Kpenavoun CIRAD 2010


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Kpenavoun CIRAD 2010

  1. 1. Agricultural Market Information Systems in Africa: renewal and impact Montpellier (CIRAD), March 29-31, 2010 Measuring the Impact of Public Market Information System g p y on Spatial Market Efficiency in maize markets in Benin: Application of Parity Bounds Model. Sylvain KPENAVOUN CHOGOU University of Abomey-Calavi, Benin
  2. 2. Outline Introduction Estimation approach and data Results and discussion Results and discussion Conclusion
  3. 3. Introduction In Benin, 70% of the total labour force is  employed in agriculture and the share of the  employed 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 up  Benin 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, etc. reforms supported by FAO and GTZ etc
  4. 4. 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 institutions 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 areas. being of the producers who live in rural areas
  5. 5. 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  (market or contract) when selling their surpluses?  ( k t t t) h lli th i l ? Have small farmers obtained better access to  market? What is the extent to which the reforms have  improved the spatial market efficiency? improved the spatial market efficiency?
  6. 6. Introduction • Research objective Measure how the agricultural reforms, in particular  M h th i lt l f i ti l the PMIS, have affected the market performance of  maize, the major staple food crop in Benin.  , j p p
  7. 7. Empirical model: Parity Bounds Model p y Distinction between market efficiency and  market integration Spatial market efficiency is an equilibrium condition  whereby all potential profitable spatial arbitrage  opportunities are exploited; t iti l it d Spatial market integration is defined as the extent to  which demand and supply shocks arising in one  which demand and supply shocks arising in one location are transmitted to other locations (Barrett  and Li, 2002). Market analysis depends on available data 7
  8. 8. Table 1: A Hierarchy of Market Analysis Methods Method group Examples Characteristics Level I methods  Price correlation test Price correlation is relatively simple way to measure utilize only price  (Lele, 1967; Jones, 1968) market integration but suffers from various weaknesses data, assume Test market integration for the marketing system as a constant  inter‐ Delgado’s variance whole instead of pair-wise test of market integration; market transfert cost  decomposition approach The method purge out the common trends and seasonality (Delgado, 1986) present in the price series before testing for market integration This method allow testing market segmentation, short‐run The Ravallion method market integration, long‐run market integration between local  (1986) and central markets after controlling for seasonality, the common trends and autocorrelation Engle and Granger Take into account the presence of stochastic trends in the (1987) cointegration price series but pair-wise test of market integration. analysis Take into account the presence of stochastic trends in the Johansen (1988) price series Multivariate cointegration Test market integration for the marketing system as a analysis whole .
  9. 9. Table 2: A Hierarchy of Market Analysis Methods  Method Examples Characteristics group Level II Based on the idea that the presence of methods Threshold Autoregression or ransaction cost creates a « neutral band » combine Threshold Cointegration g whithin which the prices in different markets transaction (Blake, and Fomby, 1997; are not linked; cost and Goodwin and Piggott. 2001; Does not require the observation of price etc.) transaction cost; data, and thus more Allows to measure the probability of being closely in different market effciency regimes that resemble are consistent with the equilibrium notion spatial Parity Bounds Model of that all spatial arbitrage opportunities are equilibrium Spiller and Wood (1986); being exploited (Enke 1951; Samuelson theory eo y Se o , Sexton, Kling a d Carman g and Ca a 1964; Takayama and Judge 1971); 96 ; a aya a a d 9 ); (1991); Park, Rozelle and Can indicate not only wether the markets Huang (2002); Baulch (1997); are efficient but also the extent to which the Penzhorn et Arndt (2002). markets are efficient; Possible estimate with incomplete price series
  10. 10. Table 1: A Hierarchy of Market Analysis Methods Method group Examples Characteristics Level III methods combine trade flow, Parity Bounds Model of Allow a clear distinction between spatial market price data Negessan and Myers (2007) effciency and spatial market integration integration. and time- or Barrett and Li (2002). series transaction cost data
  11. 11. Table 2: Trade regimes between two markets  Trade regimes Trade Pit − Pjt = TC jit (1) Pit − Pjt p TC jit (2) Pit − Pjt f TC jit (3) λ11 λ21 λ31 With Trade Perfect market efficiency or Imperfect integration Imperfect integration perfect market integration Market inefficiency Market inefficiency λ12 λ22 λ32 No trade Market efficiency Market efficiency Segmented disequilibrium λ1 = λ11 + λ12 λ2 = λ21 + λ22 λ3 = λ31 + λ32 With or witout trade Market efficiency condition Autarky market condition Market inefficiency condition λi is the probability of being in regime i et λij is the probability of being in sub-regime j of regime i. TC jit is the transfer cost for trading from market j to market i at time t. g Pit et Pjt are prices in markets i and j, respectiv ely. 11
