Market information services and spatial asymmetry price transmission in Togolese maize market

Market information services and spatial asymmetry price transmission in Togolese maize market
AJAERD
Market information services and spatial asymmetry price
transmission in Togolese maize market
Koffi Yovo
Department of Agricultural Economics, Agricultural College, University of Lomé, Lomé, Togo
E-mail: koffiyovo@yahoo.fr
This paper examined the asymmetry price transmission in maize market in order to understand
how it has been affected by prices dissemination issued by the market information system since
2008 in Togo. To this end, we use concurrently the symmetric error correction model (ECM) of
Engle and Granger and the asymmetric error correction model of Enders and Granger (AECM) to
evaluate the prices transmission. The monthly retail maize prices collected from ten markets for
the period without service (2000-2007) and the period with service (2008-2015) are considered.
The results show that despite of price dissemination, the asymmetric price transmission still
dominates the maize market in Togo. In fact, neither the price adjustment speed nor the
asymmetric transmission of positive and negative variations of prices did not significantly change
during the prices dissemination period. Through the collusive behaviour of wholesalers and the
agreements on buying and selling prices in maize markets, some actors may exert some market
power which dampens the prices dissemination effect. Even though the overall effect is mitigated,
the results suggested that the market information services are not useless. They need to be
improved in order to increase the efficiency of the functioning of maize market in Togo.
Keywords: Market information system, Asymmetric price transmission, maize markets, Togo
INTRODUCTION
Economic theory relies heavily upon the assumption that
market agents have the necessary information to engage
in optimal arbitrage, and that this information is symmetric.
In reality, however, information is rarely costless or
symmetric. Due partly to costly information, market power,
asymmetric price transmission across markets is a
common occurrence and is especially acute in developing
countries (Peltzman, 2000; Abdulai, 2000). In specific
case of Togo, the general concern raised is that the
transmission of price changes between rural and urban
markets may be different. This occurs in such a way that
powerful actors in that particular market segment try to
realize profits in excess of normal rates of return.
Precisely, Farmers often complain that decreases in farm
prices are more fully and rapidly transmitted to the urban
markets than equivalent increases in consumers’ price.
Yovo (2017) has already showed that price asymmetry is
manifested in three ways in maize market in Togo: firstly,
when upward price movements in one market are not
transmitted in the same way to another market as
downward price movements, secondly when a price in a
market increases faster than it falls, thirdly when the rate
of adjustment of equilibrium prices differs from one market
to another. These three manifestations of market failure
highlighted in the maize market in Togo imply welfare
redistribution from agents downstream to agents upstream
and have serious political and social consequences.
To deal with the issue of asymmetric price transmission
and getting prices right, the agricultural policies in
developing countries have promote the creation of Market
information system in order to improve the availability and
the accessibility of small farmers to market information
(Courtois and Subervie, 2015). In the case of Togo, market
information services (MIS) was created in 2008.
This public service entrusted in collecting and
disseminating information about the mercurial of
Journal of Agricultural Economics and Rural Development
Vol. 3(3), pp. 259-269, October, 2017. © www.premierpublishers.org, ISSN: XXXX-XXXX
Research Article

Yovo K. 260
agricultural products aim to improve the spatial agricultural
prices transmission between markets through the
development of efficient arbitrage in Togo. This paper
assesses the impact of price dissemination on the
asymmetric price transmission in maize market. More
precisely, the article tries to answer the following question:
did the weekly dissemination of information on the maize
prices improve the asymmetric price transmission in maize
market urban and rural markets or between northern
markets and southern markets in Togo? To respond this
question, the article begins with an analysis of the role of
information in the functioning of the market (section 2).
Section 3 presents the methodology and data used to
analyze the impact of MIS on the asymmetric price
transmission. Section 4 discusses the results and draws a
conclusion.
LITERATURE REVIEW
Economists have long recognized the important role that
information, specifically price, plays in the efficient
functioning of markets. Jensen (2007) emphasized that in
a market economy, price enables the efficient coordination
of large numbers of consumers and producers, each
acting only in self-interest and only with information about
their own preferences, technology and constraints. Price
differentials across markets in excess of transportation
costs for example serve as signals to profit seeking agents
to re-allocate goods towards the higher priced market. In
doing so, they also increase aggregate welfare. However,
optimal arbitrage requires agents have full information on
prices.
The modern economic theory places information in the
centre of the performance of the markets. In the theory of
the competitive walrasian general equilibrium, information
concerning the scarcity of the resources is available to all
the economic agents. This facilitates the optimal allocation
of the resources. Thus, the price summarizes information.
A high price indicates too short supply or too long demand,
and vice versa. In other words, the equilibrium price
transmits information to the economic agents and plays to
some extent the part in signals of the market (Friedman,
1990).
According to the Efficient Market Hypothesis (EMH), a
market is “informationally efficient” if prices at each
moment incorporate all available information about future
values. Informational efficiency is a natural consequence
of competition, relatively free entry, and low costs of
information. If there is a signal, not incorporated in market
prices, that future values will be high, competitive traders
will buy on that signal. In doing so, they bid the price up,
until it fully reflects the information in the signal (Jarrow,
2012).
