DP2020-30

The Covid-19 Impact on
Agricultural Market Arrivals and
P r i c e s i n I n d i a : A P a n e l VA R
Approach
Katsushi S. IMAI
Nidhi KAICKER
Raghav GAIHA

Revised December 1, 2020

The Covid-19 impact on agricultural market arrivals and prices in India: A
Panel VAR approach
Katsushi S. Imai (*)
Department of Economics, University of Manchester and ROEB, Kobe University, Japan
Nidhi Kaicker
Ambedkar University Delhi
Raghav Gaiha
GDI, University of Manchester and Population Studies Centre, University of Pennsylvania
This version: 30th November 2020
Abstract

Using the panel data on market arrivals and prices for the 17 Indian states from July 2019 to June 2020,
the present study examines whether the growth of Covid-19 pandemic influenced fractional changes inmarket arrivals and prices. A point of departure of our analysis from the literature is that we take into
account the dynamic and lagged interactions between the fractional changes in market arrivals andprices of food commodities, namely, rice, onion, potato, and tomato, and the growth rate in the severity
ratio of the Covid-19 pandemic, using a panel VAR model based on the GMM. Our results suggest that
there was virtually no effect of the Covid-19 pandemic growth on fractional changes in market arrivals
while the former negatively influences fractional food price changes in the short run. However, once
we consider feedback effects in the VAR model based on Impulse Response Functions, the overall
elasticity of the fractional change in the market arrival with respect to the Covid-19 pandemic growthturns from weakly positive to zero in a relatively short term. The overall elasticity of the fractionalchange in the market price with respect to the Covid-19 pandemic growth turns from positive to zero or
negative in onion and tomato, and from negative to zero in rice and potato. We also find a great deal ofregional heterogeneity where, for instance, the negative effect of the pandemic growth on the fractionalchange in price is larger in Maharashtra, the state with the worst pandemic. While the effect of thepandemic growth is relatively short-lived, policymakers need to take into account dynamic effects overtime given the complexity of the transmission mechanism.
Key words: Covid-19 pandemic, food prices, a panel VAR model, lockdown, India
JEL codes: E 31, E 61, E 65.
(*) Corresponding Author: Katsushi S. Imai (Dr), Address: Department of Economics, School of Social Sciences,
Arthur Lewis Building; University of Manchester, M13 9PL, Email: Katsushi.Imai@manchester.ac.uk;
Acknowledgements: This study is funded by Asia & the Pacific Division, IFAD. We are grateful to the IFAD
Country Director, Ms Rasha Omar, for her support and advice; we are also indebted to Radhika Aggarwal for her
outstanding research support. The views are personal and not necessarily of IFAD.

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The Covid-19 impact on agricultural market arrivals and prices in India: A
Panel VAR approachI.

Introduction

A nearly one year has passed since the 1st of December 2019 when the first case of Covid-19
was confirmed in China (Wu et al., 2020) and more than nine months have passed since thefirst positive Covid-19 case was registered in India on 30 January, 2020, in Kerala. The numberof cases started increasing rapidly in early March. India reported its first death due to Covid19 on 13 March, 2020, soon after which the Indian government sealed its international borders,suspended all visas to India, banned domestic travel by rail as well as air, and eventually
announced a complete lockdown of the country to prevent community spread of the virus(Kaicker, Imai and Gaiha, 2020). As of 12th November 2020, the total coronavirus infection
cases in India were 8,727,900 (the second next to USA) with death numbers 128,380 (the third
next to USA and Brazil).1 Though both daily cases and death numbers showed signs of slowing
in September, there still exists risk for the second wave and thus a huge impact of the pandemic- both direct and indirect – on the Indian society. Hence sound policies to mitigate such impact
need careful analysis. The impact has had a huge negative impact – including direct and indirect- , but despite its importance we do not know exactly the extent to which the Covid-19
pandemic damaged the Indian economy and society or how various policy measures taken by
the central and state governments have mitigated any negative impact of the pandemic. Among
numerous issues, the present study investigates whether the Covid-19 pandemic affected
agricultural market arrivals and prices by taking into account the dynamic interactions between

1

Source: https://www.covid19india.org/ (accessed on 12 November 2020).
2

the mandi prices and the arrivals2. More specifically, we will use the weekly panel data of 17
Indian states from July, 2019, to June, 2020, and will examine whether there exist any causal
associations between the Covid-19 pandemic growth and fractional changes in the agricultural
market prices and the market arrivals of food commodities, namely, rice, onion, potato andtomato.3 For this purpose, we use a Panel VAR (Vector Autoregressive) Model based on the
GMM (the generalized method of moments) to allow the dynamic interdependence of
fractional changes in mandi arrivals, prices, and the Covid-19 pandemic growth.
Covid-19 pandemic had a negative impact on agricultural production, sales, prices andincome of farmers in India and it has caused a huge disruption to the country’s food systems
and livelihoods (Harris et al. 2020). Harris et al. undertook a telephone survey with 448 farmers
in 4 states, Jharkhand, Assam, Andhra Pradesh, and Karnataka during 5-12 of May, 2020, andfound that a majority of farmers experienced negative impacts on production, sales, prices andincomes. Price reductions were reported by over 80% of farmers, and reductions by more thanhalf for 50% of farmers. Harris et al.’s study suggests that the immediate or short-term impactof the Covid-19 pandemic on prices and market arrivals was likely to be negative. FAO (2020)
also reported a huge loss in agricultural production in India, but rather emphasised a surge infood prices: ‘food prices skyrocketed across the nation as transportation services were halted
and fresh supplies were unavailable. Urban residents all over India found it difficult to buy
groceries as the commodities became scarce in the beginning. The major reason was panic
buying and hoarding among the people’. Globally, the Covid-19 impact on food prices is likely

2

3

Mandi means market place.

We focus on the first difference of these variables as some of the level variables of arrivals
are non-stationary while price and the Covid-19 severity are stationary as we will discuss laterin detail.
3

to depend on crops or items as well as the extent to which food supply chains are disrupted(Laborde et al., 2020).4 However, Reardon et al. (2020) observe that “COVID-19 is likely toincrease food prices, both as a cause and consequence of food shortages. Restrictions on foodsupply chains (FSCs) logistics will increase transaction costs and thus consumer prices.
Speculative hoarding may occur and trigger price increases” (Reardon et al, 2020, page 80).
ADB (2020) has also noted significant price increases in staple prices in developing Asian
countries.
While the effect of the Covid-19 pandemic on agricultural production and food supplies iscomplex as it may vary across different products and different regions, it is important tounderstand how the pandemic and the government lockdown policies influence food supply
chains and the agricultural market – its functioning and access. To understand the effect of theCovid-19 pandemic on food systems, it is necessary to analyse how it affects food prices andmarket arrivals at the same time given the complexity of the system. In their admirably richcontribution, Varshney et al. (2020) assess the impact of the spread of COVID-19 and the
lockdown on market prices and quantities traded in agricultural markets. They compare
whether the impact differs across non-perishable (wheat) and perishable commodities (tomato
and onion), and the extent to which any adverse impacts are mitigated by the adoption of agreater number of agricultural market reform measures. Varshney et al. use a granular dataset
comprising daily observations for 3 months (i.e. April-June 2020, relative to the samemonth/period in 2019) from nearly 1000 markets across five states and use a double- and tripledifference estimation strategy. Indeed, as the authors rightly claim, this study is probably one‘Since the onset of the pandemic, world wheat prices have been quite volatile…, but prices
have declined by around 10% between January and early July. By contrast, world market pricesof rice rose around 20% between January and April and became highly volatile in May’ (IFPRI,
2020, cited by Laborde et al., 2020, p. 502).
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of the first to estimate the causal impacts of COVID-19 on food prices. They found that wheat
saw a decrease in price differentials in June, but the overall impact across the 3 months was
non-significant. This is likely because government procurement operations helped anchor
wheat prices at the minimum support price (MSP). Prices for tomatoes fell in May, but there
was no statistically robust impact. Also, onion prices were unaffected—this may reflect theconcentrated nature of its supply, and the relatively dispersed nature of its demand. In
comparison, all the market arrival impact magnitudes were positive and significant, especiallyfor the two perishable goods. That the magnitudes of differentials in market arrivals were muchhigher than those in prices suggests that supply constraints began easing beginning in May. In
the case of the perishables, the positive coefficients on market arrivals may well be a reflectionof distress sales and/or the need to address cash flow constraints. Together, these results suggest
that, while there were undoubtedly short-term disruptions in agricultural markets, they werealso relatively resilient, in the sense that market arrivals were quick to recover after the initialmonth, and that possible distress sales did not result in a disproportionate fall in prices.
The methodology used is, however, debatable. First, it ignores the price-quantity interaction.
Running double and triple differences on wholesale prices and mandi arrivals, respectively,
raises the concern whether the results on the prices (or arrivals) might be different if
instrumented mandi arrivals (or prices) are used as an explanatory variable5. Second, the main
explanatory variable based on the Covid cases is misleading as it depends on the number oftests which are not randomly distributed across different states or districts. Their claim that5

