impact of e-markets in karnataka

Impact of E-mandi on the Prices and Market Arrivals of Copra and Rice in Karnataka Tanpreet Singh, M.Sc. Agribusiness Economics student in Gokhale Institute of Politics & Economics

Abstract

The paper analyse e-mandis in Karnataka with the objective of knowing the impact of e-mandi scheme on

the prices and market arrivals of Copra and Rice. There is 172 % increase in average prices of Copra in e-

mandis compared to only 108% in non-e-mandis between 2007 and 2015. In case of Rice, 76% increase in

average prices is noticed in e-mandis and 66% increase in non e-mandis between the period 2007 and

2015. Tabular analysis and difference in difference approach is used to identify the impact of the e-mandi

scheme. The difference in difference approach shows positive impact in the prices of Copra implying

farmers receiving better prices after the introduction of e-mandi scheme. The prices of Rice and the

market arrivals didn’t seem to have any significant impact and thus, it will be better to give the scheme

more time so that the farmers get used to it and reap the benefits.

1. Introduction

A market is a place which allows buyers and sellers to meet at a certain place and at a certain time in

order to have commercial dealings with each other. Whereas taking into notice an e-market, place and

time restrictions have weakened and cyberspace has become the new meeting point. Internet use has

increased significantly around the world in the past few years. It is believed that e-market redefines the

rules of doing business, its future is spectacular, those who embrace it early will be the winners but the

hesitant will be eliminated. E-markets have become increasingly popular and are treated as an alternative

to physical markets. An e-market is defined as an internet-based solution that links businesses interested

in buying and selling related goods or services from one another. It can be distinguished from

procurement or distribution system insofar as it must be neutral, taking into account the interests of both

buyers and sellers in its governance (Lipis et al., 2000).

The internet provides an infrastructure to the buyers and sellers at a cheaper rate for executing auctions

and bids. The host website on the internet acts as a broker, they offer services for sellers to post their

goods and services for sale and allow buyers to bid on those items. Detailed information given by the

sellers on every item for sale is available online and bidders look at the descriptions and then start the

bidding (Turban, 2007). Electronic auctions remove the deficiencies of traditional auctions by giving

buyers sufficient time to make their decisions which helps the sellers in getting the highest possible price.

Also, bidders don’t have to visit the auction site and thus the potential bidders which would have been

excluded in the traditional auctions, are included. The commissions are also fairly low as compared to the

traditional auctions as there is no requirement of place for auction, auctioneer and other employees

(Turban, 2007).

Information and Communication Technologies, have enabled many functions and capabilities that were

simply inconceivable a few years ago and the same has boosted both the secondary and tertiary sectors

in the country. It was quite evident that to remove the deficiencies faced in the agriculture sector, policy

reforms should get accompanied by the usage of technology.

Agricultural marketing scenario in the country has undergone a sea change since independence, owing to

the increase in the quantity and the variety of commodities produced, the marketable surpluses,

changing consumption pattern in the society, linkages with the international market, etc. Therefore, the

framework under which markets for agricultural produce function in the state and the factors that

influence the Farmer prices has to be understood afresh and reckoned suitably.

E-mandi is a real-time electronic auctioning platform offering online trading which enables farmers,

traders, processors, exporters and importers to buy and sell agricultural commodities in a transparent

manner. It is a comprehensive system, meeting all the requirements of the APMCs and designed by

incorporating the rules and regulations defined in APMC Act. Karnataka is the first state to implement the

model and has encouraged other states to learn and replicate the Karnataka model of e-mandis.

Karnataka has connected all its major 55 markets and has set up a web-enabled portal that record all the

lots of products available for sale. Each of the state’s traders have been given a username and password.

The Agricultural Marketing Reforms Committee 2013, setup by the Government of Karnataka,

recommended the use of technology in agricultural marketing system. The objective is to increase

competition, provide symmetric information and augment capacity-building. Establishment of e-mandis

can result in the following manner:

The farmers being able to choose from a wide range of traders (both offline and online) and sell

to the one with the right price for their produce.

Any transaction made will be recorded. This will reduce the chances of middlemen adding any

extra cost or seeking double commission as a result, inducing transparency in the market system.

