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 = 𝜷 𝟎 + 𝜷 𝟏 ∗ 𝑻𝒊𝒎𝒆 + 𝜷 𝟐 ∗ 𝑰𝒏𝒕𝒆𝒓𝒗𝒆𝒏𝒕𝒊𝒐𝒏 + 𝜷 𝟑 ∗ (𝑻𝒊𝒎𝒆 ∗ 𝑰𝒏𝒕𝒆𝒓𝒗𝒆𝒏𝒕𝒊𝒐𝒏)
Here, 𝜷 𝟎 is the constant term and indicates the baseline average of prices/arrivals before e-mandi,
𝜷 𝟏 Indicates price/arrival trend in the control group (non e-mandis),
𝜷 𝟐 Indicates the difference between the two groups for prices/arrivals before the introduction of e-
mandis, and
𝜷 𝟑 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
Prices/Arrivals
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
Prices
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
Prices
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
5110
3997
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
651
13461621
2857 2821
5597
863
1300
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
172
192
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
76
98
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
… , 2015 is 9)
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
8
16
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
MarketArrivals
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
… , 2015 is 9)
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
MarketArrivals
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
… , 2015 is 9)
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
205
226
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.
References
1. Banerji, A., & Meenakshi, J. V. (2004). Buyer collusion and efficiency of government intervention
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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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