Agricultural Market Information Systems in Africa: renewal and impactand impact

Montpellier (CIRAD), March 29-31, 2010

Measuring the Impact of Public Market Information System g p yon Spatial Market Efficiency in maize markets in Benin: Application of Parity Bounds Model.

Sylvain KPENAVOUN CHOGOUUniversity of Abomey-Calavi, Benin

kpenavoun@yahoo.fr

Outline

Introduction

Estimation approach and data

Results and discussionResults and discussion

Conclusion

Introduction

In Benin, 70% of the total labour force is employed in agriculture and the share of theemployed in agriculture and the share of the sector in  export earnings is more than 60% (Cotton);

Agricultural liberalization reforms undertaken by Benin Government in the 1990s to build upBenin Government in the 1990s to build up efficient markets that benefit poor or smallholder farmers;

MIS promoted as an accompanying measure of reforms supported by FAO and GTZ etcreforms, supported by FAO and GTZ, etc.

Introduction

MIS was expected to:

correct the information asymmetries;correct the information asymmetries; 

give more bargaining power to farmers;

make a more transparent market, strengthen competition get the institutionsstrengthen competition, get the institutions right, reduce transaction costs and improve market integration and market efficiency;g y

and then contribute to improve the well being of the producers who live in rural areasbeing of the producers who live in rural areas.

Introduction

So, large positive impacts are expected from MIS, but empirical suffisant works to show them are missing in SSA (Tollens, 2006).

Several key questions still not answered carefully: e.g.

Have small farmers obtained better arrangements ( k t t t) h lli th i l ?(market or contract) when selling their surpluses? 

Have small farmers obtained better access to market?market?

What is the extent to which the reforms have improved the spatial market efficiency?improved the spatial market efficiency?

Introduction

• Research objectiveM h th i lt l f i ti lMeasure how the agricultural reforms, in particular the PMIS, have affected the market performance of maize, the major staple food crop in Benin. , j p p

Empirical model: Parity Bounds Modelp y

Distinction between market efficiency and market integrationSpatial market efficiency is an equilibrium condition whereby all potential profitable spatial arbitrage 

t iti l it dopportunities are exploited;

Spatial market integration is defined as the extent to which demand and supply shocks arising in onewhich demand and supply shocks arising in one location are transmitted to other locations (Barrett and Li, 2002).

Market analysis depends on available data

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Method group Examples Characteristics

Table 1: A Hierarchy of Market Analysis MethodsMethod group Examples Characteristics

Level I methods utilize only price data, assume

Price correlation test (Lele, 1967; Jones, 1968)

Price correlation is relatively simple way to measuremarket integration but suffers from various weaknessesTest market integration for the marketing system as a

constant  inter‐market transfert cost 

Delgado’s variance decomposition approach(Delgado, 1986)

whole instead of pair-wise test of market integration;The method purge out the common trends and seasonalitypresent in the price series before testing for marketintegration

The Ravallion method(1986)

This method allow testing market segmentation, short‐runmarket integration, long‐run market integration between local and central markets after controlling for seasonality, the common trends and autocorrelationcommon trends and autocorrelation

Engle and Granger (1987) cointegrationanalysis

Take into account the presence of stochastic trends in the price series but pair-wise test of market integration.

Johansen (1988) Multivariate cointegrationanalysis

Take into account the presence of stochastic trends in the price seriesTest market integration for the marketing system as a whole .

Table 2: A Hierarchy of Market Analysis Methods Methodgroup Examples CharacteristicsgroupLevel IImethods combine

Threshold Autoregression or Threshold Cointegration

Based on the idea that the presence of ransaction cost creates a « neutral band » whithin which the prices in different markets

transaction cost and price

g(Blake, and Fomby, 1997; Goodwin and Piggott. 2001;etc.)

are not linked;Does not require the observation of transaction cost;

data, and thus more closely resemble

Allows to measure the probability of beingin different market effciency regimes that are consistent with the equilibrium notionresemble

spatialequilibrium theory

Parity Bounds Model of Spiller and Wood (1986); Sexton, Kling and Carman

are consistent with the equilibrium notion that all spatial arbitrage opportunities are being exploited (Enke 1951; Samuelson 1964; Takayama and Judge 1971);eo y Se o , g a d Ca a

(1991); Park, Rozelle and Huang (2002); Baulch (1997); Penzhorn et Arndt (2002).

