The matching problem (and inventories) in private
negotiation.
by Menkhaus, Dale J.^Phillips, Owen R.^Bastian, Christopher T.^
Gittings, Lance B.
Menkhaus, D.J., O.R. Phillips, A.F.M. Johnston, and A.V. Yakunina.
2003. "Price Discovery in Private Negotiation Trading with Forward
and Spot Deliveries." Review of Agricultural Economics 25:89-107.
Noussair, C.N., C.R. Plott, and R.O. Riezman. 1995. "An
Experimental Investigation of the Patterns of International Trade."
American Economic Review 85:462-91.
Parks, R.W. 1967. "Efficient Estimation of a System of
Regression Equations When Disturbances Are Both Serially and
Contemporaneously Correlated." Journal of the American Statistical
Association 62:500-09.
Phillips, O.R., D.J. Menkhaus, and J.L. Krogmeier. 2001.
"Laboratory Behavior in Spot and Forward Markets."
Experimental Economics 4:243-56.
Sexton, R.J., and M. Zhang. 1996. "A Model of Price
Determination for Fresh Produce with Application to California Iceberg
Lettuce." American Journal of Agricultural Economics 78:924-34.
Ward, C.E. 2005. "Beef Packers' Captive Supplies: An
Upward Trend? A Pricing Edge?" Choices 20:167-71.
(1) Supplementary Appendix A (Menkhaus et al. 2007) provides more
detail of inventory loss risk when sellers produce in advance of sale.
(2) Additional description of matching risk is contained in
supplementary Appendix A (Menkhaus et al. 2007).
(3) Hong and Plott (1982) conducted experiments where trading
occurred via negotiation by telephone with chosen trading partners. They
found that as the number of matching opportunities increased in
bilateral trading, prices approached the predicted competitive
equilibrium and quantities traded were slightly higher than the
competitive level. Private negotiation with many bargaining rounds
resembles the matching rich double auction when there is no advance
production. The double auction is "matching rich" in that when
a bid or offer is announced, it is immediately available to all agents
in the market. As a result, market participants have no difficulty
finding a trading partner.
(4) This is a version of Akerlof's (1970) lemons problem. In
our case, units held late in the production cycle are the
"poorer" quality and these units drive down the price of units
sold earlier in the cycle.
(5) There is some empirical evidence that directly suggests this
type of control. For example, the price effects of advance production in
the case of California iceberg lettuce are reported by Sexton and Zhang
(1996). They document a decline in spot lettuce prices related to
greater advance production. Their model allows for imperfect competition
among buyers. From this, they estimate the loss in seller bargaining
power due to a larger sunk lettuce harvest. Sexton and Zhang argue the
buyers have an advantage "when the sellers' asset is sunk and
highly perishable" (p. 932).
(6) On the rare occasion, it was necessary to ask individuals who
happened to be in the vicinity of the experiment location, whether or
not they had participated in a previous experiment, to fill in for a no
show. Otherwise, new recruits were used for each replication of each
treatment. Individual influences were minimized by averaging the data
across individuals and replications in the analysis.
(7) For completeness, the estimated [[GAMMA].sub.j] (the starting
level coefficients) and estimated starting levels are presented in
supplementary Appendix B (Menkhaus et al. 2007) for the trade and price
equations for each base category.
(8) The Parks method of estimation requires an equal number of
observations in the times series (trading periods) of each cross section
(treatment). There are twenty observations for each of the four test
treatments (3M, 5M, 2B5M, and 2S5M) and another twenty observations for
the base treatment for a total of a hundred observations for each
equation in each of the three base categories (competitive, Cournot, or
monopsony).
(9) We estimated the convergence models (trades and prices) for the
Cournot solution for two sellers. This model did not perform as well as
the competitive and monopsony models.
Dale J. Menkhaus is professor in the Department of Agricultural and
Applied Economics. Owen R. Phillips is professor in the Department of
Economics and Finance. Christopher T. Bastian is assistant professor,
and Lance B. Gittings is former graduate assistant in the Department of
Agricultural and Applied Economics. Menkhaus, Phillips, and Bastian are
at the University of Wyoming.
