privatization and price convergence: evidence from four markets in kyiv

Privatization and Price Convergence:Evidence from Four Markets in Kyiv1

Patrick Conway

Department of Economics, Gardner Hall, CB 3305, University of North Carolina,Chapel Hill, North Carolina 27599-3305

E-mail: [email protected]

Received May 21, 1998, revised February 5, 1999

Conway, Patrick—Privatization and Price Convergence: Evidence from Four Marketsin Kyiv

The evolution of prices of three commodities at four market locations in Kyiv, Ukraine,is examined. The interconnected effects of increased market orientation and of privatiza-tion are separated and evaluated through use of an error-correction statistical methodol-ogy. The data cover the period from April 12, 1993 to December 31, 1996. There issignificant evidence of price convergence due to arbitrage by buyers and sellers at thesemarkets. There are also a secular trend toward greater market integration over time and aonce-off reduction in price differentials associated with the privatization of formerly stateshops.J. Comp. Econom.,June 1999,27(2), pp. 231–257. Department of Economics,Gardner Hall, CB 3305, University of North Carolina, Chapel Hill, North Carolina27599-3305. © 1999 Academic Press

Journal of Economic LiteratureClassification Numbers: O12, D40.

1. INTRODUCTION

Since its independence in late 1991, Ukraine has progressed greatly in thetransition from a planned economy to reliance on markets. This transition isevident in all locations and types of markets, but it is especially evident in themarkets for foodstuffs in Kyiv. The transition to market reliance has both supply-

1 This paper is dedicated with heartfelt appreciation to my colleague Inna Shevtsova, late of Kyiv,for her assistance with this project. None of the results from this ongoing research program wouldhave been possible without her. Thanks to Jaffrey Ali and Kim Wildner for data entry support. Thanksto Ritu Anand, Wafik Grais, Daniela Gressani, John Hansen, Chandrashekar Pant, and MichelleRiboud for logistical support. Thanks as well to these, to two anonymous referees, to the editor of thisjournal, and to Thomas Anderson, Stanley Black, Barry Goodwin, and Miguel Herce for usefulcomments and criticisms.

Journal of Comparative Economics27, 231–257 (1999)Article ID jcec.1999.1582, available online at http://www.idealibrary.com on

231 0147-5967/99 $30.00Copyright © 1999 by Academic PressAll rights of reproduction in any form reserved.

and demand-side elements. On the supply side, firms are converted from shopscontrolled by the state to more independent joint-stock companies. On thedemand side, consumers are given increased freedom of choice in their pur-chases. This paper quantifies the effects of these two elements on the evolutionof prices in Kyiv.

The theoretical analysis presents a testable model of economic competitionwithin a market with geographically separate locations. This realistic theoreticalinnovation separates the consumer-choice aspects of activity from the firm-specific aspects. The empirical analysis tests the implications of this theoreticalmodel using daily quotations for equal-quantity and comparable-quality beef,milk, and butter in four marketplaces in Kyiv over the period from April 12, 1993to December 31, 1996. Two of the marketplaces were private bazaars throughoutthe period, while the remaining two began the period as state shops and wereprivatized in mid-1994.

The analysis begins with the benchmark of a market supplied by a combinationof private competitors at the bazaars and state-administered shops at geograph-ically separate locations.2 The government had enforced price controls through-out the economy during its participation in the Soviet Union and in 1991 afterindependence. Upward adjustment of these controlled prices began in 1991,reflecting the excess demand for commodities engendered by the ruble overhang(Conway, 1995). These adjustments became larger and more frequent beginningin January 1992, but the government maintained its effective control overcommodity prices in state shops past that time. By April 1993, the population ofKyiv was accustomed to higher prices and more plentiful supplies of commod-ities in the bazaars and to controlled prices and frequent shortages of goods in thestate shops.

The null hypothesis for the statistical tests performed here is that marketintegration and competition had reached their limit by April 1993. The alternativehypothesis has two parts. First, market integration became more profound in thesubsequent period through a secular process of intensified competition as citizensbecame better informed and more confident in comparison shopping, whilequality of goods and shopping experience converged through competition. Sec-ond, market integration received a positive and discontinuous impetus with theprivatization of the state shops in mid-1994. The statistical evidence permitsrejection of the null in favor of these alternatives and provides a quantification ofboth the secular integration and privatization effects. It also, however, points up

2 In this paper, the universe of transactions in a commodity is considered a market for thatcommodity. The market is characterized by locations where supply is made available. The locationsconsidered can be separated into the private bazaars and the originally state-owned shops. Detailsabout the specific bazaars and shops considered are provided in the appendix. Figure 2 is a map ofthe locations.

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a surprising reduction over time in the speed of convergence to price parity acrosslocations. This may be an indicator of the solidification of delivery relationshipsby those locations with the ultimate sources of the commodities.

While there is evidence of increased market integration, substantial pricedisparities remain throughout the sample. These disparities and the rather slowdaily percentage rate of convergence of prices in four marketplaces separated byat most 3.2 km suggest that modeling the institutional and quality differencesacross locations will be important in interpreting empirical results on priceconvergence found in the literature on transition.

2. THE TRANSITION FROM PLAN TO MARKET: METHODOLOGY

Existing empirical studies of market integration in transition economies haverelied upon a simple theoretical structure. Market integration is evident in theprogressive bidding away of price differentials in two markets and is thus amanifestation of arbitrage. Empirical tests then are restatements of the tests ofstatic market efficiency as defined by Fama (1970) with allowance for anunspecified arbitrage dynamic.3

There are two potential sources of arbitrage behavior in this market. Consum-ers of products in two locations of the market may bring about integration byshifting demand from the market in which the good sells at high cost to themarket in which the good is available at low cost. Producers, or resellers, ofproducts may also practice arbitrage by reallocating supply of goods in responseto price differentials. The line between producers and consumers becomesblurred, in fact, as intermediaries buy extra in the low-cost market for resale inthe high-cost market. Either activity will lead to price equalization if there are nospatial or regulatory costs to travel between markets and if the two goods areperfect substitutes in demand. However, quality differentials or distance providesan alternative rationale for equilibrium price differentials.

Empirical tests of the randomness of price differentials in a static modelrepresent a joint hypothesis of market efficiency and invariant spatial or policy-related transactions costs.4 Engel and Rogers (1996) apply this approach toanalyze price convergence between North American cities, with a focus upon the

3 Fackler (1996) provides a summary of these static concepts as applied to agricultural commoditymarkets.

4 Market integration can also be examined from the quantities traded. One empirical method oftenemployed for this purpose is the gravity equation. Collins and Rodrik (1991) and Havrylyshyn andPritchett (1991) apply the equation to determine trade within Eastern Europe and the former SovietUnion under normal market integration. Saxonhouse (1993) and Frankel (1993) use the method tomeasure the supernormal market integration due to formation of trading blocs or free trade areas.McCallum (1995) employs this structure to examine the impact of the U.S.–Canada border on marketintegration. See Haveman and Hummels (1997), however, for a critique of the use of gravityequations in this context.

PRICE CONVERGENCE IN KYIV 233

role played by the United States–Canada border in discouraging convergencebetween cross-border locations. Goodwin et al. (1996) and Berkowitz et al.(1996) have applied econometric tests to price dynamics in food markets withinRussia. Goodwin and coauthors examine the differences in state shop and bazaarprices of the same product across cities widely dispersed geographically during1993 and 1994, and find evidence of some market integration. The integration ismore visible among retail shops, a result the authors attribute to continuedregulation of these shops by government. Berkowitz and coauthors examinemarkets in different, and more closely situated, cities; they find significantlygreater integration of markets than was evident in Goodwin et al. (1996). Bothused econometric tests based upon vector autoregressive (VAR) techniques toserve as a reduced form for the unspecified arbitrage dynamic. Neither examinedthe temporal evolution of the price transmission mechanism across markets thatis at the heart of this paper.

