Wal-Mart’s impact on supplier proﬁts - Semantic Scholar€¦ · Wal-Mart’s impact on supplier proﬁts Qingyi Huang1 Vincent Nijs2 Karsten Hansen3 Eric T. Anderson4 September

Wal-Mart’s impact on supplier profits

Qingyi Huang1 Vincent Nijs2 Karsten Hansen3 Eric T. Anderson4

September 11, 2009

1Kellogg School of Management, Northwestern University2Kellogg School of Management, Northwestern University3Rady School of Management, University of California, San Diego4Kellogg School of Management, Northwestern University

Abstract

Previous academic research on the expansion of dominant retailers such as Wal-Mart has

looked at implications for incumbent retailers, consumers, and the local community. Little

is known, however, about Wal-Mart’s influence on suppliers’ performance. Manufacturers

suggest Wal-Mart uses its power to squeeze their profits. In this paper we study the validity

of that claim. We investigate the underlying mechanisms that may cause changes in manu-

facturer profits following Wal-Mart market entry. Our data contains information on supplier

interactions with retail stores, including Wal-Mart, for a period of five years.

We find that post-entry supplier profits increased by almost 18% on average, whereas

profits derived from incumbent retailers decreased only marginally. Contrary to predictions

from analytical work our results show wholesale prices are not the main driver of post-entry

supplier profit changes; market expansion is. We observe a significant increase in shipments

to 45% of markets studied. Furthermore, our analysis demonstrates supplier shipment and

profit increases are highest for markets in which incumbents o!er a wide variety of products

and carry items that Wal-Mart does not sell.

Key words: Wal-Mart, market entry, supplier profits, wholesale prices, shipments, prod-

uct line, assortment.

“It’s a complicated question, but for business people, it is the essential question about Wal-

Mart: Will doing business with Wal-Mart help my business or hurt it?”

—The Wal-Mart E!ect

1 Introduction

Wal-Mart, reporting over $400 billion in net sales for 2008 (Wal-Mart Stores Inc. 2009), has

been ranked the number one non-oil company on the FORTUNE 500 for the past nine years.

As the world’s largest retailer Wal-Mart has become the biggest buyer for manufacturers

such as Disney, Gillette, Kellogg’s, Mattel, Procter & Gamble, and Sarah Lee (Useem 2003).

Passing-through cost savings and quantity discounts, the low-overhead, low-inventory retailer

won the battle for the grocery dollar by o!ering consumers its (in)famous “Every Day Low

Prices”.

Although over 200 million people shop at Wal-Mart’s 4000+ U.S. locations each year

(Wal-Mart Stores Inc. 2008), public opinion is not all favorable. Trade unions and special

interest groups (e.g., McGee and Festervand 1998, Kinzer 2004), local politicians (Moreton

2006, Parsons 2006), and the media eagerly portray the retailer as a “world-wide chain of

exploitation” destroying communities while squeezing its employees and suppliers alike (e.g.,

Akron Beacon Journal 2000). Its sheer size and power compelled manufacturers to ask

whether or not to “... say no to Wal-Mart?” (Bowman 1997). The demise of Rubbermaid is

an often cited example of the retailer’s power. When the durable housewares producer, then

one of America’s most reputable companies, tried to pass-on a cost based price increase,

Wal-Mart pulled all Rubbermaid products from its shelves in 1994. As the big-box retailer

was Rubbermaid’s largest outlet, the manufacturer could not recuperate and was forced to

sell-out to Newell several years later. Over 700 suppliers have now built o"ces close to

company headquarters in Bentonville, Arkansas in an attempt to strengthen ties with the

dominant chain (Fishman 2006).

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Previous academic research on discount chains mainly focuses on implications for in-

cumbent retailers (e.g., Stone 1988, 1995, Basker 2005, Gielens et al. 2008, Ailawadi et al.

2009), consumers (e.g., Basker 2005, Hausman and Leibtag 2005, Luchs 2008), and the local

community (e.g., Bianchi and Swinney 2004, Moreton 2006, Goetz and Rupasingha 2006,

Neumark and Ciccarella 2008, Halebsky 2009). Five recent empirical studies on Wal-Mart in

marketing and economics emphasize its market entry impact on incumbents and consumers.

