The Impact of One Stop Shopping- Induced Purchasing Complementarities in Structural Econometric Models of Retails Competition

Transcription

1 The Impact of One Stop Shopping- Induced Purchasing Complementarities in Structural Econometric Models of Retails Competition Ulrich Heimeshoff, Gordon Klein & Dennis Rickert Heinrich-Heine-Universität Düsseldorf March 16, 2015 Abstract This paper uses a standard structural econometric framework to investigate the effect of One-Stop Shopping on bargaining power in a retailer manufacturer interaction. It is the first -to the best of our knowledge-, which incorporates the utility of cross-category consumption and the concentration of purchases in the demand estimation. The results of this estimation then allow identify a Nash Bargaining Framework and simulates shifts in the shopping behavior, i.e. cross-category consumption and concentration of purchases on bargaining power. It becomes evident that this concentration is a major driver of bargaining power. Keywords: Structural Econometrics, One-Stop Shopping, Antitrust, Vertical Relationships Heinrich-Heine-Universität Düsseldorf, Düsseldorf Institute for Competition Economics (DICE), Universitätsstr. 1, Düsseldorf, ulrich.heimeshoff@dice.hhu.de, klein@dice.hhu.de., rickert@dice.hhu.de. Chains. Draft, please do not cite. We thank participants of the ANR-DFG project Workshop in Rennes for comments and suggestions. We thank Joeffrey Drouard for valuable comments. Gordon J. Klein acknowledges financial support from the ANR-DFG research grant of the project Competition and Bargaining in Vertical Chains. 1

2 1 Introduction The empirical analysis of competition in retail markets is a topic of interest for more than a decade. This interest can shown easily by various antitrust investigations starting by the of the British Competition Commission (e.g., Competition Commission 2000, 2008). This interest translated into many academic research, particularly interested in the vertical structure of the retail sector (e.g., Chen and Rey 2013, Caprice and von Schlippenbach 2012, Bonnet et al. 2012, Draganska et al. 2011). 1 The analysis of vertical value chains in retail markets, has gained enormous attention in acacademic research within the last years This is motivated by high interest of this sector in recent antitrust analysis investigations. Hereby, the analysis is heavily reliant on particular consumer behavior as for instance one-stop shopping. This particular behavior implies a concentration of purchases by customers. The literature shows that given this effect there arise externalities leading to complementary relationships of products across categories (e.g., Stahl 1982, Klemperer 1992). 2 This effect is not innocuous from a competition economics point of view. Complementarities have been shown in the theoretical literature to have an effect on firms strategies, e.g., loss leading pricing (Lal and Matutes 1994, Klemeperer and Padilla 1997, Chen and Rey 2013) and bargaining between retailer and manufacturer (e.g., Competition Commission 2000, 2008, Caprice and von Schlippenbach, Johansen 2012). Still, the empirical identification and analysis of those effects is incomplete. Besides a reduced form analysis of a natural experiment by Heimeshoff and Klein (2012), there is no identification in structural market analysis, which is one of the strongest instruments in the antitrust analysis. Neither there is any empirical assessment of the impact of One- Stop Shopping on important factors important for the antitrust analysis as buyer power. The recent literature achieved to identify bargaining power in structural econometric models using a Nash-Bargaining framework and found several correlates of bargaining power (Draganska et al. 2011, Bonnet and Bouamra 2013, Haucap et al. 2012). It is not clear how a change in concentration of purchases affects the bargaining power between 1 See for a discussion of the problem also own previous work in Heimeshoff and Klein (2012), Haucap et al. (2012). 2 In this context the literature claims an increasing concentration of sales explained by an changing environment lead to shopping costs and the concentration of sales to shopping cost reduction. 2

