price variability is interpreted as:

{\bar{θ}}_{i} = \frac{1}{T} \sum_{t = 1}^{T} θ_{i t}

(15)

Similarly, the contribution of the average share of real, inflation, and interaction component of $i$ th commodity to relative price variability, respectively, are defined as follows:

{\bar{θ}}_{i}^{R} = \frac{1}{T} \sum_{t = 1}^{T} θ_{i t}^{R}; θ_{i}^{- 1} = \frac{1}{T} \sum_{t = 1}^{T} θ_{i t}^{I}; and {\bar{θ}}_{i}^{RI} = \frac{1}{T} \sum_{t = 1}^{T} θ_{i t}^{RI}

(16)

Preliminary Analysis

Food Inflation and Distribution of Relative Price Variability

We have used annualized monthly data on food items for India to analyse the trend and pattern of relative price variability. The mean, variance and skewness of the cross-sectional distribution of relative price variability in each period have been calculated and presented in Table 1. The results depict that an average value of skewness is positive, which indicates that persistent positive skewness in the distribution of price variability is present in India. In other words, the aspect of asymmetries in the price adjustment is appeared due to downward rigidity in prices or differential adjustment lags.

Table 1.

Descriptive Statistics.

Variables	Mean	Median	Std. Dev.	Skewness	Minimum	Maximum
Inflation	0.072	0.074	0.034	0.650	−0.006	0.179
Variance	0.020	0.018	0.007	0.936	0.009	0.043
Skewness	0.112	0.299	1.430	0.526	−4.167	3.445

Source: The authors.

Additionally, we have also represented the graph of food price inflation and relative price variability to understand the association between the variables, which is shown in Figure 1. The high relative price variability is observed when food inflation is high. Inflation encourages variation in relative price variability. The results of disaggregated food items also exposed that variation in very few commodities creates a high fluctuation in the whole relative price variability.

Figure 1.

Source: The authors.

Empirical Analysis

Shares of Commodity Prices in Relative Price Variability

We use regression coefficients of α and β from Equation (15), in order to find out the average share of ith commodity price

({\bar{θ}}_{i})

in relative price variability. The sector-wise contribution to relative price variability is reported in Table 2. The first column represents the components of the food basket, which consists of both the primary food articles and manufactured food products. The second column shows the percentage share of primary food articles and manufactured food products for determining the variation in relative price changes. The 82% of relative price changes is identified from primary food articles, whereas manufactured food products contribute 18% of relative price changes. It implies that primary food articles are mainly responsible for determining the variation relative price changes. Again, the third and fourth columns indicate that 53% of the relative price variability in primary food articles is derived from real factors, and the remaining 47% comes from inflationary factors.⁶ However, relative price variability in manufactured food products is mainly explained by inflationary factor (70%), and the rest (30%) is due to real factors. Therefore, we can say that a rise in relative price variability in primary food articles occurs due to real factors, and relative price variability in manufactured food products occurs due to inflationary factors in India. These results indicate that variability in relative price changes in India occurs due to both real and inflationary factors. These results contradict previous studies that claimed that relative price variability of food items arises due to inflationary factors only, known as supply-side factors.

Table 2.

Sector-wise Contribution to Relative Price Variability of Food Items.

Food Inflation	$(s {\bar{θ}}_{i})$	$({\bar{θ}}_{i}^{R})$	$({\bar{θ}}_{i}^{I})$
Primary food articles	82	53	47
Manufactured food products	18	30	70
Total	100	—	—

Source: The authors

Note: $s {\bar{θ}}_{i}$ is the sum of average shares of commodity prices $({\bar{θ}}_{i})$ in particular sector, ${\bar{θ}}_{i}^{R}$ is shares of real factors in relative price variability and ${\bar{θ}}_{i}^{I}$ is shares of inflationary factors in relative price variability.

