However, it is vell known that agriculture is subjected to considerable .... LE p. ) (5) it it. Pit. 8. When the policies are independent of the world prices, b will be ...... Herlihy, Michael, Stephen Magiera, Richard Henry, and Kenneth Baily (1989).
Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized
Affairs and Extemral Policy,Research,
WORKING
PAPERS
Markets Conmodity Internatlonal
International Economics Department TheWorldBank March1990 WPS383
On the Relevance of World Agricultural Prices Yair Mundlak and Dona'd F. Larson
Is it appropriatefor market analysts to use intemational agricultural prices as a proxy for domestic prices when domestic prices are unavailable? A firm yes.
The Policy.Research,andExternalAffairsCosplex distributesPRE WorkingPapersto disseninatethe findingsof workin progrems and toencouragetheexchangeof ideasamongBankstaffandallothersinterestedin developenntissues.Thesepaperscartythe names Thefundings,interpetations,andconclusionsarethe reflectonlytheirviews,andshouldbe used andcitedaccordingly. of the authors, authore'own.Theyshouldnot be atuibutedto theWorldBank,its Boardof Diectors,its manageaent,or anyof its membercountries.
Policy,Resoarch, and ExtemalAffairs
Intornational Commodity Markets
This paper - a product of the Intemational Commodity Markets Division, Intemational Economics Department- is part of a larger effort in PRE to model the global markets for primary commodities and to use these models for forecasting purposes as well as forpolicy analysis. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Dawn Gustafson, room S7044, extension 33714 (30 pages with tables).
In a free market, domestic prices on agricultural products could be expected to vary with world prices. But intervention is so common with agricultural products that prices vary between countries and gaps exist between world and domestic prices. The Intemational Commodity Markets Division is often forced to use intemational prices as a proxy for domestic prices. But it is often claimed that world prices are irrelevant to agricultural development in countries that intervene in agricultural pricing.
Mundlak and Larson examined the appropriateness of this substitution in measuring, say, the agricultural supply response to price changesparticularly in the long run. They conclude that on the whole world prices are indeed relevant. The results - for 18 countries and 17 commodities - are surprisingly robust, and invariant to both data sources and time/commodity pooling.
're PRE Working Paper Series disseminates the findings of work under way in the Bark's Policy, Research. and Extemal Affairs Complex. An objective of the series is to get these fundingsout quickly, even if presentations are less than fuOy polished. The futdings, interpretations. and conclusionscin these papers do not necessarily represent official Bank policy. Ptoduced at the PRE Dissemination Center
Foreword
In the absenceof domesticpricesfor primarycommoditiesthe International CommodityMarketsDivisionis oftenforcedto use internationalpricesas a proxyfor of usingintenationalprices, domesticprices. This projectexaminesthe appropriateness say in measuringagriculturalsupplyresponseto pricechanges- particularlyin a long-nm contexL This projectis part of theDivision'sprogam of researchon commodityprice behaviorand supplyresponsivenessin agriculture.
TABLE OF COUTEIrS
Introduction.
*
**
*9***9**
*****
.....
..
...
..
*9
..
.- l
The Framework.3 Empirical Reoults.5 Pooled, Country Results.8 Decomposition by Sources... .o.....
o. oo.oo...
o.oo.......o..........
Technical Digression................................. Additional Results
8 0... ...... ll
1eoe...e.ooosooooooo*e*.oo6
Pooled Country Data.17 Di
scussion......oo ..
o.o.o.....
References eoo..*o.*.o*o.ooo*oooooo*oo.o29
e. ooo......
oo.........
.........
26
ON THE RELEVANCEOUF CORLDAGRICULTUIRAL PICRS *
by
Yair Mundlakand Don Larson
Introduction
1.
Agriculturalproducts are on the whole tradablesand every country
trades in some agriculturalproducts. In the absence of interventionit is expected that domestic prices of such productswill vary with world prices. However, it is vell known that agriculture is subjected to considerable intervention which creates a gap between world and domestic prices, and generates cross-countryvariationsin agriculturalprices. Therefore,it is often claimed that world prices are irrelevant for the development of agriculturein countrieswhich intervenein the pricing of their agricultural products.
The authors are grateful to Ronald C. Duncan and D.C. Johnson for comments and suggestionson an earlierdraft.
*
-2-
2.
It to
true that interventionaffects the relationship between
domestic and world prices. Interventionsin agricultureare well documented and it is rare to find a country which does not intervene(see for esample, McCalla, 1969; Johnson 1973; Bale and Lutz, 1981; Australian Bureau of AgriculturalEconomics, 1985; Anderson, Hayami and Honma, 1986; World Bank 1986).
The discussionon interventiondeals primarilywith the effect of
policies on domestic prices and the consequencesfor domestic production, consumption trade, welfare and the spill over to the world market.
Such
policies are costly and thereforethe reasons for their implementationare discussed as well.
There are basicallytwo approachesto the reasoning of
governmentpolicies. The first one considerspolicy to be endogenouswithin the economic system. Examplesof this approachare: Bullock,1989; Rausser and !reebairn,1°74; Rau ser and St^nchouse,1978;
Shei and Thompsoor,1977.
The second one treats policies within a broad framework where political pressures play a dominant role and thereforethe responseof government to changes in the economy are strongly hindered by political considerations. Examples of this approach to agriculturalpolicy are: Abler, 1987; Gardner, 1987; Miller, 1986; Binswangerand Scandizzo,1983.
3.
In this paper we ask a differentquestion;to what extent are world
prices transmitted to domestic prices?
This is a
crucial topic for
understandingthe relationshipsbetweendomesticand world markets. It is of particular interest in studyingthe dynamicsof world agriculture(Mundlak, 1989).
-3 -
4.
The insulationof a countryfrom world prices requiresresources. As
the gap betweendomesticand world prices increases,the cost of such policies increasesaccordingl.y and eventuallybecomes excessive. Hence, it leads one to believe that the gap is boundedand if this is the case then we should see a transmissionof world prices to domestic prices.
This is the working
hypothesisto be tested in this paper. Its implicationis discussed in the concludingsection.
The Framework
5.
The simple frameworkdraws on the (relative)law of one price where
the domestic price, P, is expressedas a productof the world price, P , the &niGizal exchanSagrate, E,
nd the tax policy S
t
(I 't), where rthe
4
tAx
rate.
P
=
p*ES
(1)
This formulationassumes that the product is homogeneousin that world and domestic prices refer to the same product; marketing margins and other domestic non-tradableinputs are ignored. This is an unrealisticassumption and any interpretationsshould be modified accordingly. We return to this below. At present it is assumed th t the systematiccomponentsof the nontradableinputs are confoundedin E and S whereas a disturbanceU is added to account for the transitorycomponent. Rewriting(1), with lower case letters indicatinglogs, we have for commodityi in year t:
-4-
*
p
-p
it
+e
it
t
+ s
+u it
(2)
.
it
Let the relativedifferencein prices be d
p
-
p* and refer to z - e + a as
the policy variable,then
it
it
where u 6.