3 retailer and manufacturers, although it is claimed by competition authorities to increase the retailers bargaining power. Our paper contributes to this question. First, it uses a demand estimation and incorporates the extend of cross category shopping into the analysis of one particular category. The importance of the other categories consumption in the estimated utility is used to estimate the size of a One-Stop Shopping - or in more general terms- cross-category consumption externality. In a second step, a structural bargaining framework is used to show how a shift in the size of this externality has an impact on the bargaining power of manufacturer vis-a-vis the retailer. The market used is the German market for diapers, of which we have a consumer panel data set, which describes all purchases in that particular category of a representative panel of customers across all different store formats. The market is attractive for this analysis, since it comprises shopping at different shop formats as full-line distributors, discounters or drugstores. Therefore, very heterogeneous shopping occasions are covered, which is important for the analysis. This information allows us to identify first, the distribution of rents and bargaining power among the retailer and manufacturers and, second, the impact of others categories consumption within the purchase inside the diaper category. This is -to the best of our knowledge- the first attempt to incorporate One-Stop Shopping into a structural ecoonometrics framework. Given the structural framework used this enables us to simulate an exogenous shift of the concentration of purchases on the bargaining power between manufacturers and retailers. We find a bargaining power reducing effect if the overall part of the shopping cart is relatively low and a high effect when the effect is relatively high. This effect is particularly strong for the market leader. Our results show, that besides other structural factors consumer behavior is essential for retailers bargaining power and the whole market outcome. 2 Related Literature The concentration of purchases and the preferences of customers for larger shops has been analyzed in the literature since Stahl s (1982) paper. He motivates these preferences with convex shopping costs leading to a decrease in utility in the number of shopping trips. Therefore, the customers tend to concentrate the purchases. Given that this implies 3

4 complementarities among unrelated products this offered the opportunity to find new business strategies, of which loss leading is the most prevalent (e.g., Lal and Matutes 1994, Chen and Rey 2013). Given that in the retail sector there have been many fundamental changes in particular the tendency to concentrate the purchases even more -in the extreme case at only one store- so called one-stop shopping. This has been accused by competition authorities to unilateral decrease the manufacturers bargaining power towards retailer (e.g., Competition Commission 2000, 2008). Still the evidence is not clear since there are only few theoretical and empirical works. Exemptions in the theoretical literature are Caprice and von Schlippenbach (2013) analyze in a theoretical framework with one retailer and two manufacturers using two-part tariffs how one-stop externality can lead to rent extraction of one of the manufacturers. Johansen (2012) shows a different effect leading to an increase of bargaining power of multi-product retailer when negotiating with a manufacturers. Schlippenbach and Wey (2011) on the other hand, investigate the impact manufacturer and retailer bargaining positions and find an increasing difference between agreement and disagreement profits of retailer, i.e. less bargaining power. On the empirical side, the effects on bargaining power are rare. In particular, there are only few studies that explicitly identify bargaining power. Our bases on the identification of a Nash-Bargaining framework provided by Draganska et al. (2011). They analyze the German coffee market using aggregated data and find a rather heterogeneous distribution of bargaining power. Besides the development of the framework they find interesting correlates of bargaining power as for instance size, which is widely acknowledge to be important to be a proxy of bargaining power. Haucap et al. (2014) use this approach also to analyze the German coffee market, but use disaggregated data and find that shopping patterns as concentration of purchases at a retailer have an significant effect on the distribution of bargaining power. Still, they can only show correlations of one-stop shopping preferences on bargaining power. Bonnet and Bouamra-Mechemache (2013) find building on that framework providing an analysis of the french milk market that product differentiation, i.e. organic products lead to a shift in bargaining power. Heimeshoff and Klein (2013) show in a reduced form case analysis of the German milk market that onestop externalities lead to a shift in bargaining power since the outside options of retailers becomes weaker. This reason is that manufacturers can successful implement product differentiation strategies which may lead to a loss of a whole customers shopping cart if a particular important product is not available. 4

5 3 Framework To analyze the relationship between retailer and manufacturer, we assume similar to Draganska et al. (2011), Bonnet and Bouamra-Mechemache (2013) and Haucap et al. (2012) that Retailer and Manufacturer play a three-stage game: Stage 0 Manufacturer and Retailer decide to participate in the market. Stage 1 Manufacturer and Retailer bargain over the intermediate input price. Stage 2 Retailer compete in Bertrand-Nash pricing. Stage 3 Consumers choose a product at a specific retail outlet. This game is then solved via backward induction. Hence, the first step is to set up the demand function. If consumers maximize their utility and retail stores play a Bertrand- Nash game, then retailers set prices that are profit maximizing. Hence, the optimal markups can be recovered with information on retail prices and demand elasticities. Because retailers play their market game given the knowledge of input prices, we are also able to derive information on the vertical relationship when retailers and manufacturers bargain over wholesale prices. 3.1 Demand Side As in all structural econometrical estimations the fist and most important step is a precise demand estimation. Given the structure of the data we are able to observe individual purchasing decisions of households. Therefore, we are able to estimate the household specific demand. Since the literature highlights unobserved heterogeneity in the customers willingness to pay, we use a random-coefficient model (see for a discussion Nevo 2000), to take unobserved heterogeneity in the willingness to pay into account. In general, there are two main approaches of estimating these kind of models: random-coefficient models identified per GMM (e.g., Berry et al 1995, Nevo 2001) which use aggregated demand side information and random coefficent models which are identified via a control function approach (Bonnet and Dubois 2010). Given the disaggregated character of our data the control-function approach seems to be more suitable as the GMM approach cannot deal 5