Shares of Disaggregated Food Items and its Decomposition

To determine which commodity price has a significant impact on variability in relative price changes, we have presented the shares of a particular commodity to relative price changes in Table 3. The results revealed that only 25 among 105 commodities are predominately contributing 93% of the variation in relative price changes in the food basket. Among them, 15 commodities are under the primary food articles and contribute 81% of the relative price variation. Whereas, 10 commodities under manufactured food products contribute 12% of the relative price variation. We reported the commodities whose share has equal to 1% or above. The highest shares of commodities that contribute to larger relative price variability are mainly vegetables. The cabbage alone contributes 24% of the variability in relative price, followed by brinjal (19%), onion (12%), potato (9%) and so on. It implies that few commodity prices are determining the higher relative price of food items in the basket. Fruits and vegetables, and milk, fish prices play a dominant role in the variability of relative prices. The increase in variability of the relative price of food items is due to changing dietary patterns of food products from cereals-based food to high-value products. Further, decomposition results indicate that 41% of the relative price variability of cabbage is because of real factors and the residual 59% by inflationary factors. Similarly, for brinjal, 42% of the variation in relative price changes is attributed by real factors, whereas 58% of variability is from inflationary factors. However, for fish-inland, 92% of its share is caused by real factors, and the remaining 8% is caused by inflationary factors. Overall, the commodities having the highest share contribute to larger variability in relative price changes by both demand and supply-side factors. In particular, relative price variability in food items is mainly contributed by vegetable prices, which are determined by both real and inflationary factors. However, inflationary factor contributes more than 50% of the variation in relative prices in food prices. Whereas, the commodities contributing lesser variation in relative price changes are determined by inflationary factors except for few commodities like gram, fish, and tapioca, which are determined by real factors.

Table 3.

Shares of Decomposed Commodities.

Commodities	$({\bar{θ}}_{i})$	$({\bar{θ}}_{i}^{R})$	$({\bar{θ}}_{i}^{I})$
Primary food articles
Cabbage	24	41	59
Brinjal	19	42	58
Onion	12	42	58
Potato	9	46	54
Milk	5	50	50
Fish-inland	2	92	8
Fish-marine	2	67	33
Rice	1	42	58
Wheat	1	44	56
Gram	1	99	1
Tapioca	1	84	16
Coconut (fresh)	1	36	64
Mutton	1	56	44
Poultry chicken	1	28	72
Chillies (dry)	1	56	44
Manufactured food products
Vanaspati	3	1	99
Powder milk	1	34	66
Maida	1	23	77
Wheat flour (atta)	1	38	62
Biscuit/cookies	1	10	90
Molasses	1	28	72
Palm oil	1	6	94
Mustard and rapeseed oil	1	20	80
Soyabean oil	1	19	81
Sunflower oil	1	3	97

Source: The authors.

Shares of Wholesale Price Index Non-food and Its Decomposition

We considered WPI non-food items to check to what extent non-food items are accountable for the relative price variability of food items. We have presented the results of non-food items whose average share is 2% or more in Table 4. The results show that among the 564 commodities, 10 commodities contribute 23% of the variability in relative price changes in food items.⁷ Most of the commodities that responsible for the variability in relative price changes are from agricultural inputs like power and fuel. These are the inputs used as major inputs of the agricultural sector directly and indirectly in the production and transportation of output/produce. An increase in the price of fuel and other major inputs of the agricultural sector, like agricultural machinery, pesticides and equipment would increase the cost of production. Thus, an increase in variability in the relative price of foodstuffs.

Table 4.

Shares of Commodity Prices and its Decomposition Analysis using WPI Non-food Data.

Commodities	$({\bar{θ}}_{i})$	$({\bar{θ}}_{i}^{R})$	$({\bar{θ}}_{i}^{l})$
Sugarcane	2	60	40
Coking coal	2	67	33
Non-coking coal	2	36	64
High-speed diesel	3	52	48
Cotton shirts	3	0	100
Urea	2	10	90
Grey cement	2	31	69
HRC (steel)	2	14	86
Gold & gold ornaments	2	80	20
Motor vehicles	3	9	91

Source: The authors.

It is interesting to notice that proportion of real factors in the share of cotton shirts is found to be zero. However, the share of urea in the proportion of real factors seems negligible as 90% of the variation is due to inflationary factors. For sugarcane, 60% of the variability in relative price changes is due to real factors and 40% by inflationary factors. For high-speed diesel, 52% of the variability is initiated by real factors and 48% by inflationary factors. From the above analysis, it is clear that an increase in non-food items has contributed to higher relative price in food items as it implies upward pressure on input prices. Few non-food commodities lead to relative price changes, and most of them are attributed by inflationary factors. However, sugarcane, gold and gold ornaments and coking coal are determined by real factors.

