Early spatial price analyses examined "co-movement" among
prices at different locations (e.g., simple price correlations as in
Timmer 1974; and co-integration as in Goodwin and Schroeder 1991; Asche,
Bremnes, and Wessells 1999; and Gonzalez-Rivera and Helfand 2001). It is
now well recognized that co-movement in prices is neither necessary nor
sufficient for spatial efficiency (Barrett 1996; McNew and Fackler 1997;
Fackler and Goodwin 2001; Barrett and Li 2002). Correlation and
co-integration analyses generally do not take explicit account of
transfer costs, and so do not provide a formal test for spatial market
efficiency.
More recently, two newer methods, which focus directly on spatial
market efficiency, have been employed. The first is threshold
autoregression, which recognizes possible "thresholds" in how
spatial prices respond to shocks, depending on whether the shock is
large enough to raise spatial price differentials above transfer cost
(Blake and Fomby 1997; Mainardi 2001; Goodwin and Piggott 2001; Goodwin
and Harper 2000). Threshold autoregressions estimate "neutral
bands" associated with unobservable transfer costs and therefore
pay explicit attention to the role of transfer costs in spatial market
efficiency.
The second newer method is the parity bounds model (PBM), first
introduced by Spiller and Huang (1986) and developed further by Sexton,
Kling, and Carman (1991); Baulch (1997); Park et al. (2002); and Barrett
and Li (2002). The PBM estimates the probability of being in spatial
price regimes that are consistent with the equilibrium notion that all
spatial arbitrage opportunities are being exploited (Enke 1951;
Samuelson 1964; Takayama and Judge 1971). Transfer costs are included
explicitly in the notion of spatial equilibrium underlying the PBM, and
if transfer cost data are unavailable the PBM requires an assumption
about the way transfer costs evolve over time.
Despite the advantages of the PBM, it has itself been subject to
criticism. First, results can be sensitive to underlying distributional
assumptions (Fackler 1996; Barrett and Li 2002). Second, the PBM is
usually applied to just one pair of markets at a time to manage the
large number of trading regimes that can emerge in a multimarket context
(Fackler 2004). Third, the standard PBM assumes shocks are serially
independent, and does not provide information on the path of dynamic
adjustment to deviations from spatial equilibrium. Fourth, the standard
PBM assumes that, while a pair of markets may switch between alternative
trading regimes in different periods, the probability of being in a
particular trading regime at a particular point in time is
time-invariant. Put another way, the standard PBM assumes the extent of
spatial efficiency (or inefficiency) between a pair of markets remains
constant over time, even in the face of changes in marketing policies
and new investments in marketing infrastructure. This assumption of
time-invariant regime probabilities is a serious limitation because in
many instances policy changes are specifically designed to improve
spatial market efficiency.
This article extends the PBM by relaxing the assumption that the
PBM regime probabilities (and hence the extent of spatial efficiency)
are constant over time. This allows investigation of whether changes in
marketing policies have increased or decreased spatial efficiency. One
simple means of achieving this goal would be to identify different
periods associated with different marketing policies and then estimate a
different PBM for each subperiod. Differences in regime probabilities
for each subperiod would indicate the effects of the alternative
policies. This is essentially the approach taken in Park et al. (2002).
The problem is that this approach assumes the impact of a policy change
on trading regime probabilities (and hence on the extent of spatial
efficiency) is discrete and instantaneous. In reality, it is likely that
the effects of a policy change are gradual and evolve slowly over time
as traders learn more about the effects of the policy change.
The approach introduced here allows for a gradual transition in
trading regime probabilities in response to policy changes. The method
also allows estimation and hypothesis testing on the length of the
adjustment period. The remainder of the article is organized as follows.
The next two sections introduce the PBM and then extend it to allow
policy changes to have a gradual dynamic effect on trading regime
probabilities. Next we provide an application to Ethiopian maize and
wheat markets, which highlights the approach and provides estimates of
the effect of the 1999 grain marketing reform on Ethiopian grain
markets. Finally, we discuss the empirical results and provide
concluding comments.