-
it
W)
2 * (O, a ), and B(zu) - E(p u)
0.
Given the stochasticidentity(2'), the elasticityof domesticprices
with respect to world prices depends on the relationshipbetween z and p*, z(p*).
For example, where governmentactions reduce fluctuationsin world
prices z'(p*) c 0.
At this point, we introducethe linear (in parameters)
;Cr.ionfor z(t*' arA re'e- to it a- L*e policy equation;
*
Z-
it
7.
= I
*
0
, where E(p v) - O.
+ v
+ Ep it
(3)
it
The empiricalrelationshipbetweendomesticand world prices is given
by the regressioncoefficientof p on p*, referredto as the elasticityof domesticprice with respectto world price:
b
b =
*
EZpitpit /
E*
2
Pit
(4)
and by the correlationcoefficientof the two prices. The summationin (4) is over comodities and time and the variablesare measuredas deviationsfrom their overall means. As explainedbelow, the variablesare unitless.
Using (3), the expectedvalue of b is evauated:
E(b - I) - E(EE dt p it
8.
it
/LE p. Pit
)
(5)
When the policies are independentof the world prices, b will be
nearly 1. On the other hand, b is smallerthan 1 when the policiesreduce the fluctuationsin world prices and larger than 1 in the opposite case. The closer b is to 1, the more closdlydomesticprices reflect,on average,world prices. The quantitativeimportanceof world prices in the determinationof domesticprices is measuredby the contributionof p* to the variationsin p. This is representedby the degree of fit, (R2), of the regressionof p on p*.
EmpiricalResults
Data
9.
The elasticityparameter b was estimatedfor 58 countries for the
period 1968-78:the sample covered some 60 products. The number of products varied by countries. Productsnot producedin a country were excluded from the analysis.
10.
The domesticprices are FAO prices describedby FAO as:
"Farm prices are in theory determinedby farm gate or first point-ofsale transaction when farmers participate in their capacity as
sellersof their own products. Of course,data may not always refer to the same sellingpoints dependingon the prevailinginstitutional set-up in the countries. Also different practicesmay prevail in regard to individualcommuniAies."1/
11.
The domesticprices are convertedfrom local currenciesto US dollars
using exchangerates (annualaverages)publishedby the IMP. The world price is an export unit value calculatedin nominal US dollars. It is a ratio of the total world value of exports for each of the commoditiesdivided by the total world exportedquantitiesfor the corresponding comodities.
12.
Note that in this study the domestic prices are expressed in US
dollars, or as P/E in terms of (1).
However, there may be a difference
between the exchange rate used in convertingprices from domestic currencies to dollars and between the "true" rate. ThereforeE in (1), or e in (2) is viewed as a correctionfactor for the exchangerate and is unitless. With this interpretation,the policy variable z is unitless. It representsthe proportionaldeviationof domesticprices from world prices. Also, note that the deviations of P it from their mean (p*0.) are unitless. They simply representthe log of the proportionalchange in prices.
1/ FAO ProductionYearbook,1987.
-7 Table 1: ELAST!'ITYOF PRODUCER PRICE WITH RESPECT TO WORLDPRICES
COUNTRY
POOLED
Arigentina Australia Austria Bangladesh Belgium-Lux. Brazil Burundi Cameroon Canada Chile Colombia Costa Rica Cyprus Denuark Ecuador Egypt El Salv^dor Finlan France Germany F.R. Greece Guatemala India Ireland Israel Italy Japan Kenya Korea, Rep. Malawi Malaysia Mauritius Mexico Netherlands New Zealand Norway Pakistan Panama Peru Philippines Portugal South Africa Spain Sri-Lanka Sweden Switzerland Syrla Tanzania Thailand Trinidad Turkey United Kingdom United States Uruguay Venezuela Yugoslavia Zambia Zimbabwe
0.966 0.930 0.979 0.715 0.972 0.902 0.862 0.890 0.999 0.878 0.922 0.908 0.925 1.037 0.987 1.208 0.903 0.967 0.949 ).989 (.912 0,907 0.737 1.022 0.972 0.909 0.942 1.064 0.921 0.888 0.858 1.041 0.985 0.985 1.029 0.977 0.744 0.937 0.868 0.804 0.959 0.972 0.928 0.814 0.930 1.039 0.978 0.977 0.897 1.015 0.952 0.95. 1.00' 0.796 0.910 1.011 0,893 0.956
I 0.759 0.847 0.790 0.630 0.824 1.097 0.579 0.865 0.797 0.641 0.648 0.659 0.831 0.944 0.719 0.964 0.759 0.636 0.846 0.751 0.846 0.697 0.438 0.809 0.798 0.690 1.142 0.750 0.997 0.488 0.836 0.989 0,654 0.C16 0.7;4 0.807 0.362 0.603 0.803 0.577 0.814 0.626 0.821 0.686 0.581 1.051 0.872 0.765 . 774 0.876 0.904 0.796 0.815 0.730 0.603 0.851 0.720 0.698
WITHIN t 0.990 0.933 0.991 0.710 0.981 0.847 0.884 0.867 1.018 0.920 0.944 0.933 0.927 1.033 1.012 1.231 0.904 0.998 0.951 1.001 0.908 0.924 0.775 1.033 0.995 0.942 0.883 1.091 0.894 0.923 0.846 1.033 1.036 0.999 1.051 0.977 0.803 0.965 0.861 0.825 0.960 1.005 0.931 0.827 0.966 1.018 0.977 0.989 0.891 1.020 0.943 0.962 1.027 0.800 0.939 1.020 0.905 0.97k
it 0.551 0.486 0.106 0.074 0.290 0.316 0.128 0.147 0.316 0.490 0.017 0.417 0.425 0.208 0.244 0.123 0.213 -0.051 0.340 0.183 0.208 0.309 0.173 0.150 0.397 0.296 -0.008 0.294 0.231 -0.057 0.358 0.378 0.373 0.166 0.134 0.012 0.004 0.213 0.124 0.238 0.185 0.147 0.326 0.588 0.021 0.111 0.108 0.233 0.130 0.364 0.265 0.405 0.596 0.427 0.043 0.111 0.326 0.023
I
BETHEEN t
1.000 0.944 1.009 0.731 0.997 0.865 0.901 0.894 1.033 0.929 0.972 0.946 0.942 1.055 1.036 1.271 0.926 1.023 0.967 1.024 0.925 0.940 0.795 1.050 1.010 0.960 0.909 1.112 0.910 0.950 0.862 1.048 1.055 1.020 1.068 1.005 0.829 0.963 0.880 0.844 0.981 1.028 0.947 0.833 0.989 1.037 1.002 1.013 0.918 1.035 0.961 0.975 1.040 0.809 0.966 1.041 0.916 0.994
0.794 0.907 0.902 0.748 0.929 1.268 0.667 1.030 0.8&' 0.67, 0.