Competition can be increased due to large number of farmers selling the same product on the

portal leading to increase in business over time.

Objectives like higher returns to farmers, lower transaction costs for buyers, and stable prices

and availability to consumers can be achieved.

2. Phase-wise amendment of e-mandi project

Karnataka Agricultural Produce Marketing (Regulation and Development) Act, 1966 was amended in 2007

to facilitate e-tendering. Further, Karnataka Agricultural Marketing Policy (2013) emphasized on

increasing competition among traders through e-mandi scheme. The pilot of e-mandi scheme was started

in 2011, and by December 2012, it modernized 13 mandis (APMCs) in Phase I. In the phase-I, only eight

commodities were covered. The first phase of the project, merely replaced the existing e-tender system

with an integrated Internet-based e-auction with additional feature of entering details of lots entering

into the market at the gate itself.

In phase-II, grading was introduced in 11 commodities. Further, an additional forty four APMCs were

modernized (from January 2013 to December 2015). E-mandis number increased to 55 markets in the

state.

In phase-III, infrastructure and technology were developed for web-based mandis in order to facilitate

national trading in line with National Agricultural Market scheme of government of India. Phase-III was

expected to be completed by June 2016 with all 155 major mandis upgraded in to e-mandis.

3. Process Flow of e-mandi in Karnataka

Note: As given in the NCDEX website

4. Objective

The overall objective of this paper is to analyze the impact of e-mandis on the prices and arrivals of Copra

and Rice in Karnataka. The specific objectives are

(i) Whether e-mandi is having any influence on the prices received by the farmers? ,

(ii) Whether there is an increase in the market arrivals in e-mandis compared to non-e-mandis?

Hypothesis –

(i) Implementation of e-mandi scheme influenced the prices received by the farmers

(ii) Market arrivals has increased with the introduction of e-mandi scheme and thereby increasing the

competition among the producers.

Research method – Tabular analysis and Difference-in-difference approach is used to know the impact of

e-mandis on the prices and arrivals of the selected commodities ie Copra and Rice.

Farmer lot wise entry and creation

of Lot ID

Unloading at Commission Agent/ CA inventory update

Sample/heap

Bidding through screens/mobile

based on unique lot ID

Best price- Winner

SMS sent to winner/CA/Farmer

Farmer option accept or reject the

best price

Weighting of lot-Authorized Personnel

Generation of Sale receipt

Cess payable

Booking CA/Buyer Account

Generation of Farmer Receipt

Update of Buyer Inventory

To Secondary sale/exit process

5. Methodology

The yearly data of prices and arrivals of both Copra and Rice, from 2007 to 2015 is collected from

AGMARKNET. There are 40 mandis in case of Rice (out of which 20 are e-mandis and 20 are non e-

mandis) and 18 mandis in case of Copra (out of which 9 are e-mandis and 9 are non e-mandis). The

selection of the mandis is on the basis of market arrivals. The mandis with the highest arrivals are taken

for the study (refer to Appendix A for the names of mandis selected for the study).

In this paper, Difference-in-Difference (DID) methodology is used to compare the prices and arrivals of e-

mandis with non e-mandis. DID is a quasi-experimental design that makes use of longitudinal data from

treatment group and control group to obtain an appropriate counterfactual to estimate a causal effect of

the treatment. The approach is used to estimate the effect of a specific intervention or treatment by

comparing the changes in outcomes over time between the intervention group and the control group.

Also, the DID approach focuses on the change and not the absolute levels.

The treatment group is the implementation of e-mandis in the state and non e-mandis are the control

group. Here, DID is used to estimate the effect of implementation of e-mandi by comparing the changes

in prices and arrivals of Rice and Copra over time between e-mandis and non e-mandis.