Another interesting study, Mahajan and Tomar (2020), quantifies the level of disruption in
the food supply chains in India due to COVID-19 induced lockdown. While the methodology
is rigorous, a limitation is that the analysis is confined to data from one of largest online grocery
retailers in India. Overall, the study tracks 789 products across three cities (Delhi, Chennai andKolkata). It evaluates the impact across four product categories, vegetables and fruits (i.e.,
perishables, and edible oils), and cereals and pulses (i.e., non-perishables). For an appraisal,
see Kaicker, Gaiha and Aggarwal (2020).
5

there are states with relatively high Covid cases and those with relatively low cases is broadly
valid, but this overlooks time-series variation or development of Covid cases or pandemic
within the former (taking 1 in their analysis) and within the latter (taking 0). Third, they claimthat the difference- in- difference approach is valid if the parallel trend assumption (based on
the comparison between 2018 and 2019) is valid. In addition to the parallel trend assumption
DID assumes that the treated and the controls are comparable in the sense that the macroenvironment is similar, or there is no factor that will uniquely influence only the former, or
only the latter. It is unlikely that this condition will be satisfied, for instance, under different
weather conditions that they did not control for. Our study based on the weakly panel VAR
covering the period from July 2019 to June 2020 attempts to overcome some of the limitations
by taking account of the dynamic (and endogenous) interactions among prices, mandi arrivals,
and the Covid-19 pandemic based on the severity measure.
Another detailed study of the impact of Covid-19 pandemic on agriculture prices in India
during March-May 2020 is by Seth et al. (2020). Its merits are that it analyses producer andconsumer price changes in a large number of agricultural commodities in 11 cities, from March
1, 2020, to May 31, 2020, relative to the same period in 2019. Seth et al. (2020) found that
cereal prices remained stable relative to last year and across the weeks following the lockdown.
This stability is explained through India’s cereal-centric policies, which resulted in hugestockpiles of grains across the country. On the other hand, among the non-cereal food groups(e.g. pulses, vegetables, and eggs), pulses have exhibited a consistent increase in the retailprices across cities, and the prices have not stabilized after more than a month of lockdown.
An increase in demand for pulses due to panic buying and disruptions in the supply chainplausibly contributed to the rising trend in prices. The disruptions in the supply chain include
the inability of farmers to move produce to APMCs due to the lack of transport. Further, stock
replenishment was reported to have been affected due to reduced availability of labour. Potato
6retail prices increased for all cities relative to last year and across weeks after the lockdown.
Onion retail prices more than doubled in almost all the cities studied, relative to last year. The
price rise was due to decreased deliveries that occurred because of transportation bottlenecks.
However, these conclusions by Seth et al. (2020) may not be robust as their analysis primarily
draws upon the comparison of means in a descriptive analysis, without a t-test or rigorous timeseries econometric analyses.6 The present study attempts a more rigorous study based on thepanel data econometric method where the price-quantity interaction is fully modelled.
The rest of the paper is organised as follows. The next section states the hypotheses anddefines the variables we use in this study. This is followed by specifications of our econometricmodels. Section IV reports and discusses the results based on our econometric results. The final
section summarises the results with policy lessons.

II.

Hypotheses, Data, and Econometric Models

We will examine the following hypotheses based on the mandi level weekly panel data on
commodity prices (based on the data collated from Price Monitoring Division of the
Department of Consumer Affairs 7 ), market arrivals (based on Agricultural Marketing
Information Network) as well as the weekly panel data of the Covid-19 cumulative severity

Seth et al.’s (2020) assessment of impact of the price changes on nutrition is thin. It rests on
the premise that a disproportionate rise in prices of non-cereals may divert consumer spending
toward staples (that is, wheat and rice), resulting in inadequate intakes of protein-rich foodgroups. However, the analysis needs to take account of dietary diversification that is associatedwith food prices, income/expenditure, household characteristics and its location, and timerelated changes transmitted through prices and expenditure, and residually through life-style,activity patterns, and improvements in the epidemiology of disease environment (Kaicker etal., 2014).
7
https://fcainfoweb.nic.in/reports/report_menu_web.aspx
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ratio (CSR) as a proxy for the pandemic, after controlling for the state-level time-variant andtime-invariant determinants.
Hypothesis 1: The Covid-19 pandemic growth negatively influenced the fractional change inmarket arrivals (namely, rice, onions, potatoes and tomatoes) in India after taking account of
the interaction between price and market arrivals.
Hypothesis 2: The Covid-19 pandemic growth negatively influenced the fractional market
price change (namely, rice, onions, potatoes and tomatoes) in India after taking account of theinteraction between the price and the market arrivals.
Hypothesis 3: The Covid-19 pandemic growth negatively influenced the fractional change inmarket arrivals differently in Maharashtra (or Meghalaya, Uttar Pradesh, Madhya Pradesh,Rajasthan, Punjab, Haryana, Jharkhand or Meghalaya) –in comparison with the rest of India.
Hypothesis 4: The Covid-19 pandemic growth negatively influenced the weekly market pricechange differently in Maharashtra (or Meghalaya, Uttar Pradesh, Madhya Pradesh, Rajasthan,
Punjab, Haryana, Jharkhand or Meghalaya) –in comparison with the rest of India.
The Price Monitoring Cell (PMC) in the Department of Consumer Affairs was created in
1998, with the task of monitoring prices of 14 essential commodities across 18 centres in the
country (PMC, 2011). PMC is the only organization in the country collating and disseminating
absolute prices (retail and wholesale) of select essential commodities on an almost real timebasis every day (ibid., 2011). Mandi prices are collected by 49 centres for 22 commodities
either by online networking (26 centres), by email (8 centres) or by fax (19 centres) based on
their connections to the common vendors (ibid., 2011). Weekly mandi prices are updated every
Friday by email. The prices are then carefully checked by the PMC staff. Quality and varietyof the item for which prices are reported remain same for each centre though these may varyfrom one centre to another. We have constructed the panel data of market prices based on theprice data collated by PMC. Given that the prices are reported for the average quality of the8item for a given centre, the data are comparable over time. There remains an issue of crosssectional comparison of the price data (e.g. due to the different methods of data collection ordifferences in the average quality), but it is unlikely that the nature of the price data
significantly varies across different regions. For the purpose of our study, this dataset isundoubtedly the best source which we could use. Given the time-consuming nature of the data
construction, we have created the centre-state-weekly panel data for market prices of rice,onions, potatoes and tomatoes. Not only the effect of the pandemic on the consumer price andon the farm gate price but also its effect on the difference between the consumer and the farm
gate prices will also be estimated.8
The market arrival data are Agricultural Marketing Information Network (AGMARKNET)
which was launched by the Union Ministry of Agriculture and links around 7,000 agricultural
wholesale markets in India with the State Agricultural Marketing Boards and Directorates for
effective information exchange. This e-governance portal AGMARKNET, implemented by
National Informatics Centre (NIC), facilitates generation and transmission of prices,
commodity arrival information from agricultural produce markets, and web-based
dissemination to producers, consumers, traders, and policy makers transparently and quickly.
We have constructed the variable on mandi arrivals, the total quantity (in tonnes) of foodcommodities arrivals from agricultural produce markets. The weekly mandi arrival quantities
have been collated at the state level for 17 states9 of India for four food commodities, namely,
potato, tomato, onion and rice. The time period is 1st July 2019 to 30th June 2020.

8

Appendix Table 1 provides a summary of descriptive statistics of the variables we use in thisstudy.
9
They are Andhra Pradesh, Assam, Chhattisgarh, Gujarat, Haryana, Himachal Pradesh,
Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Meghalaya, Odisha, Punjab,
Rajasthan, Uttar Pradesh, and West Bengal. These states are selected on the basis of the data
availability as there are relatively few missing observations for these states in our study period.
They are by no means representative of India, but the share of GDP of the 17 states in India
9A new indicator ‘relative severity’ proposed by the World Bank is used to illustrate theunequal distribution and progression of covid-19 deaths across states10. The relative severity
ratio is defined as the ratio of the total deaths attributable to Covid-19 over a given period tothe expected total deaths from all causes under the counterfactual assumption that the pandemic
had not taken place over a base period of the same length. Comparison with pre-pandemic
mortality patterns provide a state- specific measure of the severity of the pandemic, and theexcess burden on the health system
Algebraically,
𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝐶𝑜𝑣𝑖𝑑 𝐷𝑒𝑎𝑡ℎ𝑠𝑡

𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜𝑡 = 𝑁𝑜.𝑜𝑓 𝐷𝑒𝑎𝑡ℎ𝑠 𝑖𝑛 𝑎 𝑝𝑟𝑒 𝑝𝑎𝑛𝑑𝑒𝑚𝑖𝑐 𝑦𝑒𝑎𝑟(

365

∗𝐿𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑃𝑎𝑛𝑑𝑒𝑚𝑖𝑐𝑡 )(2)where,
𝐿𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑃𝑎𝑛𝑑𝑒𝑚𝑖𝑐𝑡= 𝑁𝑜. 𝑜𝑓 𝐷𝑎𝑦𝑠 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝐷𝑎𝑡𝑒 𝑜𝑓 𝐹𝑖𝑟𝑠𝑡 𝐶𝑜𝑣𝑖𝑑 𝐿𝑖𝑛𝑘𝑒𝑑 𝐷𝑒𝑎𝑡ℎ 𝑎𝑛𝑑 𝑡 𝑖𝑛 𝑡ℎ𝑒 𝑅𝑒𝑔𝑖𝑜𝑛
The Covid-19 data are obtained from Ministry of Health and Family Welfare, India. The dataon past mortality patterns is based on the State-wise Number of Registered Deaths in 2017from the Ministry of Health and Family Welfare, Government of India. For the purpose of the
Cum-SR, the number of reported deaths in 2017 is scaled down from annual estimates to thelength of the pandemic in each state, calculated as the number of days since the first death in
the state till the cut-off date for this analysis, i.e. 21 June 2020. For instance, in Maharashtra,
the first death was reported on 17 March 2020, implying the length of the pandemic as 97 days.

was 76.4% and the population share was 77.8% in 2017-2018, which implies that we covermajor states in our dataset.
10
For details, see Schellenkens and Sourrowuille (2020). Kaicker, Imai and Gaiha (2020 b)
examined the determinants of the Covid -19 severity ratio in India.
10

The expected total deaths under the no-pandemic situation is calculated as the total number ofdeaths in each region in 2017 * 97 days / 365.11
As we noted earlier, we estimate the following panel VAR model based on the GMM, whichdraws upon Abrigo and Love (2016). As suggested by Tomek and Myers (1993) who reviewed
various econometric models of agricultural market behaviours (e.g. price, demand, supply) andpublic policies, the use of VAR model for hypothesis generation would be less controversial
than other alternative econometric methods under the uncertainty where not all the variables
are observed.
𝑌𝑖𝑗𝑡 = 𝑌𝑖𝑗𝑡−1 𝐴1 + 𝑌𝑖𝑗𝑡−2 𝐴2 + 𝑌𝑖𝑗𝑡−3 𝐴3 + 𝑋𝑗𝑡 𝐵 + 𝜇𝑖 + 𝑒𝑖𝑡(1)

where 𝑌𝑖𝑗𝑡 is a (1 × 𝑘) vector (where 𝑘 is 3 or 2 in our study) of dependent variables (namely,
the first differences of the logarithm of mandi arrivals and prices (and the first logged differenceof Covid-19 severity ratio in Model A) and 𝑋𝑗𝑡 is a (1 × 𝑙) vector of exogenous explanatory
variables (namely, weekly variables on temperature and rainfall at state levels and the Covid19 severity ratio at state levels and various state dummies in Model B). The number of lags has
been determined by statistical significance of lagged variables and it is 3 in our case. 𝜇𝑖 and𝑒𝑖𝑡 is a (1 × 𝑘) vector of panel fixed effects and idiosyncratic errors at mandi levels,
respectively. The Covid-19 severity ratio is treated as an endogenous variable in Model A,
while it is made exogenous in Model B where the state-level effect of the pandemic growth on
the fractional change of mandi arrivals and prices is examined. 𝑖 stands for mandi (1 to 93), jstands for state (1 to 17) and t stands for time, or week (1 to 52, or the first week of July 201911