The Standard Parity Bounds Model
Consider two markets i and j located in different regions that
trade a homogenous commodity. Three mutually exclusive regimes can be
identified, based on the relative sizes of spatial price differentials
and transfer costs. (1)
In regime 1, the spatial price differential is equal to transfer
cost:
(1) [P.sub.it] - [P.sub.jt] = [TC.sub.jit]
where [P.sub.it] and [P.sub.jt] are prices in markets i and j,
respectively, and [TC.sub.jit] is the transfer cost for trading from
market j to market i at time t. This regime is consistent with spatial
market efficiency irrespective of whether trade occurs. When trade does
occur the market prices [P.sub.it] and [P.sub.jt] will differ from
autarky prices and demand and supply shocks in one market will be
transferred to the other market.
In regime 2, the spatial price differential is less than transfer
cost:
(2) [P.sub.it] - [P.sub.jt] < [TC.sub.jit].
Here there are no profitable arbitrage opportunities between the
two markets and they are spatially efficient if no trade is occurring
(market prices equal autarky prices). If trade is occurring, however,
then the regime is inefficient because traders are making losses. This
regime emphasizes that spatial efficiency does not necessarily require
physical trade flows between markets.
Finally, in regime 3 the spatial price differential is greater than
the transfer cost:
(3) [P.sub.it] - [P.sub.jt] > [TC.sub.jit].
This condition violates spatial arbitrage and the markets are not
spatially efficient, irrespective of whether or not trade occurs,
because there are opportunities for profitable spatial arbitrage that
are not being exploited. Among several conditions that may lead to
regime 3 are noncompetitive pricing practices, restrictions on the
amount of product that can flow between regions, government price
support activities, licensing requirements, and quotas (Tomek and
Robinson 1990; Baulch 1997). (2)
To derive the standard PBM, examine a particular market pair (so
that the i and j subscripts can be dropped), and assume that transfer
costs are unobservable but known to be explained by a vector of
observable variables [Z.sub.t] according to:
(4) [TC.sub.t] = [alpha] + [Z.sub.t][beta] + [e.sub.t]
where [alpha] and [beta] are unknown parameters that can differ
across market pairs, and [e.sub.t] is a random shock that is usually
assumed to be normally distributed with mean zero and standard deviation
[[sigma].sub.e] (which can also differ across market pairs). In
practice, transfer costs are usually assumed to be a constant plus a
random shock (i.e., [beta] = 0), as in Sexton, Kling, and Carman (1991);
or it is assumed that transfer costs are observed with error ([Z.sub.t]
= [T[??].sub.t] and [beta] = 1 where [T[??].sub.t] is the observed
transfer cost estimate), as in Barrett and Li (2002).
Using (4) the conditions for the three regimes can be written:
(5) [P.sub.it] - [P.sub.jt] - [alpha] - [Z.sub.t][beta] = [e.sub.t]
(6) [P.sub.it] - [P.sub.jt] - [alpha] - [Z.sub.t][beta] = [e.sub.t]
- [u.sub.t]
(7) [P.sub.it] - [P.sub.jt] - [alpha] - [Z.sub.t][beta] = [e.sub.t]
+ [v.sub.t]
where [u.sub.t] and [v.sub.t] are nonnegatively valued random
variables that measure the negative (regime 2) and positive (regime 3)
deviations (if any) between price differentials and transfer costs. The
[u.sub.t] and [v.sub.t] terms are usually assumed to be half-normal and
distributed independently of each other and of [e.sub.t], with standard
deviations [[sigma].sub.u] and [[sigma].sub.v], respectively.