To examine the secular evolution of market integration and to test for theeffect of privatization on that evolution, the research reported here derives amodel of arbitrage-based adjustment in prices that introduces quality- anddistance-based rationales for equilibrium price differentials. The theoreticalstructure allows identification of two distinct sources of price convergence at anypoint in time. The first, demand-driven, can be identified with the temporal lagin transmission of shocks from one market to another.5 The second, supply-driven, measures the gradual convergence of price in one market to those incompeting markets due to supply reallocations. The theoretical decompositionsuggests empirical implementation through the use of error-correction estimationmethodology. As Engle and Granger (1982) demonstrate, the error-correctiontechnique reveals the same features of cointegration tested separately in thepreceding studies; it has the advantage of doing so simultaneously with itsestimates of the dynamic interrelation of prices. Given this structure, the nullhypothesis of the paper is weighed against the alternatives by testing for secularevolution of the parameters of this model and for a discontinuous shift in themodel with the privatization of the state shops.

The use of daily data from four separate markets within a single city highlightsthe key contribution of competition in the transition from planning to the market.While evidence of convergence and more rapid transmission of shocks betweenmarkets is found in the data, so also are persistent premiums to goods in onebazaar relative to another and in bazaars relative to state shops.

2.1 A dynamic model of two potentially integrated markets.Privatization andincreased reliance on competitive forces in the transition to free market will havetheir effects, if any, against the backdrop of supply and demand forces that

5 This is the effect that the previous studies have captured, although those studies used a lowerfrequency of observations.

PATRICK CONWAY234

characterize these markets. Among the alternative sources of convergence ornonconvergence in commodity prices are the relative quality of the two goods,the location of the two markets, the speed with which information about pricesis transmitted to consumers, and the ability of suppliers to transfer stocksbetween the two locations. A variant of the Hotelling (1929) model provides aneffective framework for summarizing these various stimuli to price differentials.6

2.2 Demand.Consider a uniform density ofN consumers along the line [0, 1]with location indexed byz.7 Each consumer purchases one unit of the goodsavailable at either of the two locations (zA, zB) on the line each dayt.8 Theselocations are denoted “A” and “B” in what follows. There are proportionaltransport costs to the consumer,t $ 1, associated with the purchase of goodssupplied at any point on the line other than the consumer’s location. For example,suppose the consumer at locationz0 betweenzA andzB will have the followingpayoffs from consuming the two goods consistent with the demand functions ofthe text, wherezA is assumed, without loss of generality, to be less thanzB:

payoff for good 1 from location A: (a1/P1t)s( zA/z0)

t

payoff for good 2 from location B: (a2/P2t)s( z0/zB)t.

The variableai represents the quality of the good, whiles is the price elasticityof the payoff. Note that each payoff is increasing in the quality of the good,decreasing in own price, and decreasing in the distance between the consumerand the supplier. This individual will prefer good 1 to good 2 when the firstpayoff exceeds the second. The individual at locationzct will be indifferentbetween the two goods on dayt, where locationzct is defined by

ln zct 5 ~1/~2t!!@s ln~P1t/P2t! 2 s ln~a1/a2! 1 t~ln zB 1 ln zA!#. (1)

Individuals at locations [0zct] will buy good 1, while individuals at locations [zct

1] will buy good 2. If information on prices is available instantly to allNconsumers at each location, the volume of demand will beD 1t 5 Nzct for good1 andD 2t 5 N(1 2 zct) for good 2.

6 Schmitt (1993) is an alternative use of the Hotelling approach for two spatially separate markets.Anderson et al. (1992) provides an excellent summary of the use of address models, of which theHotelling model is a prominent example.

7 The growth rate of the aggregate consumer population could be specified. This would not changethe following modeling exercise.

8 In theoretical models of this sort, the location of the markets can be endogenously derived. SeeAnderson et al. (1992) for a general discussion and Conway (1996) for a specific example. In the caseat hand, the location of the markets is predetermined. The modeling results are also dependent uponthe absence of another, closer, competitor market. In the empirical work, the shops chosen are in closeproximity to the central bazaar. The second bazaar considered is the closest among other bazaars tothe central bazaar.


When there are asymmetries in the availability of information about relativeprices, the model must be adapted; the crossoverzct will differ depending on thevintage of the information available to that consumer. This is illustrated in Fig.1. The horizontal dimension of this rectangle is the physical distance of con-sumers from suppliers A and B, as above. The vertical dimension is the stock ofconsumersN at each location. If these consumers have access to relative-priceinformation of differing vintage, without loss of generality, group those consum-ers with the same vintage. The top slice of the rectangle represents thoseconsumers with same-day information on relative prices, and their share in thetotal consumers is denotedb0. The next slice groups those consumers withinformation one day old, and their share in the total isb1. Continue through theconsumers until everyone is represented. In Fig. 1 this occurs after seven daysdelay.9

9 Since every consumer shops every day, every consumer observes one of the prices daily.However, learning the other price requires some cost, or effort, and the distribution of those obtainingthat information is measured by theb k.

FIG. 1. A theoretical model of consumer choice between two alternatives. Horizontal dimension:physical location of consumers. Vertical dimension: stock of consumers at physical location. Theshaded portion of the rectangle indicates the share of the total consumers purchasing good 1 fromlocation A. The rows of the rectangle indicate the shares (b k) of the consumers having informationon relative prices that isk days old.

PATRICK CONWAY236

Defineu t as the share of the stock of consumers purchasing good 1 on dayt.It can be written

u t 5 u0j t

u0 5 @~a1/a2!~s/ 2t!~ zAzB! 1/ 2#

j t 5 exp~v t!P k507 ~P2,t2k/P1,t2k!

b~s/ 2t!. (2)

The initial term u0 indicates the division of consumers between the twolocations in the absence of price differentials. The share of consumers purchasinggood 1 will rise with the relative quality of good 1. It will rise as well the closerto the upper boundary the two locations are. The termj t captures the dynamic inthe share of demand due to informational differences. It is a weighted harmonicmean of lagged relative price ratios, with the weights defined by the share ofconsumers having information of that vintage on relative prices. The zero-meanrandom errorv t indicates the stochastic nature of this share. In Fig. 1, thedeviation fromu0 of the share of the market purchasing good 1, i.e., the shadedsection, illustrates the demand pattern today from various vintage groups due toprice-ratio deviations from unity in previous days.

With this specification, the ratio of demands for the two goods can be statedin logarithmic form as approximately:10

d1t 2 d2t > a0 1 aq 1 S k50` bk~s/t!~ p2,t2k 2 p1,t2k! 1 v t

a0 5 ~ln zB 1 ln zA!

aq 5 ~s/t!ln~a1/a2!. (3)

The ratio in demands has two price-invariant components. The first,a0, is acomponent determined by the location of the two markets relative to the con-sumer population. It will change in response to shifting population structure orpotentially due to the entry of competing suppliers. The second,aq, is a com-ponent determined by the relative quality of the two goods and, more generally,the shopping experience of the two locations. The summation of lagged pricedifferentials indicates that these differentials provide incentives to switch de-mand from one location to another. In a market with instantly available relative-price information,b0 5 1 and the otherb k equal zero. If demand for the twogoods is not price-elastic,s 5 0, or if travel costs are very high,t 3 `, thenrelative prices and demand ratios will not exhibit any correlation. If the twolocations are in fact segmented markets, so that each consumer can only shop atone location, then there will be no correlation between relative prices anddemand ratios.