But the fact is that in reality, markets often operate with
asymmetric information, as is the case in developing
1
For the literature related to the second category (bilateral coordination), see Courtois and Subervie (2015).
countries. However, one of the lessons of the economy of
information is that in an environment where information is
asymmetric, the agents manage a risky situation which
hampers the functioning of markets (Stiglitz, 1991). Two
types of risk are generally quoted in the literature: the anti-
selection also called adverse selection is a situation where
the market is disturbed by the fact that a party knows better
the characteristics of the goods exchanged at the time of
signature of the contract and moral hazard is a situation in
which one party called the principal cannot control the
action of the other called agent or does not have the
means to evaluate his opportunity. When buyers
imperfectly observe the quality of goods they want to buy,
sellers have an incentive to overestimate the quality of
their products and sell them at the highest possible price.
The purchasers cannot thus trust the declarations of the
sellers, nor to deduce that a high price means a good
quality. In such situation, the sellers of goods of good
quality which are worth indeed a high price can have no
possibility to sell their product at their true price insofar as
the buyers doubt his quality. The price is any more a
perfect signal of the value of the good and can no longer
play its role in information. Under these conditions, the
competitive market cannot function effectively any more.
The agent victim of a lack of information is likely to select
a product which does not correspond to the displayed
price, or requires a price so low that the good products are
withdrawn from the market. With regard to cereal markets,
the existence of asymmetric information between buyers
and sellers may provoke problems such as adverse
selection and modify the nature of their transaction, or
more generally, the process of arbitrage. Asymmetric
information about prices, quantities and quality is a barrier
to trade between urban markets and rural markets as it
hampers producers and consumers to rationalize their
buying and selling decisions. This makes exchanges
unequal. Consequently, the availability and accessibility of
information leads to the reduction of transaction costs and
oligopolistic markets which are often based on possibility
for certain traders who hold specific information. Many
failures in the arbitrage process among different markets
can be explained by the concentration of information in the
hand of these traders. This concentration of information in
some segments of the market causes asymmetric in price
transmission (Abdulai, 2000; Yovo, 2017; Von Cramon-
Taubadel and Meyer, 2004).
How the dissemination of information can help to correct
asymmetric price transmission?
The empirical literature that deals with the impact of MIS
on economic development in poor countries can be divided
into two categories. The papers of the first category
analyze how MIS improves spatial integration (global
coordination) and the second category, the efficiency of
transaction (bilateral coordination). In this paper, we only
focused on the first category due to the macroeconomic
nature of the study1.

J. Agric. Econ. Rural Devel. 261
In this first category, three papers are reviewed: Yovo
(2015); Bizimana and al (2013) and Bassolet and Lutz
(1998). Yovo (2015) used autoregressive distributed lag
(ARDL) to assess the impact of price dissemination by MIS
on the spatial integration of maize markets in Togo. To this
end, the weekly retail maize prices collected from 13
markets for the period without service (2003-2007) and the
period with service (2008-2012) were considered. The
results show that the impact of price dissemination on the
spatial integration of maize markets is mitigated. By
reference to Lome, neither rural markets nor northern
markets have significantly improved their level of long-term
and short-term spatial integration.
In the same vein, Bizimana and al. (2013) analyze the
impact of a newly introduced market information system
“E-Soko”, on beans markets integration by comparing the
period before and after the system was implemented in
Rwanda. Beans, both bush and climbing, are the most
important traded crop in rural areas of Rwanda, and third
most important in urban areas in terms of value. Bi-weekly
prices on beans were analyzed for the two-time periods:
one before the introduction of the market information
system “E-Soko” (1999 to 2003) and another one after “E-
Soko” was introduced (2007-2012) on eight markets
across Rwanda for each period. The Vector
Autoregressive (VAR) method was used to analyze the
data and assess the level of market integration in both
periods. Stationarity tests show that the price series in the
period after the introduction of E-Soko (2007-2012) are all
(except one) stationary, which raises the question of the
market efficiency. Prices in the period before E-Soko
(1999-2003) indicate a good level of integration with
Musanze market leading the group. No clear conclusions
were drawn from this study regarding the impact of the E-
Soko market information system.
As concerns Bassolet and Lutz (1998), using ARDL, they
did not find a significant effect of the information system on
the integration of sorghum markets in Burkina Faso. The
authors showed that the prices issued by the information
services did not really affect the arbitrage of cereals
traders. According to the authors’ opinion, this is due to the
quasi-general ignorance about the day of price diffusion on
the radio.
METHODOLOGY
In this section, we present, firstly the model of asymmetric
price transmission, secondly the model assessing the
effect of price dissemination on the asymmetric price
transmission and thirdly the data used in the analysis.
Modelling asymmetric price transmission
Most analysis of the transmission of food prices between
different markets use the modified model of Houck (1977).
However, Enders and Granger (1998) and Goodwin and
Piggot (2001), Hansen and Seo (2002), Meyer and Von
Cramon-Taubadel (2004) and von Cramon-Taubadel and
Meyer (2004) criticized the forms of specification of this
model because they do not allow to represent the
asymmetry of the cointegration relation due to the non-
stationarity of the transaction costs. Enders and Siklo
(2001) explain that the cointegration approach of Engle
and Granger 1987) may be incorrectly specified if the
transmission is asymmetric.