6 well with individual heterogeneity in the observed choice sets (for a discussion see Petrin and Train, 2010). Therefore, we use a random coefficient logit with a control function (Petrin and Train 2010), which is widely used with similar datasets (Bonnet et al. 2013). The final specification for the demand function is: U = α + β R1 P rice + β R2 OneStop + β j X j + λ 1 CF 1 + λ 2 CF 2 + ɛ (1) The utility U is hereby defined as a function of the constant α, two random coefficients P rice resp. OneStop and a vector of brand fixed effects X j with corresponding coefficients beta j. We assume that customers are effected by the product price, the percentage share of their overall groceries they spend at the retailer and the interaction of both. To control for endogeneity we insert two control functions, the first for the price and second for the percentage share of overall grocery spendings. Both variables are endogeneous because their are correlated with unobserved factors in the error term. Prices, for instance, might be correlated with quality or position in the shelf. A one-stop measure might be correlated with retail assortment decisions. Hence, we use cost shifters as control for product prices, i.e. plastic and paper prices because there is a high correlation between retail prices and input prices. A similar argument can be made for the one-stop measure: rurality of the living location and the regional GDP are correlated with number of stops and expenditures at the retailer and thus are good controls for the variable OneStop. The estimation has an error term ɛ which is i.i.d from a type I GEV distribution if the control functions are correctly specified. The consideration of potential endogeneity and the resulting correlation of unobserved consumer heterogeneity and product attributes produces a demand curvature that implies general substitution patterns. Particularly in the presence of one-stop shopping behavior neglecting a control for heterogeneity in shopping preferences might imply unreasonable substitution patterns. Consumers are not able to substitute among all retailers without restrictions. Some might be attracted by the low price of a special product and rather prefer to switch within a retailer to a private label product than to another retailer when the diaper price of a branded manufacturer is too expensive. Given recent discussions on loss-leading as an exploitative practice (e.g., Chen and Rey 2013), it seems intuitive that consumers optimize the utility of their whole shopping basket rather than optimizing the utility for each item separately. Letting the One-Stop measure, which can also be 6

7 interpreted as shopping costs at a specific retailer, enter into the utility of the consumer, extracts unobserved heterogeneous buying patterns from the error term of estimation. Without correction of these buying patterns, results would be biased. This estimation allows us to derive several marginal effects. By using numerical differentiation methods, the random-coefficients allow the identification of S i p i with E = OneStop. as well as S i Supply Side With demand estimates at hand, we are able to recover retailers optimal mark-ups in stage 2. 3 Each retailer optimizes its profit regarding the price for each product in its assortment. Retail profits of retailer R are the sum over the the profits for all products i = 1,..., I in his assortment Ω R : 4 π R i = i Ω R (p i w i c i ) S i (p i ) (2) Taking the First-Order-Condition results in: π R p = S n + (p n w n c n ) S i(p i ) = 0 (3) p n Ω R n Importantly, since we observe equilibrium prices, we do not need to solve for the equilibrium, but have this already such that we can derive the different parts of the equation. In particular the margins are then given by: Margin R i = n Ω R (p n w n c n ) = S n S i (p i ) p n (4) After having retail profits recovered, we are able to solve the first stage of the model, the optimal wholesale prices. These are is defined as the solution of a Nash bargaining 3 The Supply side s setup follows the approached developed by Draganska et al. (2011) and applied by Bonnet and Bouamra-Mechemache (2013). This section (3.1.1), therefore, summarizes their approach and follows their general notation. 4 At this stage, we assume that retailers do not account for consumers shopping costs resp. that their are not able to gather information on these costs. 7