10 Lower-case representations of previously upper-case letters indicate logarithms.


The demand side of the market is predicted to evolve during the transition tothe market. Specifically, information dissemination is predicted to become morerapid, so that theb k would increase for smallk and decrease for largek. Further,the substitutability of goods across markets is predicted to increase with a rise in(s/t). There could as well be a convergence of the quality of goods in the twolocations, leading to a secular trend inaq separate from the effect of (s/t).

2.3 Supply.Total supply of the commodity grows at an annual rate ofr . In theplanned economy, the supply would be allocated among markets according toplan. In a more market-oriented economy, however, supply will be reallocatedamong the markets in response to price incentives, with that allocation subject torandom errorut. Thus, the ratio of supplies is

~S1t/S1t21! 5 ~S2t/S2t21!~P1t21/P2t21!fexp~ut!. (4)

In the absence of price differentials, supplies to the two locations would grow atthe rater . However, when a price differential opens between the two locationsthe suppliers respond by shifting supply to the higher-priced location.11 Theparameterf is a measure of the costs to suppliers of rerouting goods from onelocation to another. Asf becomes larger, there are less restrictive spatial orpolicy-related barriers to transfer.12 In a totally planned economy,f will be zero.In an economy with less-than-complete planning control over suppliers,f will belarger.13 Privatization, if effective in changing supplier behavior, should cause adiscontinuous jump inf.

2.4 Equilibrium.In a planned economy, supply need not equal demand in eachmarket. There is the possibility, and in Kyiv during the beginning of that periodthe probability, of shortages and rationing with fixed price. For market-orientedoutcomes, by contrast, supply equals demand in each location. This can bestated as

s1t 2 s2t 5 d1t 2 d2t. (5)

Balance with flexible prices can be rewritten by substituting demand functionsfrom (3) into (5):

11 The product is thus homogeneous. The analysis of demand admitted the possibility of qualitydifferentials between the two goods; in this instance, to reflect the nature of the locations to beexamined empirically, those differences are assumed to be attached to the bazaar or shop and not tothe products themselves.

12 This dynamic trade-off can be thought of as an example of Marshallian adjustment to illustratethe role off. Suppose that the Marshallian adjustment process for locationi takes the form (Sit /Sit21)5 (Pit21/1)f with Pit21 the demand price and 1 the supply price.f parameterizes the response toprice incentives, withf close to zero indicating sluggish response andf large a more rapid, fluidresponse. The ratio of the adjustment processes for the two locations yields Eq. (4).

13 There is an important distinction in the Ukrainian case between plan-governed suppliers andplan-governed retailers. If the shop is state-owned but the suppliers of foodstuffs have a choice ofwhere to market their products, then (4) can govern the decision to supply, e.g., state shops vs bazaars.

PATRICK CONWAY238

s1t 2 s2t 5 a0 1 aq 1 S k50` bk~s/t!

3 ~ p2,t2k 2 p1,t2k! 1 v t for all t, or alternatively

~ p1t 2 p2t! 5 ~t /sb0!@2~s1t 2 s2t! 1 a0 1 aq 1 v t#

2 S k51` ~bk/b0!~ p1,t2k 2 p2,t2k!#. (6)

Total differentiation of Eq. (6) and substitution for (ds1t 2 ds2t) from Eq. (4)yields

~dp1t 2 dp2t! 5 ~t /s!@~1/b0!daq 2 ~f/b0!~ p1t21 2 p2t21!

2 S k51` ~s/t!~bk/b0!~dp1,t2k 2 dp2,t2k!# 2 ~1/b0!~ut 2 dv t!#. (7)

The differential of price movements in the two locations thus has four potentialsources. One source is the contemporaneous random shocks to supply anddemand sides of the market, i.e., (ut 2 dv t). A second source is the demandresponse to price movements in previous periods; the nonnegative coefficients(b k/b 0) indicate the elasticity of the demand ratio to past price differentials dueto information dissemination. The third source is the allocation of goods betweenlocations by suppliers in response to price differentials; its importance is mea-sured by the coefficientf, also defined to be nonnegative. The final source is apotential trend growth in the price differential due to the evolving qualitydifferential daq.

The transition-to-market literature cited above has focused upon the autore-gressive relationship captured by the summation of terms in (dp1,t2k 2 dp2,t2k),although it has done so with lower frequency data. Through the use of the richerspecification in (7), the following empirical analysis can decompose thesedemand-side effects from those due to supply-side choices. There is further thepossibility of capturing the effect of privatization of state-owned firms through adiscontinuous shift inf and other estimated parameters as suppliers make moreprofit-oriented decisions.

2.5 More commodities and locations.The fundamental relation represented by(7) should hold for any comparison of goods in spatially separated markets. In theempirical work that follows, the model will be estimated for a number ofcommodity pairs and a number of location pairs. Denote the logarithmic differ-ence between prices of a particular commodity pair asr, so thatr t21 5 ( p1t21 2p2t21) anddr t 5 (dp1t 2 dp2t). If commodity pairs are indexed byi and locationpairs byj , then a general statement of (7) is

dr ijt 5 ~t j/b j0!~1/s i!@~daqj 2 f ijr ijt 21

2 S k51` ~s i/t j!b jkdr ijt 2k 2 ~uijt 2 dv ijt!#. (8)


The coefficientsb jk and (t j /b j0) are hypothesized to be location-specific and tovary across different location pairs.14 The coefficientss i are hypothesized to becommodity-specific and to vary across different commodity pairs. The supply-response coefficientf ij is hypothesized to vary potentially across both locationand commodity pairs.

The existing literature typically captures only one measure of the demandspillover response, an aggregate of theb jk, in its estimation. The presentstructure, as illustrated by Eq. (8), provides a more detailed structure for analysisof transition. The supply-side effect of goods reallocation can be disentangled.Further, the information-dissemination effect of integration can be decomposedinto separateb jk in these higher frequency data. Finally, the possibility ofstructural shifts with privatization can be separated from the other effects throughuse of the knowledge of specific dates of privatization for the state shops.

3. DATA AND ESTIMATION RESULTS

The relation outlined in Eq. (8) will hold in theory for every pair of commod-ities in a market-oriented economy. Its explanatory power is investigated forthree commodities in four locations in Kyiv. The prices of milk, butter, and beefare available with daily frequency for the Bessarabski and Vladimirski bazaarsand for state shops 9/127 (30 Khmelnitsky) and 6/123 (40 Kreschatik) for theperiod from April 12, 1993 to December 31, 1996.15 The goods are sampled toensure comparable quality, so that noninfinite relative price elasticity will be dueto relative features of shopping experience at the four locations. The bazaars arelocations with large numbers of buyers and sellers in each commodity; whileonly one seller is sampled, there is competitive pressure upon that individual.State shops were, in many cases, established originally on the ground floors ofapartment blocks to service the local residents. Travel time will be less for thenatural constituency; the shop may be cleaner and less chaotic than the bazaar,but there will be no explicit competition at close hand. Although these shopswere originally state-owned and -managed, the two sampled here are nowmanaged by joint stock companies and rent space from the city administration.The Appendix provides more details on the size and volume of business at allthese markets and on the dates of privatization of the shops.