First, studying seventeen products in ten categories, Basker (2005) reports a post-entry de-

cline in market prices. E!ects for cigarettes, cola, pants, shirts, and underwear are, however,

not significant. Singh et al. (2006) – focusing on changes in sales and consumer purchase be-

havior following one Wal-Mart entry – conclude the incumbent supermarket studied su!ers

a 17% volume loss, mainly resulting from fewer consumer visits. Interestingly, the authors

detect little change in basket size; the number of products bought by the supermarket’s

loyal customers is comparable to pre-Wal-Mart conditions. Third, Jia (2008) finds that the

multinational’s expansion in the 1980s and 1990s accounted for a 34% to 41% drop in the

number of discount stores. Gielens et al. (2008) study incumbents’ stockprice changes after

Wal-Mart acquired UK based Asda supermarket group. The authors demonstrate assort-

ment as well as positioning similarities between Wal-Mart and incumbents are detrimental

to the latter’s post-entry stock performance. Finally, a recent paper on competitive reactions

to eleven Wal-Mart entries by Ailawadi et al. (2009) reports negative e!ects on incumbent

retailers’ revenues. Moreover, the authors show increasing assortment size can successfully

mitigate entry impact.

Despite discount chains more than doubling their market share since the late 1960s (Jia

2008), analytical and empirical work analyzing entry implications for manufacturers is scarce

and provides inconsistent results. The trade press proclaims suppliers are forced to o!er at-

tractive trade deals, merchandise support, and slotting allowances to please powerful retailers

(Bloom and Perry 2001). Although suppliers have reported for years that big-box retailers

demand financially-damaging concessions that undermine channel profits (Huey 1998, Brunn

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2006), current analytical studies provide limited support for a directional hypothesis on how

dominant retailers’ actions a!ect supplier performance.

Chen (2003) concludes that a dominant retailer’s bargaining power reduces a manufac-

turer’s share of joint profits. He theorizes that manufacturers should decrease incumbent

wholesale prices, who, in turn, will lower retail prices to boost sales and counter the domi-

nant retailer’s rise. Dukes et al. (2006), on the other hand, suggest that a dominant retailer’s

power need not diminish supplier profits. The authors argue that suppliers should increase

rather than lower wholesale prices charged to incumbents. Their analytical results suggest

that while rival retailers may su!er, manufacturers can achieve enhanced performance by

leveraging the channel e"ciencies the dominant retailer o!ers.

Dukes et al. (2009) study product line decisions in the presence of a dominant retailer.

They argue that incumbent retailers should broaden their assortment when a dominant

retailer chooses to stock fewer products. Their analytical results imply that both the size of

the incumbents’ assortment and its overlap with the dominant retailer’s product selection

a!ects their performance. In addition, depending on assortment costs, suppliers may choose

to distribute specialty items through incumbents only. Selling to Wal-Mart, which carries

a shallow assortment in many categories, will therefore impact manufacturers’ profits and

product line decisions (Dukes et al. 2009).

Even though some authors examine the link between Wal-Mart and supplier performance

in an empirical setting, their results are also inconsistent and none of them, to the best of our

knowledge, quantifies Wal-Mart entry impact on suppliers. Ailawadi et al. (1995)’s industry

level analysis of relative retailer and manufacturer profitability shows Wal-Mart surpassed

manufacturers on most performance metrics between 1982-1992. Whether working with

Wal-Mart is detrimental or beneficial for suppliers remains unclear, however, as they do

not examine the relationship between Wal-Mart and its vendors directly. Using Compustat

data, Bloom and Perry (2001) find a negative correlation between financial performance

and collaboration with Wal-Mart for most manufacturers who self-identified the retailer as

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a ’primary customer’. However, Mottner and Smith (2009), who test Bloom and Perry’s

model on a longer time-series of Compustat data, conclude working with Wal-Mart will not

a!ect supplier performance. In contrast, Gosman and Kohlbeck (2006), using Compustat

and CRSP data, estimate higher gross-margins for Wal-Mart vendors.

In sum, as studies on the costs and benefits for manufacturers of working with Wal-

Mart are scarce and inconclusive and empirical research on the impact of Wal-Mart entry

on supplier performance is lacking, we propose the following two research questions:

• Does Wal-Mart market entry impact supplier profits?

• What processes drive post-entry profitability changes?

The fact that some retailers do not share data with third parties, such as Nielsen and

IRI, is one of the most important reasons empirical work on big-box retailers is still lacking

at present. As Wal-Mart has refused to contribute to national consumer sales databases

since 2001 (Sachdev 2001), our study significantly adds to the scarce empirical literature

examining the company’s impact on suppliers by directly analyzing historical data collected

by a vendor. Our unique database contains detailed records on profits, wholesale prices,

and shipments for a major consumer packaged goods manufacturer, allowing us to quantify

changes in supplier performance following Wal-Mart entry. Our data not only captures the

items shipped to each Wal-Mart every week, but also their price. As our dataset spans

thousands of retail stores and hundreds of products for a period of five years, we are able to

to analyze Wal-Mart entry impact in a broad set of geographical markets while accounting

for market structure e!ects.