8 framework between retailer and manufacturer, who bargain over the intermediate input price: max(π R i d R i ) λ i (π M i d M i ) 1 λ i (5) In this setting the parties bargain over the intermediate input good w i, where the FOC w.r.t. to the wholesale price gives the profit maximizing solution. It is important to note, that the difference between agreement profit(π R/M i ) and disagreement profit (d R/M i ) determines parties endogenous bargaining power, also referred to bargaining positions, while the factor λ i determines parties exogenous bargaining power, which indicates how much of the difference between both one party can capture. In the simplest version the outside option is equal to zero. 5 Then this can be simplified to the following expression. πi R πi M = 1 λ i λ i (6) The solution of the lambda parameter is implemented empirically since the identification of the intermediate input price is analytically not tractable. Therefore, we reformulate the FOC by expressing the manufacturer margin as a share of the retailers margin, i.e. margin m = 1 λ λ marginr. This allows us to write down the pricing equation as follows: p i = θ z + margin R i + 1 λ λ margin m i (7) with margin m i = margin R i. This equation is straightforward to estimate and identified given the argument of Bresnahan (1991) Comparative Statics The core contribution of this paper is the comparative statics analysis in this section where we show how the λ-parameter is affected by customer behavior. In particular, we show how consumer one stop shopping behavior drives the size of the λ parameter and how exogenous shifts of consumer behavior have impact on the split of the profit pie. It 5 The construction of the outside option is currently work in progress. In particular we try to implement the shift in market shares as well as the value of the potentially affected other goods if a customer is lost, i.e. the one-stop shopping externalities. 8

9 is worth mentioning, that we are well aware that decisions over the firms strategy may lead to a higher degree of one-stop shopping customers, but those strategies are adapted slowly. A more important driver is given by customers shopping costs, such as time and money required for shopping. We assume that stores are exogenously affected by the customers decisions to one-stop shopping in the short time period of the sample. The relationship we are interested in is the impact of a change in one-stop shopping behavior E j of all households on the bargaining power λ of a particular retailer j : λ i = ( π M π R +π M ) j Φ (8) To solve this equation we take advantage of the envelope theorem which states that partial differentiation can be used to gain information on the marginal change of the function under the assumption that the equilibrium outcome does not change. The partial derivative of retail profits w.r.t. one-stop shopping behaviour is: π R p=p = S i margin R i + S i marginr i (9) and analogously for the manufacturer: π M p=p = S i margin M i + S i marginm i. (10) Still, these necessary formulas require more information to be solved. First we focus on the retail margins, which can be differentiated using the envelope theorem by the partial derivative of equation (4): margin R i p=p = S S 1 i + S i p 2 i S i (11) p i p i s All necessary information are provided by the results of the demand estimation, i.e. E and s p, except for the derivative. To get this effect we take advantage of the price p E equation (7). Given that the price is dependent on margins and on the size of E j, 7 can be reexpressed by using the size of the Externality instead of the margins. Clearly, this leads to an omitted variable bias, which is tackled by the use of a control function. p i = θ z i + β Ej E j + β CF CF 2 + ɛ (12) 9

10 The coefficient β E can be interpreted as the derivative of the Externality on the price p. Having this information recovered we proceed and take advantage of the fact that E the margin is defined as i Ω R(p i w i c i ) and we know that costs are fixed by assumption, and only p i and w i are dependent on the one-stop behavior E j. Consequently, we reformulate (11) as: such that margin R i p=p w i = p i marginr i = p i w i (13) p=p = marginm i (14) Now, all elements are identified and we can rewrite the local differentiation of λ at the equilibrium price regarding the one-stop shopping behavior as: λ i = πm i (π R + π M ) 1 π M (πr + π M 2 ) π E (15) Since profits in equilibrium are well defined, we have identified all elements and can show how changes in the one-stop shopping behavior affect the bargaining power distribution. 4 Empirical Analysis 4.1 Descriptive Statistics We use micro level data for the German diaper market obtained from GFK Panel Services, a German market research company. The GFK tracks a panel of up to consumers which is representative for the whole sample of Germany. Consumers are equipped with home-scanning devices and are able to scan all their purchases which gives information on the purchased products, retail outlet, actual transaction price, promotion, total bill, number of shopping trips, time as well as information on household characteristics such as income, number of children, age. These type of information simplifies identification strategy because it contains information on sales across all stores and identifies purchases by the same consumer at other outlets. For the construction of the control function additional data on cost shifters is used from Thompson Reuters (plastic and paper) and from the German Federal statistical office (GDP per capita and rurality index). 10