DID is nothing but an interaction term between time and dummy variable treatment group in the

regression model cited below

𝒀 = 𝜷𝟎 + 𝜷𝟏 ∗ 𝑻𝒊𝒎𝒆 + 𝜷𝟐 ∗ 𝑰𝒏𝒕𝒆𝒓𝒗𝒆𝒏𝒕𝒊𝒐𝒏 + 𝜷𝟑 ∗ (𝑻𝒊𝒎𝒆 ∗ 𝑰𝒏𝒕𝒆𝒓𝒗𝒆𝒏𝒕𝒊𝒐𝒏)

Here, 𝜷𝟎 is the constant term and indicates the baseline average of prices/arrivals before e-mandi,

𝜷𝟏 Indicates price/arrival trend in the control group (non e-mandis),

𝜷𝟐 Indicates the difference between the two groups for prices/arrivals before the introduction of e-

mandis, and

𝜷𝟑 Indicates the impact of e-mandi on the change in prices/arrivals in e-mandi over non e-mandi.

To tackle the problem of heteroscedasticity, robust standards are taken in the model.

In the absence of treatment (introduction of e-mandi scheme), the unobserved differences between the

treatment and control groups are the same overtime. For better understanding, one can look to table 1

and figure 1.

Table 1. Interpretation of difference-in-difference regression parameters

6. Results

A. Prices of Copra and Rice

For comparing prices before and after the implementation of e-mandis, triennium average of prices for

the years 2007, 2008 and 2009 and prices for the year 2015 are taken respectively. Descriptive analysis is

done on the basis of maximum, minimum and average price. Although maximum and minimum prices are

not a good indicator of price volatility but it can help in reporting the range of prices.

90

110

130

150

170

190

210

230

250

2007 2012 2016

Pri

ces

/ A

rriv

als

Figure 1. Difference-in-difference approach

emandi non-emandi

B

A

C

D

Pre-emandi emandi

Coefficient Calculation Interpretation Β0 B Baseline average (before e-mandi)

Β1 D-B Price trend in control group (non-e-mandi)

Β2 A-B Difference between two groups before introduction of e-mandi

Β3 (C-A)-(D-B) Difference in change in prices over time

For both Copra and Rice, the prices in e-mandis are greater than the prices in non e-mandis after the

introduction of e-mandi scheme (Figure 2 and 3).

In case of Copra (Figure 4), average price in e-mandis (Rs.3997/q) before the introduction of e-mandi

scheme was slightly higher than average price in non e-mandis (Rs.3717/q). After the introduction of e-

mandi scheme in the state, the average price in e-mandis (Rs.10876/q) is very high as compared to

average price in non e-mandis (Rs.7748/q). In case of minimum prices, base line price in e-mandis

(Rs.3264/q) was slightly higher than the non e-mandi (Rs.3190/q) but after the introduction of e-mandi

scheme, minimum price in e-mandi (Rs.6351/q) is very high than that of non e-mandi (Rs.5110/q). This

proves that both the average and minimum prices are positively influenced by the e-mandi scheme.

However, in case of maximum prices, the introduction of e-mandi scheme increased the maximum price

in e-mandis (Rs.12750/q) but it is still lower as compared to non e-mandis (Rs.14125/q).

0

2000

4000

6000

8000

10000

12000

14000

16000

2007 2008 2009 2010 2011 2012 2013 2014 2015

Pri

ces

Year

Figure 2. Prices (in Rs/q) of Copra in e-mandis and non e-mandis

E-mandi Non E-mandi

0

500

1000

1500

2000

2500

3000

3500

2007 2008 2009 2010 2011 2012 2013 2014 2015

Pri

ces

Year

Figure 3. Prices (in Rs/q) of Rice in e-mandis and non e-mandis

e mandi Non e-mandi

Now taking Rice into consideration (Figure 5), both the average price and the maximum price has shown

positive impact of the e-mandi scheme. The average price in e-mandis (Rs.1621/q) before the

introduction was slightly higher than the average price in non e-mandi (Rs.1274/q). After the introduction

of e-mandi scheme, the average price in e-mandis (Rs.2857/q) is very high as compared to average price

in non e-mandis(Rs.2114/q). The same can be seen for maximum price that before the introduction of e-

mandi scheme, it was Rs.2821/q in e-mandis higher than Rs.1962/q in non e-mandis. The after effect of

e-mandi scheme resulted in the increase in the maximum price in e-mandis to Rs.5597/q. However, in

case of minimum prices e-mandi scheme did not show significant increase in prices.