As we expect Wal-Mart to strategically choose markets in which to open new stores,

controlling for endogeneity in entry decisions is crucial to accurately estimate its impact.

We use propensity score matching to construct a research design of experimental and control

markets. Subsequently, we specify a hierarchical Bayesian model to estimate Wal-Mart’s

entry impact on supplier profits. We use a di!erences-in-di!erences format for the first stage

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model and link entry impact variation to retailer assortment profiles in the second stage

(Dukes et al. 2009).

The remainder of the paper is structured as follows. We introduce the data and the

propensity score method in Section 2 and discuss the hierarchical Bayesian model in Section

4. Section 5 describes our empirical results while Section 6 provides managerial implications.

2 Data

In this study we use weekly data for a packaged goods category o!ered by a major man-

ufacturer in the industry. Detailed information is available on supplier profits, wholesale

prices, and shipments for over 200 products from December 1999 to January 2005.1 The

data provides complete retail coverage in the U.S. including supermarkets, convenience and

drug stores, and mass-merchandisers. Figure 1 shows the spatial distribution of the 759

Wal-Mart market entries observed in the data. None of these markets has another Wal-Mart

store in the same zip-code area. The number of entries by year are shown in Table 1.

[Insert Figure 1 about here]

Year No. of entries2000 1052001 1672002 1522003 1442004 191

Table 1: Number of Wal-Mart entries in the U.S. by year

For each market we observe the key dependent variables (profits, wholesale prices, and

shipments) both before and after Wal-Mart entry. Shipments are the number of standard

units of goods transported to a market in a week. Wholesale price reflects the average weekly

price charged to retailers across all goods sold in a market. Supplier profit is measured as

1For confidentiality reasons we can disclose neither manufacturer nor industry.

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wholesale price minus supplier cost of goods times shipments. As an initial estimate of entry

impact we calculate the change in weekly supplier profits, wholesale prices, and shipments

in pre- versus post-entry periods (see Table 2).

% changeSupplier profits +22.011%Wholesale prices +6.247%Shipments +13.509%

Table 2: Pre versus post Wal-Mart entry change in weekly supplier profits, wholesale prices,and shipments

Supplier profits increased by 22% on average, shipments to entry markets by nearly 14%,

and wholesale prices charged to retailers by just over 6%. Note that the results in Table 2

should be interpreted with caution. For example, demand for the supplier’s products might

have increased regardless of entry. Since data on the same markets over the same time period

but without entry do not exist, we compare outcome data for markets with and without Wal-

Mart entry. To the extent that markets with and without entry experience similar changes

over time the latter serve as valid controls. Table 3 shows changes in outcome variables for

entry and non-entry markets.

Before After Di! Di!-in-Di!Supplier profits Non-entry 1.000 1.101 0.101 0.637

Entry 3.350 4.087 0.738Wholesale prices Non-entry 1.000 1.062 0.062 0.003

Entry 1.037 1.102 0.065Shipments Non-entry 1.000 1.029 0.029 0.399

Entry 3.172 3.601 0.429For confidentiality reasons numbers are indexed to the value of the outcome measure for

non-entry markets before the Wal-Mart entry date

Table 3: Before and after Wal-Mart entry measures of profits, wholesale prices, and shipmentsfor entry and non-entry markets

Estimating Wal-Mart entry e!ects using a di!erences-in-di!erences approach controls for

changes common to both types of markets (Angrist and Krueger 1999). Impact estimates on

supplier profits, wholesale prices, and shipments, expressed as a percent of the average value

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in entry markets before Wal-Mart, are 19% (0.637 / 3.350), 0.2% (0.003 / 1.037), and 12.6%

(0.399 / 3.172), respectively. As each of these e!ects is smaller compared to the weekly

pre versus post entry changes (Table 2), particularly wholesale price for which the impact

estimate is near zero, the results reported in Table 2 may not be attributable to entry.

Even though these numbers are more reliable than those in Table 2, they are not without

limitations. They have a causal interpretation only if we can reasonably assume that, except

for entry, markets with and without entry are comparable. This would imply that Wal-

Mart selects entry markets at random. The fact that entry markets appear, on average, to

generate much higher supplier profits and shipments suggests such an assumption is highly

questionable. If Wal-Mart is strategic in its selection of markets to enter, the results in Table

3 are invalid. Our approach to dealing with potential selection bias is described next.