11 Table 1 summarizes household characteristics and shows how consumers shopping behavior varies over the household panel dependent on household preferences for shopping plans and on characteristics of the household, such as household size, number of children, income, age and so on. Table 1: Summary Statistics I Variable Mean SD Shopping trips/month Tot.Bill/Purchase (in Euro) Monthly Expenditures (in Euro) OneStop Measure The table summarizes the average consumers shopping behavior conditional on initiating a shopping trip. If she decided to shop, she does it one average 1.22 times a day with a standard deviation of The difference in shopping patterns is more clearly shown by the summary of shopping trips per month: A standard deviation of close to 5 shopping trips and 107 Euros in monthly expenditures underline heterogeneous consumer shopping patterns. That already indicates that consumers are heterogeneous regarding their choice of numbers of stops a day and month. A significant amount of consumers plan their trip in a way that their are able to buy all purchases at one specific retailer, but some prefer to divide their shopping basket over at least two retailers. The average consumer spends Euros per purchase and 164,62 Euros in a month. Total bill and monthly expenditures are adjusted for the value of diaper purchases which are subtracted from the amount of the total bill. We measure one-stop shopping behavior as the fraction of the total bill of a consumer spent for a retailer and her monthly purchases, which is on 24% on average in the sample, again with a rather high standard deviation of 21 percentage points among households. Table 2 sums up strategic competition variables of retailers, i.e. price, share of promotion, private label shares, share of premium labels (either from private label or brand label manufacturers) and average package size. Between the different retail format, there are significant differences in competitive determinants, for instance, prices in hypermarkets are on average 3 cents higher (measured per diaper in a package) than in both other formats and private label share is highest in discounters, whereas the share of premium 11

12 Table 2: Summary Statistics II Format Price Promo PL Prem Pack. purch. No. purch. Discounters Drugstores Hypermarkets Total labels is lowest at discounters. The most surprising fact seems that the share of promotion, this can be a price reduction or a free add-on in the package size, in drugstores is around 30 percentage points higher in Hypermarkets than in drugstores. The difference is closely related to different pricing policies in hypermarkets (more promotions and higher average price) and discounters resp. drugstores (less promotions and lower average price). Table 3: Buying Patterns among Retailers Retailer Format Trips/month Tot.Bill Month.Exp. OneStop 1 Discount Drugstore Full-Line Full-Line Discount Full-Line Full-Line Drugstore Total Table 3 shows that consumers with different shopping preferences might also prefer different shopping formats. For instance, customers visit drugstores, that are retailers 4 and 30, more frequently than customers of other formats but the average bill per visit is lower for Drugstore than for the remaining formats. Furthermore, there seem is some variance across retailers regarding monthly expenditure, where customers of retailer 1 (discounter) spend most and customers of another discounter spent least. Unfortunately, we are not able to separate this measure into an income effect and price effect because we only know the total bill not the number of items purchased nor prices of these items. That is why we 12