O0;. 0.900 1.080 0.816 1.102 0.891 0.789 0.930 0.902 0.948 0.799 0.503 0.934 0.877 0.754 1.347 0.858 1.097 0.607 0.958 1.117 0.724 0.923 0.858 0.977 0.437 0.720 0.958 0.675 0.948 0.721 0.914 0.706 0.772 1.198 0.992 0.889 0.939 1.026 1.000 0.891 0.862 0.776 0.726 0.951 0.787 0.834
a8-
Pooled.CountryResults
13.
As the policy variable is unitloss,it is possibleto pool the data
over all commoditiesfor all years. The estimatesof the elasticityb for the poolvd data ap-ar in column 1 of Table 1.
The elasticityvaries between
0.715 and 1.208 with a median of 0.945. The values for 35 out of 58 countries fall in the range of 0.9-1.0. The implicationis that x, the elasticityof domestic prices with resx.i..to the policy variable,has a median value of -0.055 (1 minus 0.945) which is indeed very small.
14.
The conclusion is that world prices are transmitted to domestic
prices. This is a qualitativefinding. The quantitativeaspect is related to th9 importance of ::^Ch tran;mission. It is
to be noted that in all
regressionsthe values of R2 are quite high. This indicatesnot only that world prices are transmitted,but that they also constitutea major component of the variationof domesticprices.
Decompositionby Sources
15.
The policy equation(3) assumesa uniformpolicy for all commodities
and all years. This
assumptionmay be too strong and should therefore be
examined. This can be done by generalizing(3).
This is done by first
assuming that the policy varies by commodity. In this case, the assumption E(p v) = 0 made in (3) is violated. Therefore(3) is rewritten:
-9-
*
Z. = W; + w (I + it 0
)Pip + v's u it
*
(3i) *
= 0, and cov (wiPi )
where E(p v')
0, for all t.
The error term, v', is now defined in accordancewith (3i) and, therefore,it is amsumed to be orthogonal to p . commodity-specific deviation,wi.
The extension in (3i) allows for a
A directway to estimatethe importanceof
this extensionis to computethe between-commodity regression. Letting
1 p
Z p ' the commodity-price average over time, the betweencommodity T it regressioncoefficientis: =
-
* 2 / £ p
*
b(i) = Z p p 1.* 1.
(4i)
I.
Eilb(i)-1] = w + A (i,
(5i)
where A(i)is a weightedaverageof wr, so that I
h(i) = ZEl X,
and
= (P*;2
2
(6)
The values obtained for b(i) appear in column 5 (bet%ieni) cf Table 1. The differencesbetween these resultsand the pooled resultsare negligible. The median is 0.975, as compared with 0.945 for the pooled regression. Thus, roughly speaking,the averagevalue of wi is 0.03. It can then be concluded that either w.
are generally small, or else they differ in signs and
thereforetheir weightedavera!,R is nearly zero. We return to this below.
-
16.
10
-
A similar analysis follows for an alternativespecificationwhich
allows for systematicvariationsof policy overtime. In this case,
z
= Ne + (1 + I ) pi it 0 t it
where E (p*v") = 0 and cov (t
Definingp.
=
-
E Pi
+ V"I it,
(3t)
P*it) = 0 for all i.
as the commodity-averageprice, the between-time
regression,with variablesagain writtenas deviationsfrom theirmeans, is: * 2
*
b(t) = Ep
p
Izp
.t .t
(4t) St
and
E [b(t)-11 =
1
+ A(t),
(5t)
*2
where h(t) - Ew A , and X = (p ) t t t et average of
*2 /£(p ) ,a weighted .t
X
t
17.
The results are reported in column 6 of Table 1.
There is now a
larger spread in country results. This may reflect the fact that the sample consistsof eleven years only, and thereforethe estimatesof the between-time regressionare less precisethan those of the between-commodity. Nevertheless, for most countriesthe resultsdo not differ much from the pooled regression. The median value of b(t) is .905. Consequentlythe averageestimateof t is approximately-0.04, similarin magnitudeand sign to that of wi.
-
18.
11
-
The policy equationcan now be extendedto allow for both commodity
and year effects. Combining3i and 3t: *
it
0
i
t
*
where E(p
it
it *
v...)
= 0; cov (i,
p it)
*
cov
(
VP
it
O0 for all iit.
19.
Finally,an interactionterm, witpit, can be added to zit, with the assumption that the covariance of wit with pit is zero. This will add additionalterms to the expectationsof the two between-regressions. However, in the present case, this additionis quantitativelyunimportant, as we shall see below.
TechnicalDigression
20.
The foregoinganalysisdiffers somewhatfrom more familiar forms of
analyzingpanel data. We thereforeturn now to evaluatethe resultswithin a uniform framework. The reader who is interestedonly with the empirical results can skip this discussionand move directlyto the next section. Let W, B(i), B(t), W(i), and W(it) be projection (symetric and idempotent) matrices that generate residuals. They can be defined in terms of their operationon an arbitraryvector x of order IT:
Wx = (sit - x..), B(i)x = (xi. - x..), B(t)x = (x.t - x..),
W(X
= (Kit
xi.), W(t)s = (xit - x.d) W(it)s - (sit - xi. - x.
+ x.)
-
12 -
The bracketed parenthesescontain the typical elements of the vectors in question. The followingidentitiescan then be derived.
21.
W = Wti) + B(i)
(7a)
= W(t) + B(t)
(7b)
= W(i) + W(t) - w(it)
(7c)
B(i) + B(t) + W(it)
(7d)
Let p and p* be the vectors of the two prices, then the regression
coefficientsobtainedabove can be derivedfrom: a = p*Ap / p*Ap*.
When A = W, B(i), B(t), the resultingestimatorsare b
(pooled), b(i) (between commodity) and b(t) (between time) respecrively. Also, when A = W(i), W(t) and W(it), the coefficientscan be referredto as: within commodity,w(i), within time, w(t), and within time and commodity, w(it), respectively. Let A and C be two arbitrarymatrixesand define:
r(A/C) = p*Ap* / p*Cp*.
(8)
It then followsthat:
b = r[B(i)/W]b(i) + r[B[t)/W]b(t)
+ [1 - r(B(i)/WJ - r[B(t)/WI w(it)
(9)
- 13 -
where, in view of (8), r[B(i)/WJand r[B(t)/WIare the ratios of the between comodity and betweentime variancesto the totalvarianceof p* respectively. Table 2 presentsa decompositionof the sum of squaresof p* by sources. As p* is world price, the sums of squares should be the same for all countries. However,the set of commoditiesanalyzedvaries somewhatbetween countriesand thereforethe numbers in the table differ accordingly. Taking Argentinaas an example, r[B(i)/WI = 481.5/562.8 = .856, r[B(t)/Wl = 69.8/562.8 = .124. Thus,
the between commodityvariancedominatesthe other components. Also note that 1
-
r[B(i)/Wl- r[B(t)/WI = .02, implying that there are hardly any variations
left in the world prices after the time and commodityeffectswere eliminated. Consequently,using the values in Table 1, it is possibleto approximatethe pooled regressionwith .856b(i)+ .124b(t)= .954, as comparedto the actual value of .966 for the pooled regression. The difference is due to the interactionterm that was neglected. It then followsthat under the present framework,the expectedvalue of the pooled regressionis:
E[b - 1] a r + r[B(i)/WI A(i) + r[B(t)/W]
h(t)
(10)
that is, the deviation from 1 (perfecttransmission)consistsof an overall deviation(X), and a weightedaverage of commodityeffects and time effects, and they are all relativelysmall.