Due to the criticism of the variants of the Houck model and
the size of the transaction costs on the Togolese cereal
markets, we use the model proposed by Enders and
Granger (1998) to examine the nature of the relationship
between prices observed in maize markets in Togo. The
choice of this model is also justified because of the
simplified form of its specification which uses the null value
as a threshold delimiting two price variation regimes.
According to Hansen (1996), thresholds delimiting
adjustment regimes for unknown values cause inference
problems due to the presence of nuisance parameters in
threshold models. However, this author proposes a
statistical test to test the statistical significance of the
selected thresholds.
According to Engle and Granger (1987), when non-
stationary series are integrated of the same order, it is
possible to establish a long-term relationship between
them as follows:
𝑃1𝑡 = 𝛽0 + ∑ 𝛽𝑖
𝑛
𝑖=2 𝑃𝑖𝑡 + 𝜇 𝑡 (3)
where 𝜇 𝑡 represents the error term. Engle and Granger
(1987) examine the cointegration relation between the𝑃𝑖𝑡
series by testing the non-stationarity of residues 𝜇 𝑡 from of
the estimation of the long-term relationship (3). The non-
stationarity of these residues is tested on the basis of the
following relation:
∆𝜇 𝑡 = 𝜌𝜇 𝑡−1 + 𝜀𝑡 (4)
where ∆𝜇 𝑡represents the first difference between
𝜇 𝑡 𝑎𝑛𝑑 𝜇 𝑡−1 and the error term 𝜀𝑡, a white noise. Enders
and Granger (1998) then split relation (4) as follows:
∆𝜇 𝑡 = {
𝜌1 𝜇 𝑡−1 + 𝜀𝑡 𝑖𝑓𝜇 𝑡−1 ≥ 0
𝜌2 𝜇 𝑡−1 + 𝜀𝑡 𝑖𝑓𝜇 𝑡−1 < 0
(5)
where the terms 𝜌1 and 𝜌2 respectively represent the
positive and negative adjustment parameters of the lagged
error term 𝜇 𝑡−1and the term 𝜇 𝑡, the error term. The system
of equations (5) can be written in another way:
∆𝜇 𝑡 = 𝐼𝑡 𝜌1 𝜇 𝑡−1 + (1 − 𝐼𝑡)𝜌2 𝜇 𝑡−1 + 𝜀𝑡 (6)
Where𝐼𝑡 is an indicator variable defines as follows:
𝐼𝑡 = {
0 𝑠𝑖𝜇 𝑡−1 ≥ 0
1 𝑠𝑖𝜇 𝑡−1 < 0
(7)

Yovo K. 262
The critical threshold for this model is zero. Enders and
Granger (1998) show that the residuals resulting from the
estimation of the long-term relationship (3) are stationary
if the values of the parameters 𝜌1 𝑎𝑛𝑑 𝜌2 are within the
interval ] 0 2 [. According to them, in so far as there is a
cointegration relationship between the series, the standard
error correction model (ECM) according to Engle and
Granger (1987) can integrate the positive and negative
adjustment parameters to give an Asymmetric Error
Correction Model (AECM) as follows:
∆𝑃1,𝑡 = 𝐼𝑡∅1 𝜇 𝑡−1 + (1 − 𝐼𝑡)∅2 𝜇 𝑡−1 + ∑ ∑ 𝛽𝑖,𝑗∆𝑃𝑖,𝑡−𝑗 +𝑘
𝑗=1
𝑛
𝑖=2
𝜗1,𝑡 (8)
where the terms ∅1 𝑎𝑛𝑑∅2 represent the parameters of
adjustment of the positive and negative deviations and the
term 𝜗1,𝑡 a white noise. The adjustment of the variations of
the variable is symmetric when the parameters
∅1 𝑎𝑛𝑑∅2are significant and equal. In this case, the Engle
and Granger MCE becomes a specific form of the
asymmetric ECM of Enders and Granger.
The F-test of Enders and Granger allows to test the null
hypothesis (𝜌1 = 𝜌2) and(∅1 = ∅2).
Modelling the effect of prices dissemination on prices
transmission
Test of asymmetric cointegration approach
In order to assess the impact of prices dissemination on
the prices transmission, we consider two periods: period 1
is the period without prices dissemination, period 2 is the
period with prices dissemination. For the two periods 1 and
2, the equation (6) can be written respectively:
∆𝜇 𝑡 = 𝐼𝑡 𝜌1 𝜇 𝑡−1 + (1 − 𝐼𝑡)𝜌2 𝜇 𝑡−1 + 𝜀𝑡 (9)
∆𝜇 𝑡 = 𝐼𝑡 𝜌1
′
𝜇 𝑡−1 + (1 − 𝐼𝑡)𝜌2
′
𝜇 𝑡−1 + 𝜀𝑡 (10)
with 𝜌1 ≠ 𝜌1
′
; 𝜌2 ≠ 𝜌2
′
To capture the effect of prices transmission, we introduce
a dummy variable D in the model as specify below:
D=1 for the period with prices dissemination;
D=0 the period without prices dissemination.
