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Agricultural & Applied Economics Association

Testing Market IntegrationAuthor(s): Martin RavallionSource: American Journal of Agricultural Economics, Vol. 68, No. 1 (Feb., 1986), pp. 102-109Published by: Oxford University Press on behalf of the Agricultural & Applied Economics Association

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T e s t i n g M a r k e t IntegrationMartinRavallionA model of spatial price differentials for a tradable good is proposed which avoidsthe inferential dangers of received methods using static price correlations. Theproposed method also extracts more information on the causes of price differentialsfrom the same data. The method is illustrated using monthly rice price data forpostindependence Bangladesh, including the very substantial regional price shocksduring the 1974 famine. Impediments to market integration are indicated.Key words: Bangladesh, famine, market integration, modeling spatial pricedifferentials.

Though much maligned, static price correla-tions remain the most common measure ofspatial market integration in agriculture.' Bythis method, bivariate correlation or regres-sion coefficients are estimated between thetime series of spot prices for an otherwiseidentical good or bundle of goods at differentmarket locations.2There are a number of inferential dangers inbivariate modeling of this sort. The followingexample expands on an important point madeelsewhere in the literature on market perfor-mance in agriculture (Blyn, Harriss). Supposethat trade is infinitely costly between two mar-ket locations but that the time series of prices

at the two locations are synchronously, identi-cally, and linearly affected by another vari-able. Possible examples include the price of arelated third good traded in a common marketor a shared dynamic seasonal structure in pro-duction. Then one can readily express price inone market as a linear function of price in theother market, with slope unity, even thoughthe markets are segmented. Of course, anymeasurement errors or omitted variableswould yield imprecision in a test equationbased on the static bivariatemodel. But it re-mains that under these conditions the receivedmethod fails hopelessly as a test for marketintegration. The likelihood of serial depen-dence in the residuals obtained from a staticmodel calibrated to nonstationary time seriesalso leads one to be suspicious of the conclu-sions drawn from this method (Granger andNewbold).However, with the same data, the staticbivariate method can be readily extended intoa dynamic model of spatial price differentials.By permitting each local price series to haveits own dynamic structure (and allowing forany correlated local seasonality or other char-acteristics) as well as an interlinkage withother local markets, the main inferential dan-gers of the simpler bivariate model can beavoided.3 Most important, the alternative hy-

The author is a research fellow, Queen Elizabeth House, Oxford,presently at Department of Economics, Research School ofPacific Studies, Australian National University.Financial assistance from the Overseas Development Adminis-tration (U.K.) is also acknowledged, subject to the usual dis-claimer.The author wishes to thank the Journal's referees for usefulcomments on an earlier draft.Review was coordinated by Hans P. Binswanger, associateeditor.

Another, less common, method of testing for market integra-tion is to calculate the spatial variance of prices and test for long-run convergence to zero or close to it (for an example, see Hurd).However, it can be readily shown that if prices at different mar-kets are generated by identical but independent stationary auto-regressive processes then they will asymptotically converge tozero variance (see Ravallion, forthcoming). Thus, nothing can beinferred about the interlinkage of markets from the results of suchtests.SA statistically significant coefficient is sometimes taken to im-ply market integration. Of course, if testing for a nonzero result,the null hypothesis being (invariably) rejected is market segmenta-tion, i.e., the absence of any relationship between the two priceseries. One could easily reject this without accepting that the mar-kets are integrated in the sense of having a reasonably stable pricedifferential. This problem can be readily solved by changing to theappropriate null hypothesis for market integration, as some stud-ies have done.