22.
Table 1 also reports the within estimators. Allowing for commodity
effects yields the within commodityestimate(column 2). Their median value is about .78 which is somewhat lower than that of the pooled regression. Allowingfor time effect results in the within time estimates(column3) with
- 14 -
Table 2:
SUM OF SQUARES OF WORLD PRICES
Pooled Country
Argentina Australia Austria Bangladesh BelgiumrLux. Brazil Burundi Cameroon Canada Chile Colombia Costa Rica Cyprus Denmark Ecuador Egypt El Salvador Finland France Germany Greece Guatemala India Ireland Israel Italy Japan Kenya Korea, Rep. Malawi Malaysia Mauritius Mexico Netherlands New Zealand Norway Pakistan Panama Peru Philippines Portugal ,Sourh Africa Spaiua Sri-Lanka Sweden Switzerland Syria Tanzania Thailand Trinidad Turkey United Kingdom Uruguay United States Venezuela Yugoslavia Zambia Zimbabwe
Within
Between
(1)
(2)
(3)
(4)
(5)
(6)
(Total)
(i)
(t)
(it)
(1)
(t)
11.5 10.6 7.7 11.0 9.2 17.5 7.5 13.0 6.6 9.8 13.5 11.5 7.8 7.9 14.1 10.6 12.5 6.2 9.9 10.3 10.9 11.2 15.1 5.8 10.9 11.0 12.6 12.9 7.8 9.0 13.1 6.3 14.8 7.5 6.7 6.9 11.6 8.4 16.9 13.2 12.2 12.1 13.6 9.7 10.7 6.4 9.7 14.5 11.9 10.9 10.2, 8.2 8.5 13.2 11.3 9.2 7.6 8.8
481.5 436.0 370.4 338.3 357.6 483.6 339.3 413.0 316.9 318.7 456.1 384.1 309.8 250.9 459.5 293.8 393.2 235.0 388.7 371.9 427.6 379.2 436.6 268.5 330.5 370.0 486.5 459.8 424.7 325.8 390.3 265.9 433.2 268.7 348.6 234.6 337.2 280.5 462.6 377.8 448,0 453.1 483.0 386.3 315.1 301.3 325.6 457.3 348.3 341.8 377.0 292.1 318.0 445.8 355.0 411.4 359.7 372.0
69.8 64.5 53.4 51.9 56.8 77.0 38.6 57.9 46.4 61.1 68.8 52.4 53.0 48.3 69.3 64.7 56.5 38.4 60.1 56.7 68.0 51.9 70.9 28.1 67.1 75.6 68.3 59.2 57.7 41.1 53.0 29.8 82.8 53.6 44.8 39.6 65.5 34.0 74.3 57.1 63.2 60.7 75.4 47.6 56.4 41.0 61.7 65.8 47.3 44.1 68.4 44.2 56.5 70.5 53.9 67.5 37.3 47.3
62.8 511.0 426.4 401.2 414.7 576.0 385.4 475.8 370.0 386.1 538.3 442.4 363.2 297.1 542.9 369.1 457.3 273.2 459.2 429.8 507.6 437.6 519.8 296.2 404.6 453.3 562.8 430.2 482.4 375.8 446.4 301.9 522.9 322.3 400.1 274.5 411.6 320,5 556.7 445.0 516.6 525.9 568.2 443.6 365.1 351.6 397.4 535.0 404.4 388.6 455.6 341.8 383.0 528.0 420.8 488.1 400.9 426.0
81.3 75.1 57.2 62.9 58.9 91.8 46.1 63.6 53.1 68.8 82.3 59.2 56.0 48.5 83.4 75.3 64.7 39.6 69.2 55.6 78.5 58.9 84.0 35.1 72.5 84.5 78.4 70.3 63.0 50.1 58.2 36.0 92.6 56.1 51.5 38.8 75.2 40.2 90.0 65.4 69.9 72.8 85.8 57.3 53.2 46.5 71.8 78.2 58.5 47.6 78.6 45.9 65.0 81.5 64.3 76.7 47.8 54.1
493.0 446.6. 373.2 349.3 358.6 499.3 346.8 418.7 323.6 325.1 469.5 390.4 310.7 249.5 473.6 304.4 401.3 235.3 399.2 375.0 439.7 386.1 449.0 268.1 338.3 377.8 494.6 471.1 425.1 334.7 394.3 272.1 440.5 269.1 355.3 236.1 346.2 286.7 483.0 388.9 453.9 465.1 493.2 396.0 310.5 310.8 335.9 469.3 357.6 345.3 387.2 298.6 326.5 457.8 367.0 420.6 363.6 378.8
Check t
(7)
0.0 0.0 -0.3 0.0 -0.7 -0.3 0.0 -0.8 0.0 -0.1 0.0 -0.4 -0.4 -0.8 0.0 0.0 -0.4 -0.4 -0.1 -2.0 -0.1 -0.4 -0.1 -0.1 -0.7 -0.1 -0.1 -0.1 -0.4 0.0 -0.9 0.0 -0.4 -0.4 0.0 -1.3 -0.1 -0.2 -0.5 -1.0 -0.5
e. -0.O 0.0 -1.7 -0.2 -0.1 -0.1 -0.5 -0.8 0.0 -1.1 0.0 -0.3 -A.1 0.0 01.1 -0.1
- 15 -
a median value of .955 which is almost equal to that obtainedfor the pooled regression. However,allowingjointlyfor the two effectsgives very low, and in some cases even negative, elasticities. The question is what is the relationshipbetweenthese variousestimates. The answer is given in terms of the identitiesin (7a) to (7d) above. For inetance,
w(i) = pW(i)p* / p*W(i)p*= b + [b - (b(i)] r[B(i)/W(i)]
(11)
E(w(i)] = (1 + w) - r[B(i)/W(i)J a(i)
(12)
and
To illustrate,using the values for Argentinataken from Tables 1 and 2:
b
=
.9656,
b
-
b(i)
=
-.0344
and
r[B(i)/W(i)] =
481.6/81.3 =
5.92.
Substitutingthese values in (11) results in the value reportedfor w(i) in Table 1. 1/ Thus, the reason that the within commodityestimatordiffersfrom the pooled estimator is largely due to the ratio of the two variances in question rather than due to the differencebetween b and b(i). A similar expressioncan be obtainedfor w(t) and w(it). The latter is of a particular interestbecause of its big variancewith the other estimates. To illustrate this point, write:
1! The results are reportedhere with more decimal points than in Table 1. A minor discrepancystill exists due to roundingerrors.