𝜌1
′
= 𝜌1 + 𝐷𝛿 𝜌1
𝜌2
′
= 𝜌2 + 𝐷𝛿 𝜌2
The equation (10) becomes:
∆𝜇 𝑡 = 𝐼𝑡 𝜌1 𝜇 𝑡−1 + 𝐷𝛿 𝜌1
𝐼𝑡 𝜇 𝑡−1 + (1 − 𝐼𝑡)𝜌2 𝜇 𝑡−1 + 𝐷𝛿 𝜌2
(1 −
𝐼𝑡)𝜇 𝑡−1 + 𝜀𝑡 (11)
The null hypothesis is tested as follows :
𝐻0: 𝜌1 = 𝜌2=0 and 𝛿 𝜌1
= 𝛿 𝜌2
=0
H0 : 𝜌1 = 𝜌2 and 𝛿 𝜌1
= 𝛿 𝜌2
3
We use nominal instead of real prices because traders’
arbitrage is not based on real but on nominal prices.
Moreover, monthly inflation of cereals prices was not so
Test of asymmetric prices transmission approach
Let consider the asymmetric error correction model
(AECM) for the two periods:
∆𝑃1,𝑡 = 𝐼𝑡∅1 𝜇 𝑡−1 + (1 − 𝐼𝑡)∅2 𝜇 𝑡−1 + ∑ ∑ 𝛽𝑖,𝑗∆𝑃𝑖,𝑡−𝑗 +𝑘
𝑗=1
𝑛
𝑖=2
𝜗1,𝑡 (12)
∆𝑃1,𝑡 = 𝐼𝑡∅1
′
𝜇 𝑡−1 + (1 − 𝐼𝑡)∅2
′
𝜇 𝑡−1 + ∑ ∑ 𝛽𝑖,𝑗
′
∆𝑃𝑖,𝑡−𝑗 +𝑘
𝑗=1
𝑛
𝑖=2
𝜗1,𝑡 (13)
with ∅1 ≠ ∅1
′
; ∅2 ≠ ∅2
′
; 𝛽𝑖,𝑗 ≠ 𝛽𝑖,𝑗
′
As aforementioned, we have:
∅1
′
= ∅1 + 𝐷𝛿∅1
∅2
′
= ∅2 + 𝐷𝛿∅2
𝛽𝑖,𝑗
′
= 𝛽𝑖,𝑗 + 𝐷𝛿 𝛽𝑖,𝑗
(13) becomes:
∆𝑃1,𝑡 = 𝐼𝑡∅1 𝜇 𝑡−1 + 𝐷𝛿∅1
𝐼𝑡 𝜇 𝑡−1 + (1 − 𝐼𝑡)∅2 𝜇 𝑡−1 +
𝐷𝛿∅2
(1 − 𝐼𝑡)𝜇 𝑡−1 + ∑ ∑ 𝛽𝑖,𝑗∆𝑃𝑖,𝑡−𝑗 +𝑘
𝑗=1
𝑛
𝑖=2
∑ ∑ 𝐷𝛿 𝛽𝑖,𝑗
∆𝑃𝑖,𝑡−𝑗 + 𝜗1,𝑡
𝑘
𝑗=1
𝑛
𝑖=2 (14)
The prices transmission effect is tested with Fisher test on
the null hypothesis as follows:
𝐻0: ∅1 = ∅2=0 and 𝛿∅1
= 𝛿∅2
=0
H0 : ∅1 = ∅2 and 𝛿∅1
= 𝛿∅2
Those tests will enabled us to know if the positive or
negative variations are better or less transmitted in the
period of prices dissemination.
Data
The data used are nominal3, monthly retail maize prices
for the periods from January 2000 to December 2015.
These time series are extracted from the price database of
DSID, ANSAT and RESIMAO. DSID and ANSAT are the
two departments of agriculture’s ministry in charge of
prices statistics. RESIMAO is the network market
information system in West Africa. The study used ten
markets: Lome, the capital of Togo, is the main consumer
market of maize. It records the most important and regular
deficits in maize despite the convergence of maize
produced in the others regions. This is due to the
concentration of the population whose main staple food is
maize. Lome stands for urban market in the sample. The
nine others markets are the big rural maize markets. They
are selected on the basis of the importance of the maize
volume transaction they established with Lome as well as
the availability of price series. They are: Ahepe, Assahoun
in caostal region, Tohoun and Anie in Plateaux region,
Tchamba in central region, Bassar and Ketao, in Kara
region, Gando and Cinkasse in Savannah region (see the
map in appendix).
important in Togo to affect significantly the efficiency of
traders’ arbitrage during the most period of study.

RESULTS AND DISCUSSION
To assess the impact of prices dissemination on prices
transmission, we use concurrently the Engle and Granger
approach and the Enders and Granger approach. For each
approach, two tests are carried out: the test of
cointegration and the test of error correction model.
Tests of symmetric and asymmetric cointegration
between maize price series
In this paragraph, we examine the tests of symmetric and
asymmetric cointegration between maize price series.
Before carrying the test of cointegration, we performed the
unit root test.
Test of unit root
In general, prices of agricultural products are affected by
seasonal variations. The periods of heavy rains during
which roads linking markets are impracticable may explain
this seasonality. The presence of seasonality does not
allow capturing the intrinsic evolution of a series and thus
its relation with another. To take into account this
seasonality, the introduction of dummy variables is the
most used solution. Abdulai (2000) suggests identifying
seasonal periods based on observable a priori information
on the functioning of markets. He suggests correcting only
these seasonal periods by introducing the corresponding
dummy variables. This identification of months of high
seasonality being subjective, our approach consists in
seasonally adjusting all price series using the moving
averages method in order to remove any cyclical influence.
