Estimating policy effects on spatial market efficiency: an extension to the parity bounds model.

by Negassa, Asfaw^Myers, Robert J.

American Journal of Agricultural Economics • May, 2007 •

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(1) This is a static model and so there is an implicit assumption that spatial arbitrage can take place within the observation period of the data (i.e., there are no delays in moving commodities between markets).

(2) Barrett and Li (2002) take each of these three trading regimes and separate them further according to whether trade is occurring or not, which leads to a taxonomy of six trading regimes that can distinguish between market efficiency and market integration. However, trade flow data are required to implement their approach and these data are not always available. The method we develop here to allow changes in marketing policy to have a gradual dynamic effect on the probability of being in different trading regimes could also be applied to the six-regime taxonomy of Barrett and Li. Here, however, we restrict ourselves to the three regimes of the standard PBM because it is a simpler framework to explain our approach, and because in our empirical application to Ethiopian grain markets quantitative data on trade flows are not available.

(3) Functional forms that allow for different speeds of adjustment during the transition period could also be used (e.g., Goodwin and Brester 1995).

(4) Of course, transfer costs themselves can still change as [Z.sub.t] varies--see equation (4). A change in the structure of the transfer cost model is equivalent to a systematic change in the magnitude of unobservable transfer costs, say by building a shorter road between two markets.

(5) Based on the data from IFPRI/ILRI sample surveys of grain traders, in 1996 (2002) about 61% (72%) of variable grain marketing cost can be attributed to transport (Negassa, Myers, and Gabre-Madhin 2004).

(6) Of course, these data may not account for discounts associated with food aid deliveries which often cover round-trip transport costs leaving backhaul capacity from outlying areas available at significantly lower cost than outbound shipments.

(7) Using this procedure, higher likelihood values were found during the grid search only in a few isolated cases.

(8) Note also the positive mean arbitrage profit for Nekemte-Addis maize after the policy change (see table 3).

(9) The negative mean arbitrage profit figures for these maize routes for the pre-policy change period are consistent with this finding (see table 3).

(10) As discussed earlier, there is some ambiguity as to whether the elimination of these roadblock charges should be viewed as an increase in spatial efficiency or a reduction in transfer cost. Our view is that if one defines "transfer cost" broadly enough to include every possible reason for trade not occurring, then markets are always, by definition, "spatially efficient" and a model such as the PBM would have no meaning or value. We view the restriction that the structure of transfer costs does not change with elimination of the roadblock as an identification assumption that forces the effect of the roadblock removal to be interpreted as an increase in spatial efficiency. We would argue that this is the most sensible interpretation for this particular case.

Asfaw Negassa is an economist with the International Livestock Research Institute, Addis Ababa, Ethiopia and formerly a graduate research assistant in the Department of Agricultural Economics at Michigan State University. Robert J. Myers is University Distinguished Professor in the Department of Agricultural Economics at Michigan State University. Table 1. Maximum Likelihood Estimates of Extended Parity Bounds Models for Maize

Maize Trade Route EPBM Jima & Nekemte & Addis & Parameters Addis Addis Dese Regime probabilities

[[lambda].sub.1] 0.041 0.475 0.055

(0.030) (0.239) (0.182)

[[lambda].sub.2] 0.586 0.524 0.872

(0.077) (0.239) (0.187)

[[lambda].sub.3] 0.373 0.000 0.073

(0.075) -- (0.058) Policy effects

[[delta].sub.1] -0.006 -0.475 0.653

(0.048) (0.239) (0.242)

[[delta].sub.2] -0.099 -0.525 -0.676

(0.129) (0.239) (0.217)

[[delta].sub.3] 0.105 1.000 0.023