3Spurious co :elations can also be avoided by filteringthe priceseries prior to calculating pairwise correlations; this can be doneby testing for residual cross correlations amongst univariateARIMA models of each price series (Haugh; Fase applies themethod to international share prices). Rather than follow this ap-proach here, I have preferred to formulate and test market integra-tion conditions as nested hypotheses within an explicit mul-tivariate model of spatial market structure. As will be seen, thispermits a much greater range of null hypotheses of economic in-

Copyright 1986 American Agricultural Economics Association

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Ravallion Testing Market Integration 103potheses of market integrationand marketsegmentation anthen be encompassedwithina more general model and so tested as re-stricted forms.A dynamic model also has the advantagethat one can distinguishbetweenthe conceptsof instantaneous market integrationand theless restrictive dea of integration s a long-runtarget of the short-rundynamic adjustmentprocess. This distinction seems important. nmanysettingsit will be implausible hattradeadjustsinstantaneously o spatial pricediffer-entials, and so one would be reluctantto ac-cept short-runmarketintegrationas an equi-libriumconcept. But, given enoughtime, theshort-runadjustmentsmightexhibit a patternwhich converges to such an equilibrium.Ifshort-runntegrations rejected,thenit wouldbe nice to know if there is any long-run en-dency towardmarketintegration.By permit-ting the investigator o answer this new ques-tion, a dynamic model can extract moreinformation boutmarkets romthe samedataused by the static model.Thispaperoffers an approach o testingag-riculturalmarket ntegrationalongthese linesand illustrates it using data on the interre-gionalpricedifferentialsor rice inBangladeshduring he turbulentpostindependenceperiod,1972-75.The periodof analysishas beencho-sen to include the substantialregional priceshockswhichoccurredduring he 1974 amine(SeamanandHolt, Alamgir,Sen). ElsewhereIhaveexamined he intertemporal erformanceof rice markets n Bangladeshduringthis pe-riod (Ravallion1985b).

Why StudyMarketIntegration?It is well-known hat,underregularly ssumedrestrictions on the continuity, slope, curva-ture, and domain of utility and productionfunctions, a competitive equilibriumfor acomplete set of markets will exist and beefficient in the Paretiansense. In general,thiswill also hold for the spatialcompetitiveequi-libriumof aneconomyconsistingof a set of N-regions among which trade occurs at fixedtransportcosts (Takayamaand Judge). Suchan equilibriumwill have the propertythat, if

trade takes place at all between any two re-gions, then price in the importing regionequals price in the exportingregion plus theunit transportcost incurredby moving be-tween the two. If this holds then the marketscan be said to be spatially ntegrated.Market ntegrations by no meanssufficientfor the Pareto optimality of a competitiveequilibrium.Even whenbasedon a soundem-piricalmethodology,the conclusion thatmar-kets are well integrateddoes not, of itself, im-ply an efficient spatial allocation (see, forexample, Newbery and Stiglitz).Nonetheless, one can be interested in em-piricallytestingfor spatialmarket ntegrationwithoutwishingto rest the case for or againstPareto optimalityon the outcome. Measure-ment of marketintegrationcan be viewed asbasic data for an understanding of howspecificmarketswork.For example, in the presentsetting,a studyof the dynamicsof market ntegrationshouldthrow some light on one of the oldest ques-tions concerning famines in market econo-mies: how long can an initially ocalizedscar-city be expected to persist? Policies ofnoninterventionwith marketsduringfamineshave often been advocatedor defendedalongthe following lines: given that the necessarytransportnfrastructurexists, the unaidedre-sponse of graintraders to the inducedspatialprice differentialswill quickly eliminate anylocalized scarcity. For example, this assump-tion was a cornerstoneof the governmentofIndia'sfaminereliefpolicy duringmuchof thenineteenth and early twentieth centuries(Bhatia,Ambirajan).Againstthis view, it hasoften been argued that markets will be tooslow to respond;for example, Ambirajan e-portsthatduring he severefamineof the mid-1870s, the local government in Madras re-belled against the government of India'spolicy, arguing hat"if timewere givento themarket,the necessary grainwouldeventuallycome, but time was whatcould not be given"(Ambirajan, . 95).4 Since independence,gov-ernmentsof India and other countries in thesubcontinenthave tended to adopt highly in-

terest than is possible with simple correlations. However, the pre-whitened bivariate correlation method is likely to be useful insome applications, particularly if one has no a priori basis foridentifying a model of market structure; for an example, in a simi-lar setting, see Ravallion (forthcoming).