The null hypothesis of unit roots specifying the non-
stationarity of the seasonally adjusted price series is tested
using the augmented test of Dickey-Fuller and Phillips-
Perron without trend. The optimum number of lags is
selected from the Schwartz criterion. The results of the unit
root tests reported in Table 1 indicate that all series are
non-stationary in level and integrated in first difference.
Table 1: Results of unit root tests on maize price series
ADF test Phillips-Perron test
Level Difference Level Difference
Lome
0.12
(1.20)
-0.01***
(-7.05)
0.23
(0.13)
-0.07***
(-5.10)
Ahepe
0.15
(1.07)
-0.03***
(-7.62)
0.23
(0.13)
-0.04***
(-8.10)
Assahoun
0.17
(1.12)
-0.01***
(-7.02)
0.31
(0.19)
-0.08**
(-5.12)
Tohoun
0.18
(1.22)
-0.00***
(-5.05)
0.28
(0.14)
-0.06***
(-4.10)
Anie
0.26
(1.22)
-0.00***
(-5.05)
0.28
(0.14)
-0.06*
(-4.10)
Tchamba
0.27
(0.20)
-0.10***
(-6.01)
0.20
(0.11)
-0.04**
(-6.10)
Bassar
0.14
(1.13)
-0.01***
(-4.13)
0.15
(0.59)
-0.03***
(-11.02)
Ketao
0.35
(2.02)
-0.01***
(-6.00)
0.42
(0.12)
-0.02***
(-6.10)
Gando
0.32
(1.09)
-0.00***
(-4.05)
0.17
(0.18)
-0.04***
(-4.26)
Cinkasse
0.16
(2.01)
-0.02***
(-7.02)
1.13
(0.18)
-0.03***
(-6.12)
Source: Calculation of the author using data from DSID, ANSAT and RESIMAO
Values without parentheses are the estimated coefficients of the parameters. Those in parentheses are the test
statistics to be compared to the MacKinnon critical values of -3.59, -2.93 and -2.60 at the 1%, 5% and 10% thresholds
respectively. The critical values of Perron are (-5.07) to 1% and (-4.22) to 5%. The asterisks *, ** and *** correspond
respectively to 10%, 5% and 1% of the significance thresholds.
Tests of standard cointegration and prices
dissemination effect
We test the cointegration relationship between the price
series observed in the Lome market and those observed
in each rural market.
Since the price series are stationary in first difference, the
null hypotheses of absence of cointegration and symmetry
of the cointegration relation between the markets are
tested using the equation (3). The results presented in
Table 2 are obtained by estimating cointegration
relationships (3) for non-integrated series of the same
order. The estimated coefficients are very significant for
each pair of markets considered.
Table 2 shows the results of the cointegration tests
according to Engle and Granger carried out on the
residues resulting from the estimation of the relation (3).
According to this test, the zero hypothesis of no

Yovo K. 264
Table 2: Results of the dissemination effect through symmetric cointegration approach (𝝆 = 𝟎)
Source: Calculation of the author using data from DSID, ANSAT and RESIMAO
a. Values without parentheses are the estimated coefficients and those in parentheses are test statistics to be compared to McKinnon's
critical values). The asterisks *, ** and *** correspond respectively to 10%, 5% and 1% of the significance thresholds used for the
McKinnon critical values of -1.615, -1.942 and -2.577, respectively.
b.  measure the dissemination effect. In parenthesis are the p-values.
c. The term Q stands for Ljung-Box statistics for which the first autocorrelation orders (p) of the residuals are jointly equal to zero.
cointegration between the price series is rejected for all
market pairs. The comparison of statistics calculated
under the null hypothesis with the critical values of the
McKinnon table confirms the cointegration relationship
between the Lome price series and the nine rural markets.
The analysis of the table showed that the dissemination of
prices information has a marginal effect on the prices
transmission. In fact, only three markets among the nine
have improved the level of their integration with Lome.
Test of asymmetric cointegration and prices
As mentioned earlier, Enders and Granger (1998) modified
the standard cointegration test of Dickey-Fuller so that the
hypothesis of a cointegration relation between prices can
be tested without maintaining the symmetry hypothesis in
the long-term adjustment. Indeed, the Dickey-Fuller
standard test based on the symmetric adjustment
hypothesis may tend to reject the assumption of
cointegrated price series in the presence of asymmetry in
the cointegration relation. As in the standard cointegration
test, the asymmetric cointegration test is based on the
stationarity of the residue. Enders and Granger (1998) use
the F-test to test the hypothesis that the coefficients 𝜌1 and
𝜌2 are jointly different from zero (the critical values are
given in Enders and Granger (1998).
Table 3 shows the results of the tests of the asymmetric
cointegration relation between the price series according
to Enders and Granger. The Ljung-Box test is also
performed to ensure that residues are not correlated. The
Ljung-Box statistics denoted Q also reported in Table 3
indicates that residues are not significantly correlated. The
null hypothesis is not rejected by comparing the Fisher
statistics to the critical values of the table of Enders and
Granger (1998) for all market pairs except for Tchamba
and Cinkasse.