A question is whether the death numbers in 2017 would serve as a valid counterfactual. First,
the national level death rate has been fairly stable and gradually declining from 7.4 to 7.3
deaths/1,000 population since 2012 and the year 2017 is not an exceptional year. Second, while
India has experienced frequent and widespread droughts, there were no major droughts in 2017.
The death numbers in 2017 would thus serve as a reasonable counterfactual for the present
analysis of Covid-19 (https://www.indexmundi.com/, accessed on 18 July 2020).
11

to the last week of June 2020). More specifically, we estimate the following two versions of
the panel VAR model.12
Model A:𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡 = 𝛼1 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡−1 +𝛼2 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡−2 + 𝛼3 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡−3 +𝛼4 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡−1 + 𝛼5 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡−2 +𝛼6 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡−3 + 𝛼7 𝑑 log 𝐶𝑆𝑅 (𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜)𝑗𝑡−1 +𝛼8 𝑑 log 𝐶𝑆𝑅 (𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜)𝑗𝑡−2 +𝛼9 𝑑 log 𝐶𝑆𝑅 (𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜)𝑗𝑡−3 + 𝛽1 𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒𝑗𝑡 + 𝛽2 𝑅𝑎𝑖𝑛𝑓𝑎𝑙𝑙𝑗𝑡 +𝑃ℎ𝑎𝑠𝑒 𝐷𝑢𝑚𝑚𝑖𝑒𝑠𝑡 𝛽3 + 𝜇𝑖 + 𝑒𝑖𝑡 ……….(2)𝑑 log 𝑊ℎ𝑜𝑙𝑒𝑠𝑎𝑙𝑒 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡 = 𝛼1 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡−1 +𝛼2 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡−2 + 𝛼3 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡−3 +𝛼4 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡−1 + 𝛼5 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡−2 +𝛼6 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡−3 + 𝛼7 𝑑 log 𝐶𝑆𝑅 (𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜)𝑗𝑡−1 +𝛼8 𝑑 log 𝐶𝑆𝑅 (𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜)𝑗𝑡−2 +12

One issue is whether it is appropriate to use a (first-differenced) log linear specification forthe panel VAR model where the underlying relationships among dependent variables may be
non-linear. First, use of a non-linear function in a VAR model is feasible (e.g. Altissimo andViolante, 2001), but computationally demanding in our data settings. Second, if quadratic terms
are inserted, they are not statistically significant, implying that there is no quadratic relationship.
Also, plotting key variables does not show any particular form of non-linearity. We have thus
decided to use a log linear specification in estimating a panel VAR. A linear panel VAR modelcannot capture non-linearity among variables, but it can model non-linearity in a time
dimension, as we discuss later.
12𝛼9 𝑑 log 𝐶𝑆𝑅 (𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜)𝑗𝑡−3 + 𝛽1 𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒𝑗𝑡 + 𝛽2 𝑅𝑎𝑖𝑛𝑓𝑎𝑙𝑙𝑗𝑡 +𝑃ℎ𝑎𝑠𝑒 𝐷𝑢𝑚𝑚𝑖𝑒𝑠𝑡 𝛽3 + 𝜇𝑖 + 𝑒𝑖𝑡 ……….(3)𝑑 log 𝐶𝑆𝑅 (𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜)𝑗𝑡 = 𝛼1 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡−1 +𝛼2 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡−2 + 𝛼3 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡−3 +𝛼4 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡−1 + 𝛼5 𝑑 log 𝑊ℎ𝑜𝑙𝑒𝑠𝑎𝑙𝑒 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡−2 +𝛼6 𝑑 log 𝑊ℎ𝑜𝑙𝑒𝑠𝑎𝑙𝑒 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡−3 + 𝛼7 𝑑 log 𝐶𝑆𝑅 (𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜)𝑗𝑡−1 +𝛼8 𝑑 log 𝐶𝑆𝑅 (𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜)𝑗𝑡−2 +𝛼9 𝑑 log 𝐶𝑆𝑅 (𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜)𝑗𝑡−3 + 𝛽1 𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒𝑗𝑡 + 𝛽2 𝑅𝑎𝑖𝑛𝑓𝑎𝑙𝑙𝑗𝑡 +𝑃ℎ𝑎𝑠𝑒 𝐷𝑢𝑚𝑚𝑖𝑒𝑠𝑡 𝛽3 + 𝜇𝑖 + 𝑒𝑖𝑡 ……….(4)In estimating (2)-(4), the first difference is taken for dependent variables because the panel unit
root test results show that some time-series data of mandi arrivals (in particular, rice) are notstationary (or I(1)) while those of market prices and the Covid-19 severity ratio are stationary(or I(0)) (Tables 1 and 2). This means that these three variables are not co-integrated and wecannot examine their long-term association based on the co-integration or the error-correction
model. If mandi arrivals in level are not stationary, we cannot estimate the panel VAR model.
As the first differenced data of these time-series are strongly stationary (Table 2), we will
examine the relationships of the first differences of these variables.13

13

When we include the equation for the pandemic growth (Equation (4)) in a panel VAR,
lagged variables of the fractional changes in mandi arrivals and prices need to be included.
This captures in principle how market changes will affect the pandemic, for instance, throughfood shortages or worsening poverty.
13

Mandi arrivals is the aggregate amount of each commodity transacted in each market place
and as such it represents the equilibrium point based on the producer supply and the consumer
demand, both of which respond to mandi prices of each commodity, which is roughly same as
wholesale prices14, and are known daily to producers, retailers, and consumers. As both mandi
arrivals and mandi prices are determined ex post to reflect both demand and supply, neither
consumer nor producer theories are directly applicable to them. For instance, the suppliersconsider daily or weekly whether and how much they should bring their products to the market
depending on the price (where a price-quantity relation is broadly positive). On the other hand,buyers have stronger incentives to purchase more if the prices are getting lower (i.e. a negative
price-quantity relationship) and so the short-term price-quantity relationship can be complex
and possibly non-linear. For instance, Sharma and Singh (2014) showed that the relationshipbetween mandi arrivals of pearl millet can be positive or negative across different markets
within Rajasthan. There is also a long-term price-quantity association that is associated with along-term planting and product decisions of producers which are influenced by the expectedmarket price of commodities –predicted by the long-run weather forecast, in particular, of
rainfall and other factors as well as the input prices. Consumers will adjust the components inthe basket of various foods depending on their budget constraint in the middle/or mid- term.
The application of the panel VAR to the first differenced mandi price and arrivals as well as
the Covid-19 severity ratio by no means reflect all the complexities in the quantity-pricerelationships. However, an advantage of our approach based on the panel VAR for the first

In this study, we use ‘mandi prices’ or ‘market prices’, and ‘wholesale prices’interchangeably. Wholesale price is a broader concept where all the prices retailers will pay to
the wholesalers are averaged at a certain point of time (e.g. prices of some direct transactions
between large farmers and retailers or supermarkets outside mandi are omitted), while themandi prices are the average prices transacted between sellers and buyers at mandi or market
places. However, mandi prices are used as a basis for constructing the wholesale prices of foodcommodities in India.
14
14

differenced variables is that we can capture a complex, dynamic and endogenous price-quantity
association over time by analysing the effect of the Covid-19 pandemic growth on either priceor quantity of agricultural products.
In Equations (1)-(3), the first differences of log (equivalent to the growth rate, or thefractional change) of mandi arrivals ( 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡 ), market price(𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝑃𝑟𝑖𝑐𝑒𝑘 𝑖𝑗𝑡

)

and

the

Covid-19

severityratio(𝑑 log 𝐶𝑆𝑅 (𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜)𝑗𝑡 ) for the kth product in the ith market, the jth state
in week t are estimated jointly by their lags up to the third lags. We have access to the data ofmandi arrivals only as an aggregate of all the markets in each state, while we constructed the
mandi level prices for all markets in our earlier study (Imai et al., 2020). So all the regressions
are estimated at mandi levels where the standard errors are clustered at state levels to takeaccountofthewithinstate

correlationsofprices.

Ourmainvariable,𝑙𝑜𝑔 𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜𝑗𝑡 , the logarithm of Cumulative Severity Ratio (CSR) of
Covid-19, is also first differenced to examine how the pandemic growth affects the fractionalchanges in the market price and market arrivals.
The model also controls for the weekly data on temperature and rainfall at state levels from
MERRA (Modern-Era Retrospective analysis for Research and Applications – Version 2 webservice). We use the weather data of capital (or a major city) of each state as a proxy for thoseat state levels. It delivers time series of temperature (at 2m), relative humidity (at 2m) andrainfall. The data source is a NASA atmospheric reanalysis of the satellite era using the
Goddard Earth Observing System Model (GEOS-5) and focuses on historical climate analyses
for a broad range of weather and climate time scales (GMAO, 2015). Weather also can affect
the Covid-19 pandemic development. Ma et al. (2020) used the data on daily death numbers
from Wuhan, China, in January-February in 2020 and found that death counts are positively
15

associated with temperature and negatively with relative humidity, while Kaicker, Imai and
Gaiha (2020) found that in India temperature is positively and significantly associated with theCovid-19 pandemic, but rainfall is not.
To capture time effects, the model also has 5 dummy variables for Phase 1, Phase 2, Phase
3, Phase 4 and Phase 5 of the lockdowns announced by the Government of India. The first
lockdown spanned a period of 21 days from 25 March 2020 to 14 April 2020, where nearly all
factories and services were suspended, barring “essential services”. The second lockdown
started on 15 April 2020 and continued till 3 May 2020, with conditional relaxations for regions
where the Covid-19 spread had been contained. With additional relaxations, the phase three ofthe lockdown was from 4 May, 2020, to 17 May, 2020, and the fourth phase was from 18 May,
2020, to 1 June, 2020. Phase 5 of the lockdown (1 June 2020 to 30 June 2020), also known as
Unlock 1.0, was the first phase of the reopening in stages, with an economic focus. We havecontrolled for the unobservable mandi-level fixed effects. This captures whether prices, arrivals
and the severity of pandemic differs due to unobservable factors at market levels, such as,
institutional factors or cultural factors which are unchanged in our study period.