3717

7748

4571

14125

3190

51103997

10876

4371

12750

3264

6351

0

2000

4000

6000

8000

10000

12000

14000

16000

Before After Before After Before After

Mean Max Min

Figure 4. Impact of e-mandi on prices of Copra (Rs/q)

non e-mandi e-mandi

1274

2114 1962

3909

65113461621

2857 2821

5597

8631300

0

1000

2000

3000

4000

5000

6000

Before After Before After Before After

Mean Max Min

Figure 5. Impact of e-mandi on prices of Rice (Rs/q)

non e-mandi e-mandi

The increase in prices of e-mandis and non e-mandis after the introduction of e-mandi scheme when

compared to base year (triennium ending 2009) Is presented in Figure 6 and 7.

In case of copra, the average price in e-mandis increased by 172% compared to only 108% in non e-

mandis. Also, the minimum price in e-mandis increased by 95% compared to only 60% in non e-mandis.

However, in case of maximum price, no such significant impact is seen after the introduction of e-mandis.

Considering the case of Rice, the average price in e-mandis increased by 76% compared to 66% in non e-

mandis. Both the maximum and minimum price increased after the introduction of e-mandi scheme but

not as much as the increase in non e-mandis.

108

209

60

172192

95

0

50

100

150

200

250

Average Max Min

Figure 6. Increase in prices of Copra (%) after the project (TE 2009 and 2015)

non e-mandi e-mandi

66

99 107

7698

51

0

50

100

150

Average Max Min

Figure 7. Increase in prices of Rice (%) after the project(TE 2009 and 2015)

non e-mandi e-mandi

Table 2. Difference in Difference regression in prices of Copra

Model-1 Model-2

When trend is followed – value given to time (2007 is 1 , 2008 is 2, … , 2015 is 9)

When time before the introduction of e-mandi (2007-2012) is 0 and after introduction (2012-2015) is 1

Coefficients t-value Significance Coefficients t-value Significance

Constant 2319.7 5.53 0.000 4046.763 26.38 0.000

Time (year) (β1) 544.4 4.29 0.000 2239.51 3.40 0.001

Intervention (e-mandi=1, non e-mandi=0) (β2)

-997.3914 -1.79 0.075 542.6738 2.59 0.011

Interaction between time and intervention (β3)

505.0674 3.04 0.003 2227.624 2.31 0.022

R2 0.4777 0.3316

Number of Observations 162 162

Note: Dependent variable= Prices in Rs. per quintal. In the difference-in-difference regression, the interaction term between time and intervention (e-

mandi=1; non-e-mandi=0) indicates the impact of e-mandi on the prices.

model 1 - When trend is followed

The regression results shows that in e-mandi prices are higher by Rs.505.06/quintal compared to non-e-

mandis(Table 2). With each year, on average, prices increases by Rs.544.47/quintal. And in base year

prices of e-mandi markets are lower by Rs.997.39/q compared to non-e-mandi.

model 2 - When trend is not followed

In e-mandis, prices are higher by Rs.2227.62/quintal compared to non-e-mandis. With each year, on

average, prices increases by Rs.2239.51/quintal. And in base year prices of e-mandi markets are higher by

Rs. 542.67/q compared to non-e-mandi (Table 2).

Table 3. Difference in Difference regression in prices of Rice

Model-1 Model-2




Constant 970.1338 13.29 0.000 1399.175 28.74 0.000

Time (year) (β1) 155.06 9.00 0.000 779.0828 8.19 0.000


261.1906 2.22 0.027 365.4387 4.73 0.000


25.66985 0.94 0.348 54.22754 0.36 0.717

R2 0.3492 0.3055


Note: Dependent variable= Prices in Rs. per quintal.

The same approach is used in analyzing the impact of e-mandi scheme on the prices of Rice. The results

are presented in Table 3.

Model 1 - When trend is followed

The regression results shows that in e-mandi prices are higher by Rs.25.66/quintal compared to non-e-

mandis (though it is not statistically significant). With each year, on average, prices increases by

Rs.155.06/quintal. And in base year prices of e-mandi markets are higher by Rs.261.19/q compared to

non-e-mandi.