The prices dissemination effect measured by the
coefficients 𝛿 𝜌1
𝑎𝑛𝑑 𝛿 𝜌2
shows that 𝛿 𝜌1
= 𝛿 𝜌2
. This result
is explained by the fact that the effect of prices
dissemination is equal both for the positive and the
negative variations of the prices denoting the marginal
effect of the prices dissemination policy.
Analysis of Prices Transmission and prices
Since there are cointegration and symmetry relations
between Lome market and all rural markets, we test the
transmission of prices between markets and show how the
prices dissemination has influenced this transmission.
First, in compliance with the Engle and Granger (1987)
representation theorem, we test the transmission of prices
using the standard error correction model (ECM). Then,
considering the presence of significant transaction costs,
we examine the asymmetric price transmission between
symmetrically cointegrated market pairs using the Enders
and Granger asymmetric error correction model (AECM).
The Schwartz criterion is used to determine the number of
lag that must be considered in the estimated models for
each pair of markets (Enders, 1995).
Cointegration coefficients (
𝛽a
)
Dissemination effect (  )
Qc
Ahepe
-0.14***
(-6.12)
-0.12**
(0.04)
1.23
(0.17)
Assahoun
-0.80***
(-4.01)
-3.70
(0.14)
2.0
(0.25)
Tohoun
-0.42***
(-3.30)
0.22
(0.45)
0.20
(0.21)
Anie
-0.60***
(-5.03)
0.42
(0.15)
2.14
(0.22)
Tchamba
-0.49***
(-3.75)
-1.05
(0.15)
2.16
(0.15)
Bassar
-0.65***
(-2.87)
-3.10**
(0.03)
0.59
(0.16)
Ketao
-0.11***
(-7.09)**
3.11
(0.17)
0.43
(0.12)
Gando
-0.80***
(-5.01)
-3.70*
(0.09)
2.15
(0.22)
Cinkasse
-0.42***
(-3.20)
0.22
(0.45)
2.16
(0.15)

Table 3: Results of the dissemination effect through asymmetric cointegration approach (𝝆 ≠ 𝟎)
Coefficients of asymmetric cointegration and tests Dissemination effect
𝜌1
𝑎
= 0 𝜌2
𝑎
= 0 Φ 𝑏 𝜌1 = 𝜌2
𝑐
Qd
𝛿 𝜌1
𝛿 𝜌2
𝛿 𝜌1
= 𝛿 𝜌2
Ahepe
-0.55**
(0.03)
-0.15**
(0.02)
10.30 0.15
(0.99)
2.23
(0.99)
-0.12**
(0.03)
-0.05**
(0.02)
0.12
(0.83)
Assahoun
-0.95***
(0.00)
-0.80**
(0.01)
11.45 0.80
(0.24)
0.07
(0.24)
-0.07***
(0.00)
-0.09**
(0.01)
0.47
(0.21)
Tohoun
-0.54**
(0.04)
-0.42**
(0.01)
7.53 2.42
(0.67)
2.45
(0.60)
-0.04**
(0.04)
-0.02**
(0.01)
1.42
(0.37)
Anie
-0.67**
(0.04)
-0.69**
(0.03)
10.12
.
0.69
(0.85)
0.95
(0.80)
-0.07**
(0.04)
-0.19**
(0.03)
0.69
(0.85)
Tchamba
-0.41
(0.13)
-0.49
(0.75)
-
- -
-0.41
(0.13)
-0.49
(0.75)
-
Bassar
-0.09**
(0.06)
-0.61**
(0.07)
7.22 0.01
(0.87)
0.01
(0.76)
0.09**
(0.06)
-0.21**
(0.07)
0.01
(0.87)
Ketao
-0.20**
(0.03)
-0.11**
(0.04)**
11.15 0.11
(0.54)
0.11
(0.89)
-0.23**
(0.03)
-0.15**
(0.04)**
0.11
(0.51)
Gando
-0.95**
(0.02)
-0.80**
(0.01)
9.46 0.00
(0.88)
0.01
(0.88)
-0.05**
(0.02)
-0.06**
(0.01)
0.20
(0.45)
Cinkasse
-0.64
(0.14)
-0.12
(0.20) -
-
-
-0.04**
(0.04)
-0.14**
(0.01)
1.10
(0.20)
Source: Calculation of author using data from DSID, ANSAT and RESIMAO.
a) The values without parenthesis are the estimated coefficients and those in parenthesis are the probabilities
corresponding to the Student test for 𝜌1 and 𝜌2.
b) The term Φ corresponds to the F-statistics calculated under the null hypothesis (ρ1 = ρ2 = 0). These statistics
are compared with those of the critical values tabulated by Enders and Granger (1998). These critical values
are 3.10; 3.82 and 5.53 for thresholds 10%, 5% and 1% respectively.
c) The values without parenthesis are the F-statistics and those in parenthesis are the values of the probabilities
corresponding to these Fisher statistics under the null hypothesis of symmetry (ρ1 = ρ2). Values without
parenthesis are F-statistics and those with parenthesis are the values of the corresponding probabilities.
d) The term Q stands for Ljung-Box statistics for which the first autocorrelation orders (p) of the residuals are
jointly equal to zero.