3 Selection bias

Matching and Instrumental Variables are two common techniques to correct for selection bias

(Angrist and Krueger 1999). In the context of our study it is di"cult to identify instruments

that are correlated with Wal-Mart’s entry decisions but uncorrelated with supplier outcomes

(see Qian 2007, Gensler et al. 2009, Tripathi 2009 for similar arguments). Matching replicates

a randomized experiment by using covariates to pair experimental (EM) and control markets

(CM) (Rubin 2006, Gensler et al. 2009). It ensures that, conditional on covariates, the

assignment of markets to the experimental or control condition is independent of market

outcomes (Rosenbaum and Rubin 1983).

Whereas matching on one or a few binary variables is generally straightforward, exact

matching on multiple, possibly continuous, variables is infeasible (Angrist and Krueger 1999,

Gensler et al. 2009). Propensity Score Matching (PSM) is a commonly used method to reduce

the dimensionality of the matching problem (Rubin 2006). Rosenbaum and Rubin (1983)

have shown that if the conditional independence assumption is satisfied by conditioning on

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Variable Estimate Standard errorIntercept -22.666!! 2.998Median age -0.024! 0.011log(Population density) 2.423!! 0.222log(Population density)2 -0.211!! 0.018log(Income per capita) 9.919!! 1.976log(Income per capita)2 -1.519!! 0.329No. of other supercenters -0.021!! 0.005Herfindahl index -10.592!! 0.574N 22186Nagelkerke’s R2 0.360

!! p-value < .01, ! p-value < .05

Table 4: Logit estimates for propensity score matching

covariates (X), it is also satisfied by conditioning on the propensity score P (X). When the

propensity scores for two markets are identical, they are equally likely to receive a treatment

because “as far as we can tell from the values of the confounding covariates, a coin was tossed

to decide who received treatment 1 and who received treatment 2” (Rubin 2006, p. 448).

In our study the propensity score is the probability that Wal-Mart will enter a market

given the value of observables.2 Propensity scores are calculated as the predicted value from

a logistic regression with market treatment as the dependent variable (i.e., 1 if Wal-Mart

entered a market, 0 otherwise) (Angrist and Krueger 1999). To minimize selection bias and

ensure only relevant covariates are included in the model a stepwise estimation procedure

was employed (Rosenbaum and Rubin 1984). Table 4 shows the estimated coe"cients and

standard errors for the selected variables.

Population size has been used in previous research on Wal-Mart entry (Jia 2008, Zhu

and Singh 2009). Our estimated non-linear e!ect suggests entry is less likely for markets

with extremely low or extremely high population density. Although Wal-Mart is known to

prefer lower income markets (Gra! and Ashton 1994, Moreton 2006, Vedder and Cox 2006,

Halebsky 2009) we find the retailer avoids both the lowest and the highest income markets.

The negative coe"cient for age suggests Wal-Mart opts for areas with younger families (see

2In this study we equate markets to zip-code areas.

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alsoSingh et al. 2006). The number of non-Wal-Mart supercenters within a 20-mile radius

captures competitive interaction with Target and K-Mart (Jia 2008, Zhu and Singh 2009).

The coe"cient for the Herfindahl index indicates a preference for markets with more but

smaller competitors. We also include state fixed-e!ects to control for unobserved regional

di!erences (Jia 2008).

It is important to ensure that the distribution of propensity scores for experimental and

control markets share a common support to avoid biased estimates (Busse et al. 2006). Figure

2 shows the propensity score distributions for EM and CM. To achieve common support, we

trimmed the dataset using bounds suggested by Gertler and Simcoe (2006), i.e., we excluded

CMs with propensity scores below the 1st percentile of P (X) for EM and excluded EMs with

propensity scores above the 99th percentile of P (X) for CM. Trimming reduced our sample

size to 629 EM and 10,728 CM. Figure 3 shows the adjusted propensity score distributions.



After ensuring selected markets lie on the overlapping support of observables, markets

with and without entry were paired based on propensity score similarity (Gensler et al. 2009)

using nearest available matching (Rosenbaum and Rubin 1985). The steps in this procedure

are as follows: 1) EMs and CMs are listed in random order; 2) when the first EM is matched

to the nearest CM based on P (X) both markets are removed from the list; 3) repeat step 2

until every EM is matched. After matching the propensity score distributions of EM and CM

are virtually identical (see Figure 4). Matching each EM with one CM allows us to avoid

bias in the estimated treatment e!ect that may occur when linking multiple, potentially

dissimilar, CMs to an EM (Smith 1997). Moreover, by treating each EM-CM pair as a

separate experiment, we are able to investigate variability in entry e!ects.