13 construct our one-stop measure as the percentage share of the expenditures a household spends at a retailer in relation to the total household expenditures in a month. If the household purchased diapers in the certain month then the value of the diaper purchases is subtracted from both, the numerator (monthly expenditures at a retailer) and denominator (total monthly expenditures). Our measure shows variation across retailers and two full-line distributors (16 and 27) seem to be typical retailers for one-stop shoppers: Consumers have to pay a rather high total bill for their shopping basket and with 31% they shift a high amount of their total expenditures to these certain retailers. Some assumptions have to be made in the context of discrete choice models. Appreciating that demand elasticities may vary among retailers and manufacturers, we define product-retailer combinations as product level, which means that the same brand is regarded as two different products when sold at two different outlets. We only consider the eight largest retail chains and simplify the choice of private label brands. We define that consumers perceive private labels within a retail format (drugstore, discounter or hypermarket) as similar but different across retail formats. Because, there is only one large branded manufacturer, this reduces the number of parameters to estimate significantly and all other choice are in the outside option. We further assume that consumers decide to buy diapers every two weeks, where the no purchase choice constitutes the outside option, which is buying from a retailer which is not a major player or alternative products, such as cotton or fleece diapers. Furthermore, customers could be able to buy large amounts of diaper packages during promotional periods and store them. Because diaper packages are heavy and quite unhandy to carry, we feel that storage is not a big issue. 4.2 Demand Estimation Results Table 4 and table 5 show results of the demand estimation. All coefficients have the expected sign. Paper and plastic input prices have a positive and significant effect on the retail price, population density has a negative effect on one-stop shopping, which means that people in congested areas do more shopping trips than people in regions with low population densities and vice versa (see table 4). Furthermore, the price has a negative effect on purchasing behavior, such has our one-stop 13

14 Table 4: Control Functions (1) (2) Price One-Stop Measure One-Stop (-10.06) Paper (4.96) Plastic (8.40) Price (-9.23) Population Density (-14.60) Regional GDP per Capita (10.97) N t statistics in parentheses p < 0.05, p < 0.01, p < shopping measure. This is not surprising because the relationship between purchasing probability and number of stops are negative by definition. More importantly, we are interested distribution of this preference among households and the magnitude of the standard deviation indicates a high degree of heterogeneity. Because both, the price and the one-stop shopping measure are endogeneous, we use the control function approach by Petrin and Train (2010) to correct for the endogeneous part in the estimation equation. (see tables 4 and 5). Because coefficients of nonlinear models are difficult, we report also elasticities of the relevant variables, which allow a percentage/ percentage interpretation. Table 6 shows the mean price and onestop elasticities with their corresponding standard deviations for all retailers. Although the difference of retailer price elasticities is higher in absolute terms, the one-stop shopping elasticities range between -0.8 for retailer 16 and -3.8 for retailer 29. (Note that the latter can be interpreted as 1-% increase in onestop shopping leads the a decrease of x-% further shopping occasions). Analysis of price elasticities show some interesting patterns. First of all, we have isolated the price effect for the product from the retailer price effect, which is defined as the price effect from changing the price of the shopping basket. This leads to heterogeneous price elasticities than neglecting the one-stop measure. In our case, elasticities range from 3.2% to 9.2%. Not surprisingly, customers of discount stores are most sensitive to price 14

15 Table 5: Estimation Results (1) (2) Logit RC-Logit Coefficient Price (-25.22) (-25.83) One-Stop Measure (-0.79) (-14.86) CF1b 0.14 (19.96) 0.25 (39.87) CF2b 0.00 (28.20) (-0.44) Brand (-11.31) (-11.51) Brand (21.53) 0.31 (9.82) Brand (4.35) 0.06 (3.11) SD One-Stop Measure (-49.99) price (-48.14) N t statistics in parentheses p < 0.05, p < 0.01, p <

16 Table 6: Elasticities Retailers retailer format own price cross price own onestop cross onestop 1 Discount Drugstore Full-Line Full-Line Discount Full-Line Full-Line Drugstore Total

17 increases. A price increase of 1% for diapers at a discount store would lead to a demand decrease of 9.2% resp. 8.2% decrease. Consumers from Full-Line distributors are least price sensitive, their product elasticities range from 3.1% to 5.7%. This results supports our hypothesis that customers are less price sensitive regarding a certain product when they are able to buy a full-range of products at a certain retailer. Also own one-stop elasticities are lowest for the two one-stop-retailers 16 and 27, that are 1.8% and 0.8%, which indicates that for consumers of these retailers the utility gain by an increase in onestop shopping behavior is lower than for customers of other retailers and formats. Most profitable would be an increase for drugstores, customers would on average postpone around 3.3% of their future shopping baskets if they had the opportunity to spend 1% more of their monthly expenditures at the drugstores Supply Side Results To get the effect the onestop shopping effect on the price, we regress the one stop shopping measure of each retailer on the diaper prices and find that a one percent increase in onestop shopping may effect the price per diaper in a range of 1.75 cents to 2.05 cents. This value is equal to the differentiation p i. Table 7: dpde retneu mean sd Total In the next step, we use the information on the information we have to estimate the lambda factors. We use information on the costs as plastic to ensure identification. Table 17