Symmetrical prices transmission and prices
The results of the standard ECM test presented in Table 4
indicate that the price transmission is in both directions, i.e.
from the market of Lome to the nine rural markets, on one
hand, and from the rural markets to Lome market on the
other hand. The adjustment speed of the pairs Lome - rural
markets is ranged from -0.55 to -0.18 with an average of -
0.23. The adjustment speed of the pairs rural markets -
Lome, is ranged from -0.57 to -0.20 with an average of -
0.40. Overall, the speed of price adjustments of the rural
markets is greater than the speed of price adjustments in
the market of Lome. Thus, the rural markets correct the
long-term disequilibrium at a higher rate than Lome, the
biggest consumer market. On average, 40% of price
changes from Lome are eliminated during the month by
the rural markets. The market in Lome corrects the
disequilibrium in a proportion of 23% monthly. The
correction of the disequilibrium is therefore slower in
Lome.
The dissemination of price did not fundamentally alter the
situation prevailing before the introduction of the MIS. Over
the 18 relations studied, only 5 pairs of markets have seen
their speed of adjustment improve with respect to Lome
due to the dissemination of the cereals prices. Therefore,
we can conclude that, the dissemination effect is mitigated.
Asymmetric price transmission and prices
The results of the estimation of the asymmetry relation
according to Enders and Granger are presented in Table
5. The Schwartz criterion is used to determine the number
of lags considered in the estimated models for each pair of
markets. The Ljung-Box test is also performed to ensure
that the residues are not significantly correlated. It appears
that, contrarily to the standard error correction model, the
asymmetric error correction model detects 15 over 18
cases of asymmetry in the transmission of positive and
negative price changes observed on pairs of maize
markets. Overall, the speed of adjustment is 34% for
positive deviations and 41% for negative deviations. It
means that within one month, on average, 34% of the
positive deviations and 41% of the negatives deviations
are eliminated from the long-run equilibrium relationship.
Moreover, the results show that the speed of long-term
prices adjustment in rural markets is higher than the prices
adjustment in Lome market.
The table 5 shows that prices dissemination effect
measured by the coefficients 𝛿∅1 and 𝛿∅2
is different from
aforementioned results. The results suggest that positive
shocks are better transmitted than negative shocks in the
MIS diffusion period. The results also appear to indicate a

Yovo K. 266
Table 4: Results of the dissemination effect on the symmetric transmission (∅ = 𝟎)
Adjustment speed()a Dissemination effect (𝜹) b Qc
Lome-Ahepe -0.32*
(0.09)
0.08*
(0.06)
0.32
(0.24)
Ahepe-Lome 0.38*
(0.07)
0.24
(0.17)
0.54
(0.19)
Lome-Assahoun -0.18**
(0.01)
-0.18
(-0.11)
0.01
(0.12)
Assahoun-Lome -0.44***
(0.00)
0.01
(0.14)
0.47
(0.13)
Lome-Tohoun -0.27**
(0.04)
0.13**
(0.01)
0.08
(0.11)
Tohoun-Lome -0.28*
(0.09)
0.18
(0.13)
0.62
(0.13)
Lome-Anie -0.44***
(0.00)
0.03
(0.05)
0.30
(0.20)
Anie-Lome -0.57***
(0.00)
0.03**
(0.02)
1.22
(0.19)
Lome-Bassar -0.26**
(0.03)
-0.16
(0.31)
1.20
(0.19)
Bassar-Lome -0.37**
(0.02)
0.08**
(0.03)
0.20
(0.21)
Lome-Ketao -0.27*
(0.08)
0.07
(0.15)
2.15
(0.22)
Ketao-Lome -0.34*
(0.07)
0.13
(0.18)
0.03
(0.15)
Lome-Gando -0.35**
(0.04)
0.09*
(0.07)
0.47
(0.26)
Gando-Lome -0.42***
(0.00)
0.13**
(0.04)
0.36
(0.15)
Source: Calculation of author using data from DSID, ANSAT and RESIMAO.
a) The values without parenthesis are the estimated coefficients () of standard ECM and those in parenthesis are the
probabilities corresponding to the Student test.
b) The values without parenthesis are the estimated coefficients of dissemination effect and those in parenthesis are
the probabilities corresponding to the Student test.
c) The term Q stands for the Ljung-Box statistics for which the first autocorrelation orders (p) of the residuals are jointly
equal to zero.
The asterisks *, ** and *** correspond respectively to 10%, 5% and 1% of the significance thresholds.
change in negative and positive variations. However,
these changes are symmetrical, leading to a persistence
of asymmetry in the transmission of price changes. The
situation even deteriorated in 6 over 18 cases.
How can we explain the persistence of asymmetry in the
transmission of price changes in spite of the dissemination
of mercurial? This can be explained by the power of some
actors in the sector who hold and exploit market
information to the detriment of others.
The presence of wholesale traders, often with collusive
behaviour and the importance of the role they play in the
exchange of maize, seem to be highlighted by the results.
These wholesale traders who are more present in the rural
markets adopt different strategies to preserve their
commercial margins. Given that they exploit trade
information better than retail traders in Lome, they
influence the pricing and transmission of prices by
controlling market supplies. Because they have a collusive
behaviour in the local market, they are led to correct price
disequilibria more quickly.
For example, when prices decline in Lome market,
wholesale traders due to their collusive behaviour reduce
the supply in Lome market either by storing their products
or by marketing them to other markets where the
conditions of transport and arbitrage are better. This
strategy consists in increasing the price level in the market
of Lome. In the case of prices increase in this market, trade
between Lome market and the rural markets continues. In
addition, by pre-financing the agricultural activities, traders
can also position themselves as unavoidable buyers. This
positioning results in incomplete transmission of prices
between markets.