The goal of the PBM is to estimate parameters [[lambda].sub.1],
[[lambda].sub.2], and [[lambda].sub.3], which represent the
probabilities of being in regimes 1, 2, and 3, respectively. To derive
the likelihood function, define the difference between spatial price
differentials and expected transfer costs to be the random variable
[[pi].sub.t] = [P.sub.it] - [P.sub.jt] - [alpha] - [Z.sub.t][beta]. Then
the joint density function for [[pi].sub.t] over all trading regimes is
given as the mixture distribution:
(8) [f.sub.t]([[pi].sub.t] | [theta]) = [[lambda].sub.1] [f.sub.1t]
([[pi].sub.t] | [theta]) + [[lambda].sub.2] [f.sub.2t] ([[pi].sub.t] |
[theta]) + [[lambda].sub.3] [f.sub.3t] ([[pi].sub.t] | [theta])
where [f.sub.kt] (k = 1, 2, 3) are densities for regime k; and
[theta] is a parameter vector ([alpha], [beta], [[sigma].sub.e],
[[sigma].sub.u], [[sigma].sub.v]) to be estimated. The likelihood
function for a sample of observations is:
(9) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
and estimation proceeds by maximizing the logarithm of (9) subject
to the constraint that the probabilities lie between zero and one and
sum to one. This is the standard PBM and does not allow for changing
regime probabilities.
The Extended Parity Bounds Model
The extended PBM (EPBM) uses the PBM framework but allows gradual
probability changes over time. Suppose the joint probability density
function and likelihood function for the standard PBM are modified as
follows:
(10) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
(11) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
where [[delta].sub.k] measures the change in the probability of
being in regime k due to the policy change, and [D.sub.t] is a
transition variable that characterizes the time path of adjustment in
regime probabilities. The transition variable [D.sub.t] is constructed
following Ohtani and Katayama (1986). Let the end date of the old
marketing policy and the beginning date for realization of the full
effect of the new policy be denoted by [[tau].sub.1] and [[tau].sub.2],
respectively. Then [D.sub.t] takes a value of 0 for dates [[tau].sub.1]
and earlier, between 0 and 1 for the period between [[tau].sub.1] and
[[tau].sub.2], and 1 for [[tau].sub.2] and later dates. Hence, the
[[delta].sub.k] parameters measure the full effect of the change in
regime probabilities in response to the policy change, but there can be
an adjustment path to that full effect.
The pattern of transition from [[tau].sub.1] to [[tau].sub.2] can
be represented using alternative functional forms but in our application
below we assume a simple linear time path, which imposes a constant
speed of adjustment. (3) Hence, if the length of the transition period
is ten months, then 1/10 (10%) of the adjustment occurs every month and
by the fifth month half of the adjustment is complete. In our model,
[[tau].sub.1] is known but [[tau].sub.2] is treated as a parameter to be
estimated. The log-likelihood function is maximized for alternative
[[tau].sub.2] values and the period that has the highest likelihood
value is selected. We allow [[tau].sub.2] to be any period from the date
of the policy change (instantaneous adjustment) to the last date of the
sample (adjustment takes the entire sample period). The case where the
effect of the policy change is instantaneous (i.e., [[tau].sub.2] =
[[tau].sub.1] + 1) is a special case of the EPBM, which is essentially
equivalent to estimating PBM probabilities for different subperiods (see
Park et al. 2002). A joint test of no structural change in any regime
probability can be conducted using a likelihood ratio (LR) test based on
the restricted (no structural change) and unrestricted EPBM estimations.