These data were collected on a daily basis by my colleague Inna Shevtsova andher collaborators. There were potentially 1350 observations for each commodity

14 The components of the ratio (t j /b jo) cannot be separately identified in this specification. Aparametric assumption on the speed of information dissemination or the relation of physical distanceto cost could provide such identification, but there is no theoretical justification for such anassumption here.

15 Observations for the Bessarabski bazaar exclude the period from June 28, 1995 to August 2,1995, since the market was closed for renovation.

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in the sample. On days when the commodity was not available, no price wasentered and the observation has been dropped.16 When these data are comparedwith governmental and nongovernmental reports of commodity inflation rates,there is a strong coincidence of movement.

3.1 Premiums in prices.Consider three measures of price dispersion in theselocations. These are (1) the private dispersion of prices between the Bessarabskiand Vladimirski bazaars, (2) the state dispersion of prices between theKhmelnitsky and Kreschatik shops, and (3) the hybrid dispersion of pricesbetween the Bessarabski bazaar and the Kreschatik shop.17 These measures ofdispersion are considered below first as a ratio of prices and then as the differencein percentage change in prices in the two locations.

3.2 Average premiums.Table 1 presents the average daily price premium forthe three commodities in the Bessarabski bazaar relative to the Vladimirskibazaar and relative to the Kreschatik shop.18 A positive premium is observed onaverage for the entire sample in the Bessarabski bazaar relative to both othermarkets. The premium relative to the Vladimirski bazaar was most evident inmilk, with an average premium of 56% paid on milk purchased at the Bessarabskibazaar. The premium on butter was smaller, but still sizeable, at 41%; and forbeef the premium was 6% on average. The premium in the Bessarabski bazaarrelative to the Kreschatik shop was even more striking, ranging from 142% inmilk to 17% in sugar over the entire sample.

The average premiums mask substantial annual differences in premiums. Thetheory behind Eq. (6) suggests that the premiums observed in the Bessarabskimarket would be reduced over time as producers shifted supply to the Bessarab-ski market and increased competition caused a convergence in quality (aq). Thisdownward trend in premiums is observed in beef prices, but not elsewhere.Interestingly, the premiums on the two other commodities grow relative to their1993 levels, with the 1996 values of the milk premium representing a 67%increase relative to 1993.

16 We recognize that this understates the relative price effect in the market. Those days ofnonavailability are most likely days in which the market clearing price would be quite high. If thiscould be modeled then the price-responsiveness picked up in estimation would be more marked.

17 There are two hybrid price dispersions for Bessarabski market; the other, with the Khmelnitskyshop, yields quite similar results.

18 Standard deviations are given in parentheses under each average. There are missing values fromeach time series during the sample period. Of the possible 1356 observations, the following areavailable for each premium: Bessarabski/Vladimirski—beef 1192, milk 1186, butter 1135;Bessarabski/Kreschatik—beef 1235, milk 1289, butter 1225. More limited observations are availablein seven other commodities, information on those will be provided on request. Further, the 1993observations begin on April 12. The period average will then not be precisely the average of theannual observations.


Examination of the standard deviation in premiums reinforces this unexpectedresult. As Engel and Rogers (1996) note, volatility in price differentials should bereduced through integration. This is also a prediction of Eq. (7); if integrationleads to greater information dissemination, thenb0 will rise and cause a reductionin the impact of random shocks on percentage price deviations. In this sample itis reduced monotonically over time only in the market for beef; in the othermarkets the standard deviations of the premiums are constant or increasing overthe sample period.

Equation (6) above suggests that a stable premium throughout the sampleperiod could be due to a locational advantage of the Bessarabski market for Kyivconsumers (a0), or perhaps the relative cleanliness or convenience of use of thatmarket (aq). As the map of downtown Kyiv in Fig. 2 illustrates, the locationaladvantage will certainly play a role in the Bessarabski/Vladimirski premium. Itshould not be evident in the Bessarabski/Kreschatik premium, since the twolocations are both on the Kreschatik throroughfare, are separated by about 250 m,and are both quite close to Metro entrances.

As Berkowitz et al. (1996) note, premiums evident between bazaar and stateshops can reflect the government’s desire to subsidize food consumption. This

TABLE 1

Average Premium on Purchases at the Bessarabski Bazaar

Beef Milk Butter

Private Dispersion Relative to the Vladimirski Bazaar

1993a 1.14 (0.13) 1.21 (0.16) 1.15 (0.14)1994 1.05 (0.08) 1.42 (0.28) 1.45 (0.21)1995 1.04 (0.07) 1.83 (0.43) 1.28 (0.15)1996 1.01 (0.05) 1.78 (0.20) 1.59 (0.27)Total period 1.06 (0.10) 1.56 (0.37) 1.41 (0.27)

Hybrid Dispersion Relative to the Kreschatik Shop

1993a 3.68 (1.31) 4.12 (3.25) 2.42 (1.36)1994 1.90 (0.36) 3.00 (0.69) 1.90 (0.29)1995 1.60 (0.24) 1.71 (0.39) 1.73 (0.30)1996 1.73 (0.15) 1.36 (0.28) 1.81 (0.32)Total period 2.11 (0.98) 2.42 (1.80) 1.93 (0.69)

a For the period April 12 to December 31, 1993. Standard deviations are in parentheses. Use of theother hybrid dispersion, i.e., relative to the Khmelnitsky shop, leads to nearly identical statistics.These measures of dispersion are based upon the actual ratio of prices. If the premium is measuredas one plus the absolute value of the logarithm of the price ratio, as for example in Engel and Rogers(1996), the premiums and standard deviations are both uniformly smaller but the distribution overtime and across commodities remains the same. Details are available from the author on demand.

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provides a competing explanation for reduction in premiums over time, asgovernments confront the budgetary consequences of such a policy and chooseto reduce such subsidies. Such excess premiums are clearly evident in the datawhen Bessarabski prices are considered relative to the Kreschatik shop in Table1. For all commodities, the average premium observed in 1993 was greater thanthat in the Bessarabski/Vladimirski comparison and was 314% on average formilk. By 1996, by contrast, the Bessarabski/Kreschatik premium was less thanthe Bessarabski/Vladimirski premium in milk. Examining the standard deviationsof these annual premia provides a picture of declining volatility in premiums overtime, although the standard deviations of the bazaar/shop premiums remainabove those of the bazaar/bazaar premiums even in 1996.

3.3 Cyclical and temporal variation in premiums.There is evidence of tempo-ral evolution as well as cyclical variation in the data on premiums, as reported inTable 2. The first panel of the table reports the results ofF tests of the significanceof day-of-the-week, month-of-the-year, or year-of-the-sample effects in the data. Asthese test coefficients illustrate, there is no evidence of significant day-of-the-weekdifferences in price premia, either between the two private markets or between

FIG. 2. Kyiv: locations of markets: (1) 40 Kreschatik St.; (2) 30 B. Khmelnitsky St.; (3)Bessarabski Market; (4) Vladimirski Market. Scale: 0.75 cm5 0.58 km.


Bessarabski bazaar and Kreschatik shop. There are, however, strong month-specificcycles and year-specific evolution evident for the three commodities considered. Thepattern of these effects is illustrated in the second panel for the Bessarabski/Vladimirski premium. For each good, the average January premium is taken as thebenchmark for monthly cyclical effects while the average 1996 premium is thebenchmark for yearly evolution of the premia.