- 16 -
w(it) = pW(it)p*/ p*W(it)p*
= b + [b - b(i)l r[B(i)/W(it)J + lb - b(t)j r[B(t)/W(it)1
(13)
and therefore,
Ejw(it)J = (1 + n) - a(i) rlB(i)/W(it)J - A(t)
r[B(t)/W(it)J
(14)
AlthoughA(i) and A(t) are relativelysmall, the r's are large. In the case of Argentina, r[B(i)/W(it)J = 41.87 and r[B(t)/W(it)] = 6.07, b - b(i) =
-.03443 and b - b(t) = .176. Using these values in (13) gives, aside from roundingerrors, the value of w(it) presentedin Table 1.
AdditionalResults
23.
As indicatedearlier,A(i) is estimatedby the differenceb(i) - b.
A referenceto Table 1 indicatesthat, for most countries,this differenceis rather small. By way of summary,the differencebetween the median values of b(i) and b is .03, which is small relative to the reference point of unit elasticity. This by itself does not imply that the individualin(i)sare small. They may be numericallylarge but of oppositesigns. To shed light on this point the analysishas to be conductedfor a smallerset of commodities as well as for individualcommodities.
24.
It is often stated that staple foods are more susceptible to
intervention which insulates domestic markets from world prices.
Also,
commodities which are traded under some sort of cartel arrangementsare
17 -
-
expected to show a larger gap in the variations of domestic and world prices. It is thereforeof interestto analyze such commodities. Table 5 presents
country results for
individual commodities based
observations: wheat, coffee and cocoa.
on
11
For wheat, the median value is
approximately0.65, and only 8 out of the 58 countries had a coefficient smallerthsn 0.5. The median value for coffee is 0.68; for cocoa it is within the range of 0.84-0.93. The conclusionis that the policy elasticitiesfor these commoditiesare negative,but on the whole they are modest and by and large world prices are well transmitted.
Pooled CountryData
25.
There is another,not independentquestion:to what extent does the
world price used here represent the domestic country price. This is not a trivialquestion. Recall that the world price is the export unit value and as such it is not an average of domestic pricer.. After all, world trade constitutes only a small fraction of world production. To examine this question,the regressionis estimatedwith all countriespooled together. The analysis is greatly simpliedwhen the sample is balanced,in the sense that there are no missing observations. As not all countriesgrow the same crops every year, a subsample was selected which consistsof 17 commodities,18 countriesand ll years, altogether3366 observations.In such an analysisthe because they all face the individualcountriesserve as repeatedobservations, same world price Pit, for commodityi in year t. The pooled elasticityfor this sample is .976,with R2 = .729.
- 18 Table 3: CEREALSONLY: ELASTICITY OF PRICE WITH RESPECT TO WORLD PRICES
COUNTRY
POOLED
Argentina Australia Austria Bangladesh Belgium-Lux. Brazil Burundi Cameroon Canada Chile Colombia Costa Rica Cyprus Denmark Ecuador Egypt El Salvador Finland France Germany, F.R. Greece Guatemala India Ireland Israel Italy Japan Kenya Korea Rep. Malawi Malaysia Mauritius Mexico Netherlands New Zealand Norway Pakistan Panama Peru Philippines Portugal South Africa Spain Sri Lanka Sweden Switzerland Syria Tanzania Thailand Trinidad Turkey United Kingdom United States Uruguay Venezuela Yugoslavia Zambia Zimbabwe
0.870 0.975 0.916 0.685 0.696 0.786 0.682 0.909 0.919 0.650 0.558 0.572 0.645 0.849 0.613 0.483 0.642 0.596 0.812 0.827 0.791 0.685 0.489 0.949 0.608 0.630 0.908 0.805 1.681 0.633 0.757 0.648 0.668 0.615 0.031 0.604 0.246 0.371 0.716 0.430 0.844 0.881 0.879 0.729 0.460 0.864 0.905 0.755 0.720 0.648 0.923 0.879 0.925 0.686 0.666 0.922 0.872 0.803
I 0.911 0.975 0.742 0.876 0.714 0.901 0.686 1.142 0.988 0.949 0.672 0.596 0.657 0.980 0.774 0.739 0.613 0.592 0.751 0.708 0.840 0.693 0.608 0.895 0.878 0.708 1.227 0.834 1.035 0.400 0.682 0.879 0.773 0.671 0.733 0.638 0.311 0.583 0.751 0.613 0.657 G.611 0.718 0.830 0.517 1.114 0.859 0.819 0.941 0.738 0.809 0.844 0.909 0.918 0.755 0.807 0.680 0.592
WITHIN t 0.859 0.973 0.926 0.245 0.678 0.426 0.684 0.034 0.907 0.537 0.302 0.543 0.296 0.201 0.172 -0.083 0.548 0.318 0.814 0.829 0.779 0.579 0.190 0.947 -0.253 0.378 0.863 0.644 0.634 1.004 0.774 0.000 0.394 0.204 1.051 0.176 0.221 -0.123 0.599 -0.016 0.856 0.906 0.892 0.508 0.238 0.838 0.962 0.525 0.362 0.223 1.115 0.877 0.925 0.167 0.475 0.930 1.126 1.114
BETWEEN it 0.160 0.743 -0.045 -0.084 0.714 0.231 0.830 0.031 -0.018 2.103 0.508 0.646 0.280 0.222 0.132 0.109 0.074 0.017 0.108 0.251 0.088 0.325 0.283 0.065 0.722 0.096 0.063 0.129 0.349 -0.070 0.380 -0.112 0.238 0.060 0.043 -0.130 0.741 0.181 0.373 0.190 0.062 0.046 0.196 -0.136 0.021 0.177 0.282 0.216 1.055 0.121 -0.178 0.422 0.749 0.655 0.164 0.807 0.680 -0.008
i 0.865 0.975 0.932 0.268 0.694 0.453 0.675 0.282 0.913 -0.336 0.286 0.509 0.653 0.202 0.175 -0.098 0.711 0.621 0.820 0.839 0.785 0.654 0.206 0.955 -0.533 0.396 0.870 0.729 0.640 1.079 1.008 0.011 0.380 0.336 1.058 0.447 0.077 -0.196 0.620 -0.131 0.866 0.913 0.898 0.547 0.307 0.842 1.014 0.602 0.176 0.343 1.231 0.880 0.927 0.130 0.496 0.936 1.271 1.313