4 The assumption that a localized shortfall will be reflected inspatial price differentials has also been questioned. For example,Sen has pointed to the possibility that the shortfall may be associ-ated with a fall in incomes and, hence, demand (due to, for ex-ample, the effect of a drought on employment) and that this willmitigate the upward price response needed to encourage imports.Elsewhere I have examined the theoretical and empirical groundsfor this argument (Ravallion, forthcoming).


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104 February 1986 Amer. J. Agr. Econ.terventionist policies concerning food grainmarkets(see, for example, Bhatia, chap. 12;George). An empirical assessment of thespeed of marketadjustmentto spatial pricedifferentials may help resolve this long-standingdebate.Modeling Spatial Market StructureThe specificationof an econometric modelofspatialprice differentialswill depend, in part,on assumptions about spatial market struc-ture. Here I shall assume that there exists agroupof local (rural)marketsanda singlecen-tral(urban)market. While theremaybe sometradeamongthe local markets, t is trade withthe central market which dominates localpriceformation.Dependingon the numberoflocal markets and their sizes, one can alsoposit that the central market price is in-fluencedby variouslocal prices.Thus, the static patternof price formationamongN markets,wheremarketI is the cen-tralmarket,maybe summarizedby a model ofthe form(1) Pl = fl(P2,P3, .. ,* PN,XI)(2) Pi =

fi(Pl,X,)(i = 2, . . , N)

where X, (i = 1, . . . , N) is a vector of otherinfluenceson local markets. The functionsf.(i = 1,..., N) can be thought of as solutionsof the appropriate onditionsfor marketequi-librium, taking account of the main spatialchoices and the costs of adjustment facingtraderswhendecidingwhere to sell. The deri-vation of these functions does not seem topose any newtheoreticalproblemsorinsights,and so I shall take them as given.At first sight, this model only seems wellsuitedto a simpleradialconfiguration f mar-kets in which each local market is directlylinkedwith the centralmarket.In most appli-cations a more plausibleconfigurations onein which some local marketsonly tradewiththe central marketvia other markets. How-ever, providedone is willingto forgo identifi-cation of at least some of the nonradial ink-ages, the radialmodel given by equations(1)and (2) can often providea useful characteri-zationof morecomplexmarketstructures.Bysubsuming inkages between intermediate o-cal markets,an (implicit)binaryrelationcanbe obtained between each local marketandthe centralmarket,and so the radialmodel ispreserved.

Clearly, this approachhas its limitations.Since spatialprice differentialsbecome moreaggregated, t produces inferentialdifficultieswhen investigating he linkage ocationof anyrevealed mpediment o trade.Indeed,if thereis a large number of local market linkages,then(dependingon what other local non-pricevariables are relevant)it may become impos-sible to identify even the indirect radial ink-age. As always, the merits of the model needto be judged in its specificapplications.Since the main aim in estimating he modelis to test alternativehypotheses to do withmarket integration, its econometric spec-ification should not prejudice the outcome.This is most easily assured if the alterna-tive hypotheses can be nested within a moregeneral model and so tested as restrictedforms. For estimation, t is also convenient toassume that the functions f, (i = 1, . . . , N)can be given a linear representationby in-troducingan appropriate tochastic term.The econometric version of equations (1)and(2) shouldalso embodya suitabledynamicstructure;as is well known, dynamiceffectscan arise from a numberof conditionsin theunderlyingbehavioral relations includingex-pectations ormationandadjustment osts (fora survey of the possibilities see Hendry, Pa-gan, and Sargan).Combininghese considerations, he follow-ing econometric model of a T-period eries ofpricesfor N regionsis assumed:

n N n(3) P, => alP1t-1 + btPkt-j=1 k=2j=0

+ XSC1 + ell(4)

Pi,= >

a.Pit1+ >

b1.Pltjj=1 j=0o+ Xitci + eit (i = 2, . . . , N)where the e's are appropriate rrorprocessesand the a's, b's, and c's are fixed.A wide rangeof possible hypothesesaboutinterregionaltrade can be formulated andtested as parameterrestrictionson equations(3)and(4). Indiscussingthese, I shall concen-trate on equation(4) since, in many applica-tions, equation(3) will be underidentified. Ishall return o this point later.)

