Spatial Price Adjustment with and without Trade*
| Date | 01 June 2012 |
| DOI | http://doi.org/10.1111/j.1468-0084.2011.00651.x |
| Author | Christopher B. Barrett,Edward Mabaya,Emma C. Stephens,Stephan von Cramon‐Taubadel |
| Published date | 01 June 2012 |
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OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 74, 3 (2012) 0305-9049
doi: 10.1111/j.1468-0084.2011.00651.x
Spatial Price Adjustment with and without TradeÅ
Emma C. Stephens†, Edward Mabaya‡, Stephan von
Cramon-Taubadel§ and Christopher B. Barrett¶
†Pitzer College, Claremont, CA 91711, USA (e-mail: estephen@pitzer.edu)
‡Emerging Markets Program, Cornell University, Ithaca, NY 14850, USA
(e-mail: em37@cornell.edu)
§Department of Agricultural Economics and Rural Development, University of Göttingen,
Göttingen, Germany (e-mail: scramon@gwdg.de)
¶Department of Applied Economics and Management, Cornell University, Ithaca, NY 14850, USA,
(e-mail: cbb2@cornell.edu)
Abstract
We investigate the possibility that price transmission between spatially distinct markets
might vary during periods with and without physical trade flows. We test for differences
between trade and non-trade regimes by using generalized reduced rank regression (GRRR)
techniques suggested by Hansen (2003). We apply these techniques to semi-weekly price
and trade flow data for tomato markets in Zimbabwe and find that intermarket price adjust-
ment occurs in both trade and non-trade periods. Indeed, the adjustments are generally
larger and more rapid in periods without physical trade flows. This finding underscores the
importance of information flow for market performance.
I. Introduction
A large literature explores the behaviour over time of distinct markets that are linked
together in a network. This network can be spatial, as in markets for a commodity within a
given region or country, or it may represent other kinds of integration, perhaps through ver-
tical marketing channels as product is transformed or through intertemporal arbitrage via
storage (Williams and Wright, 1991; Deaton and Laroque, 1996; Br¨ummer, von Cramon-
Taubadel and Zorya, 2009). The primary purpose of such research is often to determine
how quickly markets respond to shocks and how these shocks transmit through the network
via price adjustment. The dominant analytical approach has exploited spatial market equili-
brium conditions, described in detail by Takayamaand Judge (1971). Deviations from these
equilibrium conditions yield key information on overall market efficiency. Understanding
ÅThe authors would like to thank Harry Kaiser, Stefan Klonner, attendees at the Agricultrual and Applied Eco-
nomics Association 2008 meetings in Orlando, Florida and several anonymous referees for very helpful comments
on earlier drafts of this paper. Stephan von Cramon-Taubadelalso acknowledges support from the Courant Research
Centre ‘Poverty, Equity and Growth in Developing Countries’,which is funded by the German Research Foundation
(DFG).
JEL Classification numbers: Q13, R12, C32, P42.
454 Bulletin
these dynamics with respect to food markets may be of particular importance for policy
makers in developing countries (Fackler and Goodwin, 2002), where large subpopulations
are employed in the agricultural sector and where the considerable budget shares devoted
to food expenditures leave many poor households vulnerable to price spikes commonly
associated with market disequilibrium.
The underlying theory of spatial market equilibrium suggests particular patterns of
price behaviour under competitive arbitrage, based on the transactions costs associated
with movement of goods between markets and observed trade flows. More specifically, if
physical trade flows occur between markets, these markets are said to be in competitive
spatial equilibrium if and only if the price differential exactly equals the costs of moving
goods between them, such that excess returns to trade are completely exhausted. Further,
with constant per unit costs of commerce, any price change in one market due to a local
demand or supply shock should generate an equal price change in the other market. This
strong spatial price transmission is the familiar Law of One Price.
The absence of trade may also imply that markets are in spatial equilibrium. This can
occur when price differentials exactly equal transactions costs, leaving traders indifferent
between trading and not trading, or when the intermarket price differentials are insufficient
to cover the costs of arbitraging between the markets. Where competitive spatial equili-
brium with trade implies strong spatial price transmission, however, these latter, segmented
spatial equilibria are consistent with uncorrelated price series as well as with the Law of
One Price. Thus spatial price transmission dynamics may be markedly different in periods
with and without trade even when markets are always in competitive spatial equilibrium.
Markets may also be out of equilibrium, with either apparent missed arbitrage oppor-
tunities (i.e. no trade in spite of intermarket price differentials in excess of the costs of
arbitrage) or positive trade in the face of negative returns to arbitrage. The different possible
combinations of trade flows and returns to trade are examined in detail in the switching
regime branch of the spatial price analysis literature, which finds empirically that markets
are frequently out of spatial equilibrium (Baulch, 1997; Barrett and Li, 2002). However,
this literature is limited in its ability to comment on the actual process of transition between
equilibrium and non-equilibrium regimes, as the approach is inherently static and does not
make explicit use of the time series nature of the data at hand.1
There is, however, a large body of work that performs spatial price analysis dynami-
cally and includes the possibility of disequilibrium periods, primarily through the use of
threshold autoregressive and vector error correction models to characterize price dynam-
ics (Goodwin and Piggott, 2001; Hansen and Seo, 2002). But one of the main, implicit
assumptions of these models is the primacy of trade flows in bringing about spatial equi-
librium. This assumption has been largely unexamined thus far in the literature. For the
most part, lack of available complementary price, trade flow and transaction cost data has
hampered the analysts’ ability to test empirically whether or not trade flows are the main
mechanisms behind spatial equilibrium patterns.2Further, until recently, the appropriate
methods in cointegration analysis necessary to fully compare spatial price dynamics in the
presence of multiple trading regimes did not exist.
1Negassa and Myers (2007) offer a dynamic extension to the parity bounds model, but like the rest of the switching
regime literature, their approach relies heavily on strong, atheoretical distributional assumptions.
2See Barrett (1996) for a break down of the types of market analysis methods by data classification.
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