Robustness Check

The food contributes higher weights, which accounts for nearly 39.8% of aggregate CPI basket whereas, 24.6% weights of aggregate WPI basket. Therefore, we use CPI disaggregated data to check the robustness of the study. The results are displayed in Table 5. The results are reported whose share is 2% or higher. It shows that 22 commodities have been identified among the 106 commodities, which contribute 94% of the variation in relative price changes. The results show that nine commodities are together contributing 83% of the variability in relative prices. However, among them, tomato determines 45% of the variability in relative price changes, followed by potato (10%), onion (9%), cauliflower (7%) and so on. It is also revealed that, in the case of tomato, 39% of relative price variability is attributed by real factors, and 61% is by inflation factors. For potatoes, 36% of the variation in relative price changes is due to real factors, and 64% of the variation is due to inflationary factors. However, for onion, 25% of fluctuation in relative prices is decided by real factors, and 75% by inflationary factors. Based on the above results, we found that most of the responsible commodities in the fluctuations of the relative price changes are identified as vegetables. The relative price variability of all the commodities is largely determined by inflationary factors and shared as highest against real factors except few items, like fish prawn and goat meat/mutton. Since food contributes significant weight (nearly 40%) to the CPI basket, it implies that any increase in food prices would surge the CPI inflation.

Table 5.

Shares of Commodity Prices and its Decomposition Analysis using CPI Food Data.

Commodities	$({\bar{θ}}_{i})$	$({\bar{θ}}_{i}^{R})$	$({\bar{θ}}_{i}^{I})$
Parwal/patal, kundru	2	52	48
Brinjal	2	30	70
Milk: liquid (L)	2	45	55
Cabbage	3	36	64
Peas (vegetables)	3	33	67
Cauliflower	7	36	64
Onion	9	25	75
Potato	10	36	64
Tomato	45	39	61

Source: The authors.

Further, we included CPI non-food commodities to check to what extent non-food items attribute to relative price variability of food items. The results are displayed in Table 6. The 14 among the 184 commodities have been identified, contributing 56% of the variability in relative price changes in food items.⁸ The results show that only one commodity, house rent; garage rent has been contributed 16% variation of relative price variability followed by medicine (non-institutional, 6%), petrol for the vehicle (5%), electricity (standard unit) 4% and so on. It is also observed that 42% variation in house rent; garage rent is due to real factors and 58% is due to inflationary factors. For petrol for a vehicle, 33% variability in relative prices is due to real factors, and the remaining 67% is due to inflationary factors. Overall, it is observed that both the food and non-food items are significantly responsible for the variability in relative prices of food items and originated from inflationary factors. Inflationary factors contribute to the larger proportion of variability in relative prices of food items. The results are similar to WPI non-food in Table 4. We here found a pass-through of fuel price to variation in the relative price of food commodities.

Table 6.

Shares of Commodity Prices and its Decomposition Analysis using CPI Non-food Data.

Commodities	$({\bar{θ}}_{i})$	$({\bar{θ}}_{i}^{R})$	$({\bar{θ}}_{i}^{I})$
Cooked meals purchased (no.)	3	39	61
Cooked snacks purchased	2	34	66
House rent; garage rent	16	42	58
Electricity (std. unit)	4	14	86
LPG (excl. conveyance)	3	45	55
Kerosene in PDS (L)	2	82	18
Firewood and chips	3	31	69
Medicine (non-institutional)	6	36	64
Petrol for vehicle	5	33	67
Bus/tram fare	2	33	67
Air fare (normal): economy class (adult)	2	29	71
Telephone charges: mobile	3	24	76
Tuition and other fees (school; college; etc.)	3	52	48
Gold	2	39	61

Source: The authors.

Furthermore, we also used CPI-IW food data for robust results.⁹ The outcomes are reported in Table 7. We have reported the commodities whose share is equal to or greater than 2%. Only 11 among 164 items are identified, which contributes 84% variability in relative prices of the food basket. However, out of them, tomato contributes higher variation (29%), onion (15%), potato (11%) and so on. The majority of the identified commodities are vegetables and mainly contributing to the higher relative price variability of the food items. Further, decomposition results demonstrated that higher variability in tomato is attributed by both real and inflationary factors. However, the relative contribution of tomato is determined by inflationary factors (54%) against real factors (46%). A similar result is observed for onion and potato, and others also. We found similar results as consumer price index-combined for food (CPI-C).

Table 7.

Shares of Commodity Prices and its Decomposition Analysis using CPI-IW Data.

Commodities	$({\bar{θ}}_{i})$	$({\bar{θ}}_{i}^{R})$	$({\bar{θ}}_{i}^{I})$
Rice	2	38	62
Onion	15	49	51
Potato	11	44	56
Brinjal	4	45	55
Cabbage	2	47	53
Tomato	29	46	54
Lady’s finger	6	41	59
Cauliflower	4	45	55
Peas	5	46	54
Palak	3	41	59
Sugar	3	38	62

Source: The authors.