In the EPBM, probabilities for the different trade regimes are
determined simultaneously for the three periods: (a) before the policy
change; (b) during the transition period; and (c) after the full effect
of the policy change. For the period before the policy change, the
probability estimates for the different trade regimes are given by the
[[lambda].sub.k]. Probability estimates for the transition period and
after the full effect of the policy changes is realized are given by
[[lambda].sub.k] + [[delta].sub.k][D.sub.t]. Because the parameter
estimates are probabilities, the probabilities should sum to one at
every period, which requires the following restrictions:
(12) 0 [less than or equal to] [[lambda].sub.k] [less than or equal
to] 1
(13) 0 [less than or equal to] [[lambda].sub.k] + [[delta].sub.k]
[less than or equal to] 1
(14) [summation] [[lambda].sub.k] = 1
(15) [summation] [delta].sub.k] = 0
It is important to note that the EPBM outlined above allows regime
probabilities to shift in response to policy changes but maintains the
assumption that the parameters of the transfer cost model, [alpha] and
[beta] in (4), remain constant under alternative policy regimes. (4)
This is reasonable in cases where changes in marketing policy would not
be expected to change the structure of transfer costs, but would be
inappropriate if a policy change could reasonably be expected to
influence the structure of transfer costs as well as regime
probabilities (or if there were other structural changes to the transfer
cost relationship which occurred around the same time as the policy
change, e.g., a new road is built). If it was necessary to allow
parameters other than the regime probabilities to shift in response to a
policy change this could be accommodated in the EPBM by taking each of
the additional parameters [[theta].sub.i] and expressing them as
[[theta].sub.i] + [psi].sub.i] [D.sub.t] so that [[theta].sub.i]
represent the parameter prior to the policy change and
[[psi].sub.i][D.sub.t] represents the path of change in the parameter
value in response to the policy change. Of course, allowing all PBM
parameters to change (including the transfer cost parameters) would add
additional complexity to an already highly nonlinear and complex
likelihood function. In our application to Ethiopian grain marketing
policy below, we explain why we think the assumption of constant
transfer cost parameters is reasonable for this case.
Application to Ethiopian Grain Markets
The EPBM was applied to Ethiopian maize and wheat markets to
estimate the effects of policy changes on spatial grain market
efficiency. We begin with an overview of grain marketing policy changes
in Ethiopia and previous research on the effects of these reforms on
spatial market efficiency.
Grain Marketing Policy Changes and Previous Research
There is a long history of Government regulation and control of
Ethiopian grain trade. A military coup in 1974 ushered in seventeen
years of socialist control during which the government set grain prices
and restricted interregional grain movements through a system of
marketing parastatals and cooperatives. The negative effects of these
policies on the development of grain markets, the agricultural sector,
and the national economy have been well studied (e.g., Lirenso 1987,
1994; Franzel, Colburn, and Degu 1989; Dadi, Negassa, and Franzel 1992;
and Gabre-Madhin 2001). In response to this poor performance, the
government undertook major policy reform in 1990 by removing
restrictions on interregional grain trade and abolishing price controls,
forced quota delivery, and eliminating the monopoly power of the
Agricultural Marketing Corporation (AMC).
Soon after the 1990 policy reform, the civil war being waged
between the socialist government and a force of Tigrayan militia came to
an end with the government being overthrown in May of 1991. With the
socialist government ousted, many of the war-related disruptions to
grain trading came to an end. Additional formal policy liberalization
followed soon after. In 1992, the AMC was reorganized to operate more in
the open market in competition with the private sector and its name
changed to the Ethiopian Grain Trade Enterprise (EGTE). Its new stated
objectives included stabilization of grain markets and prices to
encourage increased output, protect consumers from unfair grain prices,
earn foreign exchange through exporting grain to the world market, and
maintaining a strategic grain reserve.
In October of 1999, the government amalgamated the EGTE with the
Ethiopian Oil Seeds and Pulses Export Corporation and reestablished it
as a public marketing enterprise designed to compete with the private
sector. The amalgamated EGTE was ostensibly relieved of its grain price
stabilization responsibilities and directed to focus more on exports
(Bekele 2002).