In terms of the parametersof equation(4),the following hypotheses will usuallybe test-able.(a) Market segmentation. Central market


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Ravallion TestingMarket ntegration 105prices do not influenceprices in the ith localmarket f(5) b 0 (j = 0 ... , n),in which case the data would be better de-scribedby the correspondingrestrictedformof equation(4).(b) Short-run market integration. A price in-crease in the central market will be im-mediatelypassed on in the ith marketprice if(6) bio = 1.Of course, there will also be laggedeffects onfutureprices unless, in additionto (6):(7) aij = byij= 0 (j = 1,... , n).If both (6) and (7) are accepted as parameterrestrictions,then one can say that market isintegratedwith the centralmarketwithinonetimeperiod.A weakerform of market ntegra-tion will also be tested in which the laggedeffects need only vanish on average:

n(8) aij + b.

= 0.j=1

(c) Long-run market integration. A long-runequilibriums one in which marketprices areconstant over time, undisturbedby any localstochastic effects. So consider the form thatequation (4) takes when Pit = P'i, Pit = P*1,and el, = 0 for all t; thennP*IZ

bi+Xci(9) P*i = j=o n

1- ai1=1It can be seen that market ntegrationnow re-quiresthatn n

(10) a + b = 1.j=1 j=0

If this parameterrestriction s accepted, thenthe short-runprocess of price adjustmentde-scribed by the model is consistent with anequilibriumn whicha unit increasein centralprice is passed on fully in local prices. Noticethat acceptance of the short-runrestrictionsimplies long-runmarket integrationbut thatthe reverse is not true.If the long-runmarket ntegration estrictionis accepted, then more efficient estimates ofthe remainingparametersand more powerfulstatisticaltests will be possible if the model is

reestimated with long-run integration im-posed. For example, under long-run ntegra-tion, equation(4)can be written n the follow-ing equivalentform:(11) APit = (ail - 1)(Pit-i

-Pit-)n

+aij(Pit-j

-P1,t-)

+bioAP1tj=2

n-1 j+ (bio - 1 + aik + bik) Plt-j=1 k=l+ Xici + ei,.

This is a member of the class of error cor-relation models discussed by Hendry andRichard,and Hendry,Pagan,and Sargan.Byinterpretation, hanges in local prices are at-tributed o changes in centralprices and pastspatialprice differentials; he latter variablesallow for the possibilitythat the marketsarenot observed in an integratedequilibrium t agiven point in time, and so there is feedbackfrom prior disequilibria.(See Salmon for acontrol theoretic interpretationof errorcor-rectionmodels.)There are a numberof possible variationsaroundthese three mainhypotheses, particu-larly to do with the nonprice influences oneach local market.Theexistence of significantlocalizedmarketcharacteristicsalso indicatesthatarbitrages imperfect n eliminatingpricedifferentials.Thus, stronger ntegration ondi-tions can be formulatedby addingc = 0 to theabove restrictions. I shall returnto this pointin discussingthe resultsfor Bangladesh.One problemthat may arise when applyingthe aboveapproach s multicollinearity mongthe regressorsof the unrestrictedmodelgivenby equation 4). Theinferentialdifficulties hatcan arise are well known;a high standarder-roron, forexample,the coefficientfor centralmarketprice maybe dueto its highcorrelationwithlaggedlocal pricesrather hanweak mar-ket integration.However, such collinearity sa poor reason for adoptinga more restrictivemodelspecification.Thebiasinducedby omit-ting relevant variables is well known. Also,the removal of such variablesfrom a modelcan actually worsen the precision in estima-tion of the remaining egressorsevenwhen thetwo sets of variables are highly(albeitimper-fectly) correlated(Davidsonet al. give an ex-ample).Rather hanimposeparameter estric-tions to reducecollinearity,a betterapproachin this settingis to firsttest for long-run nte-


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106 February 1986 Amer. J. Agr. Econ.gration; since this restriction involves all ofthe price variables, a good deal of the col-linearity danger is avoided (possible correla-tions with Xi remain). If long-run integration isaccepted, then it should be imposed on themodel with subsequent tests based on a re-stricted form such as equation (11), for whichthe collinearity problem is likely to be a gooddeal less severe. If long-run integration is re-jected, then this avenue is best closed off andso one should apply extra caution in rejectingthe short-run integration conditions.