Model 2 - When trend is not followed

In e-mandis, prices are higher by Rs.54.22/quintal compared to non-e-mandis( not statistically

significant). With each year, on average, prices increases by Rs.779.08/quintal. And in base year prices of

e-mandi markets are higher by Rs. 365.43/q compared to non-e-mandi.

It is quite evident from both the tabular analysis and regression estimates that there is a positive impact

of e-mandi scheme on the prices of Copra. There are some indicators in case of Rice those are showing

positive impact of the scheme on its price but not all the indicators. The reason for low impact of e-mandi

scheme on rice can be due to the Minimum Support Price (MSP) issued by the government. What MSP

does is that it creates a focal point of prices for the farmers in the country. The farmers realizing it sells

their produce near that price and doesn’t wait for better prices of the produce. Whereas, without MSP,

the farmer is not aware about the focal point so they prefer high prices for their produce.

Price variability (coefficient of variation calculated by using monthly average prices of mandis) is

calculated before and after the introduction of e-mandi scheme. The change in CV(%) before and after is

presented in Figure 8 and 9. Figure 8 shows the price variability of copra and it is less for e-mandis (16%)

even after the introduction of e-mandi scheme as compared to non e-mandis (47%). This indicates that

the volatility (variability) in monthly prices is less in e-mandis compared to non-e-mandis.

21

47

816

0

20

40

60

before after

Figure 8. Price Variability (CV%) of Copra in e-mandis and non e-mandis before and after project

non e-mandi e-mandi

Figure 9 shows the price variability of rice before and after the introduction of e-mandi scheme. Before

the introduction of e-mandi scheme, price variability was equal in both e-mandis and non e-mandis but

the introducing e-mandi scheme, price variability is found to be more in e-mandis (37%) than non e-

mandis (35%).

B. Market Arrivals of Copra and Rice

It is expected that after the introduction of e-mandi scheme, market arrivals will increase as with

increased transparency and less collusion among traders, farmers prefer to sell at e-mandis compared to

local dealers, local traders and other informal channels. Hence, there will be overall shift of market

arrivals from informal to formal markets (e-mandis). The results also shows similar trend. After the

introduction of e-mandi scheme in 2012, there was steep increase in market arrivals in e-mandis

compared to non-e-mandis for both Copra and Rice (Figure 10 and 11).

28

35

28

37

0

10

20

30

40

before after

Figure 9. Price Variability (CV%) of Rice in e-mandis and non e-mandis before and after the project

non e-mandi e-mandi

0

2000

4000

6000

8000

10000

12000

2007 2008 2009 2010 2011 2012 2013 2014 2015

Mar

ket

Arr

ival

s

Year

Figure 10. Market Arrivals (in 1000 tons) of Copra in e-mandis and non e-mandis

E-mandi Non E-mandi

In the difference-in-difference regression, the interaction term between time and intervention (e-

mandi=1; non-e-mandi=0) indicates the impact of e-mandi on market arrivals. In Table 4, it can be seen

that the interaction term between time and intervention is not significant at 10% confidence level in both

the models for Copra. Only the intervention variable is significant at 5% confidence level in model 2.

Table 4. Difference in Difference regression in arrivals of Copra

Model-1 Model-2




Constant 981.39 2.42 0.017 1046.444 3.67 0.000

Time (year) (β1) 79.67 0.85 0.396 749.9722 0.87 0.383


1251.40 0.51 0.614 3925.133 2.31 0.022


761.57 1.23 0.221 2551.867 0.72 0.473

R2 0.073 0.065


Note: Dependent variable= Market arrivals (in tons) The regression analysis is not showing any significant impact of e-mandis on the market arrivals due to

the reason that the market arrivals are maintaining a constant like gap from the base year itself. The

mandis which were performing well before the scheme are turned up into the e-mandis. And thus, the

market arrivals are still maintaining that gap (figure 10).