TABLE 2

Temporal and Cyclical Features of Premiums in the Bessarabski Bazaar

Beef Milk Butter

Relative to the Vladimirski Bazaar

F test, day-of-week effect 0.18 0.28 0.13F test, month-of-year effect 6.55* 7.52* 8.75*F test, year-of-sample effect 106.50* 317.33* 244.97*

Relative to the Kreschatik Shop

F test, day-of-week effect 0.36 0.17 0.16F test, month-of-year effect 17.83* 12.40* 29.99*F test, year-of-sample effect 731.59* 558.87* 32.42*

Coefficient Estimates for the Premium in Bessarabski Price over Vladimirski Price

Average premium, January 0.09* 0.36* 0.20*Deviations from January premium:

February 20.06* 0.04 0.12*March 20.05* 0.01 0.06April 20.06* 20.02 0.08*May 20.04* 0.10* 0.13*June 20.01 0.09* 0.18*July 20.06* 20.06 0.11*August 20.04* 0.13* 0.16*September 20.02 0.12* 0.20*October 20.04* 0.11* 0.16*November 20.07* 0.09* 0.16*December 20.02 0.01 0.08*

Average premium, 1996 0.01* 0.57* 0.45*Deviations from 1996 premium:

1993 0.11* 20.38* 20.31*1994 0.03* 20.23* 20.08*1995 0.02* 0.01 20.21*

Note.Premiums are defined in logarithmic form, with positive coefficients indicating a premiumand negative coefficients a discount.

* Significance at the 95% level of confidence.

PATRICK CONWAY244

The cyclical results indicate significant differences in premia by month in eachof the commodities. In beef, for example, the average January premium in thesample is 9% but premia in the other months are uniformly lower than that. Ineight of the 11 remaining months the difference in premia from the Januaryaverage is significantly different from zero at the 95% percent level of confi-dence. In milk and butter, there are average January premiums larger than thosein beef; however, premiums observed in the other months are even higher thanthose.

The yearly evolution of premiums, as evidenced by the results reported at thebottom of Table 2, reiterates the conclusions on temporal evolution of premiumsdrawn from Table 1. For the Bessarabski market, there is a small but significantpercent premium in beef in 1996 and the premiums in years preceding declinedover time to approach that percent. For milk and butter, the average premiums in1996 are much larger with the premiums observed in earlier years significantlylower. These results reinforce the notion that the temporal evolution of premiumsmust be explicitly modeled in estimation.

3.4 Examination of the secular evolution of market integration.The model ofEq. (8) specifies a theoretically predicted error-correction mechanism for thedivergence in percentage change of prices in two markets. Table 3 reports theresults of joint estimation of that equation for the three commodities and for theprivate, state, and hybrid location pairs.19

Nine equations for three commodities apiece in the private, state, and hybridprice premiums are estimated using iterative least squares (ITLS).20 The hypoth-esis that theb jk will be the same for all commodities in each of the three locationpairs is maintained in estimation. Theory predicts that these will be larger for theprivate market pair and smallest for the state market pair.21 Equation (8) isspecified with a potentially infinite number of lagged relative-price terms, but insequential calculation of the Wald statistic for the joint significance of placing

19 As noted above, shortages can exist in locations with controlled prices. In the present dataanalysis, such shortages are noted by missing price observations. By excluding those observationsfrom estimation, information is excluded that could potentially be incorporated through switching-regression methods (Hamilton, 1994, Chap. 22). This complication will increase efficiency inestimation, but it is beyond the scope of the present analysis.

20 Iterative least squares imposes the cross-equation restrictions on parameters implied by thetheory of Eq. (8) and as outlined in the text. The weighted sum of squared errors is minimized. Errorsfrom different equations are weighted by the estimated covariance matrix of errors. Iteration followeda Gauss process, with the covariance matrix updated at each iteration. The correlation of errors wassignificant for private and state with hybrid of the same commodity, and insignificant otherwise.These results are available from the author on demand.

21 This is a logical extension of the theoretical model. There is no reason to expect that the abilityto collect relative-price information on spatially separate markets should depend upon the type ofproduct.


additional lags, a specification of seven lags was preferred.22 In the absence ofprior information on quality evolution, the predicted value ofdaq is zero.

The parameters j is estimated separately for each commodity. The datacollection procedure is designed to pick identical goods in each market, so thatthe price elasticity will in each instance be nonzero. However, it may differacross commodities due to difficulties in transporting and preserving the pur-chased good. Theory suggests, and Wald statistics calculated jointly with theestimation of Table 3 and reported at the bottom of that table cannot reject, thehypothesis that the price elasticity of demands i of goodi does not depend upon

22 The Wald statistics for the first seven lags of the dependent variable indicated that these lagscontributed significantly to the explanatory power of the regression system. The Wald statistic for theeighth lag was 5.95, with probability value 0.11, and the null hypothesis that seven lags were aseffective as eight could not be rejected.

TABLE 3

Parameter Estimates: Price Dispersion Regressions

Private State Hybrid

daq 0.024 (0.006) 0.004 (0.001) 0.031 (0.008)b1 0.331 (0.032) 0.083 (0.022) 0.232 (0.025)b2 0.265 (0.030) 0.132 (0.027) 0.184 (0.025)b3 0.172 (0.027) 0.098 (0.022) 0.080 (0.022)b4 0.094 (0.026) 0.056 (0.025) 0.043 (0.022)b5 0.048 (0.024) 0.061 (0.025) 0.027 (0.022)b6 0.009 (0.023) 0.073 (0.024) 0.040 (0.022)b7 20.031 (0.020) 0.064 (0.024) 20.066 (0.020)fM93 0.167 (0.074) 0.033 (0.026) 0.029 (0.012)fM94 0.082 (0.022) 0.043 (0.026) 0.034 (0.009)fM95/96 0.038 (0.010) 0.022 (0.008) 0.054 (0.014)fB93 1.007 (0.614) 20.744 (0.730) 0.020 (0.044)fB94 0.942 (0.535) 0.275 (0.215) 0.054 (0.033)fB95/96 1.328 (1.011) 0.066 (0.176) 0.044 (0.027)fBU93 0.245 (0.088) 0.612 (0.090) 0.049 (0.020)fBU94 0.065 (0.019) 0.103 (0.037) 0.055 (0.014)fBU95/96 0.057 (0.015) 0.058 (0.020) 0.047 (0.013)

sM 0.600 (0.166)sB 4.831 (2.657)

Note.Test:sM 5 sB 5 1. Wald statistic 54.79.Source.Author’s calculations use iterative least squares with nine equations for milk, beef, and

butter in each of the private, state, and hybrid market pairs. Private refers to price differentialsbetween the two private bazaars; state refers to price differentials between the state shops. Hybridrefers to price differentials between the Bessarabski bazaar and the Kreschatik shop.sB andsM arecalculated for all market pairs;sBU is normalized at unity. Standard errors are in parentheses.

PATRICK CONWAY246

the market pair i.e., private, state, or hybrid. The structure of elasticity isanchored by assumingsBU 5 1.23

Separatef ijt , as defined in Eq. (4), are calculated for each location-pair in yeart. Thef ipt calculated for the private markets are predicted to be larger than thosefor the state or hybrid markets in every year. The suppliers to private markets willfind it easier to shift sales among markets; the suppliers to formerly state shopswill find it more difficult to reallocate supplies. The hybridf iht is a measure ofthe ability of suppliers to shift supplies between state shops and private marketsin response to price differentials; this too is expected to be small in each year.Theory suggests thatf ijt will rise over time for each commodity as suppliersbecome better able to allocate supplies across markets to obtain profits.