Spatial Price Differentials in Bangladesh1972-75In this section the general approach outlinedabove will be applied to monthly district-leveldata on rice prices (coarse quality) for Bangla-desh in the post-independence period as moni-tored by the Bangladesh Directorate of Ag-riculturalMarketing. A thirty-six-month serieswas used, from July 1972 to June 1975. Thisperiod was chosen for its unusual price turbu-lence; there were numerous localized scar-cities (particularly during the famine year1974)and disturbances to trade and communi-cations (for a detailed economic history of theperiod see Ravallion and van de Walle). Cer-tainly this should be a hard (but important)test of the market integration restrictions.The demand for rice in Dhaka, the capitaland (by far) largest city, would appear to bethe dominant influence on interdistrict ricetrade in Bangladesh. And this is reflected inthe country's heavily monocentric transportsystem. Aside from the old transport linkagesto Calcutta in West Bengal (the previous colo-nial capital of Bengal prior to partition in1947), the main road, rail and water routes arefocused on Dhaka. The radial model seemswell suited to this setting.It was decided to concentrate on Dhaka'strade linkages with the main surplus districtsof its rural hinterland: Mymensingh, Bogra,Rangpur, Dinajpur, and Sylhet. During 1974these five contiguous districts of northernBangladesh had the five highest levels of foodgrain production per capita among the nine-teen districts of Bangladesh, while the neigh-boring district of Dhaka had one of the lowest(rank 16), see Alamgir (table 6.29) and Sen(table 9.7) for details. Table I gives summarydata for these districts. Even taking first dif-ferences, the correlation coefficients betweenDhaka and local prices are quite high and rea-

sonably significant. The average price differ-entials also accord roughly with the distancesto Dhaka, although their standard deviationsare very high indeed. It is hard to believe thattransport costs could vary so much. This leadsone to doubt the existence of reasonably sta-ble price differentials determined by transportcosts, and so the picture given by the correla-tion coefficients may well be spurious.Three variables were selected as likely non-price influences on local markets, aiming tocapture the seasonality in rice production, anylocalized effects of the 1974 famine and anylocal time trend. The main (aman) harvest isfrom mid-November to early January andseems to be quite adequately described by asingle dummy variable for November and De-cember. It is widely agreed that the worstmonths of the 1974 famine were August, Sep-tember, and October; a single dummy variablewas included for these months.As has been noted, there will often bedifficulties in identification of the parametersof equation (3). But equation (4) will generallynot pose such problems. The simultaneity inthe system can be dealt with easily using anappropriate instrumental variables estimatorsuch as the least squares estimate of (4) ob-tained by replacing P1, by its predicted valuesfrom the reduced-form equation obtained byusing equation (4) to eliminate P1, (j = 2 ...N) from (3). This is the method of estimationused here. Reduced-form estimates of P1,were obtained from a regression of this vari-able against its own lagged values, the laggedvalues of prices in all other districts and theseasonality, famine, and time trend variables.All seventeen districts of Bangladesh in thedata set were used although (because of thenumber of degrees of freedom needed) onlyone-month lags were included.

