Commodity price inflation, retail pass-through and market power

International Journal of Industrial Organization 30 (2012) 50–57

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International Journal of Industrial Organization j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / i j i o

Commodity price inflation, retail pass-through and market power☆ Timothy J. Richards ⁎, William J. Allender, Stephen F. Hamilton a r t i c l e

i n f o

Article history: Received 13 July 2010 Received in revised form 10 April 2011 Accepted 15 May 2011 Available online 19 May 2011 JEL classification: C35 D12 D43 L13 L41 Q13 Keywords: Market power Pass-through Random parameter model

a b s t r a c t When commodity prices rise, wholesalers and retailers of products derived from basic commodities respond by passing along at least a portion of the price increase to consumers. In this paper we examine whether firms respond differently to positive commodity price shocks than to negative commodity price shocks; that is, whether commodity price volatility alters market power. We exploit recent volatility in food commodity prices over the period 2007–2010 to investigate how commodity price shocks translate into market power in two different vertically-structured food product industries: potatoes and fluid milk. For potatoes, we find both wholesale and retail market power decreases (increases) during periods of rising (falling) commodity prices. Moreover, price–cost margins widen a substantially greater degree in response to negative shocks than margins narrow in response to positive shocks, indicating that commodity price volatility increases market power. For fluid milk, we find that market power likewise declines during periods of rising commodity prices; however, market power does not significantly change during periods of falling commodity prices, suggesting that commodity price volatility decreases market power. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Commodity prices have been unusually volatile in recent years. In 2008 commodity prices rose at unprecedented rates, raising concerns over general retail price inflation. For the most part, however, retail prices rose only moderately. In the case of consumer food and beverage products, for example, the farm products commodity price index rose 21.5% over the period July 2007–2008, while the consumer price index for food and beverages rose by only 5.8% (BLS, 2009a). Yet, in the subsequent period of falling commodity prices between July 2008 and 2009, prices in the farm products commodity index decreased 24.5% while the consumer price index for food and beverages continued to rise. While the divergent paths of commodity and retail prices may be partially explained by the fact that farm commodities comprise a relatively small proportion of total production and marketing costs, they may also be due to a fundamental asymmetry in the exercise of market power by food manufacturers and retailers during periods of rising and falling commodity prices. ☆ Authors are Marvin and June Morrison Chair of Agribusiness and PhD Student in the Morrison School of Agribusiness and Resource Management, W. P. Carey School of Business, Arizona State University, Mesa, AZ, and Professor in the Orfalea College of Business, California Polytechnic State University, San Luis Obispo, CA. Support from the Economic Research Service of the USDA is gratefully acknowledged. All opinions and findings described herein are, however, the authors' and do not reflect the official position of USDA. ⁎ Corresponding author at: 7171 E. Sonoran Arroyo Mall, Mesa, AZ 85212, USA. Tel.: + 1 480 727 1488; fax: + 1 480 727 1961. E-mail address: [email protected] (T.J. Richards). 0167-7187/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.ijindorg.2011.05.003

Indeed, retailers may even be able to exploit commodity price volatility to extract information rents from consumers: Farm commodity prices decreased 8.2% in nominal terms between July 2007 and 2009, whereas consumer food and beverage prices increased 6.9%. This paper seeks to shed light on this issue by empirically examining how firms exercise market power during periods of positive (negative) commodity price shocks, while controlling for changes in cost. Commodity price shocks may facilitate market power as volatility can disguise an increase in margins. The potential linkages between input-price changes and market power have been understood since at least Lucas (1973) and Barro (1976), who develop models of price inflation in which agents mistake aggregate price-inflation for relative price changes. Uncertainty over industry-specific input costs generates opportunities for non-competitive pricing. More recently, Benabou and Gertner (1993) recognize that the information content in evolving prices is endogenous to agents' incentive to search. Price inflation increases the return to consumer search, but also reduces the information content of prices. If consumer search costs are sufficiently low, search intensity rises with inflation and firms narrow their price–cost margins; however, if search costs are relatively high, inflation reduces the intensity of consumer search, resulting in higher equilibrium prices. Our paper provides a formal test of this implication. A relatively large body of empirical research examines the degree to which input-price changes pass through to retail prices in oligopoly markets. The basic message from this literature is that pass-through rates tend to be incomplete (less than 100%). Nakamura and Steinsson

