The Future of Smallholder Farming in Eastern Africa - Uganda ...

postliberalization periods.
2.3.2 Effects of Liberalization on Prices
Initial analysis of price data focused on summary statistics and plots of price trends. We
computed marketing margins, defined in this study as the difference between prices in surplus
areas (representing producer price) and deficit areas (representing consumer price), and
attempted to compare their behavior between the pre- and postliberalization periods. .
Marketing margins would be expected to decline with liberalization, due to either a decline in
consumer prices, an increase in producer prices, or both. If marketing margins declined, they
would indicate that the private traders were effectively engaging in spatial arbitrage.
Price volatility (as manifested by excessive price variability) was examined to detect whether
or not there was effective temporal arbitrage before and after market liberalization. Volatility
was assessed by computing the coefficients of variation for the two periods. Volatile prices
would indicate a lack of temporal arbitrage. Traders and farmers will fail to store grain if they
lack appropriate storage facilities and technologies or they are unwilling to engage in
temporal arbitrage because of perceived market risk. Effective temporal arbitrage would, on
the other hand, serve to stabilize prices.
2.3.3 Effects of Liberalization on Market Integration
Market integration concerns the free flow of goods and information over form, space, and
time and is thus closely related to concepts of efficiency. Spatial integration relates to
spatially distinct markets. If two markets are integrated, a shock to the price in one market
should be manifest in the other market price as well. Among perfectly segmented markets,
price series should be independent. Comovement of prices has thus become synonymous with
market integration (Barret 1996). This observation underscores the usefulness of market
integration studies in evaluating the efficiency of markets. As Barret (1996) observes,
however, market analysis depends on available data. Advances in economists’ toolkits for
market analysis, though dramatic in the last two decades, have not been accompanied by
concomitant emphasis on the collection of relevant data in developing countries. Often,
analysts must use the more easily accessible price data to carry out market analysis studies.
Although models requiring transaction cost and trade flow data (Barrett 1996) hold promise,
they are not easily implemented because of a lack of this kind of data.
In the current study, market integration was analyzed by considering whether prices from
geographically different locations move together in systematic ways over time. Several
models, which are discussed later, were employed to study market integration. Monthly price
data were divided into pre- and postliberalization periods. The preliberalization data set
covered the period between January 1991 and December 1993, and the postliberalization set
covered the period between January 1994 and December 1999. Prices were studied using
simple correlation, bivariate correlation of price differences, and cointegration techniques.
In addition, the direction of causality in maize prices was examined using the Granger
causality test. Granger causality is a useful approach in determining whether price
movements follow well-defined paths--that is, start around demand or production centers and
then spread around the country. Testing for causality among commodity markets determines
the existence of central markets (Goletti and Babu 1994). The presence of central markets
would invariably mean that there is radial transmission of prices and price changes. Analyses
for the periods before and after liberalization were used to indicate how liberalization has
affected integration of maize markets and the location of central markets.