  12. 12. Market performance measure with Parity Bounds Model Market performance has several characteristics that together help describe the development of the market: Efficiency rate; Inefficiency rate; Arbitrage rate; Arbitrage opportunity rate; Autarky rate; 12
  13. 13. Arbitrage opportunity rate ( λ1 + λ3 ) It is the probability It is the probability that the arbitrage opportunities exist between two the arbitrage opportunities exist markets.  Arbitrage rate, a more appropriate measure of integration The probability that arbitrage is observed when arbitrage opportunities  Th b bili h bi i b d h bi ii exist or the extent to which arbitrage opportunities are realized by  traders. λ1 Arbitrage rate = ( λ1 + λ3 ) Autarky rate Autarky rate The percent of trading periods in which two regions do not trade because  price differences are less than transaction costs. λ2 Autarky rate = ( λ1 + λ2 + λ3 ) 13
  14. 14. Subperiods for Parity Bounds Model estimation We estimate a parity bounds model of interregional trade for four parity-bounds subperiods to characterize how multiple aspects of market performance change during the process of liberalization and 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 s and 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.  14
  15. 15. Likelihood function In order to estimate the probability of being in one regime or another we need another, to define the likelihood function: T L = ∏⎡λ1 f1t + λ2 f2t + (1− λ1 − λ2 ) f3t ⎤ ⎣ ⎦ with the density functions for each regime: t =1 ⎡ it ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎡ ( σ ⎤⎤ P − Pjt −TCjit u ⎥⎥ 1 ⎢ P − Pjt −TCjit f⎥ = ⎢ 2 ⎥ϕ ⎢ Pit − Pjt −TCjit ⎥ ⎢1−Φ⎢ it σe ⎥ ) f1t = ϕ 2t ⎢ 2 2⎥ ⎢ 2 2 ⎥⎢ ⎢ ⎢ ⎥ ⎥⎥ σe ⎢ σe ⎥ ⎣(σe +σμ ) ⎦ ⎣ (σe +σμ ) ⎦ ⎢ ⎢ (σe +σμ ) 1 1 1 2 2 2 2 2 ⎣ ⎦ ⎢ ⎣ ⎥⎥ ⎦⎦ ⎣ ⎡ 2 ⎡ ⎤ ⎡ P − Pjt −TCjit ⎤ ⎢ ⎡ ( )σ ⎤⎤ − P − Pjt −TCjit ν ⎥⎥ ⎢ it σe ⎥ f3t = ⎢ ⎥ϕ ⎢ it ⎥ ⎢1−Φ⎢ ⎥ ⎢ (σe2 +σν2 ) 2 ⎥ ⎢ (σe2 +σν2 ) 2 ⎥ ⎢ ⎢ ⎥⎥ 1 1 1 ⎣ ⎦ ⎣ ⎦⎢ (σe2 +σv2 ) 2 ⎢ ⎣ ⎥⎥ ⎦⎦ ⎣
  16. 16. Data The data used in the study are monthly maize consumption  y y p price 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 mai e market places, well distributed over the major maize  consumption and/or production regions:  Cotonou, Azove and Ketou (located in the South),  Bohicon and Glazoue (located the central region) Parakou and Glazoue (located the central region), Parakou and Nikki are northern markets;  Parakou, Bohicon and Cotonou are urban markets;  Kétou, Glazoué, Azové and Nikki are rural markets. Kétou Glazoué Azové and Nikki are rural markets These data were collected by ONASA (Office National d’Appui à la Sécurité Alimentaire).  Following Baulch (1997), we have constructed the time series transfert costs using one‐time transfert costs estimate from  interviews with traders and for adjusting for inflation. 16
  17. 17. Results and discussion Table 3: Maximum Likelihood Estimates of Parity-Bounds Model for maize market in Benin Arbitrage Autarky rate Efficiency Inefficiency Arbitrage Time rate ⎛ λ2 ⎞ ⎞ ⎜ ( λ + λ + λ ) = λ2 ⎟ rate rate opportunities ⎛ λ1 ⎜ ⎟ Periods ( λ1 ) ⎜ ⎜ (λ + λ ) ⎟ ⎟ ⎝ 1 2 3 ⎠ ( λ3 ) rate ( λ1 + λ3 ) ⎝ 1 3 ⎠ 0.22 0.81 0.61 0.17 0.39 1988-1992 (0.11) (0 11) (0.33) (0 33) (0.34) (0 34) (0.33) (0 33) (0.33) (0 33) 0.06 0.37 0.51 0.43 0.49 1993-1996 (0.07) (0.47) (0.37) (0.40) (0.37) 0.21 0.50 0.39 0.40 0.61 1997-2000 (0.22) (0 22) (0.46) (0 46) (0.37) (0 37) (0.44) (0 44) (0.37) (0 37) 0.23 0.35 0.16 0.61 0.84 2001-2007 (0.21) (0.33) (0.30) (0.35) (0.30) 0.16 0.54 0.41 0.43 0.59 1988 2007 1988-2007 (0.13) (0 13) (0.44) (0 44) (0.36) (0 36) (0.41) (0 41) (0.36) (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 pairs of studied markets. Th are the estimates of 75 equations. i f t di d k t They th ti t f ti 17
  18. 18. Conclusion We find that: The marketing reforms did not significantly improve the  Th k ti f did t i ifi tl i th degree of efficiency or of spatial integration of markets; But they did induce new marketing opportunities, which still  B t th did i d k ti t iti hi h till remain under‐exploited; The rate of autarky, which measures the spatial range over  Th f k hi h h i l which transactions did not occur between two markets due to  high transaction costs, shows a decreasing trend over time.  g , g Improvements are observed on a few markets.  18
  19. 19. 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. 19
  20. 20. Thank you for your attention
  21. 21. Agricultural Market Information Systems in Africa: renewal and impact Montpellier (CIRAD), March 29-31, 2010 Measuring the Impact of Public Market Information System g p y on Spatial Market Efficiency in maize markets in Benin: Application of Parity Bounds Model. Dr Ir Sylvain KPENAVOUN CHOGOU University of Abomey-Calavi, Benin