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(2008), Hellerstein (2008) and Nakamura and Zerom (2010) identify three reasons why pass-through rates may be imperfect under oligopoly: (i) demand curvature and market structure, (ii) the presence of local costs unrelated to the cost of the primary input, and (iii) price rigidities and other elements reflecting dynamic, shortrun influences. However, little empirical evidence exists to date on the link between commodity price volatility and the ability of downstream firms to exercise market power. We provide some evidence using a structural model of retail demand and pricing at the retail and wholesale levels. We examine market power and commodity price pass-through rates in a vertical structure comprised of a wholesale industry, a retail industry, and a consumer market. We frame our empirical observations around two products that involve different levels of manufacturing inputs: potatoes (a fresh, random-weight product) and fluid milk (a fixed-weight consumer packaged good). Our central results are twofold. First, market power tends to decrease (increase) when commodity prices are rising (falling), as wholesalers and retailers maintain relatively stable price levels in response to fluctuation in the underlying commodity prices. Second, the extent to which market power is manifested in the supply chain depends on the vertical structure of firms. For potatoes, we find both manufacturers and retailers narrow their margins in response to positive commodity price shocks and expand their margins in response to negative commodity price shocks. Moreover, the magnitude of the margin adjustment is larger at both stages of production when commodity prices fall than when commodity prices rise, indicating that commodity price volatility raises market power in the potato industry. For fluid milk, we find lower levels of market power at both stages of the supply chain than in the case of fresh potatoes. Fluid milk manufacturers and retailers likewise narrow price–cost margins when commodity prices rise, but do not significantly adjust margins when commodity prices fall. Commodity price volatility therefore reduces market power in the fluid milk industry. Our finding of narrow margins and lack of margin adjustment in the fluid milk market during periods of negative commodity price shocks is consistent with the use of fluid milk as a “loss-leader” by multi-product retailers. Supermarkets may rely on low milk prices in all cost environments to attract customers into their stores, and, at the same time, extract information rents from consumers on lessfrequently purchased items (like potatoes) by widening margins on a subset of products impacted by negative commodity price shocks. The remainder of the paper is organized as follows. The next section outlines a structural model of retail pricing consisting of a discrete choice demand model, and a two-stage retailer/manufacturer price optimization framework on the supply side. In Section 3, we describe our potato and fluid milk data. We detail our estimation and identification methods in Section 4 and present our empirical results in Section 5. 2. Pass-through in differentiated product markets 2.1. Overview In this section, we describe the structural model used to estimate the pass-through rate of farm prices to retail prices for fresh potatoes and fluid milk. We select these categories because they represent different levels of value-added from the basic food commodity and, as such, can potentially project commodity price inflation differently through the vertical structure. In each case, the econometric model is based on a two-stage game: Wholesalers offer contracts to retailers in the first stage, and then, conditional on retailers' acceptance of the wholesale contracts, retailers set consumer prices in the second stage subject to market-clearing conditions on consumer demand. We estimate the game backward, beginning with consumer demand and the retailers' profit maximization problem, and then estimating

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suppliers' pricing decisions conditional on consumer demand and the pricing conduct of retailers. Within this traditional structural industrial organization framework, we estimate the impact of commodity price inflation on retail pass-through by introducing conduct parameters into the retail and supplier models. The conduct parameters are atheoretic, and do not represent any postulate over retailer or manufacturer behavior, but rather measure the degree of departure of the industry from perfectly competitive (or Bertrand–Nash) outcomes. Critically, we allow the conduct parameters to vary during periods of positive and negative commodity price shocks, which allow us to determine the impact of commodity price volatility on wholesale and retail market power. Our data encompass market transactions at a highly aggregated level. The implicit assumption underlying our method therefore is that a single, representative multi-product retailer serves to describe the pricing behavior of a group of oligopoly retailers. Recent theoretical models of supermarket behavior by Hamilton (2009) and Hamilton and Richards (2009) provide justification for this approach: Inter-retailer competition for store traffic uniformly reduces margins on all products offered by multi-product retailers, while the relative prices of products offered on the intra-retailer margin adopt a Ramsey-type rule that mimics the relative pricing behavior of a multi-product monopolist across products within the store. Because retailers encompassed by our empirical model are differentiated-product oligopolists, our estimated conduct parameters measure the deviation from Bertrand–Nash behavior rather than from collusive behavior. 2.2. Consumer demand Consumer demand is represented by a random utility model in which consumers are assumed to make a discrete choice of one product (brand or variety) from among those represented in our retail data sample, or some other product from another outlet, which is defined as the outside option. Because consumers can buy either potatoes or milk from sources other than those captured by our scanner data, we model the hierarchical nature of a consumer's choice process: Consumers first choose whether to buy from the traditional supermarkets described by our data or another source, and then select a specific brand or variety conditional on store choice. 1 Consequently, we adopt a Generalized Extreme Value (GEV) model of consumer demand (McFadden, 1978). Utility for consumer i from consuming product j during week t is a function of the product's price (pjt), product-specific preferences, γijt, and a set of product attributes (xjk),   K uijt = γij + αi pjt + ∑k = 1 βk xjkt + ξjt + τijt + 1−σJ εijt ;

ð1Þ

where τijt is an error-component that makes the entire term: τijt + (1 − σJ)εijt extreme-value distributed (Cardell, 1997). The product attributes included in xjk are a binary discount variable (dj) that assumes a value of 1 if the product is reduced in price by at least 10% from one week to the next and then returned to its previous value in the following week, an interaction term between the discount and price (djpjt) and a time trend, t. For the milk model, the set of product attributes also includes fat content, and binary indicators for whether the brand is organic and/or private label. ξjt is an error term that accounts for product-specific variation in demand that is unobserved to the researcher, for instance the perceived quality of the good. To circumvent the proportional draw property of the GEV model within each group, we allow the product-preference and marginal 1 Scanner data typically includes only supermarkets with over $2.0 million in annual sales, and excludes stores that do not participate in national data syndication efforts, notably Wal-Mart, Costco, and Target.

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utility of income parameters in (1) to vary randomly over consumers (BLP; Berry et al., 1995; Nevo, 2001). Specifically, the marginal utility of income is normally distributed over consumers so that: L

αi = α0 + ∑l = 1 αl zil + σα vi ;

vi ∼ Nð0; 1Þ;

ð2Þ

where α0 is the mean price response across all consumers, αl is the effect of attribute l on price sensitivity, zil is vector of individual attributes and vi is the random, consumer-specific variation in response with parameter σα. Product-specific preferences in the milk example depend on the attributes of each individual: L

γi = γ0j + ∑l

= 1 γl zil

+ σγ μ i ;

μ i ∼ Nð0; 1Þ;