Model B:As an extension, Model B examines whether the effect of the Covid-19 severity ratio differs
across different states due to different policies of lockdown or different degrees of market
reforms. For instance, an Agriculture Produce and Livestock Marketing Act (APLMA) was
introduced in 2017 with the aim to rebuild appropriate market infrastructure for the public andprivate sectors to benefit both farmers and consumers, where each state decides how it isapplied locally depending on its circumstances (e.g. this deregulation will reduce marketing
margins and lower food prices) (Varshney, et al., 2020). They used the market-level data forfive states and argued that Uttar Pradesh and Rajasthan (adopted the market reform with high16

intensity (taking 1 in their triple difference model), and Madhya Pradesh, Punjab, and Haryana
with low intensity (taking 0) depending on the number of adopted provisions in APLMA. They
found that the states with market reforms tend to prevent prices from falling under the Covid19 crisis, mainly for perishable goods (tomato, onion) not for non-perishable goods, such as
wheat. Our recent studies focused on three states, namely, Maharashtra, Jharkhand, andMeghalaya (by using dummy variables for Maharashtra and Meghlaya depending on statisticalsignificance) which showed highly contrasting development of the Covid-19 pandemic inanalysing the causes for the pandemic and its effect on wholesale and retail prices (Kaicker,
Imai, and Gaiha, 2020; Imai et al., 2020). Building upon these studies, the first difference oflogged Cumulative Severity Ratio is interacted with a vector consisting of a set of seven state
dummy variables (for Uttar Pradesh, Rajasthan, Madhya Pradesh, Punjab, Haryana,
Maharashtra and Meghlaya) out of 17 states to capture the effect of phases in these states.15 In
Model B, the Covid-19 severity ratio and the interaction terms are treated as exogenous
variables to avoid the specification becoming too complicated. Equations (5) and (6) are
estimated where the first differences (in logarithm) of mandi arrivals and price are estimatedby the panel VAR model where the first difference of the Covid-19 severity and the interaction
terms are included as exogenous variables. 𝐷_𝑠𝑡𝑎𝑡𝑒𝑗 denotes a vector of state dummies forseven states and this is interacted with the first differences of the Covid-19 severity ratio.
Everything else is same as in Model A. It should be noted that the identification of the statelevel effect is similar to Varshney et al.’s (2020) methodology of triple differencing (Covid
dummy * Year dummy * State dummy) though our identification is based on the time-series

15

State-level unobservable fixed effects are included for all 17 states to identify the interacted
effects for these seven states separately. Insertion of interaction terms only for a subset of theseven states will not change the main results as well as those of the interaction terms
significantly.
17

variation (on a weekly basis, including the period prior to the pandemic) and the cross-sectionalvariation in continuous changes in the Covid severity after controlling for separate effects ofphases and fixed effects at the mandi level.
𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡 = 𝛼1 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡−1 +𝛼2 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡−2 + 𝛼3 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡−3 +𝛼4 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡−1 + 𝛼5 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡−2 +𝛼6 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡−3 + 𝛽1 𝑑 log 𝐶𝑆𝑅 (𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜)𝑗𝑡 +𝐷_𝑠𝑡𝑎𝑡𝑒𝑗 ⨂ log 𝐶𝑆𝑅 (𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜)𝑗𝑡 𝛽2 + 𝛽3 𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒𝑗𝑡 +𝛽4 𝑅𝑎𝑖𝑛𝑓𝑎𝑙𝑙𝑗𝑡 + 𝑃ℎ𝑎𝑠𝑒 𝐷𝑢𝑚𝑚𝑖𝑒𝑠𝑡 𝛽5 + 𝜇𝑖 + 𝑒𝑖𝑡 ……….(5)𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡 = 𝛼1 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡−1 +𝛼2 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡−2 + 𝛼3 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠 𝑘 𝑖𝑗𝑡−3 +𝛼4 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡−1 + 𝛼5 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡−2 +𝛼6 𝑑 log 𝑀𝑎𝑛𝑑𝑖 𝑃𝑟𝑖𝑐𝑒 𝑘 𝑖𝑗𝑡−3 + 𝛽1 𝑑 log 𝐶𝑆𝑅 (𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜)𝑗𝑡 +𝐷_𝑠𝑡𝑎𝑡𝑒 𝑗 ⨂𝑑 log 𝐶𝑆𝑅 (𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜)𝑗𝑡 𝛽2 + 𝛽3 𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒𝑗𝑡 +𝛽4 𝑅𝑎𝑖𝑛𝑓𝑎𝑙𝑙𝑗𝑡 + 𝑃ℎ𝑎𝑠𝑒 𝐷𝑢𝑚𝑚𝑖𝑒𝑠𝑡 𝛽5 + 𝜇𝑖 + 𝑒𝑖𝑡 ……….(6)Based on the results of panel VAR models, we have carried out Granger causality tests among
endogenous variables to see the direction as well as the presence of causalities.

III.

Results(1)

Panel Unit Root Tests

18

As the long time-series data of prices or arrivals or the Covid-19 severity ration can be nonstationary, we have carried out the panel unit root tests to see whether these variables are
stationary or not. In Tables 1 and 2 we apply Levin–Lin–Chu (LLC) (Levin et al., 2002) and
Im-Pesaran-Shin (IPS) tests (Im et al, 2003) for the level and the first differences of thesevariables. LLC tests the null hypothesis that each time series contains a unit root against thealternative hypothesis that each time series is stationary in which the lag order is permitted tovary across individuals. IPS test is not as restrictive as the LLC test, since it allows forheterogeneous coefficients. The null hypothesis is that all individuals follow a unit root processagainst the alternative hypothesis allowing some (but not all) of the individuals to have unit
roots. We apply the specifications with and without a time trend. We determine the number oflags by Akaike Information Criteria (AIC).16

Table 1. Results of Unit-root Tests (Level)

Panel structure

Levin-

Levin-

Im-

Im-

Lin-

Lin-

Pesaran-Pesaran-

Chu

Chu

Shin

Shin

(LLC)

(LLC)

(IPS)

(IPS)

no

with

trend

trend

no trend

no trend

N (no of centres)

93

93

93

93

T (no of periods)

52

52

52

52

Panel means

No

No

No

No

Average lags *1

2.13

2.43

2

2.22

8.12

6.78

4.66

-0.84

I(1)

I(1)

I(1)

I(1)

1.09

1.21

0.97

1.01

Mandi
Rice

Arrivals

adjusted t or W-t(log)

Rice

16

Mandi

bar*2

Average lags

We have also applied other alternatives of panel unit root tests and the results are broadly
similar.
19

Pricet (adjusted)

(log)

-5.12

***

-4.86

***

-4.75

***

-4.64

I(0)

I(0)

I(0)

I(0)

0.78

0.85

0.65

0.7

***

Mandi
Onion

Arrivals

Average lags *1
adjusted t or W-t-

(log)

Onion

Mandi

bar*2

Average lags

-8.55

***

-5.79

***

-10.7

***

-4.86

I(0)

I(0)

I(0)

I(0)

0.5

1.11

0.52

0.93

***

Price

t (adjusted)

(log)

12.52

***

-14.8

***

-12.5

***

-14.56

I(0)

I(0)

I(0)

I(0)

0.52

0.63

0.52

0.51

2.55

-1.67

-1.48

-1.98

I(1)

I(0)

I(1)

I(0)

1.19

0.9

0.84

***

Mandi
PotatoArrivals

Average lags *1
adjusted t or W-t-

(log)

Potato

bar*2

Mandi

Average lags

1.2

Price

t (adjusted)

-8.18

(log)

***

-9.77

**

***

-12.2

***

-12.32

I(0)

I(0)

I(0)

I(0)

0.77

0.76

0.59

0.6

-1.37

-4.95

-1.19

-4.09

I(1)

I(0)

I(1)

I(0)

1.02

0.69

0.81

**

***

Mandi
TomatoArrivals

Average lags *1
adjusted t or W-t-

(log)

Tomato

bar*2

Mandi

Average lags

0.95

Price

t (adjusted)

-7.53

(log)
log CSR

***

-6.34

***

***

-11.7

***

-7.35

I(0)

I(0)

I(0)

I(0)

Average lags

1.22

1.51

1.23

1.4

t (adjusted)

-7.24

***

***

(Covid-19
Severity)

I(0)

***

-2.52
I(0)

Notes: 1. Lags are determined by Akaike Information Criteria (AIC).
*2. adjusted t is reported for LLC and W-t-bar is reported for IPS.
*3. The threshold significance level is at 5%.
*4. Cases where the null hypothesis is rejected are highlighted in
bold.

20

***

-12.1
I(0)

***

-10.58I(0)***We focus on the cases with no trend (the first and the third columns in Table 1), we canconclude that mandi arrivals of rice, potato and tomato are likely to be I (1) while all the othervariables are I(0) which would suggest that mandi arrivals and price of these commodities
cannot be co-integrated. That is why we modelled the price-quantity and Covid-19 pandemic
relationships with the focus on the first differences of these variables. We repeat the same tests
for the first differences and find that they are I (0) or stationary.