0

2000

4000

6000

8000

10000

12000

14000

16000

2007 2008 2009 2010 2011 2012 2013 2014 2015

Mar

ket

Arr

ival

s

Year

Figure 11. Market arrivals (in 1000 tons) of Rice in e-mandis and non e-mandis

e-mandi Non e-mandi

Table 5. Difference in difference regression in arrivals of Rice

Model-1 Model-2




Constant 1442.385 2.86 0.005 1769.98 3.78 0.000

Time (year) (β1) 28.96417 0.39 0.696 -411.2425 -0.76 0.449


-26.07083 -0.01 0.993 2790.485 1.89 0.060


948.9358 1.26 0.207 4338.278 1.33 0.185

R2 0.0404 0.0346


Note: Dependent variable= Market arrivals (in tons) Now, considering the difference in difference regression analysis in arrivals of Rice (Table 5), similar

results are evident. The interaction term between the time and intervention is not significant at 10%

confidence level in both the models for the similar reason as of Copra. Figure 11 shows the gap between

the arrivals is constant from the base year itself. It can be implied that the e-mandi concept is applied on

well performing mandis if we consider the regression results.

The variability (coefficient of variation calculated by using market arrivals of mandis) is calculated before

and after the introduction of e-mandi scheme. The change in CV(%) before and after is presented in

Figure 12 for Copra . Figure 12 shows that the variability in arrivals of copra more for e-mandis (226%)

after the introduction of e-mandi scheme as compared to non e-mandis (172%). Before the scheme, it

143

172

205226

0

50

100

150

200

250

before after

Figure 12. Variability (%) in arrivals (Copra) of e-mandi and non e-mandi before and after the project

non e-mandi e-mandi

was 205% for e-mandis and 143% for non e-mandis. This indicates that the volatility (variability) in

market arrivals is more in e-mandis compared to non-e-mandis.

In case of Rice (Figure 13), the variablility in e-mandis (267%) has fallen down after the introduction of e-mandi scheme, though it is still more than variability in non e-mandis (171%). Before the scheme, it was 311% for e-mandis and 241% for non e-mandis.

7. Conclusion

This paper analyzed the impact of e-mandis on the prices and arrivals of Copra and Rice in Karnataka. The

analysis shows that there has been a positive impact on the prices of Copra in the state i.e the farmers

are getting better prices after the introduction of e-mandi scheme. Whereas, the impact on prices of Rice

is insignificant due to the fact that every year Minimum Support Price is issued for the crop in the state

which creates a focal point and affects the price received by the farmers.

The arrivals for Copra and Rice in e-mandis and non e-mandis are seen to have a constant gap from the

base year. The arrivals in e-mandis are more than the arrivals in non e-mandis, as a result the regression

analysis shows no impact of e-mandi scheme on the arrivals.

One must not forget that the impact of policies in agriculture sector is J-shaped. First, it will fall and after

a certain period of time, it will start rising. The concept of e-mandi scheme should be given more time so

that a concrete impact can be seen.

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241

171

311

267

0

50

100

150

200

250

300

350

before after

Figure 13. Variability (%) in arrivals (Rice) of e-mandi and non-emandi before and after the project

non e-mandi e-mandi

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Appendix A

Mandis selected for the study of Rice

E-mandis Non e-mandis Annigeri Channapatana

Arasikere Gangavathi

Bhadravathi Gonikappal

Bidar Gundlupet

Chamaraj Nagar Hanagal

Chikkamagalore Holenarsipura

Davangere Lingasugur

Gulbarga Madikeri

Hassan Malur

Haveri Moodigere

Kadur Mundgod

Madhugiri Nagamangala

Mysore (Bandipalya) Nanjangud

Pavagada Sakaleshpura

Raichur Shikaripura

Ranebennur Sindhanur

Shimoga Sirguppa

Sira Somvarpet

Tumkur T. Narasipura

Yellapur Tarikere

Mandis selected for the study of Copra

E-mandis Non e-mandis Arasikere Bangalore

Gubbi Bantwala

Hosadurga Channarayapatna

Huliyar Kanakpura

Kadur KR Pet

Sira Kunigal

Tiptur Nagamangala

Tumkur Puttar

impact of e-markets in karnataka

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