ð3Þ

where IJ is a J × J identity matrix, “⁎” indicates element-by-element multiplication and G is a matrix of second-order derivatives of the retail first-order conditions. We then substitute the wholesale margin in Eq. (7) into the retail margin Eq. (5) to arrive at:   −1 −1 −1 p = r + c−Sp S− G Sp Sp ⁎IJ S:

Eq. (8) allows us to estimate pass-through rates from farm prices to retail prices through both wholesale and retail stages of production. Because marginal wholesale and retail costs are not separately identified, we estimate the total cost of wholesale and retail production as linear functions of input prices, νr and νw, such that I

where γ0j is the mean preference for product j, γi is a vector of individual attribute effects, and μ i is the random consumer-specific effect on product preferences. In the milk model, we also allow the marginal utility parameters for the organic, fat content and private label attributes to be random, so that βij = β0j + ∑ lL= 1βlzil + σβϖi, ϖi ∼ N(0, 1). The random-coefficient GEV model is then estimated in aggregate data using simulated maximum likelihood (SML) algorithms (Train, 2003). 2.3. Retail and wholesale pricing model We characterize the marketing channel as consisting of symmetric, multi-product oligopoly retailers, and multiple, single-product suppliers. Beginning with the retailer decision, and suppressing the time period index (t) for clarity, each retailer f = 1, 2,…, F sets a price for each product, pj, each week to solve the following problem: f



J

Π = max M∑j pj

= 1

 pj −rj −wj sj ;

f = 1; 2; …; F

ð4Þ

where M is total market demand, wj is the wholesale price, rj are unit retailing costs, and sj is the market share defined above. 2 Solving the retailers' first-order condition for the optimal retail price, and writing the result in matrix notation gives: −1

p = r + w−Sp S;

ð5Þ

where p is an J × 1 vector of prices, w is a J × 1 vector of wholesale prices, r is a J × 1 vector of product-specific retailing input prices, S is an J × 1 vector of market shares, and Sp is a J × J matrix of sharederivatives with respect to all retail prices. Each supplier sets wholesale prices to maximize the surplus over production costs. Assuming individual wholesalers sell all products sold by retailers, we index the supplier model over individual products, j, and write supplier profit as   m Πj = max M wj −cj sj ; wj

ð6Þ

where cj is the (constant) production cost of product j incurred by the supplier and the other variables are as described above. Because wholesale prices are unobserved, we follow Sudhir (2001) and VillasBoas and Zhao (2005) in deriving expressions for wholesale price changes by totally differentiating the retail first-order conditions and solving for the implied wholesale prices. Doing so allows us to write the solution for wholesale prices as   −1 −1 w−c =− G Sp Sp ⁎ IJ S;

ð7Þ

2 We refer to the price paid by retailers throughout the paper as the wholesale price, which differs from the farm price by packing costs, shipping costs and other contractual terms specified by the wholesaler.

ð8Þ

r ðνr Þ + cj ðνw Þ = η0 + ∑i

= 1 ηri νri

L

+ ηwI bj + ∑l

= 1 ηwl νwl ;

ð9Þ

where bj is the farm price paid by wholesalers. We assume wholesale costs to be separable between agricultural inputs (farm products) and non-agricultural inputs. Eq. (8) maintains retailers' and wholesalers' pricing decisions as the outcome of Nash equilibrium. In general this need not be the case. Deviation from the Nash equilibrium can occur if market power varies among manufacturers and retailers due to private information on cost or if consumer search intensity varies in response to price-volatility (Lewis, 2004). Our focus is on how positive and negative commodity price shocks affect market power and, hence, pass-through rates from farm prices to wholesale prices and from wholesale prices to retail prices. Accordingly, we allow for deviations from the hypothesized Bertrand–Nash pricing behavior by wholesalers and retailers by introducing conduct parameters in Eq.(8) that measure the deviation of the wholesale margin (θ) and retail margin (φ) from the Nash equilibrium. Specifically, we estimate     −1 −1 −1 p = r + c−ϕ Sp S −θ G Sp Sp ⁎ IJ S

ð10Þ

where θ and φ are functions of exogenous variables that influence agents' ability to markup prices over marginal cost, namely the upward and downward movements in commodity prices. We write the conduct parameters as linear functions of a constant term as well as upward and downward changes in commodity prices. Specifically, let dwj,t − τδ + and dwj,t − τδ − denote the change in wholesale prices from period t − τ to period t, where δ + and δ − are indicator functions that take the values of δ + = 1 during periods when commodity prices rise and δ − = 1 when commodity prices fall and are otherwise equal to zero. Making use of these expressions, we write the conduct parameters as θj = θ0 + (θ1δ + + θ2δ −)dwj,t − τ and φj = φ0 + (φ1δ + + φ2δ −)dwj,t − τ at the wholesale and retail levels, respectively. This specification of the conduct parameters allows us to test whether retailers or wholesalers, respectively, face greater competitive pressure in an inflationary environment, as argued by Lewis (2004), or regard inflationary periods as an opportunity to exploit buyers' confusion over what prices should be, as suggested by Benabou and Gertner (1993). We then calculate the implied passthrough rates from commodity prices to retail prices by simulating the impact of commodity price variation on retail prices under alternative specifications of the conduct parameters using the approach described by Kim and Cotterill (2008). 3. Data description Our potato data consist of 143 weeks (January 8, 2006 through September 28, 2008) of market-level retail scanner data from five markets: Atlanta, Chicago, Dallas, Los Angeles and New York. The data are obtained from Fresh Look Marketing, Inc., and comprise the dollar sales, unit volume (pounds), promotion activity and attribute indicators