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Before matching After matching

with entry without entry t-statistic with entry without entry t-statistic

Median age 35.502 37.693 -11.608!! 35.783 35.855 -0.251log(Population density) 5.887 4.893 18.289!! 5.935 5.922 0.150

log(Income per capita) 2.943 2.893 4.824!! 2.936 2.935 0.044No. of other supercenters 7.721 5.577 5.600!! 8.378 8.394 -0.028

Herfindahl index 0.119 0.466 -78.605!! 0.119 0.123 -1.008

N 759 21427 629 629!! p-value < .01, ! p-value < .05

Table 5: Variable means before and after matching

The sample means reported in Table 5 show that experimental and control groups were

successfully matched (Rubin 2006). By using PSM, “the observational study equivalent of

randomization in an experiment” (Rubin 2006, p. 461), our data were transformed into a

quasi-experimental design.

4 Model

We use a hierarchical Bayesian model to estimate Wal-Mart entry e!ects on supplier per-

formance. Our model provides a di!erences-in-di!erences estimator, as extensively used in

economics (Angrist and Krueger 1999) and marketing (e.g., Ailawadi et al. 2009, Tripathi

2009). Our dependent variables are supplier profits, wholesale prices, and shipments in week

t. For each pair of matched experimental and control markets we have

PROFITiet = !0i + !1iEMie + !2iWMiet + !3iEMie ! WMiet + "1iet, (1)

WPiet = #0i + #1iEMie + #2iWMiet + #3iEMie ! WMiet + "2iet, (2)

SHIPiet = $0i + $1iEMie + $2iWMiet + $3iEMie ! WMiet + "3iet, (3)

where i indexes a matched pair of markets i = 1, 2, ...N ; e indexes an experimental or control

market; t indexes time t = 1, 2, ..., T ; PROFITiet is supplier profit; WPiet is the wholesale

price charged; SHIPiet is shipment volume; EMie is an experimental market dummy (1 for

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a market with entry, 0 otherwise); and WMiet is the Wal-Mart entry dummy (1 if week t

is after entry, 0 otherwise). Note that the times series data for the matched experimental

and control markets are stacked for estimation. We assume ("1iet, "2iet, "3iet)" " N(0, !i) for

i = 1, 2, ..., N . The coe"cients !3i, #3i, $3i capture the treatment e!ect (i.e., Wal-Mart entry)

in our before-and-after-with-control-group analysis (Ailawadi et al. 2009). Model parameters

are allowed to vary across markets. The second stage is given by

%i = ""Zi + &i, (4)

where %"i is the vector of parameters in equations (1-3); " = ['1, '2, ..., 'nz], &i " N(0, V!); and

Zi is an nz !1 vector of covariates used to capture heterogeneity in entry e!ects. We explore

whether cross-sectional impact di!erences can be linked to variation in the products o!ered

to, and carried by, incumbent retailers and Wal-Mart. We also include the covariates from

the propensity score model in the Z matrix as controls. Additional details on the estimation

algorithm are provided in Appendix A.

5 Results

We estimate equations (1-4) twice: First with data from all retailers in a market combined,

including Wal-mart (total market), and again with data from incumbents only (incumbents).

By isolating the impact of entry on, for example, supplier profits generated from incumbents,

we are able to investigate a potential source of changes in the outcome metrics for markets

as a whole. For example, profits from a market might increase after entry due to a direct

boost from Wal-Mart at the expense of profits from incumbents. Providing results for both

total markets and incumbents separately provides additional insight into processes a!ecting

supplier performance following entry.

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5.1 Wal-Mart entry impact

The mean percentage change in manufacturer total market profits is +17.77% (see Table 7)

across all markets with Wal-Mart entry; profits from incumbents drop only slightly (-1.34%).3

The supplier studied clearly benefits from the collaboration with Wal-Mart. Compared to the

size of Wal-Mart entry e!ects on manufacturer profits the magnitude of impact on wholesale

prices is much smaller. On average, wholesale prices increased only 0.55% and 0.32% for

total market and incumbents, respectively. Interestingly, shipments to incumbents drop

only 2.52% following entry. When we include Wal-Mart, however, total market shipments

increase by 14.95%, on average, which is similar in magnitude to the post-entry profit change

mentioned above. As the estimated Wal-Mart entry e!ects di!er substantially from those

reported in Table 2, controlling for selection bias is clearly important in our application.

