Kuteesa, Annette (0001-05). Price discovery in the wholesale markets for maize and beans in Uganda. Master's Thesis. Thesis uri icon


  • Market information services established in 1999 were aimed at the promotion of market
    efficiency through provision of information across the nation. While the responsible
    bodies have improved the knowledge of prices, information exchange and flow, as a
    result of competition between markets, is not known and questions of market
    effectiveness still stand.
    This study examines market efficiency based upon response to price signals across
    Ugandan markets. We focus on information exchange for maize and beans among 16
    key markets. We study weekly price data from the first week of 2000 to the last week of
    2003 from each of the sixteen markets. Each commodity is studied separately using
    Vector Autoregessions (VARs) and Directed Acyclic Graphs (DAGs). The two
    techniques are widely used to show market risk and causal relations in time series data.
    While results are presented individually for each commodity, the markets are
    In determining market efficiency, we test for stationarity of the data, explore the
    magnitude of forecast error decompositions over time across markets, and observe the
    patterns of communication based on DAGs. We find that markets are more efficient in
    exchanging information on maize than beans. Communication of data is mostly between
    markets in eastern, western, and central parts of Uganda. Overall, markets are very slow
    in reacting to information in the short run.Information from the Mbale and Iganga markets, which are located in areas of high
    production, is very valuable in the maize trade. However, of the two markets, it is data
    from the Mbale market, located near the border with Kenya, which is of paramount
    importance. Specifically, price is discovered in Mbale in the maize trade. Our results
    also show the Gulu market, which is situated in an insecure zone, to be very responsive
    to price signals over the long run.
    In the case of beans, it is the price signals from Tororo and Jinja that cause more
    disruption in most of the markets. Price is discovered in these two markets. A majority
    of the markets is more affected by data from Jinja than Tororo. This segmentation in
    market price discovery suggests an existing market failure. Arua and Gulu are found to
    be the least responding markets in regards to price signals for beans. We do not find
    information from the Kampala market to be important in either the maize or beans trade.

publication date

  • May 0001