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for ten potato varieties (fingerling, Idaho, purple, red, red creamer, russet, white, yellow, organic, other). Farm price data are from the United Potato Growers of America (UPGA) for each variety and represent weekly FOB price data at the packing-house door. Wholesale potato prices, which are unobserved, differ from farm prices depending on transportation costs, packing costs and other wholesale services. Retail input prices consist of average weekly earnings of workers in the supermarket industry, weekly retail management earnings and market-specific indices of commercial electricity prices. Input prices for milk and potato supplies include an index of interest, taxes and wages, diesel fuel prices, and an index of business service costs. These input price indices are from the USDA (USDA-NASS) and are smoothed to produce weekly series from the native monthly data. 3 The retail wage and utility price indices are from the Current Employment Statistics (CES) survey of the Bureau of Labor Statistics (BLS, 2009a,b) and are also smoothed to produce weekly series. Utility prices are from the BLS Consumer Price Index program (BLS, 2009a) and, for current purposes, are market-specific indices. In the potato demand model, the outside option is defined as the entire potential market for table-potato sales. We calculate the size of the whole market on a weekly basis by multiplying the total metropolitan statistical area (MSA) population (Bureau of Census) by the USDA estimate of per capita consumption of potatoes (USDAERS). The difference between the inside and the outside options is then reduced to potatoes sold through convenience stores, food service outlets and retailers that do not participate in retail-scanner data syndication (Wal-Mart, Costco, and other club and super stores). 4 Finally, we take random draws from the distribution of each demographic measure (household income, age, education and household size) reported by the Current Population Survey (Bureau of Census) to capture market-specific variation in unobserved elements in the mixed logit demand model. The data used for the milk model is similar in nature to the potato data, but reflects inherent differences between data for fixed-weight consumer-packaged-goods and fresh, random-weight products. The milk data represent 10 of the largest Metropolitan Statistical Areas (MSAs) in the U.S. over the 104 week period March 4, 2007 through February 22, 2009. We focus our analysis on the top 18 brands in each market based on local market share, as many milk brands are marketspecific. Additional attributes for the milk demand model include fat content, whether the brand is organic or not, and if it is a private label. All retail input prices are the same as those defined above in the potato model (adjusted for time periods and markets), but we use an index of packaging prices instead of the interest, taxes and wages index used in the potato model.

sampling error (Berry et al., 2004). Consequently, we adopt the “control function” approach developed by Petrin and Train (2010), which is similar to the simulation method proposed independently by Gupta and Park (2009). The control function method addresses potential endogeneity of prices using a two-stage approach. In the first stage, instrumental variable (IV) regression is used to generate a set of residuals which are then used as explanatory variables in the mixed-logit demand equation. We then estimate the demand equation using simulated maximum likelihood (Train, 2003). By introducing the IV residuals into the demand model, we account for unobservables in the prices that may be correlated with errors in the demand equation. Our identification strategy is well-accepted in the literature. Following others in this literature (Berto Villas-Boas, 2007; Draganska and Klapper, 2007) we use a variety of instruments. First, we interact retail and production input prices with the set of binary productindicators (brand in the case of milk and product type in the case of potatoes). Implicitly, this assumes that product-specific variation in cost is correlated with the retail price for each product, but uncorrelated with unobservable factors in demand. Second, we include a set of lagged share values in order to pick up state dependence in demand that may arise from habit, learning or inertia. Third, we include product-specific binary variables to account for unobserved supply or quality factors that influence retail prices. Firststage instrumental variables regressions show that this set of instruments explains over 90% of the variation in our endogenous price variables. On the supply side, retail and wholesale markups in the pricing equation are also endogenous. To identify pricing conduct, we introduce exogenous variation in demand by including market-specific demographic variables such as income, age, education and average household size. We also include lagged margin values, which are likely to be correlated with current-period margins. As in the case of the demand model, the pricing instruments encompass a set of product-specific binary variables to capture brand-specific preferences that are otherwise not accounted for in the continuous instruments. We evaluate the appropriateness of our instruments on the basis of: (i) a goodness-of-fit test of the instruments in explaining variation in the endogenous variables, and (ii) a J-test of the overidentifying restrictions implied by the GMM estimator (Davidson and MacKinnon, 2004). In the case of the vertical potato model, the J-test statistic value is 971.989 and a first-stage regression of the instruments on endogenous retail prices provides an R 2 value of 0.94, suggesting that the instruments, though not ideal, are also not “weak.” For the milk model, the J-test statistic is 120.174 with a first-stage R 2 value of 0.90.

4. Estimation method and identification strategy

5. Empirical results and discussion

For both products, we estimate the entire model in two stages. We estimate the first-stage demand model using the control function method described by Petrin and Train (2010) and the second stage pricing model using Generalized Method of Moments (GMM, Davidson and MacKinnon, 2004). We test for endogeneity of retail prices in our market-level scanner data using a Hausman (1978) specification test. Addressing endogeneity using the simulated generalized method of moments (SGMM) approach of Berry, Levinsohn and Pakes has become a workhorse in the empirical industrial organization and marketing literatures; however, the BLP contraction algorithm is highly sensitive to

In this section, we first present the demand estimates for both products in order to establish the validity of the demand model, and then present the supply-side estimates that are central to our examination of pricing conduct during periods of commodity price shocks.

3 USDA calculates weighted-average input price indices that combine price data for a range of production inputs using weights derived from the annual Farm Costs and Returns Survey (FCRS). Details on the methods used by the USDA in constructing these indices are provided by Milton et al. (1995). 4 Nevo (2001) and Berry et al. (1995) follow similar strategies in defining the potential market.

