Session IV Improving the quality of price statistics: Monitoring consumer price trend using daily price data of online grocery stores in India

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1 Session IV Improving the quality of price statistics: Monitoring consumer price trend using daily price data of online grocery stores in India Anirban Sanyal Reserve Bank of India

2 Content I. Research question and scope of study II. Literature review III. Online grocery and e-retail IV. Data used V. Methodology VI. Findings VII. Remaining issues/further research

3 I. Research question & scope of study Food items drives consumer inflation in recent times Wt of food item in overall CPI: around 46%

4 I. Research question & scope of study Price stability - one of the major mandate of central banks across countries Major impediments Official estimates of CPI and WPI comes with a lag Drivers of inflation can be different High frequency data of these inflation drivers are rarely found Objective of the paper To assess the viability of using daily price data from online grocery stores to track retail inflation To check the degree of coherence at different item basket level Is there any possibility of divergence between the official estimates and such price data

5 I. Research question & scope of study Contribution of the paper Identifying alternate data source Policy implications as real time tracking of inflation Identify adverse price shocks on drivers of inflation First of its kind in Indian context Online grocery is only a step towards using high frequency data Other high frequency data can also be tried Spatial coverage and item basket coverage major challenge

6 II. Literature review Authors Cavallo (2010) Cavallo and Ribogon (2010) Cavallo (2013) Cavallo and Ribogon (2016) Cavallo (2016) Contribution Scraped Data and Prices in Macroeconomics. (Ph.D Dissertation) Initiated project titled MIT Billion Prices Project (BPP) "Online vs Official Price Indexes: Measuring Argentina s Inflation" The Billion Prices Project: Using Online Prices for Measurement and Research Are Online and Offline Prices Similar? Evidence from Large Multi-Channel Retailers MIT Billion Prices Project BPP covered over five million commodities sold across 300 online retail chains in more than 70 different countries

7 III. Online grocery and its future Survey report of Nielson (April, 2015) More than 80% of respondents across countries find it convenient to order from online grocery stores. Asia-Pacific Region has been a leading region for adopting such online initiatives in grocery market Major thrust in food and beverage Technology Preference Online grocery Access to Mobile internet Application based hassle free ordering

8 III. Online grocery store in India History of online grocery stores in India BigBasket ZopNow LocalBana ya.com Grofers Peppertap Jugnoo

9 III. Online grocery store: Prospect in India The food and grocery industry in India is now worth $383 billion and is expected to touch $1 trillion by Technopack

10 IV. Data used daily price data from one of the major online grocery of India, Bigbasket.com daily price of around 2200 commodities have been monitored over 5 months horizon (Apr-Aug 2015) The business operation of the aforesaid online grocery store has been found to be restricted only to 5 major cities namely Hyderabad, Bengaluru, Chennai, Mumbai and Pune.

11 IV. Data used Item Code Item Description City Sale Price MRP Unique identifier of each commodity which is maintained by online grocery Description of the item City name Sale price quotation of commodities Maximum retail price

12 No. of unique commodities offered V. Methodology Data cleansing and transformation The items which does not have any reported price, has been excluded from the analysis. MRP has been used as representative price quotation across commodities as the information on MRP is not available for Bengaluru, the sale price has been used as proxy for MRP for Bengaluru only Mapping online grocery commodities to CPI item basket Bangalore Hyderabad Mumbai Pune Chennai

13 V. Methodology CPI Items No. of items mapped Palak and other leafy vegetables 260 other fresh fruits 257 other vegetables 254 gourd, pumpkin 161 mango 138 beans, barbati 94 apple 92 banana (no.) 72 onion 72 processed food 70 brinjal 64 tomato 55

14 V. Methodology Construction of Index City level index CPI city it = P city ijt city P 100 ij0 Aggregate Index CPI it aggr = 1 C c=1 w i c C w i c CPIit c c=1 Comparative analysis and deep-dive CPI item level indices are rebased at Apr-15=100 for comparative purpose The comparative analysis has been carried out using visual inspection

15 VI. Findings Co-movement in potato price but sharp increase during Jun-15 which is not that high in official estimates Onion price which increased significantly during Jul-Aug 2015, is properly captured by daily data

16 VI. Findings: Co-movement in price

17 VI. Findings: Divergence Divergence between official estimates and daily data observed in some fruits and vegetables Pappaya Apr-15 May-15 Jun-15 Jul-15 Aug CPI (Derived) Composite

18 VI. Findings: Deep dive Spatial divergence

19 Apple VI. Findings: Deep dive Commodity basket: Mapping issues CPI Fresho Apple - Fuji Fresho Apple - Royal Gala Fresho Apple - Washington Fresho Apple - Green Fresho Apple - Kinnaur Fresho Apple Fuji Premium BigBasket Fresho Organically Grown - Apple Fresho Apple - Queen Fresho Apple - Shimla Fresho Apple - Golden Delicious Fresho Apple - Granny Smith Fresho Apple - Indian

20 VII. Remaining issues/further research The daily price data is able to track the price momentum across commodities which matches with the official data Divergence in price momentum is also observed in certain commodities. The divergence of price momentum can be attributed to regional coverage and items covered under these online services. Such divergence between online data and official estimates has also been observed by Cavallo (2012) As these online groceries are spread out only in metro cities, the price quoted in these online groceries are often representative of premium quality of items. Further scope includes extending the timeline of analysis along with increasing coverage of the item basket by incorporating larger information set from other online groceries.

21 Thank You