Mean 25th perc 50th perc 75th percProfits Total market 17.77% 1.45% 12.26% 30.72%

Incumbents -1.34% -15.29% -3.16% 7.81%Wholesale price Total market 0.55% -3.40% 0.65% 4.46%

Incumbents 0.32% -3.77% 0.47% 4.25%Shipments Total market 14.95% -1.34% 11.13% 26.28%

Incumbents -2.52% -16.88% -3.68% 7.99%Parameters were converted to percentages for reasons of confidentiality.

Table 6: Wal-Mart entry e!ects

The magnitudes of entry impact on supplier profits and shipments are intriguing. To

ensure these e!ects are not an artifact of the estimation procedure used we conducted

several robustness checks (see Table 7). A fixed-e!ects di!erences-in-di!erences estimator

was employed to calculate the main e!ects at the total market level (Angrist and Krueger

1999). The estimates for profits, wholesale prices, and shipments were 19.01%, 0.32%, and

12.59% respectively; very similar to the results based on matching. We also estimated two

di!erences-in-di!erences models with alternative matching procedures. First, we used the

3For confidentiality reasons we do not report the parameter estimates from Equations 1-3 directly. Rather,we transform them to a percentage change after entry relative to the average weekly profits, wholesale prices,and shipments before Wal-Mart entry.

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same covariates in the logit model as reported in Table 4 but estimated the model five times,

once for each year in our dataset. Markets were matched based on the parameters of the

logit model for the year in which entry occurred. If Wal-Mart’s entry strategy changes over

time this matching procedure should produce results di!erent from those reported in Table

5.1. The estimates for profits, wholesale prices, and shipments derived using the ’matching

by year’ procedure were 17.52%, 0.33%, and 13.91% respectively; again, very similar to our

earlier results. Finally, we estimated a matching model with a broader set of covariates

including quadratic terms for all variables but excluding state-fixed e!ects, regardless of

statistical significance. Note that we did not use trimming as part of this matching proce-

dure. The estimates for profits, wholesale prices, and shipments from the ’matching without

trimming’ model were 16.95%, -0.25%, and 15.53% respectively; again, very similar to the

results reported in Table 5.1.

Profit Wholesaleprice

Shipments

fixed e!ects di!erences-in-di!erences 19.01% 0.32% 12.59%matching by year 17.52% 0.33% 13.91%matching without trimming 16.95% -0.25% 15.53%

Table 7: Robustness checks on total market impact of Wal-Mart entry

Figures 5, 6, and 7 contain histograms of the %i estimates derived from equations 1-4. All

three graphs show that entry impact estimates vary significantly; the vertical black line in

each figure is drawn at the median value. We investigate plausible causes of this variability

in Section 5.3 below.




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Table 8 shows the percentage of positive, non-significant, and negative estimates of entry

impact on supplier profits from total markets (i.e., incumbents plus Wal-Mart) and incum-

bents respectively. In nearly 56% of the markets studied post-entry market profits increased

significantly; they decreased in only 9% of markets. Supplier profits from incumbent retail-

ers are down in nearly a third of post-entry markets, whereas in 20% of cases incumbent

contributions increased. Interestingly, over two-thirds of markets generate as much, if not

more, profits for the supplier after entry even when excluding contributions from Wal-Mart.

% (non)significant e!ects+ ns -

Total market 55.92% 34.99% 9.09%Incumbents 19.90% 47.17% 32.93%

For significant estimates zero is not contained in the 95% credibility region.

Table 8: Wal-Mart entry impacts on supplier profits

The three scatter plots in Figure 8depict the relationship between supplier profits from

Wal-Mart, incumbents, and the total market following entry. The first panel shows the

correlation between profits from incumbents and Wal-Mart is limited (r = 0.065), demon-

strating that the benefits derived from Wal-Mart’s market presence need not come at the

expense of profits the manufacturer generates from other retailers. Interestingly, the correla-

tion between profits from Wal-Mart and the total market, shown in the second panel, is not

especially strong either (r = 0.361). As Wal-Mart accounts, on average, for 19.19% of total

post-entry market profits, this result is surprising. In contrast, the correlation between prof-

its from incumbents and total market, shown in the bottom panel, is very high (r = .954),

which suggests maintaining profit levels generated from incumbents is key to the supplier’s

post-entry performance.