5.1. Demand results Tables 1 and 2 provide estimates of the random-coefficient nestedlogit potato demand and fluid milk demand models. We employ a number of specification tests to establish the validity of the randomparameter nested logit model. We first test our nested logit specification against a single-level logit model by examining the nested logit scale parameter, σ. The scale parameter is statistically different from zero for both goods, validating the nested logit model. Second, we test whether the random parameter specification is preferred to the alternative specification by testing whether the standard

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T.J. Richards et al. / Int. J. Ind. Organ. 30 (2012) 50–57

Table 1 Random coefficient nested logit demand estimates: potatoes.

Table 2 Random coefficient nested logit demand model: milk.

Non-random Parameter model

Random Parameter model

Variable

Estimate

Estimate

Discount Discount ⁎ Price Atlanta, GA Chicago, IL Dallas, TX Los Angeles, CA Fingerling Idaho Other Purple Red Red Creamer Russet White Yellow Organic Quarter 1 Quarter 2 Quarter 3 μ η σJ

0.091⁎ − 0.030⁎ − 0.752⁎ − 0.707⁎ − 0.057⁎ 0.260⁎ − 0.652⁎ − 0.446⁎ − 0.577⁎ − 0.760⁎ − 0.455⁎ − 0.514⁎ − 0.400⁎ − 0.489⁎ − 0.493⁎ − 0.596⁎ − 0.127⁎ − 0.289⁎ − 0.350⁎ 0.012⁎ − 0.002 0.952⁎

t-ratio 4.969 − 2.063 − 71.611 − 67.583 − 5.399 24.428 − 17.559 − 27.337 − 20.222 − 17.761 − 26.500 − 22.016 − 26.473 − 26.123 − 25.200 − 22.254 − 12.915 − 29.282 − 35.350 2.972 − 1.543 196.903

0.090⁎

t-ratio

0.012 − 0.002 0.953⁎

3.201 − 1.485 − 101.699 − 85.800 − 6.227 21.111 − 10.746 − 8.780 − 8.243 − 15.949 − 5.516 − 6.670 − 4.534 − 5.048 − 4.642 − 8.548 − 25.473 − 63.223 − 67.395 1.785 − 0.788 194.436

− 0.110⁎

− 6.890

− 0.030 − 0.750⁎ − 0.706⁎ − 0.061⁎ 0.258⁎ − 0.658⁎ − 0.449⁎ − 0.586⁎ − 0.755⁎ − 0.458⁎ − 0.520⁎ − 0.404⁎ − 0.490⁎ − 0.495⁎ − 0.591⁎ − 0.146⁎ − 0.268⁎ − 0.328⁎

Variable Discount Discount ∗ Price Trend Atlanta, GA Boston, MA Dallas, TX Denver, CO Los Angeles, CA Minneapolis, MN New York, NY Phoenix, AZ San Francisco, CA μ η σJ

− 0.027⁎

− 3.733

N.A.

Estimate

Estimate

t-ratio

0.005⁎ − 0.104 0.004⁎ − 0.037⁎ 0.349⁎ − 0.107⁎ − 0.119⁎ 1.658⁎ − 0.048⁎ 0.869⁎ 0.570⁎ 0.328⁎ 23.722⁎

1.979 − 1.507 54.768 − 35.236 327.337 − 106.946 − 105.238 1857.817 − 44.753 986.094 568.275 311.466 9.416 1.365 2078.949

0.001 − 0.051 0.004⁎ − 0.028⁎ 0.329⁎ − 0.116⁎ − 0.134⁎ 1.666⁎ − 0.062⁎ 0.883⁎ 0.552⁎ 0.308⁎ 0.629 − 0.001 0.998⁎

t-ratio 0.028 − 0.476 24.821 − 12.687 139.042 − 52.682 − 59.763 732.229 − 25.828 359.561 244.248 133.816 0.151 − 0.629 1396.953

0.004 0.996⁎

− 0.117 − 2.374⁎ 0.004 0.001 − 0.006

− 1.480 − 720.709 0.586 0.595 − 0.032

− 0.575⁎ − 2.384⁎ 0.002 0.007⁎ 0.012⁎

− 9.929 − 689.365 0.983 4.248 5.113

Standard deviations of random parameters

Standard deviation of price parameter N.A.

Random Parameter model

Price and intercept parameters Price Constant Fat content Binary private label Binary organic

Price parameter Price

Non-random Parameter model

0.055⁎

26.197

0.002⁎ 0.013⁎ 147.148 294.296

5.845 16.084

Price Constant Fat content Binary private label Binary organic

1.732⁎ 0.003⁎ 0.002⁎ 0.001⁎ 0.002⁎

245.293 11.092 8.852 2.108 2.455

N.A. N.A. N.A. N.A. N.A. N.A. N.A.

0.014⁎ − 0.122⁎ − 0.003 0.002⁎ − 0.002 − 0.003 0.002

4.897 − 15.181 − 1.600 3.356 − 0.211 − 0.650 0.191

N.A. N.A. N.A. N.A. N.A. N.A. − 891.542 − 16,862.901

− 0.001⁎ − 0.001⁎ − 0.002⁎ 147.148 294.296

− 2.702 − 2.670 − 4.268

N.A. N.A. N.A. N.A. N.A.

N.A. N.A. N.A. N.A. N.A.

Random parameter function

LLF Chi-square

N.A. N.A. − 891.542 − 16,862.901

N.A. N.A.