t 0.967 0.992 0.799 0.908 0.887 0.960 0.680 1.474 1.067 0.704 0.677 0.592 0.864 1.131 0.796 0.761 0.738 0.718 0.798 0.813 0.896 0.759 0.645 0.986 0.881 0.757 1.312 0.895 1.133 0.418 0.771 0.917 0.792 0.778 0.788 0.803 0.262 0.622 0.764 0.625 0.746 0.652 0.756 0.864 0.625 1.188 0.878 0.887 0.927 0.836 0.843 0.897 0.914 0.926 0.777 0.846 0.721 0.650
-
Table 4:
19
-
VEGETABLESONLY: SUMMARY TABLE FOR WITHIN AND BETWEEN COEFFICIENTS
COUNTRY
POOLED
Argentina Australia Austria Bangladesh Belgium, Lux. Brazil Burundi Cameroon Canada Chile Colombia Costa Rica Cyprus Denmark Ecuador Egypt El Salvador Finland France Germany, F.R. Greece Guatemala India Ireland Israel Italy Japan Kenya Korea, Rep. Malawi Malaysia Mauritius Mexico Netherlands New Zealand Norway Pakistan Panama Peru Philippines Portugal South Africa Spain Sri Lanka Sweden Switzerland Syria Tanzania Thailand Trinidad Turkey United Kingdom United States Uruguay Venezuela Yugoslavia Zambia Zimbabwe
1.027 0.706 0.951 0.551 1.182 1.319 0.826 0.988 0.657 0.977 1.068 1.065 1.006 1.223 1.148 1.194 0.824 1.225 1.000 0.840 0.964 1.187 0.735 0.544 0.826 0.792 1.370 0.553 0.954 0.433 0.236 1.132 0.898 1.307 0.207 1.320 0.365 0.939 0.966 0.930 0.981 0.891 0.972 0.782 1.110 0.994 0-825 1.130 0.798 0.882 0.851 1.086 0.596 0.405 0.730 0.726 0.766 0.752,
I 0.740 0.921 0.868 0.468 1.052 1.294 0.360 0.906 0.697 0.539 0.659 0.601 0.958 1.136 0.809 0.994 0.935 0.773 0.825 0.944 0.844 0.632 0.507 0.937 0.720 0.838 1.158 0.475 1.015 0.309 0.803 1.256 0.661 1.012 0.544 0.838 0.328 0.529 0.838 0.676 1.046 0.580 0.929 0.665 0.599 0.960 0.814 0.968 0.354 1.084 0.970 0.887 0.832 0.485 0.820 0.920 0.759 0.580
WITHIN t
It
1.176 0.356 0.466 0.082 0.802 1.017 0.573 -U.171 1.135 1.311 1.270 -0.099 1.083 0.234 0.993 0.114 0.587 0.193 1.350 0.277 -0.350 1.246 1.243 0.459 0.736 1.041 1.292 1.092 1.295 0.067 0.216 1.331 0.729 0.013 1.522 0.419 1.191 0.849 0.769 0.610 1.035 0.234 1.394 -0.202 0.858 0.505 0.275 0.674 0.919 0.386 0.726 0.559 1.427 -0.026 0.566 -0.205 0.815 -0.054 0.462 -0.347 0.023 -0.490 1.008 -0.047 1.005 0.031 0.250 1.528 -0.095 -0.146 1.612 0.237 0.342 -0.508 1.091 0.254 0.991 -0.279 0.681 1.054 0.853 -0.019 1.111 0.282 0.972 0.495 0.802 -0.182 1.364 0.176 0.983 0.182 0.794 0.250 1.185 0.337 0.987 -0.293 0.349 0.798 0.651 0.264 1.196 0.937 0.375 0.270 0.292 -0.152 0.654 0.094 0.585 0.491 0.788 1.169 0.805 -0.132
BETWEEN i t 1.213 0.496 0.966 0.631 1.325 1.333 1.117 1.033 0.619 1.434 1.319 1.271 1.067 1.308 1.345 1.419 0.761 1.606 1.220 0.773 1.098 1.455 0.875 0.249 0.934 0.740 1.493 0.601 0.926 0.493 0.038 1.057 1.099 1.634 -0.091 1.715 0.408 1.122 1.049 1.071 0.959 1.175 1.002 0.847 1.380 1.047 0.841 1.224 1.046 0.811 0.684 1.247 0.373 0.327 0.679 0.483 0.770 0.848
0.770 0.994 0.854 0.524 1.046 1.418 0.369 0.977 0.742 0.561 0.740 0.614 0.974 1.139 0.865 1.053 1.017 0.808 0.823 1.048 0.890 0.704 0.507 0.961 0.734 0.859 1.290 0.530 1.188 0.365 0.918 1.363 0.762 1.074 0.608 0.899 0.389 0.553 0.923 0.676 1.175 0.608 0.975 0.741 0.703 1.016 0.851 1.022 0.412 1.150 1.016 0.918 0.909 0.541 0.885 0.945 0.726 0.644
- 20 Table 5: ELASTICITYOF PRODUCER PRICES WITH RESPECT TO WORLD PRICES FOR SELECTED COMMODITIES
Country
Wheat
Argentina Australia Austria Bangladesh Belgiuw-Luxembourg Brazil Burundi Cameroon Canada Chile Colombia Costa Rica Cyprus Denmark Ecuador Egypt El Salvador Finland France Germany Greece Guatemala India Ireland Israel Italy Japan Kenya Korea, Rep. Malawi Malaysia Mauritius Mexico Netherlands New Zealand Norway Pakistan Panama Peru Philippines Portugal South Africa Spain Sri Lanka Sweden Switzerland Syria Tanzania Thailand Trinidad Turkey United Kingdom United States Uruguay Venezuela Yugoslavia Zambia Zimbabwe
0.70118 0.90514 0.58824 0.65485 0.62648 O.E1396 0.51779 1.15186 0.95447 0.83626 0.62013 0.55367 0.47708 0.89237 0.52851 0.56167 0.62115 0.41253 0.58173 0.64565 0.71502 0.6868 0.40493 0.7066 0.82092 0.65518 1.11312 0.77996 0.90314 0.50035 1.00846 0.69214 0.58558 0.58419 0.70093 0.60052 0.09736 0.49686 0.70418 0.60916 0.42215 0.45356 0.5463 0.58647 0.48239 0.90987 0.68743 0.63865 0.9951 0.72659 0.70537 0.70782 0.95847 1.15293 0.80533 0.62647 1.18678 0.62393
Coffee
Cocoa
0.65206 0.67989 0.53017
1.1923 0.61456
0.61911 0.94008
0.61176 1.07543
0.62852
0.97872
1.05404
0.92683
0.86177 0.14221
0.97473
1.00593 0.42954 0.83695
0.84433
0.85832
0.83584
0.42537 0.73226 1.01799
1.02345 1.04622 0.75422
0.55763 0.80918
1.08503
0.61596 0.46107 0.60366
0.49762 0.70243
0.83133 0.05066 0.71453 0.43381
0.50441
-
26.
21 -
A similar analysis for subsets of commoditiesgives the following
elasticitiesfor the pooled regressions,with R2 reportedin the parentheses: cereals .839 (.86), vegetables.933 (.61), oilseeds, .963 (.70), fruits .700 (.70), beverages .729 (.73), fibers .719 (.73), tobacco.599 (.86), livestock, .980 (.71).
27.
Turning to individual commodities,with the number of countries
follow the R2 in the parentheses,the resultsare: rice .692 (.83; 45), barley .770 (87; 49), maize .820 (.86; 58), rye .810 (.89; 39), oats .796 (.88; 51), millet .853 (.89; 34), sorghum .858 (.82; 45), wheat .693 (.85; 58), rubber .518 (.82; 10), sugar .199 (.87; 54).