Table 2. Results of Unit-root Tests (First Difference)
Levin-

Levin-

Im-

Im-

Lin-

Lin-

Pesaran-Pesaran-

Chu

Chu

Shin

Shin

(LLC)

(LLC)

(IPS)

(IPS)

no

with

trend

trend

no trend

no trend

N (no of centres)

93

93

93

93

T (no of periods)

51

51

51

51

Panel means

No

No

No

No

Arrivals

Average lags *1

2

2.01

1.62

1.62

(FD,

adjusted t or W-t-

log)

bar*2

Panel structure

Mandi
Rice

Rice

-45.98-48.08***-53.72***-51.82

I(0)

I(0)

I(0)

I(0)

1

0.68

0.79

Mandi

Average lags

0.87

Price

t (adjusted)-45.89

(log)

***

***

-42.93***-51.69***-48.15

I(0)

I(0)

I(0)

I(0)

0.76

0.88

0.68

0.71

***

***

Mandi
Onion

Arrivals

Average lags *1
adjusted t or W-t-

(log)

Onion

Mandi

bar*2

Average lags

-58.42***-55.66***-58.29***-56.64

I(0)

I(0)

I(0)

I(0)

1.02

1.06

0.87

0.91

21

***

Pricet (adjusted)(log)-57.37***-53.21***-60.62***-57.16

I(0)

I(0)

I(0)

I(0)

0.72

0.63

0.52

0.51

***

Mandi
PotatoArrivals

Average lags *1
adjusted t or W-t-

(log)

bar*2

-94.3

***

I(0)
Potato

Mandi
Price

Average lagst (adjusted)

(log)

***

-53.72I(0)1.2-52.19-48.08

I(0)

1.25
***

-48.96***-57.71

***

I(0)

1.09
***

-51.82

1.12***-54.36

I(0)

I(0)

I(0)

I(0)

0.78

0.93

0.52

0.57

***

Mandi
TomatoArrivals

Average lags *1
adjusted t or W-t-

(log)

Tomatobar*2***-53.49***-57.89***-55.91

***

***

I(0)

I(0)

I(0)

I(0)

0.86

0.64

0.7

Mandi

Average lags

0.67

Price

t (adjusted)-56.84(log)
log CSR-57.14***-53.92***-58.38

***

-56.4

I(0)

I(0)

I(0)

I(0)

Average lags

1.19

1.33

1.13

1.28

t (adjusted)-55.98***(Covid-19
Severity)

I(0)

***

-43.97***-67.99

I(0)

I(0)

***

-58.73***I(0)Notes: 1. Lags are determined by Akaike Information Criteria (AIC).
*2. adjusted t is reported for LLC and W-t-bar is reported for IPS.
*3. The threshold signficance level is at 5%.

(2) Covid-19 impact on market arrivals and prices
Rice
Next, we have estimated Model A (Equations (2), (3) and (4)) and Model B (Equations (5) and(6) based on the GMM applied to the panel VAR model. The results for rice, onions, potato
and tomato are given in Tables 3, 4, 5 and 6. Broadly speaking, rice is a non-perishable
commodity while onions, potato and tomato are semi perishable and perishable commodities

22and the quality of rice deteriorates over time. Below, we primarily focus on the results related
to the Covid-19 in light of our research hypotheses. Other results are only briefly noted.
Table 3 shows the results for rice for Model A and Model B. In the case of Model A where
the Covid-19 is treated as endogenous, we find that the pandemic growth negatively andsignificantly influences mandi price growth with three lags (i.e., after three weeks) andpositively and significantly influences mandi arrivals growth with 2 lags (i.e., after two weeks).
However, as in the results of Granger causality test based on the panel VAR results shown at
the bottom of Table 3, the pandemic growth does not significantly affect either fractional pricechange or change in arrivals. That is, we cannot conclude that the pandemic growth influences
any change in market price or arrivals when it is treated as an endogenous variable.

Table 3. Associations among COVID-19 Severity Ratio, Market Arrivals and Wholesale
Prices of Rice: Panel VAR model based on the GMM Robust Estimation, Weekly Data
July 2019-June 2020
Model A (Rice)
Dep Vars

Arrivals

PricesEndogenous Vars
Exp. Vars

FD log Mandi Arrivals

Endogenous

L1.
L2.

Arrivals

PricesRatio-0.0130.001-0.398

-0.01

0.002

(0.31) a(1.25)(1.47)(0.35)(1.34)

0.029-0.002

-0.16

0.029

(1.48)(0.65)(1.77)

0.014-0.001-0.0790.014-0.001(0.35)(0.77)(0.30)(0.35)(0.64)(1.76)
L3.

Model B (Rice)

Covid
Severity*b,f-0.002*(1.33)

FD log Mandi Price
L1.
L2.
L3.

0.043

0.092

(0.23)(1.96)*-1.666

0.057

0.096

(0.43)(0.32)(2.00)-0.8980.026-0.063(0.21)(0.37)(1.98)

0.012-0.063(0.18)(2.10)-0.007-0.001-0.668-0.0010(0.11)(0.05)(0.16)(0.03)(0.01)

0.004-0.001**

FD log Covid-19 Severity Ratio
L1.
L2.
L3.
Exogenous-0.004

0.001

0.075

(0.57)(1.48)(0.61)-0.001-0.0010.359(0.29)(1.31)(2.67)-0.005-0.0010.045(1.04)(1.94)

FD log Covid-19 Severity Ratio

23

*

***

(0.47)****

(0.64)

D_Maharashtra*FD Covid SR-0.013c,d,e(1.91)
D_Meghalaya* FD Covid SR
D_Uttar Pradesh* FD Covid
SR(0.39)(2.82)

D_Haryana*FD Covid SR

rainfall
Phase1

0.011-0.0010.491(1.10)(1.28)(3.17)-0.0040

5.451(1.15)(0.24)(3.85)-0.0220.008(0.97)(3.12)

Phase2-0.039

0.007

8.382

(0.73)(1.57)(4.50)

Phase3-0.017

0.001

8.773

(0.42)(0.34)(4.62)

Phase4-0.067

0.004

6.21

(1.36)(1.01)(7.39)

Phase5-0.143(2.42)

**

***
***

7.179
***

(3.89)

***
***
***
***

-0.003(2.63)

0.039

0.002
(2.07)

0.012

0.002
(2.07)

00(0.32)(1.77)

0.007

0(0.73)(0.01)-0.0020(0.77)(0.46)-0.0090.006(0.22)(1.12)-0.031-0.001(0.60)(0.15)

0.004-0.003(0.07)(0.39)-0.048-0.003(0.91)(0.32)

0-0.128(0.00)(2.44)-0.003**(0.43)-0.003-0.001(0.37)(0.77)

No. of observations

4371

4371

No of N

93

93

Average no. of T
Hansen’s J (Over Identifying
Restriction, p value)

47

47

1.00

1.00

16

22

Yes.

Yes

No. of instruments
Eigenvalue stability condition
All the eigenvalues lie inside the unit circle***(1.61)

0.001

Constant(4.92)

0.005(0.25)

Panel VAR-Granger Causality Wald Test
Yes/No

Chi2

Prob>
Chi2

Yes/No

Chi2

Prob>
Chi2

Price causes arrivals

No

0.145

0.986

No

0.282

0.963

Covid causes arrivals

No

1.824

0.610

-

Arrivals cause price

No

4.258

0.235

No

Covid causes price.

No

4.620

0.202

-

-

-

Arrivals cause Covid.

No

2.768

0.429

-

-

-

-

-

-

Arrivals (FD)

WholesalePrice (FD)

-

4.175

-

0.243

Covid (FD)Price causes Covid,
No0.427 0.935
Notes: a. The numbers in brackets show z values. They are based on robust standard errors.
b. *** = Significant at 1% level. ** = Significant at 5% level. * = significant at 10% level.
c. State dummies for all the states have been included in the regressions.

24

***
***

0.004
***

(1.16)(5.81)
D_Punjab*FD Covid SR(4.03)
0.003

0.014

D_Rajasthan* FD Covid SR*-0.004*0.003(3.25)

D_Madhya Pradesh* FD CovidSR

temperature(1.80)

***

**
**
**
*

d. D_ stands for a dummy variable (taking 1 or 0).
e. FD stands for First Difference.
f. Statistically significant cases are highlighted as bold numbers.

However, in Model B where the change in log Covid-19 severity ratio is treated as an
exogenous variable, the estimated coefficient is negative and significant (for the 10 default
states) 17. Compared with default cases, negative effects of the pandemic are more pronounced
in Maharashtra and Madhya Pradesh. Negative effects are mitigated in Haryana. Comparedwith other states, the negative effect of the pandemic on changes in market arrivals is larger in
Maharashtra, but smaller in Uttar Pradesh where the market reform has been actively
implemented (Varshney,et al., 2020). It is notable that in Maharashtra where the pandemic has
been the most severe since its onset, the pandemic growth reduces the growth of market arrivals
and prices in the short- run.
While we observe some statistically significant effects of own lags, we find no significant
causality from price changes to arrival changes or vice versa. While both temperature andrainfall raise Covid-19 severity significantly, consistent with Ma et al. (2020), they are notassociated with growth of prices or arrivals (Models A and B). The specifications are validated
as Hansen’s J statistic for over-identification test is statistically non-significant
Graphical representations of Impulse Response Function (IRF) also show that any negative
effects of the pandemic growth on changes in the market prices is short-lived, lasting only 4
weeks (the second graph from the top in the left hand side, Figure 1). The graph of IRF on the
dynamic relationship between the pandemic growth and the change in arrivals (the third graphon the left) implies that a slight negative effect of the pandemic disappears quickly. Figure 2
shows graphically that the dynamic association between arrivals and prices is weak.

17

Default cases are the average of 10 states, that is, Andhra Pradesh, Assam, Chhattisgarh,
Gujarat, Himachal Pradesh, Jharkhand, Karnataka, Kerala, Odisha and West Bengal.
25

Figure 1. Graphs of Impulse Response Function (IRF) among COVID-19 Severity
Ratio, Market Arrivals and Wholesale Prices of Rice

Note: Y axis is a response variable. X axis is an impulse variable.

Figure 2. Graphs of Impulse Response Function (IRF) among COVID-19 Severity Ratio
and Market Arrivals of Rice

26

Note: Y axis is a response variable. X axis is an impulse variable.

Overall, we can conclude that, apart from a few states, such as Maharashtra, the Covid-19
pandemic growth does not strongly influence either rice price change or any change of market
arrivals. We observe a weak negative effect of the pandemic growth on rice prices, but it will
last only 5 weeks. A caveat, however, is that if the pandemic continues to grow, the negative
effects will further accumulate over time.

Onion
The results of Models A and Model B for onion are shown in Table 4 as well as in Figures 3
and 4. It is notable that statistically significant and positive associations are observed between
lagged fractional changes in price and in quantities and the current pandemic growth (the thirdcolumn of Table 4). That could be due to the fact that food prices and availability have a strong
association with food security that will directly influence the pandemic development. However,the lagged variables on the pandemic growth negatively influence price growth and positively
affect quantity growth. The overall association of the pandemic growth on the changes inmarket prices and arrivals are positive as suggested by graphs on IRF (the middle and thebottom graphs on the left-hand side of Figure 3), consistent with Varshney et al. (2020). If wecompare the graphical analyses for rice and onion, we find that for onion the overall association
between the Covid-19 pandemic growth and price growth (or the elasticity of price growth withrespect to the pandemic growth) is initially larger and positive (particularly for price) andgradually disappears, while for rice the initial association is negative and small and thenweakens gradually.