The chi-square statistic is twice the difference between the simulated likelihood value and likelihood of a null model (all parameters restricted to zero). The parameters μ and η refer to the control function, where μ is the instrumental-variable regression residual and η is a standard-normal error component. ⁎ Indicates significance at the 5% level.

deviation of the price and intercept terms used to describe the unobserved heterogeneity in each market are statistically significant. Further, we conduct a log-likelihood ratio (LR) test in which the unrestricted version (random coefficients) is compared to a restricted version (fixed coefficients). Both specification tests indicate the superiority of the random coefficients version, and we consequently use this model as the basis of consumer demand. 5.2. Pricing model results For our supply-side estimates, we interpret the conduct parameters for each good as measuring the extent of deviation from Bertrand–Nash pricing by wholesalers (θ) and retailers (φ), respectively. If θ = φ = 0, then both retailers and wholesalers set prices as perfectly competitive firms, while values of θ and φ above 1.0 suggest that firms at the respective stage of production exercise a greater degree of market power than under Bertrand–Nash. In terms of wholesaler and retailer conduct during intervals of rising and falling commodity prices, wholesale (retail) margins widen during periods of positive commodity price shocks when θ1 N 0 (φ1 N 0) and during periods of negative commodity price shocks when θ2 b 0 (φ2 b 0). Under circumstances in which the wholesale (retail) adjustment parameters satisfy |θ1| N |θ2| (|φ1| N |φ2|), then firms adjust their margins to a greater degree during periods of rising commodity prices than during periods of falling commodity prices.

Random parameter function Price (education) Price (age) Constant (education) Constant (age) Fat content (education) Fat content (age) Binary private label (education) Binary private label (age) Binary organic (education) Binary organic (age) LLF Chi-square

N.A. N.A. N.A. N.A. N.A. N.A. N.A.

The chi-square statistic is twice the difference between the simulated likelihood value and likelihood of a null model (all parameters restricted to zero). The parameters μ and η refer to the control function, where μ is the instrumental-variable regression residual and η is a standard-normal error component. ⁎ Indicates significance at the 5% level.

Table 3 presents the results from the potato and milk margin models. The GMM and Chi-square statistics at the bottom of the table indicate the results of our specification tests. We address goodness of fit by comparing the estimated model against a null alterative with all parameters jointly set equal to zero (Davidson and MacKinnon, 2004); for both goods we reject the null hypothesis based on the Chisquare values. 5 We test for the exogeneity of retail and wholesale markups using a Hausman test (Hausman, 1978), which indicates the appropriateness of the instrumental variable estimator. Our primary concerns regard pass-through rates and the change in wholesale and retail market power following upward and downward movements in commodity prices. In terms of the pass-through effects, we find that the estimated pass-through parameter (“farm price”) is 5 We also compare the “vertical” model with oligopolistic retailers and suppliers to one in which the supply sector is assumed to be competitive. The results of this test reject the competitive-supplier model in favor of the vertical alternative.

T.J. Richards et al. / Int. J. Ind. Organ. 30 (2012) 50–57 Table 3 Retail and wholesale margin model estimates: potato and milk, GMM.

Variable Wholesale inputs Interest, taxes, wages/packaging Fuel cost Business services Retail inputs Grocery wages Management wages Electricity cost Farm price θ0 θ1 θ2 φ0 φ1 φ2 θˆ ˆ φ Lw Lr GMM Chi-square

Potato model

Milk model

Estimate

t-ratio

Estimate

t-ratio

− 3.143⁎ 0.605⁎ 2.941⁎

− 12.272 12.546 10.484

0.058⁎ − 22.408⁎ 1.173⁎

47.049 − 11.418 7.530

− 0.849⁎ 1.577⁎ 1.354⁎ 0.440⁎ 0.672⁎ − 0.075⁎ − 0.260⁎ 0.091⁎ − 0.622⁎ − 1.187⁎

− 9.306 2.454 4.827 9.979 9.705 − 4.175 − 10.858 1.985 − 3.263 − 2.431

− 2.568⁎ − 2.863⁎ 15.691⁎ 0.104⁎ 0.587⁎ − 0.178⁎ 0.031 0.753⁎ − 0.217⁎

− 34.564 − 10.826 51.951 45.345 17.096 − 9.685 0.245 10.497 − 8.652 0.397

0.511 0.096 0.562 0.262 971.989 794.358

0.006 0.584 0.131 0.194 0.179 120.174 8533.505

Chi-square compares the estimated model to a naive alternative (coefficients restricted to zero) using the GMM objective function value. Product and market indicator estimates are not included in this table, but are available from the authors. ⁎ Indicates significance at the 5% level.

positive and statistically different from zero for each good, with a larger magnitude in the case of potatoes. For potatoes, the retail price of potatoes rises by $0.44 per pound for every $1.00 per pound change in the farm price of potatoes, which implies a pass-through rate of 57.9% based on the average farm price. For fluid milk, the retail price of milk products rise by $0.104 for a $1.00 change in the unit price of raw milk, which implies a pass-through rate of 5.4% at the mean farm price, an order of magnitude smaller than the pass-through rate for potatoes. Market power at each stage of the vertical structure is shown for each good by the fitted values of the retail conduct parameter (θˆ and ˆ For potatoes, the fitted value of the wholesale conduct parameter φ). is 0.511 while the fitted value of the retail conduct parameter is 0.096. Measuring market power by Lerner Indices at each stage of production (L), these value imply Lw = 0.562 and Lr = 0.262 at the wholesale and retail levels, respectively. The relatively large degree of market power at the wholesale level of the potato market is not surprising given that potato marketing at the wholesale level is dominated by the United Potato Growers of America (UPGA) cooperative. Under the Capper–Volstead Act (1922), pricing by farm cooperatives is exempt from anti-trust laws, and the UPGA was unusually successful during the sample period at maintaining what is referred to in the industry as “stable” wholesale prices. For fluid milk, the fitted values of the wholesale and retail conduct parameters are 0.584 and 0.131 respectively, which implies wholesale market power of Lw = 0.194 and retail market power of Lr = 0.179. Relative to producers in the potato industry, milk wholesalers and retailers have lower and more balanced levels of market power. Although wholesale milk production, much like potatoes, is dominated by several large cooperatives (e.g., Dairy Farmers of America), the ability of fluid milk wholesalers to exercise market power appears limited, a finding that may be due to Federal regulations peculiar to the dairy industry. The pricing conduct of firms in each industry is influenced by commodity price trends. For potatoes, the four conduct parameter estimates attached to the indicator variables on commodity price trends (θ1, θ2, φ1, φ2) are all negative, implying that wholesale and retail margins narrow during period of rising commodity prices and widen during periods of falling commodity prices. During periods of