96 ; a aya a a d Judge 9 );Can indicate not only wether the marketsare efficient but also the extent to which the markets are efficient;Possible estimate with incomplete priceseries

Table 1: A Hierarchy of Market Analysis Methods

Methodgroup Examples Characteristics

Level IIImethods combinetrade flow,price data

Parity Bounds Model of Negessan and Myers (2007)

Allow a clear distinction between spatial marketeffciency and spatial market integration

and time-series transaction cost data

or Barrett and Li (2002).effciency and spatial market integration.

Table 2: Trade regimes between two markets  Trade regimes Trade

it jt jitP P TC− = (1) it jt jitP P TC− p (2) it jt jitP P TC− f (3)

With Trade 11λ 21λ 31λ

Perfect market efficiency or perfect market integration

Imperfect integration Market inefficiency

Imperfect integration Market inefficiency

No trade 12λ 22λ 32λ No trade

Market efficiency Market efficiency Segmented disequilibrium

With or witout trade 1 11 12λ λ λ= + 2 21 22λ λ λ= + 3 31 32λ λ λ= + With or witout trade

Market efficiency condition Autarky market condition Market inefficiency condition

iλ is the probability of being in regime i et ijλ is the probability of being in sub-regime j of regime i. i ij

jit is the transfer cost for trading from market j to market i at tiTC me t.

it jt are prices in markets i and j, respectivP et P ely.jit g j

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Market performance measure with Parity Bounds Model Market performance has several characteristics that together help describe the development of the market:

Efficiency rate;Efficiency rate;Inefficiency rate;Arbitrage rate;Arbitrage rate;Arbitrage opportunity rate;Autarky rate;

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Arbitrage opportunity rateIt is the probability that the arbitrage opportunities exist between two

( )1 3λ λ+It is the probability that the arbitrage opportunities exist between twomarkets. Arbitrage rate, a more appropriate measure of integration

Th b bili h bi i b d h bi i iThe probability that arbitrage is observed when arbitrage opportunities exist or the extent to which arbitrage opportunities are realized by traders.

λ

Autarky rate

( )1

1 3

Arbitrage rate λλ λ

=+

Autarky rate

The percent of trading periods in which two regions do not trade because price differences are less than transaction costs.

( )2

1 2 3

Autarky rate λλ λ λ

=+ +( )1 2 3λ λ λ

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Subperiods for Parity Bounds Model estimationWe estimate a parity bounds model of interregional trade for fourWe estimate a parity-bounds model of interregional trade for four subperiods to characterize how multiple aspects of marketperformance change during the process of liberalization and measure the impact of PMIS:measure the impact of PMIS:1988 à 1992: Period before reforms;

1993 à 1996: Period with PMIS: publication of monthly bulletins 993 à 996 e od t S pub cat o o o t y bu et sand the broadcasting of prices and market information on national public radio; Market infrastructure investment; Major investment in transport investment (road, easy to buy occasional cars, etc.);

1997 à 2000: Improvement of PMIS with the posting of maize prices at different locations on each market place.

2001 à 2007: Broadcasting of prices and market information on several regional and rural radios, development of GSM, sms and web services. 

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In order to estimate the probability of being in one regime or another we needLikelihood function

In order to estimate the probability of being in one regime or another, we need to define the likelihood function:

( )1T

L f f fλ λ λ λ⎡ ⎤= + +⎣ ⎦∏ with the density functions for each( )1 1 2 2 1 2 31

1t t tt

L f f fλ λ λ λ=

⎡ ⎤= + + − −⎣ ⎦∏

1 P P TC⎡ ⎤− − ( ) uit jt jitP P TCP P TC

σ⎡ ⎤⎡ ⎤− −⎡ ⎤⎡ ⎤ ⎢ ⎥⎢ ⎥

with the density functions for each regime:

11 it jt jit

te e

P P TCf ϕ

σ σ

⎡ ⎤− −⎢ ⎥=⎢ ⎥⎣ ⎦

( )2 1 1 12 2 2 2 2 22 2 2

2 1( ) ( ) ( )

it jt jitit jt jit e

t

e e e

P P TCf

μ μ μ

σϕσ σ σ σ σ σ

⎡ ⎤⎡ ⎤ ⎢ ⎥⎢ ⎥− −⎢ ⎥⎢ ⎥ ⎢ ⎥⎢ ⎥= −Φ⎢ ⎥⎢ ⎥ ⎢ ⎥⎢ ⎥+ + +⎣ ⎦ ⎣ ⎦⎢ ⎥⎢ ⎥⎣ ⎦⎣ ⎦

( )2 it jt jitP P TCP P TCνσ⎡ ⎤⎡ ⎤− − −⎡ ⎤⎢ ⎥⎢ ⎥⎡ ⎤ − − ( )