Estimation results are reported in Table 3. The market demand spillover effectsb jk take the expected sign in nearly all cases and are of appropriate size. Theprivate market is characterized by substantial demand shifts between locations,with a given differential in price between the two private markets leading todemand reallocations over a seven-day period that erase 89% of the differential.In the state and hybrid markets, the spillover exists but is less pronounced; asimilar initial price differential will lead to erasure of only about 55% of thedifferential after seven days. The dynamic over those seven days is also quitedifferent. In the private markets, spillovers during the first two days after a pricedifferential opens lead to erasure of 60% of the differential; in the state markets,the corresponding erasure is 22%, and in the hybrid market 42%.

The supply elasticitiesf ijt take the correct sign in all instances but one. Theyare significantly different from zero in six out of nine private and hybridcoefficients and in four of nine state coefficients. The major source of insignif-icance is in the market for beef; none of the supply elasticities were significantlydifferent from zero in any of the periods examined. If attention is restricted to themilk and butter markets, the prediction on the pattern off ijt over time is borneout for the hybrid comparisons, but not for the private and state coefficients,because thef ij 1993 tend to be larger than those for later years in these cases.24 Inthe hybrid comparison, it is possible that suppliers have taken the longest todevelop contacts in both kinds of markets. The fallingf ijt over time in the othercomparisons may be an indication of the institutionalization of supply channelsin more recent years. It may also be an artifact of the exclusion in this estimationof the supply-side effects of privatization on behavior.

23 This anchoring is necessary for an interior solution to the likelihood function maximization.Using different values ofsM to normalize led to nearly identical results once the change innormalization was factored in. These are available from the author on demand.

24 In this section, and in the privatization section that follows, an alternative specification of thissecular evolution was considered in which the estimated coefficient followed a constant dailyadjustment. These coefficients were imprecisely estimated and were insignificantly different fromzero at the 95% percent confidence level.


The conditional estimates of growth in market premiadaq are not zero, aspredicted, but are equal to(2.4/s i)% for private comparisons,(3.1/s i)% forhybrid comparisons, and(0.4/s i)% for state comparisons. Each of these issignificantly larger than zero. These indicate greater price growth on average inthe Bessarabski bazaar relative both to the Vladimirski bazaar and the Kreschatikshop even when demand and supply evolution have been explicitly modeled.There is as well a smaller but still significant preference for the Kreschatik shoprelative to the Khmelnitsky shop. The large growth in private comparisons maybe due to the relatively faster growth of the downtown population, or to thegrowing attractiveness of the Bessarabski shopping experience. The hybrid-comparison growth is most likely due to an artificially controlled price in stateshops at the beginning of the sample period.

3.5 Privatization: What impact?In examining the market transition in thesedata, it is important to recognize the change in status of the formerly state shops.On March 31, 1994, the Khmelnitsky shop was transformed into a joint stockcompany. On June 8, 1994, the Kreschatik shop became a joint stock companywith different owners than the Khmelnitsky shop. According to theory, thistransformation will be important since, in postprivatization shops, excess demandpressure should be translated into commodity price increases as in Eq. (8) insteadof into rationing.

The dates of privatization define three different regimes in the data.25 The firstregime, from April 12, 1993 to March 31, 1994, was characterized by stateownership of both shops. The second regime, from April 1, 1994 to June 8, 1994,was one of privatization of the Khmelnitsky shop and state ownership of theKreschatik shop. The third regime, after June 8, 1994, was one of privateownership of both shops. The joint significance of differences in estimatedparameters across these regimes can be tested by calculating anF statistic, theresults of which are reported in Part I of Table 5. Due to the small number ofobservations in the second regime, two tests were performed. One examinedwhether the period up to June 8, 1994, i.e., regimes 1 and 2, differed from thatafter June 8, 1994, i.e., regime 3. The other examined whether the periodpreceding March 31, 1994, i.e., regime 1, differed from that following in regimes2 and 3. In both cases the null hypothesis is rejected in favor of an unspecifieddifference in underlying parameters.

25 The division of the data into three samples based upon the decision to privatize the state shopsand the subsequent estimation of coefficients for subsamples assume that the decision to privatize theshops was independent of the observed price differentials. The prospective owners may have madethe decision to privatize based upon current price differentials. This potentially will introduce aselection bias into the hypothesis testing that follows. It would be appropriate in that case to estimatethe privatization decision jointly with the price differentials. We thank a referee for this suggestion;it represents an interesting direction for future research.

PATRICK CONWAY248

Comparison of the determinants of price dispersion in the first and thirdregimes provides an interesting contrast of the forms of competition evident inKyiv’s markets prior to, and subsequent to, privatization. Table 4 illustrates thediffering estimates for coefficients in the two regimes.26 In the first regime, thedaq indicate significant average differences in price growth in private, state andhybrid comparisons. The impact of supply-side adjustment on relative prices (f i)is most evident in the private and hybrid comparisons, with such adjustmentbetween state shops only significant in butter. The price elasticity parameterss i

could not be estimated precisely, and thus were given imposed values of 1 in thisregime.27 There is evidence of significant dynamic effects of demand-sideswitching in the private (b pk) and state (b sk) comparisons, as suggested by theinformation-dissemination rationale of the theoretical section, with estimatedvalues of bothb pk andb sk having the correct sign and differing significantly fromzero. However, these effects are not evident in the hybrid comparisonb hk.

In sum, the first regime is a period in which the market forces posited by thetheoretical model are observed only imperfectly. There are persistent divergencesin prices of the same commodity across private locations and across statelocations. There is no evidence of a stable nonzero price elasticity of demandacross locations for the same good. The supply-shifting response of locations inresponse to excess demand is evident in the correct signs on thef j , but thesecoefficients are significantly different from zero in just over half the cases. Thehypothesis of a dynamic demand response to relative-price differentials due tosluggish information dissemination cannot be rejected in the data, as evidencedby the significantb pk and b sk, although these coefficients indicate a small neteffect. Interestingly, this dynamic demand response takes on substantively thesame features in the private and the hybrid comparisons; as the Wald test reportedat the bottom of Table 4 indicates, the joint equalityb pk 5 b sk for all lags kcannot be rejected. Consumers evidently were willing to switch from one privatebazaar to another, or from one state shop to another, but evidently not from abazaar to a state shop. This is evident in theb hk estimated for the hybridcomparison in the first regime. These are all insignificantly different from zero.

In the third regime there is much greater evidence of market forces at play. Theaverage divergence between price growth in locations indicated bydaq issmaller, although still significantly different from zero. The price elasticities inthe goods are more precisely estimated and significant at the 10% level of

26 The specification of the regression differs slightly from that of Table 2; given the chronologicalsplit in data, time-specific error-correction coefficients were uninformative in this context. Theerror-correction mechanism was specified in this case as a single coefficient for each product in eachregime.

27 When the coefficientssM andsB were estimated jointly with the other coefficients of the systemof equations, the estimates tended toward zero and the value of the objective function diverged.