From the CPI-C and CPI-IW analysis, we conclude that very few commodities are responsible for the higher variability in relative prices, and most of them are vegetables. The variability of identified items is due to both real and inflationary. However, inflationary factors contribute a larger variability in the relative prices in the food basket.

Comparison between WPI and CPI Results

First, few commodities are predominately contributing to larger variability in the relative price of food basket as a whole. Only 25 commodities contribute 93% of the relative price variability among 105 commodities from WPI data. Among them, cabbage alone provides 24% of the variability in relative price changes. However, from the CPI-C data, it is observed that 22 commodities have been identified among the 106 commodities, which contribute 94% of the variation in relative price. Among them, one commodity, namely, tomato, which determines 45% of the fluctuation in relative prices.

Second, the changes in relative price variability are mainly due to vegetable prices determined by both real and inflationary factors but magnitude is larger for inflationary factors for both the results. However, the variability in relative price changes in all the commodities are largely determined by inflationary factors and shared as highest against real factors except fish (prawn), parwal and goat meat/mutton from CPI results. Third, both the CPI and WPI non-food items are significantly responsible for the relative price variability of food items.

From the above analysis, we can conclude that the majority of identified commodities in relative price variability (mainly vegetables) are largely determined by inflationary factors form CPI results, whereas changes in relative price variability are mainly determined by both real and inflationary factors from the WPI results. However, inflationary factor has nearly 60% share in the relative price variability. Overall, the results obtained from both the data set exhibits similar results. However, it varies with magnitudes.

Conclusions and Policy Implications

The present study empirically investigates the decomposition of relative price variability in the case of India employing monthly frequency on 105 commodities prices, spanning the period January 2005 to March 2017. This study broadly focuses on three important issues: (a) identifying the individual commodities whose contribution is higher towards variability in relative prices; (b) investigating the decomposition of relative price variability into two components due to inflationary and real factors. For that, we have used the methodology developed by Clements and Nguyen (1981, 1982); and (c) checking the robustness of CPI food disaggregated data. We have also taken into account WPI and CPI non-food commodities to verify to what extent non-food commodities are accountable in relative price variability of food items.

Based on the empirical results, we found that only 25 among 105 commodities are predominately contributing 93% of the variation in relative price changes in the food basket. The decomposition analysis results indicate that 53% of the variability in the relative price of primary food articles is due to real factors, and the remaining 47% is due to inflationary factors. Whereas 30% of the relative price variability of manufactured food products is due to real factors, and the rest 70% is due to inflationary factors. Overall, the commodity having the highest share largely contributes to variability in relative price by both demand and supply-side factors. However, the inflationary factor contributes a relatively larger share in the variability in relative prices. In particular, higher relative price variability is mainly contributed by vegetable prices. The study concluded that few commodity prices have a larger contribution to the variability of relative price. The primary food articles have a higher contribution to relative price variability, whereas manufactured food products have the least contribution. Moreover, the CPI food results demonstrated that most of the food commodities in relative price variability are largely determined by inflationary factors. Finally, it is concluded that non-food items are also responsible for the variability of relative price changes of food items. Most of the commodities responsible for variability in relative price changes are from agricultural inputs like power and fuel.

Based on the empirical findings, we can draw the following policy implications. Our results concluded that both the supply- and demand-side factors are responsible for larger relative price variability, and robustness check results revealed that relative price variability is mainly due to inflationary factors (supply side). Therefore, the government and policymakers should take important policy measures for both the supply and demand sides. First, to meet the supply-side response, we need to increase the growth of agricultural productivity. Therefore, the government should take appropriate policy decisions to boost agricultural production and productivity via adopting new technology, investment in agriculture. Second, various institutional reforms, such as allocating different crop insurance schemes and providing rural credit facilities via rural banking and small-scale cooperative societies, should be taken by the government. Third, to meet the demand-side response, the contractionary monetary policy measures might help to curb the food price inflation by reducing the money supply and credit facilities, which eventually moderate consumption demand. Fourth, both food and non-food items are accounted for the variability of relative price changes of food items. Therefore, the central bank should take suitable policy decisions by focussing on commodity-wise disaggregated inflation points of view while targeting headline inflation to maintain stability in relative prices and growth of the economy. Finally, if variability in the relative price of both the identified food and non-food items can be checked by implementing an appropriate policy stance. In that case, food inflation can be eliminated as a whole term.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Notes

ORCID iDs

Asharani Samal

Phanindra Goyari

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