Thus, the collusive behaviour and the pre-financing of the
farmers by some wholesalers are strategies which reduce
arbitrage of actors and dampen the dissemination effect.

Table 5: Results of the dissemination effect on the asymmetric transmission (∅ ≠ 𝟎)
Coefficients of AECM4
and tests Dissemination effect
Pairs of markets ∅1
a
∅2
a Fstat
b
Qstat
c
𝛿∅1
𝛿∅2
𝛿∅1
= 𝛿∅2
Lome-Ahepe -0.28
(0.14)
-0.38***
(0.00)
- 0.47
(0.25)
0.04*
(0.06)
0.08
(0.15)
-
Ahepe-Lome 0.42***
(0.00)
-0.49***
(0.00)
2.32**
(0.01)
0.51
(0.19)
0.26
(0.13)
0.24
(0.17)
0.12
(0.83)
Lome-Assahoun -0.29
(0.15)
-0.38**
(0.04) -
0.01
(0.10)
-0.18
(0.20)
-0.18
(0.11)
0.47
(0.21)
Assahoun-Lome -0.54***
(0.00)
-0.47**
(0.02)
1.34
(0.19)
0.48
(0.15)
0.01
(0.14)
0.01
(0.14)
1.42
(0.37)
Lome-Tohoun -0.21**
(0.03)
-0.13
(0.16) -
0.09
(0.16)
0.21**
(0.01)
0.13**
(0.01)
0.69**
(0.02)
Tohoun-Lome -0.48***
(0.00)
-0.56***
(0.00)
2.04
(0.13)
0.63
(0.12)
0.18
(0.13)
0.19
(0.20)
-
Lome-Anie -0.30***
(0.00)
0.26**
(0.04)
3.02***
(0.00)
0.30
(0.20)
0.03**
(0.04)
0.08
(0.07)
-
(0.87)
Anie-Lome -0.56***
(0.00)
-0.36**
(0.03)
2.21**
(0.01)
1.22
(0.19)
0.09**
(0.02)
0.03**
(0.07)
0.11*
(0.07)
Lome-Bassar -0.24**
(0.04)
-0.25
(0.23)
- 2.0
(0.18)
-0.16
(0.31)
-0.26
(0.41)
0.20
(0.45)
Bassar-Lome -0.42**
(0.02)
-0.57**
(0.01)
0.42**
(0.01)
0.20
(0.21)
0.11**
(0.02)
0.08**
(0.04)
1.10
(0.20)
Lome-Ketao -0.23
(0.13)
-0.35
(0.19)
- 2.14
(0.22)
0.07
(0.15)
0.07
(0.15)
0.12
(0.83)
Ketao-Lome -0.43**
(0.04)
-0.47**
(0.02)
2.16**.
(0.01)
2.16
(0.15)
0.13
(0.18)
0.13
(0.18)
0.47
(0.21)
Lome-Gando -0.42**
(0.04)
-0.39*
(0.08)
0.76*
(0.08)
0.57
(0.16)
(0.09
(0.07)
(0.01
(0.14)
1.42
(0.37)
Gando-Lome -0.49**
(0.03)
-0.58*
(0.09)
2.50**
(0.01)
0.43
(0.12)
0.12**
(0.03)
0.13**
(0.04)
0.69**
(0.02)
Source: Author's estimation based on data from DSID, ANSAT and RESIMAO
.a) The values without parenthesis are the estimated coefficients and those in parenthesis are the Student t-statistics
for (∅1and ∅2).
b)The test of Fisher for the null hypothesis testing that the coefficients of the correction terms are equal (∅1 = ∅2). The
values without parenthesis are the F-statistics and those in parenthesis are the corresponding probability values.
c) The term Q stands for Ljung-Box statistics for which the first autocorrelation orders (p) of the residuals are jointly
equal to zero.
CONCLUSION AND POLICY IMPLICATIONS
This paper examined the price asymmetry transmission in
maize market in order to understand how it has been
affected by prices dissemination issued by MIS since 2008
in Togo. To this end, we use concurrently the symmetric
error correction model (ECM) of Engle and Granger and
the asymmetric error correction model of Ender and
Granger (AECM) to evaluate the prices transmission. The
weekly retail maize prices collected from ten markets for
the period without service (2000-2007) and the period with
service (2008-2015) are considered. The results show that
despite of price dissemination, the asymmetric price
transmission still dominates the maize market in Togo.
Through the collusive behaviour of wholesalers and the
agreements on buying and selling prices in maize markets,
the actors exert some market power which dampens the
prices dissemination effect.
Even though the overall effect is mitigated, the results
suggested that the market information services are not
useless. They need to be improved in order to increase the
efficiency of the functioning of maize market.
4
AECM is Asymmetric Error Correction Model
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Accepted 18 October 2017
Citation: Yovo K (2017). Market information services and
spatial asymmetry price transmission in Togolese maize
market. Journal of Agricultural Economics and Rural
Development, 3(3): 259-269.
Copyright: © 2017 Yovo K. This is an open-access
article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium,
provided the original author and source are cited.

APPENDIX

Market information services and spatial asymmetry price transmission in Togolese maize market

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