27

The Granger causality shows that the Covid-19 pandemic growth causes (reduces) price,
but not mandi arrivals, while both arrivals and price cause (or increase) the pandemic
growth. Also, price changes cause (or increase) market arrivals, but not vice versa.

Table 4. Associations among COVID-19 Severity Ratio, Market Arrivals and Wholesale
Prices of Onion: Panel VAR model based on the GMM Robust Estimation, Weekly
Data July 2019-June 2020
Model A (Onion)
Dep Vars

Exp. Vars

Endogenous Vars
FD log Mandi
Arrivals

Endogenous

L1.
L2.
L3.

Arrivals

Model B (Onion)
Covid
Severity

PricesArrivals

Prices-0.014-0.01(0.78)

Ratio-0.015-0.0050.54(1.00)a(0.62)(1.59)(0.92)-0.014-0.0120.354-0.013-0.01(1.05)(1.51)(0.97)(0.96)(1.52)-0.025-0.0070.831(1.66)(0.84)(2.58)-0.023

-0.01

**b,f

(1.57)(1.04)

0.043

0.19

***

(1.29)(6.83)-0.0110.008(0.70)(0.53)

0.049

0.1

FD log Mandi Price
L1.
L2.
L3.

0.034

0.197

(1.03)(7.69)-0.018

0.014

2.157

(1.37)(1.05)(3.97)

0.044

0.106

1.699

(3.22)***(5.66)

3.406
***

***

(4.47)(4.05)******(3.14)***(5.08)******

FD log Covid-19
Severity Ratio
L1.
L2.
L3.

-0.0020.028(1.21)(0.63)(0.25)

0.003-0.013(0.61)(2.83)

0.004

0.003
(2.06)

Exogenous

**

0.255
***

(2.34)-0.0050.039(1.51)(0.44)

FD log Covid-19
Severity Ratio
D_Maharashtra*FD
Covid SR c,d,e
D_Meghalaya* FD
Covid SR
D_Uttar Pradesh*
FD Covid SR
D_Madhya
Pradesh* FD Covid
SR

**

0.004

-0.01

(1.07)(3.25)

0.005

-0.01

(1.23)(2.77)-0.0030(1.03)(0.06)

0-0.01(0.09)(2.90)

0.009(2.48)

D_Rajasthan* FD
Covid SR

-0.00428-0**(0.52)
0.001

***

***

***

(1.21)

D_Punjab*FD
Covid SR
D_Haryana*FD
Covid SR
temperature
rainfall-0.003-0.0040.434(0.69)(0.96)(3.02)

0.002

0.005

5.586

(1.14)

0.001

0.002

(1.22)(3.33)-0.006-0(1.45)(0.13)

0.003

0.004

(3.97)

0.008-0.0197.408(0.52)(1.16)(4.25)

Phase2-0.007-0.0618.908(0.36)(2.87)

0.035

-0.04

9.063

(1.06)(1.38)(4.99)

0.002-0.0286.574(0.07)(1.07)(7.78)

0.002-0.016

0

0.022

-0.04

(0.09)(0.80)(0.00)(0.85)(1.21)

Phase4Phase5(5.15)***-0(0.56)(1.01)***(4.34)-0.002Phase1

Phase3******(0.29)

***
***
***
***

Constant(1.73)(2.38)-0.03(1.14)(1.42)

0.021

-0.1

(0.74)(2.95)

0.061

-0.06

(1.94)*-0.06(0.85)(1.33)

-0.01

-0.02

(1.72)(1.85)

4371

4371

No of N

93

93

Average no. of T
Hansen’s J (OverIdentifying
Restriction, pvalue)

47

47

1.00

1.00

No. of instruments
16
Eigenvalue stability condition
All the eigenvalues lie inside the unit circle

22

**

***

(1.50)

0.029

No. of observations

Yes.

*

0.023

***

Yes

Panel VAR-Granger Causality Wald Test
Arrivals (FD)

Yes/NoProb>
Chi2

Yes/No0.003

Price causes arrivals

Yes

Chi2
13.88
***

Covid causes arrivals

No

5.28

0.152

Arrivals cause price

No

0.485

Covid causes price.

Yes

2.448
11.55
***

Prob>Chi2

Yes

Chi2
13.21
***

No

2.527

0.470

0.004

WholesalePrice (FD)0.009

Covid (FD)

8.640
**
0.034
22.90
Price causes Covid,
Yes
***
0.000
Notes: a. The numbers in brackets show z values. They are based on robust standard errors.
Arrivals cause Covid.

Yesb. *** = Significant at 1% level. ** = Significant at 5% level. * = significant at 10% level.
c. State dummies for all the states have been included in the regressions.
d. D_ stands for a dummy variable (taking 1 or 0).
e. FD stands for First Difference.
f. Statistically significant cases are highlighted as bold numbers.

29

The results of Model B suggest that there is an overall negative effect of the pandemic
growth on the fractional changes in onion market prices (for the default 11 states), but thenegative effect on price changes is much larger in Maharashtra and Uttar Pradesh (the statewith the market reform). The state-level effect of the pandemic growth on the fractional change
in market arrivals is statistically not significant (except Madhya Pradesh where the effect ispositive and significant relative to the 10 default states). Madhya Pradesh is another state that
actively implemented the market reform, which may have prevented any decline of market
transactions even during the Covid-pandemic. It is also noted that rainfall positively influences
the changes in both price and quantity in Model B (and only prices in Model A). The Covid19 pandemic tends to rise when rainfall and temperature are high. This is consistent with Ma
et al. (2020). It is noted that from Figures 3 and 4 cross-effects between market prices andmarket arrivals are weak.
Figure 3. Graphs of Impulse Response Function (IRF) among COVID-19 Severity
Ratio, Market Arrivals and Wholesale Prices of Onion

Note: Y axis is a response variable. X axis is an impulse variable.

Figure 4. Graphs of Impulse Response Function (IRF) among COVID-19 Severity Ratio
and Market Arrivals of Onion

30

Note: Y axis is a response variable. X axis is an impulse variable.

PotatoThe results on potato are given in Table 5 as well as Figures 5 and 6. We infer from the Granger
causality test as well as the estimates of relevant coefficients based on Model A that (i) the
Covid-19 pandemic growth reduced changes of market prices of onions significantly, but not
those of market arrivals; and (ii) the cross-effect between price changes and changes in mandi
arrivals are negative and the causality is from the former to the latter (not the other way around).
In Maharashtra, the effect of the pandemic growth is more negative than in other states as
before, while in Utter Pradesh it has a more positive effect than other states. It is observed thatthe pandemic development had a more positive effect on change in market arrivals in
Meghalaya and Uttar Pradesh, but the effect is negative in Madhya Pradesh, Rajasthan andPunjab. The variation in results does not match with the classification of the states according
to the degree of implementation of market reforms made by Varshney et al. (2020) and sofurther investigations are necessary to understand the underlying factors of explaining thisheterogeneity.
Table 5. Associations among COVID-19 Severity Ratio, Market Arrivals and Wholesale
Prices of Potato: Panel VAR model based on the GMM Robust Estimation, Weekly
Data July 2019-June 2020
Model A (Potato)
Dep Vars

Arrivals

PricesModel B (Potato)
Covid Severity

Endogenous Vars

Ratio

31

Arrivals

PricesExp. Vars

FD log Mandi
Arrivals

Endogenous

L1.
L2.
L3.

-0.008-0.011

0.278

-0.01

-0.011(0.38) a(1.41)(0.97)(0.38)(1.52)-0.026

0.001

0.136

-0.03

0

(0.96)(0.09)(0.32)(0.97)(0.02)-0.009

0.007

0.271

-0.01

0.005

(0.38)(1.13)(0.85)(0.39)(0.93)-0.006

0.028

-0.09

-0.009(0.28)(0.05)(3.29)-0.06-0.854-0.02

FD log Mandi Price
L1.

-0.084(3.23)

L2.
L3.

-0.016

***
b, f

***

***

(0.38)-0.068(0.81)(2.74)(1.62)(0.93)(2.98)

0-0.019-1.69-0-0.027(0.02)(1.07)(2.14)(0.10)(1.49)-0.001-0.0040.092(0.34)(1.52)(0.74)-0.004-0.013(1.17)(3.81)-0.001-0.005(0.27)(2.36)

0-0.002(0.28)(0.72)*****

FD log Covid-19
Severity Ratio
L1.
L2.
L3.

Exogenous

0.363
***

(2.68)

***

0.026
**

(0.26)

FD log Covid-19
Severity Ratio
D_Maharashtra*FD
Covid SR c,d,e
D_Meghalaya* FD
Covid SR

-0

-0.01

(1.25)(2.99)

0.008

0.002

(4.04)

D_Uttar Pradesh*
FD Covid SR
D_Madhya
Pradesh* FD Covid
SR(3.84)(4.34)

0.012

D_Punjab*FD
Covid SR***

(1.26)***(1.94)***(1.62)*(1.21)

0.008

-0.02
(7.23)

D_Haryana*FD
Covid SR

0.005

-0(1.91)-0.0010.008(0.41)(2.26)

rainfall0-0.002(0.57)(1.75)

Phase1-0.0070.021(0.67)(2.05)-0.006-0.0298.218(0.33)(1.55)(4.64)-0.019-0.047(0.82)(2.75)

32

0.484
**

(3.25)

***

5.396
*

(4.02)

***

7.271
**

(3.95)(4.81)

0.001

-0

0.007

(0.12)(1.08)

0-0.002(0.17)(0.96)

-0.01

0.025

***

(0.57)(1.16)-0.02-0.046***(0.60)(1.57)-0.02-0.045***(0.72)(1.42)

8.693
***

***

-0.004-0.02(6.70)

Phase3**

0.01

D_Rajasthan* FD
Covid SR

Phase2(0.58)

0.006(1.99)

temperature

***

***

*

Phase4

0.016-0.063(1.30)(2.80)

0-0.013(0.02)(0.90)

Phase5

6.19
***

0.011

-0.064(0.45)(1.68)

0-0-0.013(0.00)(0.16)(0.50)

0.01-0.007(7.69)***

Constant(1.88)
No. of observations

4465

4465

No of N

95

95

Average no. of T
Hansen’s J (OverIdentifying
Restriction, pvalue)

47

47

1.00

1.00

No. of instruments
16
Eigenvalue stability condition
All the eigenvalues lie inside the unit circle

22
Yes.

*

(1.00)

Yes

Panel VAR-Granger Causality Wald Test
Yes/No

Chi2

Prob>
Chi2

Yes/No

Chi2

Prob>
Chi2

Price causes arrivals

Yes

13.16***

0.004

Yes

14.19***

0.003

Covid causes arrivals

No

1.55

0.670

-

-

Arrivals cause price

No

4.68

0.197

No

3.95

Covid causes price.

