Entry and Pricing Strategies in the Polish Gasoline Distribution Market

• Wojtek Dorabialski, WSHiFM, WISER

• wojtekd@wiser.org.pl

• January 2007

Goals• To study entry and pricing strategies in an imperfectly

competitive markets • A transition economy offers opportunities for this type of

study– growing markets with a lot of entry and not much exit– firms have market power and exercise it

• I selected the local retail gasoline markets– homogenous good (although some advertising and quality

differences and loyalty programs exist)– existence of station chains, operators of such chains make entry

decisions repeatedly– evidence of imperfect competition (tacit collusion) in the US market

Literature on pricing-entry games

• Pre-entry limit pricing: difficult to justify (Spence critique, chain-store paradox )

• Milgrom-Roberts limit pricing model does not fit the market in question

• Reputation formation (Kreps-Wilson) hypothesis could be tested

• „Sixth Avenue effect” (Caplin-Leahy) hypothesis could be tested (whether entry may attract competitors due to uncertainty about market conditions)

Empirical studies on entry• Bresnahan and Reiss (1991)

– effects of entry on competitiveness in concentrated markets – significant differences between industries– the entry of a second firm had a big impact on the level of prices, but new entry

did not have much pro-competitive effect in markets with 3 or more incumbents

• Mazzeo (2002) – entry decisions and product (quality level) choice– entrants have a strong incentive to differentiate their product from the

incumbents’

• Toivanen and Waterson (2001) – entry strategies of fast-food chain operators who set uniform prices across all

markets – find evidence for learning; the probability of entry is positively affected by

competitor’s presence in a given market

Empirical studies on gasoline distribution markets

• Borenstein and Shephard (1996) – find evidence of (tacit) collusive pricing, that despite the fact that the

market concentration is low and the margin levels are far from monopolistic

– margins behave in a manner consistent with the Rottemberg-Saloner model of collusion

• Karrenbrock (1991), Borenstein, Cameron and Gilbert (1997) – asymmetries in downstream price response to changes in upstream prices – retail gasoline prices react more quickly to increases than to decreases in

the wholesale price. This asymmetry is evidence of existence of retailers’ market power

• Slade (1987) – test for tacit collusion is through direct estimation of demand, cost and

reaction functions, finds some level of collusion

Gasoline market in Poland• Production: PKN Orlen (70%), Grupa Lotos (30%)• Imports Exports (10% of consumption)• Some competition in the wholesale, but Orlen and

Lotos are major players• Retail (filling stations):

– over 3000 independent stations– Orlen: 1900 stations (former CPN)– Lotos: 360 stations– Major iternationals: BP – 300, Shell – 250, Statoil – 230– Small internationals and supermarkets

Gasoline market in Poland

• Orlen is reducing the number of stations, as they (and the independents) have the lowest average sales volume

• Internationals have the highest volume of sales (When Lotos bougth Esso stations in 2005, they claimed that the acquired stations sold on average 40% more fuel than their old stations)

Liczba stacji

0

50

100

150

200

250

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

rokstacje Statoilstacje Shell

Market definition• We assume that the markets in the geographical sense

are counties (powiaty). This is a result of compromise: – we are unable to precisely determine the proper geographical

market for each station– gmina would be too small– voivodship is too large– powiat is probably too large (variation within market-firm

exists but is smaller than between market-firms std. dev. 0.027 versus 0.078)

– problem with stations located in small local markets but on major roads. To mitigate the problem (at least with respect to pricing) we focus on the price of E95 unleaded gasoline

What we need• We want to find the determinant of entry and pricing

decisions of the international chain operators. The several things we should know:– unobservable costs:

• entry cost: we assume that the entry costs are fixed and uniform (at least within the chain)

• marginal costs: we assume that the entry costs are the uniform across stations

– demand, demographic variables• purchasing power• number of vehicles• number of inhabitants

– date of entry for each location – price at every location

What we have

• The data come from 3 sources:– station location and opening date: from Shell and

Statoil websites (we also have BP locations)

– E95 gasoline prices at Shell and Statoil stations have been collected on a weekend in February 2005 by telephone interviews

– demographic and infrastructural data come from GUS Bank Danych Regionalnych for 2003

Summary statsPowiaty with Shell and/or StatoilAll powiaty

Variable Obs Mean Std. Dev. Obs Mean Std. Dev.voivod 153 0.104575 0.30701 373 0.042895 0.202894city 153 0.385621 0.48834 373 0.174263 0.379845cena 137 3.744805 0.079969 373 .drogi_ulep~m 153 227.4941 164.0218 373 284.7517 153.9999wyd_biez 153 1.40E+08 3.24E+08 373 73222966 2.15E+08vehicles 153 57012.33 86669.22 373 41887.06 58110.52st_bezrob 153 19.17974 6.503009 373 21.97507 7.477223ludn 153 142044 170853.7 373 100748 117688.9powierzchnia 153 596.9346 525.3244 373 825.6595 525.6141west 153 0.039216 0.194745 373east 153 0.035948 0.182315 373overall 153 4.084967 5.999943 373own 153 1.856873 1.803372 373agem 153 55.9982 26.94076 373