28.
applicationis that the results A potentialproblem of any emLpirical
emerging from the study reflect the idiosyncrasiesof tha way in which the data are collected,estimatedor reportedrather than the underlyingeconomic effects. This possibility is inherent in applied work and can never be eliminated. However, in an effort to check for results that are consistent and independentof the underlyingdata source, the proceduredescribedabove was repeated on a separate data set.
Tables 6 through 8 report results
obtainedby repeatingthe procedureon 25 years of producerprices in the EC10 as published by Herlihy et. al. (1989). Producer prices for barley, butter, cattle, cheese, eggs, maize, milk, oats, pork, poultry, pototoes, rice, rye, sugarbeets,and wheat are includedin the dati. Country coverage includes Denmark,France, Greece, Ireland,Italy, the Netherlands,the United Kingdom,and West Germany. In addition,Belgiumand Luxembourgare treated as a single region. Pooled EC-aggregate resultsare reported, as well. The
-
22 -
Table 6: ELASTICITYOF PRODUCERPRICES WITH RESPECT TO WORLDPRICES FOR THE EURO'EANCOMMUNITY
b (pooled)
Standard Error
Belgium/Luxembourg Denmark France Germany F.R. Greece Ireland Italy Netherlands United Kingdom
0.979 0.957 1.009 0.956 0.988 0.909 0.991 0.935 0.957
0.023 0.019 0.021 0.021 0.020 0.029 0.020 0.022 0.019
42.78 49.76 48.85 45.21 49.65 30.93 49.51 42.96 51.28
0.85 0.88 0.87 0.86 0.90 0.81 0.87 0.85 0.89
EC Average
0.967
0.007
134.16
0.86
Country
T-Score
Adjusted R-Squared
I"
R
fi
ii
' 0
o
IOOO
00
'0
0
.
_S
0.00000000
.0 0.0H
oeoooo,ooo
0Fw..
Table 8: POXi
P
ITUY 1 EARICE
ll
WAH T MDW
PD FR1; Fat S1EEM) aZ
ITIlES
Federal Re~~~~k Greece Irelmd
Demirk
France
of Cermny
0.71435 0.52201 0.95788 0.92142 0.63558
0.97215 1.10116 1.19329 1.28692 0.91878
0.68419 0.73629 0.98713 1.02179 0.78698
0.63153 0.71555 0.82901 0.77285 0.73947
0.87775
1.18823 0.77850 0.72207 0.95483 1.07921 0.84387 0.74074 0.66116
1.7'128 0.99771 0.81864 0.95039 1.10471 0.91158 0.88504 0.90719
0.69096 0.60682 0.88884 0.84411 1.19786 0.68343 1.23026 0.71647 0.57090 0.60383 0.90602 0.69711 0.79767 0.61932
1.39492 0.75335 0.75415 0.87973 0.90102 0.82241 0.71826 0.72264
0.66893 0.74656
1.55054 0.84547 0.76728 0.74830 1.01842
CxhrDdity
ltmoug
Barley Butter Cattle heese FgpS Maize Mflk Oats Pigs 1tu1try Potatoes Rye S9arbeeta Wheat
0.41084 0.75519 0.64540 0.53690
1.1050B 0.78471
0.6982C
Italy 0.71782 0.65394 0.87287 0.83674 0.54785 0.85417 1.39304 0.77350 0.64235 0.53365 0.94985 0.84759 0.62443 0.77443
ltbherlaii
lk~~~dtai KlzWIm
0.76180 0.87974 0.91107 1.03998 0.76204
0.86865 1.07247 1.02313 1.11623 0.63434
1.31213 0.71949 0.73252 0.93656 1.01588 0.77341 0.78962 0.67367
1.17299 0.81865 0.73517 0.91322 0.92297 1.05246 0.79526 0.89776
iloEb Gmdty 0.76880 0.786C0 0.97425 0.980m0 0.77862 0.76880 1.29359 0.79441 0.71788 0.77015 0.96148 0.84759 0.81790 0.76830
- 25 -
producer prices were originally reported in the domestic currency of each country. Official exchange rates as reported in the same publicationwere used to convert the prices to a dollar denomination. World prices were derived by dividing world expo:t values by world export quantities as publishedby FAO.
29.
While the country and commoditycoverageavailablefrom the EC data
is more limited than in the original data set, the EC data provide an interesting and robust check on the findings. EC agriculturalpolicy is active, well-financed, and sophisticatedin its execution and reporting mechanisms. Because it is well-financed,any wedge between domestic and internationalprices could be expectedto be more long-livedthan in lowerincome countries.
30.
The results in Tables 6 and 7 confirmthe earlier results. Although
the commodity coverage is different,the pooled elasticitiesfor countries common to
both samples are remarkably similar.
The producer price
elasticitiesfor the EC countries in Table 1 range from 0.91 to 1.04, while the elasticitiesof Table 6 range from 0.91 to 1.01. The adjustedR2s range from .81 to .90. As with the earlierresults,the commodityand time effects are small.
Prom Table 7, the median for b(i) is .978 as compared to the
pooled result of .967, yielding a commodity effect (wi)
of 0.01.
The
absolute value of the time effect (11t) is slighly larger at -0.08. Within commodity and time effects are reported in Table 7 as well.
Again the
estimetedvalues are consistentwith results from the larger data set. Table 8 provideselasticitiesfor individualcommodities. The resultsfor wheat can
- 26 -
be directly compared to the resultsin Table 5 which were obtainedfrom the larger data set.
Again, despite different sample years, the results are
fairly comparabls.
DISCUSSION
31.
the deviation What do the results show? By way of generalization,
from unitary elasticityis, on the whole, surprisinglysmall; and while there appear to be some differencesamong commoditiesand commoditygroupings,the results appear quite robust regardlessof the manner in which the data are pooled
32.
or disaggregated.
The deviation from unitary elasticity is in part due to policy
measures and in part due to domestic inputs which are not necessarily synchronizedwith world agriculturalprices. 1/
This does not imply that
policies generatedwith respect to particularproductsare not important in affecting the prices of these products. They certainly affect the price levels and whenever a country taxes agriculture the domestic prices will differ from world prices. However,the questionwhich is of concernto us is not the existenceof price interventionmechanisms,but rather whether or not with world prices. The evidencein this these mechanismsmove systematically paper suggeststhat they do not.
1/ For an analysisof this subjectsee Mundlak,Cavalloand Domenech.
- 27 -
33.
This brings up the next question: how about policieswhich are not
related to world prices? These, by definition,will not bias the coefficient and a unitary elasticitywill be observed. What is then the role of world prices in this case? The empiricalanswer is given by the degree of fit of the model, that is, by the proportionof the total varianceof domesticprices which is accountedfor by world prices. The values are relativelyhigh.
34.
The implicationof this result is that technicalchange (and other
shocks of a more permanentnature) which originatein one country but which are big enough to affect world prices, eventually affect prices in all countries. The passive countries,which are the shock takers, cannot avoid them for very long because it is too costly to do so.