Yes

14.99***

0.002

-

-

-

Arrivals cause Covid.

No

2.42**

0.491

-

-

-

-

-

-

Arrivals (FD)-

WholesalePrice (FD)
0.267

Covid (FD)Price causes Covid,
Yes
6.75*
0.080
Notes: a. The numbers in brackets show z values. They are based on robust standard errors.
b. *** = Significant at 1% level. ** = Significant at 5% level. * = significant at 10% level.
c. State dummies for all the states have been included in the regressions.
d. D_ stands for a dummy variable (taking 1 or 0).
e. FD stands for First Difference.
f. Statistically significant cases are highlighted as bold numbers.

The graphs of IRF (Figures 5 and 6) show that (i) the negative effect of the pandemic growthon the fractional change of mandi prices weakens quickly in the second or the third steps (orafter two or three weeks); (ii) there is a weak positive effect of the pandemic growth on market
arrivals of potatoes for 3-4 weeks; and (iii) the cross-effects between mandi arrivals and prices
are weak.
Figure 5. Graphs of Impulse Response Function (IRF) among COVID-19 Severity
Ratio, Market Arrivals and Wholesale Prices of Potato

33*

Note: Y axis is a response variable. X axis is an impulse variable.

Figure 6. Graphs of Impulse Response Function (IRF) among COVID-19 Severity Ratio
and Market Arrivals of Potato

Note: Y axis is a response variable. X axis is an impulse variable.

TomatoFinally, we have estimated Models A and B for a highly perishable commodity, tomatoes. The
results are given in Table 6 and Figures 7 and 8. Similar to the case of onion (Table 4), the34

Covid-19 pandemic growth negatively influences fractional changes in mandi prices of tomato
with some lags (Model A, Table 6), while the lagged changes in mandi prices promoted thegrowth of the pandemic significantly, possibly due to the deteriorated food security. However,such relationships are not observed for mandi arrivals. Cross-effects between changes in mandi
arrivals and prices are weak and not statistically significant. Granger causality tests reflect theseresults. That is, the causality is statistically significant in both directions between changes inmandi prices and the Covid-19 pandemic growth. Interestingly, the graphical analysis of IRF
shows a non-linear relationship between the Covid-19 pandemic growth and price changes(Figure 7). Initially, the pandemic growth had a positive association with the fractional pricechange of tomato, but the effect turns negative in 4-6 weeks, after which it becomes positive
again. Initial shortages in food may cause the price change to be more positive (e.g. panic
buying, or the shortage of supply due to the lack of labour), but after some time adjustment
takes place. A slight negative association between the pandemic growth and the fractionalchange in arrivals is observed in Figure 7.
Table 6. Associations among COVID-19 Severity Ratio, Market Arrivals and Wholesale
Prices of Tomato: Panel VAR model based on the GMM Robust Estimation, Weekly
Data July 2019-June 2020
Model A (Tomato)
Dep Vars

Arrivals

Covid
Severity

PricesEndogenous Vars
Exp.
Vars

Model B (Potato)
Arrivals

PricesRatio

FD log Mandi Arrivals
L1.
L2.

-0.0580.03-0.291-0.0590.026(1.15) a(1.21)(0.79)(1.19)(1.10)

0.014

0.03

-0.22

0.013

0.025

(1.12)(0.57)(3.05)-0.0340.02-0.272-0.0340.019(1.24)(0.92)(0.56)(1.30)(0.74)

0.016-0.132(3.44)
L3.

***
b,f

***

(0.97)

FD log Mandi Price
L1.
L2.

L3.

0.004

-0.14

(0.25)(3.27)-0.005-0.22(0.21)(4.79)

0.022

-0.17

(1.64)(4.93)

35

2.929
***

(5.38)***(0.88)(1.97)

0.007-0.239***(0.31)(2.95)
00.191******(2.03)(2.66)

**

2.983
***

(4.44)***(3.80)

2.386

0.031
**

**

FD log Covid-19 Severity Ratio
L1.

0

-0.03

(0.10)(2.71)

L2.

-0.002-0.04(1.26)(4.19)

0

-0.01

0.061

(0.09)(1.42)(0.63)

L3.

0.005
***

(0.04)***(1.89)

0.268*

FD log Covid-19 Severity Ratio

0.004(2.00)

D_Maharashtra*FD Covid SR

0.004

c, d, e(2.69)

D_Meghalaya* FD Covid SR

0.013(8.55)

D_Uttar Pradesh* FD Covid
SR-0.006**(0.52)-0.084***(7.32)***(1.03)-0.0040.1(0.93)(2.90)

D_Madhya Pradesh* FD Covid
SR

0.002

-0.012(1.26)(0.71)

D_Rajasthan* FD Covid SR

0.007

0.12

(1.21)(2.23)

D_Punjab*FD Covid SR-0.0030.074(0.61)(2.10)

0.003-0.011

D_Haryana*FD Covid SR(3.97)
temperature

0.001

0.11

(0.25)(2.88)

0-0.03(0.22)(2.32)

0.006

-0.2

(0.41)(1.76)

rainfall
Phase1
Phase2-0.009-0.47(0.37)(3.08)

Phase3

0.022

-0.56

(0.64)(3.65)

Phase4-0.004-0.61(0.13)(3.68)

Phase5-0.008-0.3(0.31)(4.10)

0.589***(3.01)**(3.41)***(3.67)(2.10)-0.034(0.84)(1.69)

0.034

-0.22

***

(1.53)(1.26)

0.028-0.557***(0.87)(2.22)

0.069-0.604***(4.26)
10.537***(4.42)***(1.60)(2.30)

0.044-0.6597.334***(6.78)***(1.25)(2.51)

0.026-0.3370***(0.00)

Constant(0.82)(2.02)-0.036

0.111(4.33)(1.67)

No. of observations

4418

4418

No of N

94

94

Average no. of T
Hansen’s J (Over Identifying
Restriction, p value)

47

47

1.00

1.00

16

22

No. of instruments
Eigenvalue stability condition
All the eigenvalues lie inside the unit
circleYes.

Yes

Panel VAR-Granger Causality Wald Test
Yes/No

Chi2

Prob>
Chi2

Yes/No

Chi2

Prob>
Chi2

Price causes arrivals

No

6.23

0.101

Yes

6.46*

0.091

Covid causes arrivals

No

4.00

0.261

-

Arrivals (FD)

36

-

***

**
**
*

0.113

0.002

10.408***(1.93)(1.09)

8.618
*

***

-0.007

5.933

***

0.011

-

**
*

**
**
**
**

Wholesale
Price (FD)
Arrivals cause price

No

1.64

0.651

No

1.52

Covid causes price.

Yes

19.85***

0.000

-

-

0.678
-

Arrivals cause Covid.

No

0.756

0.860

-

-

-

-

-

-

Covid (FD)Price causes Covid,
Yes
29.01***
0.000
Notes: a. The numbers in brackets show z values. They are based on robust standard errors.
b.*** = Significant at 1% level. ** = Significant at 5% level. * = significant at 10% level.
c. State dummies for all the states have been included in the regressions.
d. D_ stands for a dummy variable (taking 1 or 0).
e. FD stands for First Difference.
f. Statistically significant cases are highlighted as bold numbers.

The results of Model B suggest that, while an overall effect of the pandemic growth on
fractional changes in mandi arrivals is positive in the default states, but the positive effect islarger in Maharashtra and Meghalaya. On the other hand, while the overall effect of thepandemic growth on mandi price growth is not significant for the default states, the effect ismore negative in Maharashtra, Madhya Pradesh and Haryarna and more positive in Uttar
Pradesh, Rajasthan (both of which are active in implementing market reforms) and Punjab(which is less active in implementing reforms than Uttar Pradesh and Rajasthan, according toVarshney et al. 2020). Whether these state-level results are due to differences in agricultural
policies or in the degree of adoption of market policies needs to be further investigated.

Figure 7. Graphs of Impulse Response Function (IRF) among COVID-19 Severity
Ratio, Market Arrivals and Wholesale Prices of Tomato

37

Note: Y axis is a response variable. X axis is an impulse variable.

Figure 8. Graphs of Impulse Response Function (IRF) among COVID-19 Severity Ratio
and Market Arrivals of Tomato

Note: Y axis is a response variable. X axis is an impulse variable.

IV.

Conclusions

Using the panel data on mandi arrivals and prices in 17 Indian states from July 2019 to June,
2020, the present study examined whether the growth of Covid-19 pandemic influenced
fractional changes in market (or mandi) arrivals and prices. A point of departure of our analysis
from the previous literature is that we take into account the dynamic interactions among thefractional changes in mandi arrivals, the mandi price change of food commodities, namely, rice,38

onion, potato, and tomato, and the change in the severity ratio of the Covid-19 pandemic usinga panel VAR model based on the GMM.
Firstly, we find that the Covid-19 pandemic growth decreases the fractional changes inmandi price of rice, onion, potato and tomato, but it does not affect the mandi arrivals, as
implied by the estimated coefficients of the panel VAR model. The Granger causality test
confirms that the causality runs from the Covid-19 pandemic growth to the fractional mandi
price change for onion, potato and tomato, but there is no causality between the Covid-19
pandemic growth and the mandi arrival (fractional) changes.18
Secondly, however, once the feedback from the fractional change in mandi prices to the
Covid-19 pandemic growth is taken into account, the graphical analysis based on the Impulse
Response Functions suggests that the overall elasticity of the fractional change in the mandi
price with respect to the pandemic growth turns from positive to zero or negative for onion andtomato, and from negative to zero for rice and potato. On the other hand, the overall elasticityof the fractional change in the mandi arrivals with respect to the pandemic growth turns fromslightly positive to zero. The results are by and large consistent with Varshney et al. (2020),
but our contribution is that we have incorporated the cross-dependence among growth paths of
the Covid-19 severity, price and quantity in the market in estimating the pandemic impact.