Pricing strategies – Conjectural variation

• The profit of an oligopolistic firm i in is:

• The first order condition can be transformed into „supply function” of an oligopolistic firm:

• where i [0, 1] is a parameter describing competitiveness of the market (1 = monopoly, 0 = p.c.). 1/ i is the equivalent of the number of firms in a symmetric Cournot model

• With proper data (quantities, cost shocks, demand shocks) the above can be estimated jointly with a demand function as a stuctural model

• Our data only allows us to estimate the above as a reduced-form model of the market

)()()()( iiiiiiiii qCqQqPqCqQP

i

icP

1

1

CV Regressions

• We will try to determine whether how the demand conditions (elasticity) and the competitiveness of the market affect prices

• We will regress the variables correlated with demand elasticity and with i on price

• We use OLS to estimate a log-linear model

ResultsNumber of obs = 320F( 11, 308) = 6.04R-squared = 0.1775Adj R-squared = 0.1482Root MSE = .08428

cena | Coef. Std. Err. t P>|t|

lnpopdens | -0.00687 0.008076 -0.85 0.396lnroaddens1 | -0.04582 0.015232 -3.01 0.003voivod | -0.03858 0.017574 -2.2 0.029lnwydatki | 0.009087 0.008905 1.02 0.308lnunemplrate | -0.06255 0.015821 -3.95 0east | 0.038134 0.026561 1.44 0.152west | 0.098054 0.022764 4.31 0lnshare | 0.01117 0.012808 0.87 0.384new1 | -0.03223 0.024593 -1.31 0.191newcomp1 | -0.01345 0.019538 -0.69 0.492kodfirmy1 | 0.030743 0.009988 3.08 0.002_cons | 3.756607 0.169459 22.17 0

ResultsNumber of obs = 170F( 10, 159) = 3.59Prob > F = 0.0003R-squared = 0.1842Adj R-squared = 0.1329Root MSE = .09283

cena | Coef. Std. Err. t P>|t|

lnpopdens | -0.01048 0.013984 -0.75 0.455lnroaddens1 | -0.03344 0.028223 -1.18 0.238voivod | -0.03076 0.027336 -1.13 0.262lnwydatki | 0.005402 0.013412 0.4 0.688lnunemplrate| -0.066 0.025399 -2.6 0.01east | 0.058194 0.048495 1.2 0.232west | 0.101693 0.029426 3.46 0.001lnshare | 0.004172 0.024552 0.17 0.865new1 | 0.047715 0.054604 0.87 0.384newcomp1 | -0.0334 0.027473 -1.22 0.226kodfirmy1 | (dropped)_cons | 3.858078 0.257527 14.98 0

ResultsNumber of obs = 150F( 10, 139) = 3.51Prob > F = 0.0004R-squared = 0.2016Adj R-squared = 0.1442Root MSE = .07411

cena | Coef. Std. Err. t P>|t|

lnpopdens | -0.00643 0.009759 -0.66 0.511lnroaddens1 | -0.04981 0.017219 -2.89 0.004voivod | -0.05155 0.022913 -2.25 0.026lnwydatki | 0.016565 0.011685 1.42 0.159lnunemplrate | -0.05039 0.020433 -2.47 0.015east | 0.023814 0.029477 0.81 0.421west | 0.104974 0.057071 1.84 0.068lnshare | 0.024104 0.015658 1.54 0.126new1 | -0.05701 0.0253 -2.25 0.026newcomp1 | 0.032072 0.029739 1.08 0.283kodfirmy1 | (dropped)_cons | 3.621155 0.222127 16.3 0

Entry

• The proper way to estimate entry is to estimate entry probability using a probit model (or an ordered probit model)

• Entry and pricing strategies could be linked, as entry is determined by the expected price-cost margin

• Work in progress, but the results should be similar to the ones in the pricing regressions, as the entry variables are highly correlated with market characteristics, but weakly correlated with price and „strategy” indicators

OLS Results Number of obs = 320F( 10, 309) = 134.44Prob > F = 0.0000R-squared = 0.8131Adj R-squared = 0.8071Root MSE = 1.7652

own | Coef. Std. Err. t P>|t|

lnpopdens | -2.0516 0.167491 -12.25 0voivod | -0.04931 0.367066 -0.13 0.893lnroaddens1 | 0.808579 0.322109 2.51 0.013lnwydatki | 3.319809 0.181755 18.27 0lnunemplrate | -1.30784 0.341847 -3.83 0east | -0.63643 0.555441 -1.15 0.253west | 0.73686 0.491568 1.5 0.135kodfirmy1 | -0.73023 0.21049 -3.47 0.001lnprice | 10.35352 4.368462 2.37 0.018leader | 0.250719 0.242581 1.03 0.302_cons | -56.1456 6.798719 -8.26 0

Conclusions• Our data does not allow us to uncover the pricing

strategies of the two firms• Location near German border, the unemployment rate

and firm-specific fixed effect have a much stronger impact on prices than „strategic” variables; the level of competitiveness is similar across local markets

• Firms’ pricing strategies differ: market share and new entry variables affect Statoils’ prices, but not Shell prices

• We find no evidence of dynamic entry deterrence (price decreases in reaction to competitor’s entry)