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
comparable.
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.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/3824 |
| Date | 16 August 2006 |
| Creators | Kuteesa, Annette |
| Contributors | David, A Bessler |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | 808743 bytes, electronic, application/pdf, born digital |
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