3 1 1 12 2 2 2 2 22 2 2

2 1( ) ( ) ( )

it jt jitit jt jit e

t

e e e v

P P TCf

ν ν

σϕσ σ σ σ σ σ

⎡ ⎤⎢ ⎥⎢ ⎥⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥= −Φ⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥+ + +⎣ ⎦ ⎣ ⎦⎢ ⎥⎢ ⎥⎣ ⎦⎣ ⎦⎣ ⎦⎣ ⎦

DataThe data used in the study are monthly maize consumption y y pprice series over the period January 1988 to December 2007.

This study analyses the price time series observed in seven market places, well distributed over the major maize market places, well distributed over the major mai econsumption and/or production regions: 

Cotonou, Azove and Ketou (located in the South), Bohicon and Glazoue (located the central region) ParakouBohicon and Glazoue (located the central region), Parakouand Nikki are northern markets; 

Parakou, Bohicon and Cotonou are urban markets; Kétou Glazoué Azové and Nikki are rural marketsKétou, Glazoué, Azové and Nikki are rural markets.

These data were collected by ONASA (Office National d’Appuià la Sécurité Alimentaire). 

Following Baulch (1997), we have constructed the time seriestransfert costs using one‐time transfert costs estimate from interviews with traders and for adjusting for inflation.

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Table 3: Maximum Likelihood Estimates of Parity-Bounds Model for Results and discussion

maize market in Benin

Time Efficiency

rate

Arbitrage rate

⎛ ⎞

Autarky rate

2λ λ⎛ ⎞

=⎜ ⎟⎜ ⎟

Inefficiency rate

Arbitrage opportunities

Periods rate ( )1λ ( )

1

1 3

λλ λ

⎛ ⎞⎜ ⎟⎜ ⎟+⎝ ⎠

( ) 21 2 3

λλ λ λ

=⎜ ⎟⎜ ⎟+ +⎝ ⎠

rate( )3λ

opportunities rate ( )1 3λ λ+

1988-1992 0.22 0.81 0.61 0.17 0.39 (0 11) (0 33) (0 34) (0 33) (0 33)(0.11) (0.33) (0.34) (0.33) (0.33)

1993-1996 0.06 0.37 0.51 0.43 0.49 (0.07) (0.47) (0.37) (0.40) (0.37)

1997-2000 0.21 0.50 0.39 0.40 0.61 (0 22) (0 46) (0 37) (0 44) (0 37)(0.22) (0.46) (0.37) (0.44) (0.37)

2001-2007 0.23 0.35 0.16 0.61 0.84 (0.21) (0.33) (0.30) (0.35) (0.30)

1988-2007 0.16 0.54 0.41 0.43 0.59 (0 13) (0 44) (0 36) (0 41) (0 36)1988 2007 (0.13) (0.44) (0.36) (0.41) (0.36)

The estimated standard errors for each parameter estimate are reported in parentheses. The results presented are averages of each parameters estimate with the level of the 15 i f t di d k t Th th ti t f 75 ti15 pairs of studied markets. They are the estimates of 75 equations.

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Conclusion

We find that:

Th k ti f did t i ifi tl i thThe marketing reforms did not significantly improve the degree of efficiency or of spatial integration of markets;

B t th did i d k ti t iti hi h tillBut they did induce new marketing opportunities, which still remain under‐exploited;

Th f k hi h h i lThe rate of autarky, which measures the spatial range over which transactions did not occur between two markets due to high transaction costs, shows a decreasing trend over time. g , gImprovements are observed on a few markets. 

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Conclusion

However, the high levels of inefficiency prevent the system from providing farmers and consumers the services they need. 

This study therefore recommends the implementation of more efficiency‐raising policies in order to encourage competition and allow the system to fulfill the expectation of farmers and consumers.

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Thank you for your attention

Agricultural Market Information Systems in Africa: renewal and impactand impact

Montpellier (CIRAD), March 29-31, 2010

Measuring the Impact of Public Market Information System g p yon Spatial Market Efficiency in maize markets in Benin: Application of Parity Bounds Model.

Dr Ir Sylvain KPENAVOUN CHOGOUUniversity of Abomey-Calavi, Benin

kpenavoun@yahoo.fr