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negative commodity price shocks, potato wholesalers and retailers appear to exercise restraint in reducing prices, thereby exploiting temporary profit opportunities that arise from consumer uncertainty on prevailing input price levels. Notice that the magnitude of the margin adjustment for both wholesalers and retailers in the potato market is much larger in response to negative commodity price shocks than to positive shocks. That is, both wholesale and retail market power rises (on average) in response to a sequence of positive and negative commodity price shocks that restore commodity prices to baseline levels. The effect of commodity price trends on retail margins is particularly strong, as retail margins adjust to a greater degree in both directions relative to wholesale margins for potatoes. At both stages of production, we find evidence that commodity price volatility raises market power in the potato industry. Such an outcome does not occur in the case of fluid milk. In response to positive commodity price shocks in the milk market, wholesale and retail margins narrow, as in the case of the potato industry; however, margins do not significantly adjust in response to negative commodity price shocks. This suggests that commodity price volatility in the fluid milk market reduces wholesale and retail market power. One reason for this may be the role of fluid milk as a “lossleader” among supermarket retailers. If retailers use milk prices to attract customers into their stores, then retailers may be reluctant to raise retail prices in response to higher commodity prices; milk wholesalers, in turn, are unable to pass increased input prices along to retailers by increasing wholesale prices for fluid milk products. Margins are relatively small in fluid milk and narrow further at each stage of production in response to positive commodity price shocks. Conversely, when commodity prices decrease, competitive pressure for store traffic among retailers forces retail milk prices down, and both wholesale and retail prices decrease roughly in proportion to the decline in input cost. Taken together, our results indicate stark differences in how commodity price changes in the potato and fluid milk markets are projected into wholesale and retail market power. Commodity price volatility in the potato (fluid milk) market serves to increase (decrease) both wholesale and retail market power. These outcomes are broadly consistent with loss-leadership among supermarkets in the case of fluid milk and the extraction of information rents in the case of potatoes. In response to a sequence of commodity price shocks that restore commodity prices to original levels, wholesaler and retailer margins narrow for fluid milk products, a finding that may be due to the importance of fluid milk prices in consumer store selection. Conversely, potato margins rise in response to commodity price volatility in the potato market, particularly at the retail level, with margins rising sharply during periods of declining commodity prices. To the extent that fluid milk serves as a reference good in store selection, retailers may be reluctant to widen their margins during periods of declining commodity prices, and instead adjust margins upward on other products (like potatoes) to extract informational rents on a subset of goods with falling input costs. Table 4 shows the outcome of simulated equilibrium changes in the retail price and wholesale price when the farm price changes by a fixed amount ($0.10). 6 We compare prices derived using coefficients from the estimated model to a scenario in which commodity price changes have no impact on market power (θ1 = ϕ1 = 0 and θ2 = ϕ2 = 0) and where they are fully articulated into market power (θ1 = ϕ1 = 1 and θ2 = ϕ2 = 1). These results reflect the average change in retail prices over all data points. Notice that the estimated passthrough rate is higher during periods of positive commodity price shocks than during periods of negative shocks in the case of potatoes,

6 We simulate pass-through using the framework described in Kim and Cotterill (2008).

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T.J. Richards et al. / Int. J. Ind. Organ. 30 (2012) 50–57

Table 4 Pass-through of commodity price inflation to retail prices, %. Potato

Rising

a

Falling

pj0 pj1 △ RF pj0 pj1 △ RF

Milk

Estimated

φk = θk = 0

φ k = θk = 1

Estimated

φk = θk = 0

φk = θk = 1

$1.4157 $1.4514 35.72% $1.4157 $1.3882 27.55%

$1.4157 $1.4598 44.04% $1.4157 $1.3716 44.04%

$1.4157 $1.4777 62.01% $1.4157 $1.3536 62.01%

$1.3440 $1.4244 10.05% $1.3440 $1.2603 10.47%

$1.3440 $1.4275 10.43% $1.3440 $1.2607 10.43%

$1.3440 $1.4434 12.42% $1.3440 $1.2447 12.42%

a In this table, △ RF is the pass-through rate between commodity and retail prices, pj0 is the initial fitted retail price (before commodity price increase), and pj1 is the new equilibrium price. The subscript k refers to either rising, k = 1, or falling, k = 2, commodity prices. Potato prices are $/lb and milk prices are $/48 oz.

but the estimated pass-through rate is higher during periods of negative price shocks in the case of fluid milk. To see how commodity price volatility affects market power, consider the average equilibrium retail price for each product across the periods of rising and falling commodity prices. If potato input prices sequentially adjust by $0.10 and then revert to baseline levels, the average equilibrium price is $1.4198, which exceeds the baseline price ($1.4157): Commodity price volatility increases market power. The opposite is true for fluid milk. If fluid milk input prices sequentially adjust by $0.10 and then revert to baseline levels, the average equilibrium price is lower than the original price ($1.3423 b $1.3440). Commodity price volatility narrows margins in the vertical market for fluid milk. The implications of these results go beyond the goods studied here. Rising commodity prices need not invite concerns regarding general price inflation, nor the monetary or regulatory responses commonly regarded as necessary to control inflation. Rather, when products are transacted in vertical structures such as those considered here, inputprice volatility and selective increases in wholesale and retail margins may be more important for consumer welfare than the underlying commodity price levels. In fact, pass-through depends critically on the nature of the product in question. For highly differentiated products sold through a vertical structure with wholesalers and retailers, multiproduct pricing considerations can be an important determinant of where, and how, retailers choose to exercise market power.