18

At first sight the results appear to be inconsistent with Imai et al. (2020) who found a positive
associations between the Covid-19 pandemic and the wholesale prices and the retail prices offood commodities, such as rice and onions. It should be noted that Imai et al. (2020) focus on
the contemporaneous association between the pandemic and price in levels, where our present
study focuses on lagged and causal associations between the variables in first difference after
taking account of the complex price-quantity interdependence. It is noted that the firstdifferenced variables of price is estimated by the Covid-19 severity with lags using the static
model, as in Imai et al. (2020), the coefficient estimate becomes negative. That is, once
pandemic occurs, this will raise commodity prices, but as the pandemic grows, the price change
tends to slow down over time. In this sense, the two studies are not inconsistent andcomplementary.
39

Thirdly, we have found that a negative effect of the pandemic growth on the fractionalchange in mandi price is stronger in Maharashtra where the pandemic remained worst among
all the Indian states (for all the commodities) but weaker in Utter Pradesh where the market
reform was actively implemented (for rice, potato and tomato).
Our detailed analysis of the data suggests that, while the effect of the pandemic is relatively
short-lived, it changes over time and the effect differs across different commodities anddifferent regions. This might be due to success of market reforms, as argued by Varshney et al.
(2020), but given the large negative effect of the pandemic growth on price changes in
Maharashtra, careful monitoring of the dynamic effect of the pandemic growth on market priceand arrivals is necessary to prevent any deterioration of food security.

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42

Appendix Table 1. Descriptive Statistics
Variable

Mean

Std.
Dev.

Min

Max

Observations

Ricelog Mandi
Arrivalsl_Mandi Prices

log_Severity
Ratio

dlog Mandi
Arrivals

dlog Mandi
Pricedlog_Severity
Ratio

temperature

rainfall

Phase1

Phase2

Mean

Std.
Dev.

Min

Max

Observations

Potato
2.01

-0.43

12.08

N = 4836

2.13

-0.73

11.89

N = 4836

betweenoverall

1.97

0.46

11.68

n=

93

2.08

-0.02

11.72

n=

93

within

0.43

3.24

11.33

T=

52

0.48

2.21

11.12

T=

52

overall

7.54

0.22

2.99

4.11

N = 4836

0.62

1.39

4.94

N = 4836

between

0.21

3.06

3.98

n=

93

0.23

2.65

4.02

n=

93

within

0.08

3.01

3.88

T=

52

0.58

1.95

4.86

T=

52

2.06

-9.21

1.24

N = 4836

2.06

-9.21

1.24

N = 4836

between

0.56

-7.53

-5.30

n=

93

0.57

-7.53

-5.30

n=

93

within

1.98

-9.93

0.44

T=

52

1.98

-9.93

0.38

T=

52

0.24

-5.16

5.02

N = 4743

0.21

-2.95

3.99

N = 4743

between

0.02

-0.09

0.03

n=

93

0.02

-0.06

0.12

n=

93

within

0.24

-5.10

5.08

T=

51

0.20

-2.90

3.89

T=

51

0.03

-0.52

0.31

N = 4743

0.17

-0.92

1.49

N = 4743

between

0.00

-0.01

0.01

n=

93

0.01

-0.01

0.02

n=

93

within

0.03

-0.52

0.31

T=

51

0.17

-0.93

1.47

T=

51

1.14

-8.15

7.63

N = 4743

1.16

-8.15

7.63

N = 4743

overalloveralloveralloverall

3.38

7.62

-6.15

-0.01

0.00

0.00

3.35

-6.16

0.00

0.00

0.00

between

0.00

0.00

0.00

n=

93

0.00

0.00

0.00

n=

93

within

1.14

-8.15

7.63

T=

51

1.16

-8.15

7.63

T=

51

6.25

277

311

N = 4836

6.27

277

311

N = 4836

between

4.34

289

308

n=

93

4.34

289

308

n=

93

within

4.51

285

313

T=

52

4.54

285

313

T=

52

7.90

0.00

63

N = 4836

7.90

0.00

63

N = 4836

overalloverall

299

5.62

299

5.60

between

5.53

0.72

29

n=

93

5.53

0.72

29

n=

93

within

5.67

-4.11

59

T=

52

5.67

-4.13

59

T=

52

0.27

0.00

1.00

N = 4836

0.27

0.00

1.00

N = 4836

between

0.00

0.08

0.08

n=

93

0.00

0.08

0.08

n=

93

within

0.27

0.00

1.00

T=

52

0.27

0.00

1.00

T=

52

0.20

0.00

1.00

N = 4836

0.20

0.00

1.00

N = 4836

between

0.00

0.02

0.05

n=

93

0.00

0.02

0.05

n=

93

within

0.20

0.00

1.03

T=

52

0.20

0.00

1.03

T=

52

overalloverall

0.08

0.05

43

0.08

0.05

Phase3

Phase4Phase5overall

0.17

0.00

1.00

between

0.00

0.00

0.04

n=

within

0.17

0.00

1.00

T=

0.17

0.00

1.00

N = 4836

between

0.00

0.00

0.04

n=

within

0.17

0.00

1.00

T=

0.24

0.00

1.00

N = 4836

between

0.01

0.00

0.07

n=

within

0.24

0.00

1.00

T=

Std.
Dev.

Min

Max

Observations

overalloverall

Variable

0.04

0.04

0.07

Mean

N = 4836

0.17

0.00

1.00

93

0.00

0.00

0.04

n=

93

52

0.17

0.00

1.00

T=

52

0.17

0.00

1.00

N = 4836

93

0.00

0.00

0.04

n=

93

52

0.17

0.00

1.00

T=

52

0.24

0.00

1.00

N = 4836

93

0.01

0.00

0.07

n=

93

52

0.24

0.00

1.00

T=

52

Std.
Dev.

Min

Max

Observations

2.01

-0.43

11.92

N = 4888

Potatolog Mandi
Arrivalsl_Mandi Prices

log_Severity
Ratio

dlog Mandi
Arrivals

dlog Mandi
Pricedlog_Severity
Ratio

temperature

0.04

0.04

0.07

Mean

N = 4836

Tomatooverall

2.01

-0.51

11.91

N = 4940

between

1.98

1.28

11.63

n=

95

1.94

0.07

11.47

n=

94

within

0.40

3.61

9.52

T=

52

0.55

2.71

11.55

T=

52

0.36

1.25

4.14

N = 4940

0.57

1.07

4.61

N = 4888

between

0.29

1.96

3.50

n=

95

0.36

2.24

4.40

n=

94

within

0.22

2.13

3.75

T=

52

0.44

1.38

4.17

T=

52

2.06

-9.21

1.24

N = 4940

2.06

-9.21

1.24

N = 4888

between

0.56

-7.53

-5.30

n=

95

0.56

-7.53

-5.30

n=

94

within

1.98

-9.93

0.38

T=

52

1.98

-9.93

0.39

T=

52

0.20

-3.38

4.53

N = 4845

0.24

-4.42

4.73

N = 4794

between

0.02

-0.06

0.08

n=

95

0.02

-0.10

0.09

n=

94

within

0.19

-3.32

4.53

T=

51

0.24

-4.40

4.64

T=

51

0.10

-0.85

0.69

N = 4845

0.20

-1.38

1.67

N = 4794

overalloveralloveralloverall

7.66

2.84

-6.16

0.00

0.01

7.45

3.00

-6.15

0.00

0.00

between

0.00

0.00

0.02

n=

95

0.01

-0.03

0.01

n=

94

within

0.10

-0.85

0.69

T=

51

0.20

-1.38

1.66

T=

51

1.14

-8.15

7.63

N = 4845

1.13

-8.15

7.63

N = 4794

overall

0.00

0.00

between

0.00

0.00

0.00

n=

95

0.00

0.00

0.00

n=

94

within

1.14

-8.15

7.63

T=

51

1.13

-8.15

7.63

T=

51

6.27

277

311

N = 4940

6.22

277

311

N = 4888

between

4.39

289

308

n=

95

4.33

289

308

n=

94

within

4.50

285

313

T=

52

4.49

285

313

T=

52

overall299.2344

299.18rainfall

Phase1

Phase2Phase3Phase4Phase5overall

5.55

7.85

0.00

63

N = 4940

5.66

7.86

0.00

63

N = 4888

between

5.49

0.72

29

n=

95

5.51

0.72

29

n=

94

within

5.64

-4.18

59

T=

52

5.64

-4.07

59

T=

52

0.27

0.00

1.00

N = 4940

0.27

0.00

1.00

N = 4888

between

0.00

0.08

0.08

n=

95

0.00

0.08

0.08

n=

94

within

0.27

0.00

1.00

T=

52

0.27

0.00

1.00

T=

52

0.20

0.00

1.00

N = 4940

0.20

0.00

1.00

N = 4888

between

0.00

0.02

0.05

n=

95

0.00

0.02

0.05

n=

94

within

0.20

0.00

1.03

T=

52

0.20

0.00

1.03

T=

52

0.17

0.00

1.00

N = 4940

0.17

0.00

1.00

N = 4888

between

0.00

0.00

0.04

n=

95

0.00

0.00

0.04

n=

94

within

0.17

0.00

1.00

T=

52

0.17

0.00

1.00

T=

52

0.17

0.00

1.00

N = 4940

0.17

0.00

1.00

N = 4888

between

0.00

0.00

0.04

n=

95

0.00

0.00

0.04

n=

94

within

0.17

0.00

1.00

T=

52

0.17

0.00

1.00

T=

52

0.24

0.00

1.00

N = 4940

0.24

0.00

1.00

N = 4888

between

0.01

0.00

0.07

n=

95

0.01

0.00

0.07

n=

94

within

0.24

0.00

1.00

T=

52

0.24

0.00

1.00

T=

52

overalloveralloveralloveralloverall

0.08

0.05

0.04

0.04

0.07

45

0.08

0.05

0.04

0.04

0.07