TABLE 4

Impact of Privatization—Comparison of Before and After

First Regime(April 12, 1993 to March 31, 1994)

Third Regime(June 9, 1994 to December 31, 1996)

CoefficientsStandard

errorT

statProbvalue Coefficients

Standarderror

Tstat

Probvalue

Private comparison

daq 0.025 0.008 3.22 0.002 0.014 0.006 2.13 0.033bp1 0.150 0.049 3.07 0.003 0.428 0.036 12.00 0.000bp2 0.198 0.057 3.49 0.001 0.345 0.035 9.92 0.000bp3 0.184 0.055 3.37 0.001 0.220 0.033 6.67 0.000bp4 0.116 0.050 2.31 0.024 0.121 0.032 3.80 0.000bp5 0.085 0.046 1.84 0.070 0.096 0.031 3.09 0.002bp6 0.066 0.040 1.66 0.102 0.022 0.029 0.77 0.440bp7 0.004 0.036 0.11 0.916 20.028 0.025 1.08 0.280fm 0.167 0.048 3.43 0.001 0.022 0.011 1.96 0.051fb 0.873 0.748 1.17 0.247 0.905 0.477 1.90 0.058fbu 0.063 0.028 2.28 0.025 0.036 0.016 2.33 0.020

State comparison

daq 0.010 0.003 2.99 0.004 0.003 0.001 2.28 0.023bs1 0.104 0.041 2.51 0.014 0.070 0.031 2.24 0.025bs2 0.178 0.049 3.65 0.001 0.104 0.032 3.21 0.001bs3 0.137 0.043 3.17 0.002 0.092 0.033 2.79 0.006bs4 0.085 0.053 1.62 0.110 0.118 0.032 3.65 0.000bs5 0.054 0.050 1.07 0.286 0.058 0.032 1.85 0.065bs6 0.088 0.049 1.80 0.076 0.110 0.032 3.65 0.000bs7 0.075 0.046 1.62 0.108 0.057 0.032 1.77 0.077fm 0.038 0.046 0.83 0.408 0.017 0.009 1.94 0.053fb 0.004 0.205 0.02 0.983 0.068 0.051 1.33 0.184fbu 0.167 0.058 2.89 0.005 0.045 0.018 2.46 0.014

Hybrid comparison

daq 0.025 0.008 3.22 0.002 0.014 0.007 2.13 0.033bh1 20.001 0.030 0.04 0.966 0.332 0.031 10.63 0.000bh2 0.002 0.030 0.08 0.937 0.290 0.032 9.07 0.000bh3 0.000 0.029 0.00 0.999 0.144 0.030 4.84 0.000bh4 0.032 0.037 0.87 0.389 0.079 0.029 2.77 0.006bh5 20.038 0.038 1.00 0.320 0.054 0.028 1.91 0.056bh6 20.011 0.037 0.31 0.758 0.056 0.027 2.05 0.041bh7 20.056 0.035 1.59 0.116 20.063 0.025 2.49 0.013fm 0.043 0.012 3.58 0.001 0.020 0.009 2.08 0.038fb 0.069 0.055 1.24 0.218 0.021 0.012 1.69 0.092fbu 0.043 0.012 3.30 0.002 0.026 0.011 2.43 0.016

Substitution parameter

sM 1.000 imposed 0.740 0.400 1.85 0.064sb 1.000 imposed 1.587 0.897 1.77 0.077

Wald test: private5 state in lagged transmission coeffs.

3.780 0.804 87.210 0.000

PATRICK CONWAY250

confidence. Supply-shifting coefficientsf j are significantly different from zeroin seven of nine cases and are similar in size across the three comparisons formilk and butter. The dynamic demand response, as illustrated in theb pk, is largerand concentrated in earlier lags for the private comparison. In this regime as well,there is an indication in theb hk of significant dynamic response between theprivate and state shop locations.

Although the state shops have been privatized in the third regime, the statecomparison remains the least in accord with the theoretical predictions. Thedynamic demand response is a significant effect, but little different from that ofthe first regime. The supply-shifting effect is weakest in this comparison as well.In the third regime, the Wald test which is reported in the last row of Table 4indicates a significant difference in the dynamic demand response between theprivate b pk and the stateb sk. The private comparison exhibits more of thecharacteristics of market theory, while the state comparison exhibits fewer.

This joint hypothesis can be decomposed into a number of interesting tests.First, the significant intercept term may be due to the timing of privatization.Second, the supply adjustment effect captured by the error-correction term maydiffer for state-owned and privatized shops; this would reflect the diminishingreliance of privatized shops on traditional, state-order suppliers. Third, thedemand-side effect of opening price differentials leading to a shift in consumervolume could be altered by the privatization record of the shops.

The binary variablesq1t and q2t are defined to be unity when shop 1(Kreschatik) and shop 2 (Khmelnitsky) are joint stock companies, respectively,and zero when they remain state-owned shops. The estimating equation (8)augmented to test the three privatization hypotheses is given in (8s) for the stateprice dispersion and in (8h) for the hybrid price dispersion.28 This specificationallows use of the entire sample of data and not just regimes 1 and 3.

dr ijt 5 ~t j/bso!~1/s i!@daqt 1 b1q1t 1 b2q2t 2 S k51` ~bsk 1 gk~q2t 2 q1t!!dr ijt 2k

2 ~f 1 f1sq1t 1 f2sq2t!r ijt 21 2 ~uijt 2 dv ijt!# (8s)

dr ijt 5 ~t j/bho!~1/s i!@~daqt 1 h1q1t 2 S k51` ~bhk 1 mkq1t!dr ijt 2k

2 ~f 1 f1hq1t!r ijt 21 2 ~uijt 2 dv ijt!#. (8h)

The coefficientsb1, b2, andh1 will register the significance of privatization onthe average rate of growth of the price ratio. The coefficientsf1s, f1h, andf2s

pick up the impact of privatization on the supply-adjustment coefficient. Thecoefficientsgj andmj measure the impact of privatization on the lagged trans-mission of price effects across markets through excess demand.

28 The hypothesis is maintained that the private dispersion is not dependent upon the timing ofprivatization elsewhere in the market.


Table 5 reports the results from using the data to test these hypotheses. Forthree potentially important effects of privatization, as measured byb1, h1, andf 1h in Eqs. (8s) and (8h), statistical tests could not reject the null hypothesis ofzero effect on price dispersion. Part II of Table 5 reports these tests. The first tworesults indicate no effect through that channel of privatization on the averagedifferential in price growth rates across markets, while the last indicates nostatistically significant change in convergence of prices between the Bessarabskibazaar and the Kreschatik shop in response to privatization. These were all setequal to zero for the parameter estimates that follow.

Privatization has the expected impact on the coefficients of lagged transmis-sion of demand from one market to another. Its significance is noted in the jointtests reported in Part III of Table 5; one must reject the joint hypotheses that allgk 5 0 and all mk 5 0. More specific conclusions are possible from thecoefficient estimates in Part IV of Table 5. In the shops, the period during whichthe Khmelnitsky shop was privatized but before the Kreschatik shop was privat-ized was characterized by a significant increase in the lagged transmissioncoefficientsgk. The Khmelnitsky shop was free to raise prices during this period,but the significance of the coefficients indicates that the Kreschatik shop fol-lowed its lead with a short lag. The theory suggests that demand pressure wouldtrigger this response.29 In the hybrid comparison, the lagged transmission coef-ficientsmk are larger during the period after the joint-stock company was formedfor the Kreschatik shop. The Bessarabski market was the higher-priced locationduring this period, but the spillover of demand to the Kreschatik shop led tohigher prices observed there with a lag. The relative size of these coefficients, andspecifically the larger values for lower k, also provide evidence of the signifi-cance of the more rapid information dissemination noted in the comparison ofregimes in Table 4.