The way transport charges are levied offersa guide to appropriate model specification inthis setting. During the winter of 1983-84, Iasked a number of wholesale rice merchants atBadamtali in Dhaka how the rice was trans-ported and how much it cost to do so. I wastold that by the main modes of rice freighttransport (road and ferry), the transport costfor any trip largely depends on the number oftrucks involved. And so, when comparinggiven markets, transport cost can be treatedas fixed per unit quantity rather than anad valorem charge.5 Thus, an equilibrium in

- For example, transport cost from Rajshahi to Dhaka was reck-oned to be Taka 22-25 per maund. This is the cost of hiringa truck


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Ravallion Testing Market Integration 107Table 1. SummaryData for MainBangladeshSurplusDistricts,1972-75

DistrictMymensingh Bogra Rangpur Dinajpur Sylhet

Distanceto Dhaka 120 150 200 250 200Meanmonthlypricedifferential 12 20 19 22 14Standarddeviationofpricedifferential 20 32 44 39 22Simplecorrelationofprice with Dhaka .97 .93 .81 .87 .96Correlation f firstdifferences .79 .57 .43 .43 .48Note:Distances n roadmiles,pricedifferentialsDhaka-Mymensingh,tc.) in Takapermaund,n = 36.which price differentials depend solely ontransportcosts would fix the unitpricediffer-ence rather han, say, the percentage log)dif-ference. For, the econometricmodel to be atleast consistentwith the possibilityof such anequilibriumunder linear parameter restric-tions it should itself be linear in nominalprices.Any laggedeffects in the model arelikelytoarise from sluggishness in price adjustment,delays in transportation nd expectationsfor-mation under price uncertainty.On a priorigrounds, maximum lags greater than sixmonths due to these causes seem highly im-plausibleand also strainthe data in termsofdegrees of freedom.The unrestrictedmodel, equation (4), wasestimated for n = 6 assumingan AR1 error.Thisgave an excellent overallfitanda reason-ably flatresidualcorrelogram ver six lagsforeach district.6Residual variance F-tests werethenapplied o the three mainrestricted ormsdescribed in the previous section. The sig-nificanceof the two postulated nonprice in-fluences was also examined. Whenever thelong-run ntegrationrestriction was acceptedthe model was reestimated in the form ofequation (11) and short-runintegrationwasthen retested.The test results are given in table 2.

Summaryand Discussionof the ResultsTo summarize he results:(a) Marketsegmentationperformspoorlyasa restricted form of the generalmodel for alldistricts.

(b) At the other extreme, short-runmarketintegrationwithin one month cannot be rea-sonably accepted for any districts exceptSylhet and in this case only for the weakerform. For allotherdistricts,residualvariancesareconsiderably ncreasedby assumingshort-runmarket ntegration,andso the assumptionis hard to justify for these data.7(c) The parameterrestriction implied bylong-runmarketintegrationperformsslightlybetter but is still difficult o acceptfor three ofthe five districts. Short-run ntegrationcon-tinuesto be weak when long-run ntegrationsimposedfor the othertwo districts.

(d) Local seasonality s in evidence for fourdistricts. For each of these, winter harvestprices were significantlylower than Dhakaprices.(e) The price series for all districts exhibitquite strong localized effects of the 1974famine. In all cases, local prices were (cete-ris paribus) significantly higher than Dhakaprices. Indeed,for two districts(Mymensinghand Rangpur)the price differentialactuallychangeddirectionbrieflyduring he famine.(f) There is evidence of a local (positive)time trend for two districts, BograandRang-pur.These resultssuggestsome quitesignificantimpediments o tradebetween Dhaka and itsmainruralsupplyareas, with the possible ex-ception of the northeasterndistrict of Sylhet.The northwesterntrade corridor to Rangpurand Dinajpurseems highly restricted. While

for one day (Taka 4,000) divided by the quantity of rice carried(about 76 bags, at 2.25 maund per bag).6 All residual correlograms comfortably passed the Box-Piercetest at the 5% level (for further discussion of this test see Harvey).

7Nor do collinearity problems appear to be the reason. Onecheck for this is to examine the significance of individual co-efficients in the unrestricted model. Over all unrestricted equa-tions, 14 of the 30 coefficients on lagged own price had absolute t-ratios over 3.0 while this was the case for 12 of the 35 coefficientson Dhaka price and its lagged values. Clearly, quite a high degreeof resolution is possible in spite of multicollinearity. Full details ofthe unrestricted estimates of equation (4) are available from theauthor.