18 (8) provides us with the information on the lambda-factors. Given that also private label diapers are not produced by retailers, we treat those diapers as not integrated. The bargaining power is in total equally distributed between manufacturer and producer. Still there is a large heterogeneity. In particular one retailer with large shopping carts market (indicated by code 16) has a strong position. Table 8: Lambda Factor, by ident Bundle Price λ 1 λ Brand Retailer 7x Market Leader Drugstore 7x Market Leader Full-line Distributer 7x Market Leader Full-line Distributer 7x Market Leader Discount 7x Market Leader Full-line Distributer 7x Market Leader Full-line Distributer 7x Market Leader Drugstore 8x Market Leader Premium Drugstore 8x Market Leader Premium Full-line Distributer 8x Market Leader Premium Full-line Distributer 8x Market Leader Premium Full-line Distributer 8x Market Leader Premium Full-line Distributer 8x Market Leader Premium Drugstore 10x Private Label Premium Drugstore 11x Private Label Discount 11x Private Label Drugstore 11x Private Label Full-line Distributer 11x Private Label Full-line Distributer 11x Private Label Discount 11x Private Label Full-line Distributer 11x Private Label Full-line Distributer 11x Private Label Drugstore Total This pattern can now be used to give results for overall margins of manufacturer and retailer provided in table (9). 18

19 Table 9: Profits and Costs, by ident ident Price Margin Retailer Margin Manufacturer cost Brand Retailer 7x Market Leader Drugstore 7x Market Leader Full-line Distributer 7x Market Leader Full-line Distributer 7x Market Leader Discount 7x Market Leader Full-line Distributer 7x Market Leader Full-line Distributer 7x Market Leader Drugstore 8x Market Leader Premium Drugstore 8x Market Leader Premium Full-line Distributer 8x Market Leader Premium Full-line Distributer 8x Market Leader Premium Full-line Distributer 8x Market Leader Premium Full-line Distributer 8x Market Leader Premium Drugstore 10x Private Label Premium Drugstore 11x Private Label Discount 11x Private Label Drugstore 11x Private Label Full-line Distributer 11x Private Label Full-line Distributer 11x Private Label Discount 11x Private Label Full-line Distributer 11x Private Label Full-line Distributer 11x Private Label Drugstore Total Comparative Statics Identification of the whole bargaining system and the relevant derivatives enables us to simulate how a change in the overall shopping cart (see table (10)). As expected, the size of the overall shopping cart is negatively related in the λ-factor, but with significant differences across the bundles products. The most striking difference is that one retailer bargaining power is most clearly negative related to an increase in the shopping-carts overall value. That means, the higher the share with other products is, the lower is the bargaining power. Still one, has to consider that the marginal effect is at a specific point and can be assumed to be non-linear. Therefore, the drastic effect will not hold for an arbitrary change in the shopping carts size, but only on the specific point identified. However, it is also interesting that the effect is by the power 10 to 50 lower for this particular retailer. This retailer in particular is associated with high revenues. However, it is important to note that the factor identified is dependent on the model used. Since the work on the outside option including the potential shopping cart will have an impact on the lambda factor as well, we consider this effect as a first working example with the effects size being subject to changes. 19

20 Table 10: Lambda Factor, by Bundle Bundle λ E Description 7x Market Leader Drugstore 7x Market Leader Full-line Distributer 7x Market Leader Full-line Distributer 7x Market Leader Discount 7x Market Leader Full-line Distributer 7x Market Leader Full-line Distributer 7x Market Leader Drugstore 8x Market Leader Premium Drugstore 8x Market Leader Premium Full-line Distributer 8x Market Leader Premium Full-line Distributer 8x Market Leader Premium Full-line Distributer 8x Market Leader Premium Full-line Distributer 8x Market Leader Premium Drugstore 10x Private Label Premium Drugstore 11x Private Label Discount 11x Private Label Drugstore 11x Private Label Full-line Distributer 11x Private Label Full-line Distributer 11x Private Label Discount 11x Private Label Full-line Distributer 11x Private Label Full-line Distributer 11x Private Label Drugstore Total