Even though earlier reforms were probably more significant, the
empirical application here focuses on the effects of the 1999
reorganization of the EGTE for three main reasons. First, this is the
major reform that occurred during the period over which the data used in
the study are available (August 1996 to August 2002--see the data
discussion further below). Second, the 1999 reorganization did lead to
elimination of some remaining restrictions on interregional trade that
may have had important implications for spatial efficiency. Third, the
effects of the earlier 1990 and 1992 reforms, and of the end to the
civil war, on spatial grain trade have already been studied by Dercon
(1995), by Negassa and Jayne (1997), and by Gabre-Madhin (2001), who
used correlation and co-integration analysis to conclude that regional
Ethiopian grain prices appear more closely connected after these reforms
than before. However, the effects of the more recent 1999 reforms have
not yet been studied.
The 1999 grain market reform was focused on the EGTE and the way
the EGTE interacts with the private sector, so there is no reason to
believe the reform would have had a significant influence of the
structure of transfer costs between markets. The one possible exception
is that as part of the 1999 policy reform a remaining roadblock
controlling and restricting trade in the north was removed (see the
discussion of results further below). One interpretation of this removal
is that transfer costs were structurally reduced at this time. However,
the roadblock charges could also be interpreted as a source of spatial
inefficiency and the effect of their removal evaluated in terms of how
much the probability of being in spatially inefficient regime 3 (price
differential exceeds transfer cost) is reduced after their removal. This
latter interpretation is the one that we prefer and to facilitate this
interpretation we impose the identification restriction that regime
probabilities may shift as a result of the policy change but transfer
cost parameters are time-invariant.
Markets, Trader Characteristics, and Data
Grain markets in Ethiopia have a radial structure with the capital
city of Addis Ababa being the central location. Maize and wheat
typically flow from the surplus production areas in the west and south
to Addis, where they are either consumed or transshipped to grain
deficit areas in the east and north. Like other studies of spatial grain
trade in Ethiopia, we exploit this radial structure in our choice of
market pairs to investigate (see Dercon 1995; Negassa and Jayne 1997;
Gabre-Madhin 2001).
Seven market pairs are investigated for maize (see figure 1). The
first two of these represent flows from the maize surplus regions
surrounding Jima and Nekemte in the west to the capital, Addis Ababa.
The next three pairs represent flows from Addis to the maize deficit
regions of Dese and Mekele in the north, and Dire Dawa in the east. The
final two pairs are designed to capture the fact that there is often a
considerable amount of maize that flows from the surplus region
surrounding Shashemene in the south through Nazret to the deficit market
of Dire Dawa, without first passing through Addis. Hence we include
Shashemene-Dire Dawa and Nazret-Dire Dawa as additional market pairs.
[FIGURE 1 OMITTED]
Seven market pairs that exploit Ethiopia's radial market
structure are also investigated for wheat (see figure 1). The first two
of these represent flows from the wheat surplus regions surrounding Robe
and Hosaina in the south to Addis. The next three pairs represent flows
from Addis to the wheat deficit areas surrounding Dese and Mekele in the
north, and Dire Dawa in the east. Then the final two pairings again
exploit the fact that grain flows from Shashemene through Nazret to Dire
Dawa without first going through Addis, leading to an examination of
wheat price relationships between Shashemene and Dire Dawa, and Nazret
and Dire Dawa.
A detailed description of the characteristics of wholesale grain
trading firms in Ethiopia can be found in Dessalegn, Jayne, and Shaffer
(1998), and in Gabre-Madhin (2001), but it may be useful to highlight
some of the more important structural characteristics here.
Maize-trading firms tend to be small scale and, in most cases, the owner
is the sole employee and manager of the business. Traders are typically
engaged in other nongrain trade activities and characterized by a low
asset base. For example, only a few own their transport capital and most
of them rent storage space. Major entry barriers to trading grain are
lack of sufficient start-up capital, high cost of finding convenient
locations, and lack of access to appropriate and adequate storage.