Realizingthis cost
limitationto an autonomouspolicy, it seems more reasonableto use, from the outset,resourcesto implementthe necessarystructuraladjustments;including the enhancementof technical change, if this is the source of the shock, rather than to delay the process through taxation. This is certainlya very general statementand it has to be properlyinterpretedwhen it comes to a particularpolicy; however, it is mentioned here in order to put possible implicationsof the analysiswithin a broaderframework.
35.
Finally,we considerhere a largenumber of commodities. In general,
the trade of a countryconcentratesonly in a few commodities,while trade in the others may be totally unimportant. The prices of the non-traded commoditiesis determinedby domesticsupply and demand and therefore,on the surface, should be independentof world prices. The explanation for the observed dependence is basically an extension of factor-priceequalization.
-
28 -
The prices of the traded commoditiesdetermine the prices of the specific agriculturalresourcessuch an land, capitaland labor in the country.
36.
To conclude,even though domesticpoliciesaffect prices, they cannot
prevent the covariationsof domesticprices with world prices in the long run and thereforedo not change the developmentscaused by fundamentals. There is a simple reason for it. Price distortionis costly and public resources,just like privateresources,are finite.
- 29 -
References Abler, D.C., (1987), "Logrolling on Farm Legislationin the U.S. House of Representatives", a Ph.D thesis, Departmentof Economics,The University of Chicago. Anderson, K., Y. Hayami, and M. Honma, (1986), "Growth of Agricultural Protection". In Kym Anderson, Yujiro Hyami, and others. Political Economyof AgriculturalProtection: The Experienceof East Asia, Sydney, Australia: George Allen & Unwin. Bale, M.D. and E. Lutz (1978), Trade Restrictionsand InternationalPrice Instability,World Bank StaffWorkingPaper 303, Washington,D.C. (1981). "Price Distortions in Agriculture and Their Effects: An InternattonalComparison",American Journal of AgriculturalEconomics,Vol. 63, 1:8-22. _
Binswanger, H. and P.L. Scandizzo (1983), Patterns of hgricultural Protection. Report ARU15, Washington,D.C.: World Bank, Agricultureand Rural DevelopmentDepartment,OperationsPo'icy Staff. Bullock, D.S., (1989), "The Volatility of Government Transfers to U.S. Agriculture, A Political Pressure Group Approach", A Ph.D thesis, Departmentof Economics,The Universityof Chicago. Bureau of AgriculturalEconomic,Australia(1985). AgriculturalPolicies in the European Community: Their Origin, Nature and.Effects on Production and Trade. Policy Monograph2. Canberra:AustralianGovernmentPublishing Service. Gardner, B. (1987). McMillan.
The Economics of Agricultural Policies, New York:
Herlihy, Michael, Stephen Magiera,Richard Henry, and Kenneth Baily (1989). Agricultural Statisticsof the European Community, 1960-1985, StatisticalBulletin770, USDA, Washington,D.C. McCalla, A.F. (1969). "Protectionismin InternationalAgriculturalTrade, 1850-1968". Agricultural History43, 3 (July): 329-44. Miller, T.C., (1986), "Explaining Differencesin AgriculturalPrice Policy Across Countries and Across Commodities Using a Model of Competition Between PressureCroups". A Ph.D thesis, Departmer.t of Economics,The Universityof Chicago. Mundlak, Y. (1989), "Agricultural Growth and World Developments",In Maunder, A., and A. Valdes (eds.),Agricultureand Governmentsin an Interdependent World, Proceedings of the XX InternationalConference of Agricultural Economists, Dartmoutha, Aldershot, England.
- 30 -
-, Cavallo, D. and Domenech. R. "Economic Policies, Tradability and Sectoral Prices, Argentina, 1913-84, The World Bank Economics Review,
(forthcoming).
Phipps, T. (1985). Farm Policies and the Rate of Return on Investmentin Agriculture. Occasional paper, Washington D.C.: American Enterprise Institute. Rausser, G.C. and J.W. Freebairn (1974), "Estimationof Policy Preference Functions: An Application to U.S. Beef Import Policy", Review of Economicsand Statistics,Vol. 56, No. 4, pp. 437-449. and D.P. Stonehouse(1978),"PublicInterventionand ProducerSupply Response",American Journal of AgriculturalEconomics,Vol. 60, No. 5, pp. 885-890. Shei, S.Y. and Thompson, R.L. (1977), "The Impact of Trade Restrictionson Price Stability in the World Wheat Market" ,American Journal of AgriculturalEconomics,Vol. 59, (4), 628-638. The World Bank, (1986) World DevelopmentReport, 1986, Washington,D.C., The World Bank.
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Do Developing CountriesDiscriminate AgainstTheirOwnTrade? WPS372 Long-Term Outlookfor the World ShahrokhFardoust Economy:IssuesandProjections AshokDhareshwar forthe 1990s
February1990
J. Queen 33740
PREWorkignPaper Series
AhAuthor
DA
Contact for pager
WPS373 Are Better-offHouseholdsMore Unequalor LessUnequal?
LawreticeHaddad RaviKanbur
March1990
J. Sweeney 31021
WPS374 Two Sourcesof Bias in Standard Partial EquilibriumTradeModels
SamuelLaird AlexanderJ. Yeats
February1990
J. Epps 33710
March1990
C. Spooner 30464
March1990
S. Shive 33761
March1990
W. Pitayatonakarn 37666
WPS375 RegionalDisparities,Targeting,and GauravDatt MartinRavallion Povertyin India WPS376 The World EconomyIn the Mid-1990s: AlternativePatternsof Trade and Growth
Colin I. Bradford,Jr.
WPS377 Securityfor DevelopmentIn a Post-BipolarWorld
John Stremlau
WPS378 How Does the Debt Crisis Affect PatricioArrau Investmentand Growth? A Neoclassical Growth ModelApplied to Mexico WPS 379 Implicationsof Policy Gamesfor MiguelA. Kiguel Issuesof High InflationEconomies NissanLiviatan WPS380 Techniquesfor Railway Restructuring
Lee W. Huff LouisS. Thompson
AlanGelb WPS381 Trade in BankingServices: Issuesfor MultilateralNegotiations SilviaSagari
WPS382 The IndonesianVegetableOils Sector: Modelingthe Impactof Policy Changes
DonaldF. Larson
March1990
D. Gustafson 33714
WPS383 On the Relevanceof World AgriculturalPrices
Yair Mundlak DonaldF. Larson
March1990
D. Gustafson 33714
WPS384 A Reviewof the Useof the Rational ChristopherL. Gilbert Expectations: Hypothesisin Models of PrimaryCommodityPrices WPS385 The Principlesof Targeting
Timothy Besley RaviKanbur
March1990
J. Sweeney 31021
WPS386 ArgentinasLaborMarketsin an Era of Adjustment
Luis A. Riveros CarlosE. Sanchez
March1990
R. Luz 39059
WPS387 Productivityand Externalities: Modelsof Export-LedGrowth
Jaime de Melo ShermanRobinson
March1990
M. Ameal 37947