21 5 Conclusion We have shown how to estimate the effect of one-stop shopping on bargaining power using a commonly used structural econometric framework. In particular we have shown that the effect of how one-stop shopping behavior has an effect on bargaining power is heterogeneous and also economically significant among the different products bundles and in particular different retailer. Our findings are important since findings can lead to a better understanding of retailer-manufacturer relationship in the shed light of consumer behavior. This should lead to more precise antitrust investigations in the long run. References [1] Berry, Steven, Levinsohn, John and Ariel Pakes (1995): Automobile Prices in Market Equilibrium, in: Econometrica, Vol. 63, [2] Bonnet, Celine (2010): Inference on vertical contracts between manufacturers and retailers allowing for nonlinear pricing and resale price maintenance. in: The American Economic Review, Vol. 41, [3] Bonnet, Celine Dubois, Pierre, Villas Boas Sofia B. and Daniel Klapper (2013): Empirical Evidence on the Role of Nonlinear Wholesale Pricing and Vertical Restraints on Cost Pass-Through, in: The Review of Economics and Statistics, vol. 95(2), [4] Bonnet, Celine and Zohra Bouamra Mechemache (2013): Organic Label and Profits Sharing in the French Fluid Markekt, Paper presented at the 134th Seminar Labels on Sustainability: An Issue for Consumers, Producers, Policy makers, and NGOs, Paris. [5] Caprice, Stephane and Vanessa von Schlippenbach (2013): One-Stop-Shopping as a Cause of Slotting Fees: A Rent-Shifting Mechanism, in: Journal of Economics and Management Strategy, Vol. 22 (3), [6] Competition Commission (2000): Supermarkets: A Report on the Supply of Groceries from Multiple Stores in the United Kingdom, Inquiry Reports Series of the Competition Commission Cm4842 (10/1000), London. 21

22 [7] Competition Commission (2008): The Supply of Groceries in the UK Market Investigation, Inquiry Reports Series of the Competition Commission, London [8] Chen, Zhijun and Rey, Patrick (2013): Competitive Cross-Subsidization, Institut d conomie Industrielle, IDEI Working Papers Vol [9] Draganska, Michaela, Klapper Daniel and Sofia Berto Villas-Boas (2011): A larger slice or a larger pie? An empirical investigation of bargaining power in the distribution channel, Marketing Science, Vol. 29 (1), [10] Johansen, Bjorn Olaf (2012): The Buyer Power of Multiproduct Retailers: Competition with One-Stop-Shopping, Working Paper 03/12, University o f Bergen. [11] Klemperer,Paul (1992): Equilibrium Product Lines: Competing Head-to-Head May be Less Competitive, in: The American Economic Review, Vol. 82, [12] Klemperer,Paul and A. Jorge Padilla (1997): Do Firms Product Lines Include too Many Varities?, in: RAND Journal of Economics, Vol. 28, [13] Lal, Rajiv and Carmen Matutes (1994): Retail Pricing and Advertising Strategies, in: Journal of Business, Vol. 67, [14] Haucap, Justus, Ulrich Heimeshoff, Gordon J. Klein, Dennis Rickert and Christian Wey (2013): Bargaining Power in Manufacturer-Retailer Relationships, DICE Discussion Paper No 107, Düsseldorf. [15] Heimeshoff, Ulrich and Gordon J. Klein (2013): Bargaining Power and Local Heroes, DICE Discussion Paper No 87, Düsseldorf. [16] Nevo, Aviv (2000): Practitioner s Guide to Estimation of Random-Coeffcients Logit Models of Demand, in: Journal of Economics & Management Strategy, Vol. 9, [17] Nevo, Aviv (2001): Measuring Market Power in the Ready-to-Eat Cereal Industry, in: Econometrica, Vol. 69, [18] Petrin, Amil and Kenneth Train (2010): A control function approach to endogeneity in consumer choice models, in: Journal of Marketing Research, Vol. 47, [19] Stahl, Konrad (1982): Location and Spatial Pricing Theory with Nonconvex Transportation Cost Schedules, in: Rand Journal of Economics, Vol. 13,

23 [20] von Schlippenbach, Vanessa and Christian Wey (2011): One-Stop Shopping Behavior, Buyer Power, and Upstream Merger Incentives, DICE Discussion Paper No 27, Düsseldorf. 23