We find that Wal-Mart entry can a!ect wholesale prices charged to incumbent retailers,

even though the size of the e!ect is, on average, small (Table 7). Table 9 shows the percentage

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of markets with a significant increase, no significant change, and a significant decrease in

wholesale prices. The distribution of wholesale price changes is clearly very balanced: we

observe each e!ect in approximately one-third of markets. Even though previous research

has suggested that Wal-Mart entry may depress retail prices (Basker 2005, Ailawadi et al.

2009), we do not find a clear directional pattern for wholesale prices.




Table 9: Wal-Mart entry impact on wholesale prices

Estimates of Wal-Mart’s impact on shipments are presented in Table 10. In 44.77% of

markets we observe significant expansion, whereas in 44.60% of markets suppliers experience

no net gain. Interestingly, after comparing pre- to post-entry conditions, we conclude that in

nearly 70% of markets incumbents generate as much, if not more, supplier shipment volume

post-entry.




Table 10: Wal-Mart entry impact on supplier shipments

The three scatter plots in Figure 9depict the relationship between supplier shipments to

Wal-Mart, incumbents, and the total market following entry. The first panel demonstrates

that the correlation between shipments to incumbents and Wal-Mart is negligible (r =

#0.011), suggesting limited post-entry cannibalization. Surprisingly, the second panel shows

that the correlation between shipments to the total market and profits generated by Wal-

Mart is not particularly strong either (r = 0.258). As Wal-Mart accounts, on average,

for 18.85% of total market shipments after entry, this e!ect is surprising. In contrast, the

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correlation between shipments to incumbents and total market, shown in the bottom panel,

is very high (r = .963), demonstrating the importance of maintaining post-entry shipment

levels to incumbents.


5.2 Drivers of post-entry profitability change

As mentioned above, Dukes et al. (2006) argue that manufacturers can boost profits when

faced with a dominant retailer by charging higher wholesale prices to incumbents, whereas

Chen (2003) theorizes they should lower them. We use the correlation between the total

market estimate for !3 and the #3 estimate for incumbents to evaluate their contradictory

hypotheses (see equations 1 and 2). A strong positive correlation between the parameters

would provide support for Dukes et al. (2006)’s hypothesis, whereas a strong negative correla-

tion would a"rm Chen (2003)’s theory. Figure 10 shows a scatter plot of Wal-Mart’s impact

on both wholesale prices charged to incumbents and manufacturer profits. The loosely scat-

tered points clearly indicate that the correlation between manufacturer profit changes and

wholesale price changes is negligible (r = #0.024). Interestingly, the results from our em-

pirical analysis support neither hypothesis. Although the results in Table 9 show wholesale

prices can change, Figure 10 clearly demonstrates they are not the key driver of manufacturer

profit changes following Wal-Mart entry.


The fact that wholesale prices are, on average, only marginally a!ected by Wal-Mart

entry implies that shipments must be the key driver of the manufacturer profit changes

reported in Table 7. The scatter plot in the left-hand panel of Figure 11 depicts the link

between changes in profits and total market shipments following Wal-Mart entry; the one

on the right shows the correlation between profit and shipments to incumbent retailers. In

16

contrast to Figure 10 both plots show a positive relationship, confirming that shipments are

indeed the prominent driver of supplier profit change. The slope in the right-hand panel

(r = 0.884) clearly demonstrates that incumbents are important to the supplier’s overall

profitability in post-entry periods. While selling to Wal-Mart generates financial benefits,

the manufacturer obviously fairs best when incumbent shipments either increase or remain

unchanged following entry.


5.3 Moderators of Wal-Mart entry impact

Results reported in Section 5.1 show considerable variation in Wal-Mart entry e!ects on

supplier performance. Manufacturers could learn how to influence outcomes in their favor by

understanding the sources of variation. As mentioned in our introduction, Dukes et al. (2009)

theorize incumbents retailers should carry a broader assortment in the presence of a dominant

retailer that chooses to o!er a limited product selection per category. Their results imply that

both the incumbents’ assortment size and the overlap with Wal-Mart impacts performance.

Recent work by Ailawadi et al. (2009) confirms that incumbents can mitigate Wal-Mart entry

e!ects by increasing assortment size.4 Retailers who’s assortments overlap substantially with

Wal-Mart’s are vulnerable according to Gielens et al. (2008). Moreover, Dukes et al. (2009)

also suggest that a supplier may choose to sell its specialty items only to incumbents in the

presence of a dominant retailer. Even though a dominant retailer will only stock the most

popular items to reduce assortment costs, incumbents will benefit from carrying a larger

assortment despite the additional cost. In sum, previous research implies that, because Wal-

Mart carries a limited selection of goods in most categories (e.g.,Stone 1988, O’Keefe 2002),

incumbents should o!er a relatively large assortment focused on products Wal-Mart does

4Since the authors do not have information on products carried by Wal-Mart, they cannot address theimplications of assortment overlap.