Economies of scale are potentially important, especially in wheat
trading, because of the growth of larger companies owned by regional
political parties. Both farmers and merchants often lack access to high
quality market information needed for making good trading decisions.
The main data required to implement the EPBM in this application
are maize and wheat prices for different market pairs, interregional
grain transfer costs, and the start date for the new policy regime.
Weekly average wholesale maize and wheat prices for each market were
obtained from the EGTE for August 1996 to August 2002. These weekly
average wholesale prices were converted into monthly average prices by
taking the unweighted mean of weekly prices for a given month. The main
reason for converting to monthly average prices is to have the same
level of aggregation for both prices and transfer costs (the minimum
data frequency for the interregional truck shipment freight rates used
to estimate transfer costs is monthly). Baulch (1997) has also argued
that, because of the static nature of the PBM model, the observation
period should be long enough to allow grain to move physically between
the markets. For every month in our sample period the recorded grain
price in the surplus market of each market pair was lower than the
recorded price in the corresponding deficit market, which is consistent
with the absence of trade reversals.
A complete time series on all interregional grain transfer costs is
rarely available, particularly in developing countries like Ethiopia.
However, monthly data are available on Ethiopian interregional truck
shipment freight rates, an important component of total transfer cost.
(5) Monthly truck freight rate data were collected from the Ministry of
Economic Development and Cooperation and the Ministry of Transport
Authority for the sample period and rates were found to be very similar
for both inbound and outbound shipments. (6) Nominal market prices and
truck freight rates are used because the arbitrage notion on which
spatial efficiency is based focuses on nominal price relationships
(i.e., deflating both the price differentials and the transfer costs
would just rescale the arbitrage profits).
October of 1999 is used as the start date for the policy regime
change because that is when the EGTE was amalgamated and reestablished
as a public enterprise, which represents the major grain market policy
shift over the study period.
Estimation Procedure
The EPBM is estimated for each pair of regional wholesale maize and
wheat markets discussed above. Spatial price differentials are
calculated as the difference between the monthly wholesale grain prices
in the importing and exporting markets. Truck freight rates are an
incomplete estimate of full transfer costs, and so full transfer cost is
specified as
(16) T[C.sub.t] = [alpha] + T[[??].sub.t] + [e.sub.t]
where [alpha] is a parameter to be estimated, T[[??].sub.t] is the
truck freight rate from the exporting to the importing market, and et is
a random error.
Combining the spatial price differentials with the transfer cost
model (16) leads to [[pi].sub.t] = [P.sub.it] - [P.sub.jt] - [alpha] -
T[[??].sub.t] which implies the EPBM can be estimated by maximizing the
log of the likelihood function (11) with respect to the parameter vector
([[lambda].sub.1], [[lambda].sub.2], [[lambda].sub.3], [[delta].sub.1],
[[delta].sub.2], [[delta].sub.3], [alpha], [[sigma].sub.e],
[[sigma].sub.u], [[sigma].sub.v]), making the standard assumptions that
[e.sub.t] is normal and [u.sub.t] and [v.sub.t] are half normal, and
imposing the probability restrictions (12) through (15). Of course, this
estimation can only take place for a given end date for the policy
adjustment process. Therefore, the estimation was repeated for every
possible end date, starting with the date of the policy reform (i.e.,
assuming immediate adjustment) and ending with the last observation in
the sample (i.e., assuming adjustment takes place over the entire
remaining period of the sample). The end date that provides the largest
log-likelihood value is then chosen as the estimated adjustment period.
There are two remaining difficulties with this estimation procedure
that need to be discussed. First, the likelihood maximization is a
highly nonlinear constrained optimization problem and, as with all such
problems, is likely to experience convergence difficulties and have the
potential for local maxima. In our application, we addressed the local
maxima problem by first obtaining a converged solution and then using a
grid search over a reasonable range of alternative starting values to
investigate whether the converged solution was globally optimal. If the
grid search led to convergence at higher likelihood values the procedure
was repeated for the new parameter estimates until no higher likelihood
values were obtained. (7) Despite this procedure, some parameter
estimates remained at the boundary of the parameter space (i.e., some
probabilities were estimated at either zero or one). In such cases we
are careful to point out the corner solutions and not to provide
conventionally estimated standard errors for such estimates, which would
be inapplicable (see the results below).