The statistical test ofb hk indicates joint insignificance of these coefficients inthe hybrid price dispersion when privatization effects are taken into account. Thisis a rederivation of the joint insignificance ofb hk noted in the first regime ofTable 4. These coefficients register the lagged transmission effect for the state-owned period of the Kreschatik shop’s operation. It is not surprising that theseare insignificant, because a state shop would find it difficult to justify raisingprices in response to demand pressures and would be more likely to run out ofthe product at the existing price.30

The remaining supply-response effects following privatization are significantand have the correct sign.f2s is positive, although insignificantly different from

29 It is also possible that the managers of the Kreschatik shop simply used the Khmelnitsky shopas an indicator of what the market would bear and followed suit without strong demand pressures.

30 The same result is suggested by examination of the significance of theb hk for the hybridcomparison in the first regime in Table 4.

PATRICK CONWAY252

TABLE 5

Hypothesis Tests: The Impact of Privatization

Part I

F test: joint significance of difference in coefficient F 1 5 4.50estimates across regimes F 2 5 4.08

F critical (35, 5150)5 1.40

Part II

Restrictionsimposed T statistic

Probabilitynot rejected

b1 5 0 0.88 0.38h1 5 0 0.57 0.57f1h 5 0 0.65 0.52

Part III

Joint tests Wald statisticProbabilitynot rejected

All bsj 5 0 43.37 0.00All g j 5 0 17.54 0.01All bhj 5 0 7.90 0.34All m j 5 0 20.10 0.00

Part IV

Coefficientestimate

Standarderror T-statistic

Probability thatcoefficient equal

to zero willbe rejected

f1s 0.166 0.091 1.82 0.068f2s 20.141 0.087 1.62 0.106b2 20.006 0.003 1.99 0.047g1 0.525 0.320 1.64 0.101g2 0.678 0.295 2.29 0.022g3 0.409 0.330 1.24 0.216g4 0.084 0.299 0.28 0.778g5 0.584 0.303 1.93 0.054g6 0.836 0.301 2.77 0.006g7 20.116 0.295 0.39 0.695m1 0.207 0.082 2.53 0.011m2 0.337 0.088 3.83 0.000m3 0.186 0.080 2.30 0.022m4 0.133 0.084 1.59 0.113m5 0.017 0.088 0.19 0.848m6 20.081 0.085 0.94 0.346m7 20.098 0.081 1.22 0.224

Note.Complete estimation results are available on demand from the author.


zero, indicating that the privatization of the Khmelnitsky shop led to an accel-eration of price divergence over time.f1s is positive and significant, indicatingthat once the Kreschatik shop was privatized, price convergence between theshops was accelerated. The coefficientb2 is the impact of the Khmelnitskyprivatization on average growth in the price ratios in the state comparison. Itsnegative value indicates that this privatization, as expected, lowered significantlythe average price ratio between the two shops.

4. CONCLUSIONS AND EXTENSIONS

Integration and privatization are theoretically distinct changes in the workingsof a market, although in the discussion of transition to the market economy theyare often combined. In this paper, these effects are disentangled theoretically. Itis demonstrated empirically that they have had complementary effects on priceconvergence in Kyiv’s commodity markets.

The results can be grouped into a number of general propositions. First, themarkets for beef, milk and butter in Kyiv were characterized, even in 1993, bysubstantial price convergence. This convergence was due to arbitrage betweenthe private bazaars, on the one hand, and between the state shops, on the other.The evidence from the estimation reported here suggests that the private and statelocations of the market were segmented one from the other, leading to twoseparate markets for these goods. The price convergence was due both todemand-side transmission of demand in response to price differentials, and tosupply-side reallocation of supplies. While price differentials existed at this timebetween the state shops in each of the commodities, the pressure for convergencemay have been bureaucratic rather than economic. Nevertheless, the convergencebehavior in the prices at state shops was statistically indistinguishable from thatof the private bazaars.

Second, there is a secular trend toward greater integration of the marketsevidenced in the evolution of demand-side price transmission coefficients. Thesewere growing significantly over time, indicating the increased spillovers fromlocation to location due to an increasingly sophisticated consumer base. There issignificant evidence of more rapid dissemination of information on price differ-entials in competing markets. Supply-side adjustment runs counter to that seculartrend, as the speed of convergence of prices to parity has slowed over time. Thisslowing can be associated with the institutionalization of supply channels to thevarious locations and with the increased mobility of consumers. There is alsoevidence of increased price elasticity of demand for these goods over time, asmay be due to nonprice competition in quality.

Third, privatization of the state shops had the expected discontinuous effect onthe evolution of price convergence. The long-run divergence from parity existing

PATRICK CONWAY254

in the data on state shops was reduced significantly by this change in ownership.There is also significant evidence of both demand-side pressure on price con-vergence and supply-side reallocation of supplies in response to price differen-tials. The net result was a once-off increase in the speed and degree of priceconvergence in the Kyiv market.

The results also suggest the limitations of market-integration analysis basedupon price convergence. These markets are all within 4 km of one another, andyet there are sizeable and sustained divergences from the law of one price. Itbecomes difficult to imagine a comparable analysis across nations or regionsproviding a clear picture of the gains possible from market arbitrage.

APPENDIX: THE FOUR MARKETS STUDIED

A. Market Description

1. Commodity shop: 40 Kreschatik Street.This began as a state shop and atthe end of the sampling period was managed by the joint stock company“Tsentralny Gastronom.” The premises are rented from the state.

Area: 1500 m2

Suppliers: 450Customers: 1500/day

This shop is in the center of the city. In previous years it had a wider assortmentof goods than was available from other shops; at present it has roughly the sameassortment as is found in other large commodity shops. As a result, fewershoppers from outlying areas come to this store at this time. This store becamea joint stock company on June 8, 1994.

2. Commodity shop (#9/127): 30 B. Khmelnitsky Street.This began as a stateshop and at the end of the sample was managed by the joint stock company“Theatre.” The premises are rented from the state.

Area: 600 m2

Suppliers 40Customers: 700/day on average

The managers report that there is less traffic during the weekend. On the whole,the number of customers has decreased in recent years. This is due to the fact thatthe residents of the communal apartments of this district were rehoused and theirlarge apartments sold to rich individuals who prefer to shop in large supermar-kets. The prices of the shop represent roughly a 25% markup over the producerprice.

This shop became a joint stock company on March 31, 1994.


3. Bessarabski Bazaar: 2 Bessarabski Square.The market is held on stateproperty.

Area: 2400 m2

Suppliers: 250 (80 meat vendors, 24 dairy vendors, etc.)Customers: 5000 to 8000/day.

Weekends are the most popular shopping days among customers at this market.The quality and assortment of goods at this market are better than other markets.This is reputed to be the most expensive in the city, with high prices attributedto the fees for market space and to the rich clientele. The sales people are for themost part intermediaries, with few producers represented.

Prices are reputed to vary as well by season. Meat is cheaper during the winter,eggs are cheaper in spring and summer, and fruits and vegetables are cheaper insummer and fall.

4. Vladimirski Bazaar: 115 Gorky Street.The market is held on state property.

Area: 5000 m2

Suppliers: 1000 (150 meat vendors, 60 dairy vendors, etc.)Customers: 10000-20000/day

Most of the sellers are the primary producers. This is the lower-price market, withhigh volume and fewer intermediaries.

B. Distance between Markets

From shop 1 to shop 2 the distance is approximately 1 km.From shop 2 to market 3 the distance is approximately .2 km.From market 3 to market 4 the distance is approximately 3.2 km.The information in this appendix was compiled by Dr. Inna Shevtsova and

Alexander Bazarov from interviews with managers and participants in themarkets.

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privatization and price convergence: evidence from four markets in kyiv

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