Funding support was provided by the U.S. Department of Agriculture
under Agreement No. 00-35400-9126 and the Lowham Research Endowment. Any
opinions, findings, conclusions, or recommendations expressed in this
work are those of the authors and do not necessarily reflect the views
of the funding agencies. Helpful comments from anonymous reviewers and
Editor Paul V. Preckel are gratefully acknowledged. Any remaining errors
are the authors'.
Table 1. Experiment Design--Private Negotiation Trading with
Advance Production, Buyer and Seller Numbers and Alternative
Buyer/Seller Matches
Treatment No. of No. of No. of Designation
Buyers Sellers Matches
1 4 4 5 5M
2 4 4 3 3M
3 2 4 5 2B5M
4 4 2 5 2S5M
Table 2. Unit Buyer Redemption Values and
Seller Costs (Tokens) Used in the Experiments
Redemption Cost
Value for for
Unit Buyers Sellers
1 130 30
2 120 40
3 110 50
4 100 60
5 90 70
6 80 80
7 70 90
8 60 100
Table 3. Asymptote Coefficients (Standard Errors) and [Convergence
Levels]--for Trades and Prices--Competitive, Cournot, and Monopsony
Bases by Treatment
Treatment Competitive Competitive Cournot
Asymptotes Trades Prices Trades
Predicted base 20 80 19.56
5M -2.72 * (a) -2.36 * (a) -2.26 * (a)
(0.29) (0.77) (0.29)
[17.28] [77.64] [17.30]
3M -5.39 * (b) -7.06 * (b) -4.95 * (b)
(0.20) (0.72) (0.20)
[14.61] [72.94] [14.61]
2B5M -7.26 * (c) -17.58 * (c) -6.82 * (c)
(0.16) (0.80) (0.16)
[12.74] [62.42] [12.74]
2S5M -6.77 * (c) -0.96 (a) -6.33 * (c)
(0.18) (1.24) (0.18)
[13.23] [79.04] [13.23]
[R.sup.2] 0.99 0.99 0.99
Treatment Cournot Monopsony Monopsony
Asymptotes Prices Trades Prices
Predicted base 86.11 16 60
5M -8.62 * (a) 1.29 * (a) 17.50 * (a)
(0.77) (0.29) (0.77)
[77.49] [17.29] [77.50]
3M -13.24 * (b) -1.40 * (b) 12.87 * (b)
(0.00) (0.20) (0.72)
[72.87] [14.60] [72.87]
2B5M -23.82 * (c) -3.27 * (c) 2.30 * (c)
(0.80) (0.16) (0.80)
[62.29] [12.73] [62.30]
2S5M -7.41 * (a) -2.77 * (c) 18.71 * (a)
(1.24) (0.18) (1.24)
[78.70] [13.23] [78.71]
[R.sup.2] 0.99 0.99 0.99
Note: Single asterisk (*) denotes estimated convergence level
significantly different from the base value, [alpha] = 0.01.
Note: (a, b, c, d)--same letter indicates no significant difference
between estimated convergence levels in the respective equations.
Different letters indicate a significant difference between estimated
asymptotes, [alpha] = 0.01.
Table 4. Average Percentage of Trades and Average Prices for Each
Bargaining Round by Treatment--Periods 16-20
Round 1 Round 2 Round 3 Round 4 Round 5
Treatment 5M
% trades 37.90 22.85 20.60 7.74 10.91
Ave. price 78.48 80.50 80.36 77.56 66.33
Treatment 3M
% trades 38.45 35.55 26.01 -- --
Ave. price 76.65 74.68 70.45 -- --
Treatment 2B5M
% trades 31.09 28.28 18.00 14.13 8.50
Ave. price 65.39 64.64 65.57 63.29 58.70
Treatment 2S5M
% trades 26.75 24.68 16.94 16.50 15.13
Ave. price 80.85 80.30 84.78 79.55 67.33
Table 5. Summary of Selected Laboratory Results Reviewed and Presented
in This Article
No. of No. of Trading Advance Resulting
Buyers Sellers Institution (a) Production Quantity (b)
Many Many CEM No [Q.sup.comp]
4 4 DA(PMK) * No [Q.sup.comp]
4 4 DA(PMK) Yes [Q.sup.comp]
4 4 EA(MPB) Yes [less than or
equal to]
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