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108 February 1986 Amer. J. Agr. Econ.Table 2. Tests for the SpatialIntegration f BangladeshRice Markets

DistrictHypothesis Mymensingh Bogra Rangpur Dinajpur Sylhet1. Market segmentation 18 7.0 14 13 162. Short-run integration 60 56 236 63 15(52) (12)3. Short-run integration 7.1 33 99 25 6.0*(weak form)4. Long-run integration 5.4* 16 184 32 9.1"5. No local seasonality 13 21 9.5 12 .50*6. No local famine 11 23 117 31 167. No local time trend 2.3* 20 92 .45* 8.4*Notes: The unrestricted model is equation (4) for n = 6 with an AR 1error estimated by maximum likelihood using the Beach-Mackinnonmethod for encorporating the stationarity condition into the likelihood function (see Beach and MacKinnon). The table gives F-tests ofthe linear restrictions on this model implied by each hypothesis. (1), (2), and (3) are distributed as F(7,13), F(13,13), and F(2,13),respectively, while the rest are F(1,13). All restrictions are rejected except * (at .01). The figures in parentheses in row 2 are the values ofF when long-run integration is imposed.

these arerelativelyremoteareas,it is also no-tablehow poorlythe short-runmarket ntegra-tion restrictionsperform or the much closerdistrict of Mymensingh.It should be emphasizedthat such test re-sults do not reveal the sources of spatialpricedifferentials.In particular, t cannot be con-cluded that the marketsare noncompetitive;the Bangladeshgovernment s also knowntoexert considerable influence on the privategrain trade. For example, having first ob-servedthat Dhakapriceswere not fallingdur-ingthe 1983-84winterharvestperiod,I askeda numberof the wholesale rice merchantsatBadamtaliwhy this was so. (This was doneduringa relatedsurvey reported n Ravallion1985a.) I was told that governmentofficialsand police were cordoningruralmarkets toprevent trade with Dhaka. The governmentwas thought o be doingthis so that rice couldbe obtainedat the government'sprocurementprice,then below the Dhakapriceallowing ortransportcosts.8Of course, governmentprocurements onlyone of the risksfacinginterdistrictrade.Mostyears see quite heavy floodingin Bangladeshandin 1974 t was very bad. This is certaintohave added to transportcosts. Also, it addsthe risk of losing all or part of the trader'sconsignment n routeand,giventhatricetrad-ers in Bangladeshare unlikely to be able toinsureagainstsuch a loss, thiswill also reduceexpected returns romtrade at given prices.

ConclusionsA procedure ortestingmarket ntegrationhasbeen proposedwhich avoids the well-knowninferentialdangersof received methodsusingspatial price correlations.The price series foreach local market s permitted o have its ownautoregressivestructure and a dynamicrela-tionshipwithmarketpricesin a trading egion.Alternativehypotheses concerning he extentof integrationcan then be encompassedandtested as restricted forms of the generalmodel. The new method also extracts moreinformationon the natureof spatial pricedif-ferentialsfrom the same data. In particular,the dynamicapproachpermitsa clear distinc-tion between short-run market integration,and integrationas a long-run endencyin theshort-runadjustmentprocess. An applicationof this approach o monthlyricepricedata forBangladesh uggestssomequite significantde-parturesfrom the conditions for both short-and long-runmarket integration.And theseare not revealedby tests using static correla-tions.

[Received March 1984;final revisionreceived January 1985.]ReferencesAlamgir, M. Famine in South Asia: Political Economy ofMass Starvation. Cambridge MA: Oelgeshlager,Gunn, and Hain, 1980.Ambirajan, S. Classical Political Economy and BritishPolicy in India. Cambridge: Cambridge UniversityPress, 1978.Beach, C. M., and J. G. MacKinnon. "A Maximum

8 This was later reported in the Bangla niewspaperDaily Ittefaq(12 Dec. 1983), although apparently the cordoning was not govern-ment policy at the time (The Bangladesh Observer, 28 Dec. 1983).


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