Second, EPBM estimation still suffers from other weaknesses of the
standard PBM, including the potential sensitivity of results to
distributional assumptions. To evaluate this sensitivity, we undertook a
Monte Carlo simulation to examine how the EPBM performs when the
underlying distributions deviate from normality and half-normality. The
conclusion is that deviations from normality can bias the results in the
direction of underestimating the magnitude of increases in regime 3
probabilities (i.e., underestimating the increase in inefficiency
resulting from a policy change), particularly when the true distribution
of [e.sub.t] is skewed. However, results are more robust to deviations
of [u.sub.t] and [v.sub.t] from half-normality (see Negassa and Myers
2006 for details).
Empirical Results
EPBM estimation results for maize and wheat are provided in tables
1 and 2, respectively. Each table contains estimates of trading regime
probabilities before the policy change ([lambda]'s), estimates of
the change in trade regime probabilities due to the policy change
([delta]'s), estimates of the parameter of the transfer cost
function ([alpha]), estimated standard deviations of profit for
different trade regimes ([sigma]'s), the estimated lengths of the
transition period for each market pair (l), and the chi-square
statistics for the LR test of the joint hypothesis of no change in
regime probabilities ([chi square]). Numbers in parentheses below the
parameter estimates are estimated standard errors, or in the case of
[chi square], the p-value of the test statistic. Results are further
summarized in table 3 which shows estimated mean price differentials,
transfer costs (freight rates plus [??]), and arbitrage profits for each
market pair and commodity over the period before the policy change, the
period after the policy change, and for the full sample. Finally, figure
2 provides example graphs of spatial price differentials and parity
bounds (90% confidence intervals around transfer cost estimates) for two
trade routes, Addis Ababa to Dire Dawa for maize and Addis to Mekele for
wheat, as an illustration of key results. We discuss the results for
maize and wheat separately.
[FIGURE 2 OMITTED]
Results for Maize
Results for maize flowing from the surplus production regions of
Jima and Nekemte to Addis are reported in the first two columns of table
1. Before the policy change, both of these trade routes were in regime 2
over 50% of the time, indicating that before the policy change there is
a high probability that any trade flow was occurring at a loss. This is
consistent with the negative mean arbitrage profit figures for these
maize routes for the pre-policy change period (see table 3). However,
these two routes are different in that the remaining probability is
allocated mainly to regime 3 for Jima-Addis but for Nekemte-Addis it is
allocated to regime 1 (see table 1). This suggests that, prior to the
policy change, Jima-Addis is frequently in regime 3 where there are
unexploited arbitrage opportunities, but Nekemte-Addis is frequently in
spatial equilibrium (regime 1). Nekemte is a bigger market than Jima
with more maize production in surrounding areas and typically more
throughput, which may explain the higher estimated level of spatial
efficiency. Nekemte is also slightly closer to Addis than Jima, and is
often the first option for sourcing maize into Addis, particularly for
the EGTE.
After the commercial reorientation of the EGTE in October of 1999
there was no statistically significant change in regime probabilities
for the Jima-Addis route but a marked and highly statistically
significant shift in regime probabilities for the Nekemte-Addis route
that occurred over an estimated five-month adjustment period (table 1).
These results suggest that the policy change had little effect on the
Jima-Addis route but Nekemte-Addis moved fairly rapidly to a situation
where price differentials rose and were exceeding estimated transfer
cost essentially 100% of the time. (8) These results suggest that, as
part of the commercial reorientation of the EGTE in October of 1999, the
Nekempte-Addis route may have bec