6. Conclusion and implications In this study, we estimate the direct and indirect pass-through of commodity price changes to the wholesale and retail prices of two differentiated food products. We model the extent of price passthrough during a period of considerable commodity price volatility in the potato and fluid milk markets and find the pricing conduct of retailers and wholesalers to vary according to the direction of the underlying commodity price shocks. Our results indicate that both retail and wholesale margins narrow in the potato market in response to positive price shocks and widen in response to negative price shocks. Moreover, market power increases during periods of falling commodity prices to a greater extent than market power decreases during periods of rising commodity prices at both stages of production, which indicates that market power rises in response to commodity price volatility in the potato industry. These results suggest that potato wholesalers and retailers are able to extract information rents that arise from consumers' uncertainty over input prices. We find commodity price volatility to have quite different implications in the fluid milk market. In response to positive commodity price shocks, both wholesale and retail margins narrow (as in the case of potatoes); however, wholesalers and retailers do not significantly adjust their margins in response to negative price shocks. Thus, commodity price volatility reduces market power at both the wholesale and retail levels of the fluid milk market.

The differing outcomes for potato and milk producers in response to changing commodity prices can arise for several reasons. First, differences can arise due to features in the production process underlying the goods: Potatoes are a fresh, random weight product while fluid milk is a fixed-weight consumer packaged good characterized by a product line that has become increasingly differentiated over time through nutritional additives, packaging and brand-line extensions. While the lower overall degree of market power in the fluid milk market may be driven, in part, by pricing complementarities among differentiated products, the stark difference in the relationship between commodity price volatility and market power in the potato and fluid milk markets provides evidence of loss-leadership behavior by retailers in the case of fluid milk. Such a view of pricing by multi-product retailers would suggest that retailers maintain low margins on fluid milk products in all cost environments to attract customers into the store, and then extract information rents from consumers on products purchased less frequently (like potatoes) by raising margins on a subset of products associated with contemporaneous decreases in procurement cost. References Barro, R., 1976. Rational expectations and the role of monetary policy. J. Monet. Econ. 2, 1–32. Benabou, R., Gertner, R., 1993. Search with learning from prices: does increased inflationary uncertainty lead to higher markups? Rev. Econ. Stud. 60, 69–94. Berry, S., Levinsohn, J., Pakes, A., 1995. Automobile prices in market equilibrium. Econometrica 63, 841–890. Berry, S., Linton, O., Pakes, A., 2004. Limits theorems for estimating parameters of differentiated product demand systems. Rev. Econ. Stud. 71, 613–654. Berto Villas-Boas, S., 2007. Vertical relationships between manufacturers and retailers: inference with limited data. Rev. Econ. Stud. 74, 625–652. Cardell, N.S., 1997. Variance components structures for the extreme value and logistic distributions. Econom. Theory 13, 185–213. Davidson, R., MacKinnon, J.G., 2004. Econometric Theory and Methods. Oxford University Press, Oxford, UK. Draganska, M., Klapper, D., 2007. Retail environment and manufacturer competitive intensity. J. Retail. 83, 183–198. Gupta, S., Park, S., 2009. A simulated maximum likelihood estimator for the random coefficient logit model using aggregate data. J. Mark. Res. 46, 531–542. Hamilton, S.F., 2009. Excise taxes with multiproduct transactions. Am. Econ. Rev. 99, 458–471. Hamilton, S.F., Richards, T.J., 2009. Variety competition in retail markets. Manag. Sci. 55, 1368–1376. Hausman, J., 1978. Specification tests in econometrics. Econometrica 46, 1251–1271. Hellerstein, R., 2008. Who bears the cost of a change in the exchange rate? Pass-through accounting for the case of beer. J. Int. Econ. 76, 14–32. Kim, D., Cotterill, R.W., 2008. Cost pass-through in differentiated product markets: the case of U.S. processed cheese. J. Ind. Econ. 55, 32–48. Lewis, M., 2004. Asymmetric price adjustment and consumer search: an examination of the retail gasoline market. U. C. Berkeley, Department of Economics, Working Paper No. CPC04-47. Berkeley, CA. Lucas, R.E., 1973. Some international evidence on output-inflation tradeoffs,. Am. Econ. Rev. 63, 326–334. McFadden, D., 1978. Modelling the Choice of Residential Location. Cowles Foundation Discussion Paper No. 477. New Haven, CT. Milton, B., Kleweno, D., Vanderberry, H., 1995. Reweighting and reconstructing USDA's indexes of prices received and prices paid. Staff Report No. ESB-95-01. Washington, D.C. Nakamura, E., Steinsson, J., 2008. Five facts about prices: a reevaluation of menu cost models. Q. J. Econ. 123, 1415–1464. Nakamura, E., Zerom, D., 2010. Accounting for incomplete pass-through. Rev. Econ. Stud. 77, 1192–1230.

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