17

not sell. Therefore, we expect that the impact of Wal-Mart entry on supplier profits will be

moderated by incumbents’ assortment choices.

Figure 12 depicts the distribution of assortment overlap between Wal-Mart and incum-

bents in experimental markets pre- and post-entry. Surprisingly, it seems that incumbent

retailers’ product assortment becomes more like Wal-Mart’s after entry. Since this e!ect

could, at least partly, be explained by changes in the supplier’s product line we express the

assortment characteristics for experimental markets relative to their matched control mar-

ket. In fact, our focal supplier’s product line contracted by 35% as depicted in Figure 13.5

We define change in assortment overlap as (ope1# ope

0) # (opc

1# opc

0) where e (c) identifies

an experimental (control) market. op1 is the percent overlap in post-entry assortment be-

tween incumbents and Wal-Mart, whereas op0 represents the assortment similarity between

incumbents before entry and Wal-Mart upon entry. Although Dukes et al. (2009) suggest

incumbents should diversify, the median assortment overlap change is +3.3%, even after

controlling for variation in the manufacturer’s product line. We define change in assortment

size as (ne

1#ne

0

ne

0

) # (nc

1#nc

0

nc

0

) where e (c) is defined as before and n1 (n0) captures the num-

ber of products in the incumbents assortment after (before) Wal-Mart entry. The median

assortment size change is -0.02%.



Equation 4 describes the second stage model used to correlate entry e!ects on supplier

performance to changes in incumbents’ assortment. Table 11 contains the " estimates for

the total market and incumbent level analyses.6 The impact of Wal-Mart entry on supplier

5Although the percentage of total shipments accounted for by Wal-Mart increases over time, it never exceeds5% in the time span of our data. Hence, we assume the reduction in assortment size is not related toWal-Mart.

6To simplify exposition we do not report parameter estimates for the control variables included in the Z

matrix.

18

profits from the total market and incumbents are both positively correlated with incumbent

assortment size changes. Consistent with predictions by Dukes et al. (2009) and extending

results reported in Ailawadi et al. (2009), we demonstrate that carrying a wider assortment

not only benefits incumbents but also boosts suppliers’ overall profits.

Total market IncumbentsProfits Shipments Profits Shipments

Change in assortment overlap -0.654%! -0.543%! -0.517%! -0.428%!

Change in assortment size 0.039%! 0.041%! 0.037%! 0.038%!

* zero is not contained in the 99% credibility region.

Parameters were converted to percentages for reasons of confidentiality.

Table 11: Posterior means for " divided by average pre-entry profits/shipments

Confirming Dukes et al. (2009) Table 11 shows the manufacturer benefits most, at both

the market and incumbent level, when incumbent retailers assortment overlap with Wal-

Mart is limited. Holding other variables constant, a 1% decrease in assortment overlap

increases weekly post-entry supplier profits from incumbents by 0.517% and shipments to

incumbents by 0.428%. Interestingly, a reduction in overlap has an even stronger impact

on supplier profits from the total market. A 1% decrease in overlap increases post-entry

supplier total market profits by 0.654% and shipments by 0.543%. This result suggests that

increased assortment di!erentiation does not diminish supplier profits generated by Wal-

Mart; to the contrary, it increases them. In addition, even though changes in assortment

size are statistically significant, e!ect sizes are small. A 1% increase in assortment size

increases weekly post-entry supplier market profits by 0.039% and shipments by 0.041%.

The impact on profits from and shipments to incumbents are similar in magnitude (0.037%

and 0.038% respectively).

19

6 Conclusion

In this paper, we study a broad set of geographical markets to determine whether Wal-Mart

entry impacts supplier profits and, if so, what processes drive post-entry profitability change.

Our unique database, collected by a Wal-Mart vendor, spans thousands of retail stores and

hundreds of products for a period of five years. We employed propensity score matching to

control for potential selection bias before analyzing the drivers of profit change: wholesale

prices and shipments. A hierarchical Bayesian model was used to quantify the e!ects of

entry and link the results to di!erences in retailer assortment characteristics across markets.

We find that mean post-entry supplier profit increased by 17.77% for the total market,

whereas profits derived from incumbents decreased only marginally. While the size of this

e!ect is surprising, various analyses demonstrate its robustness. Interestingly, over two-

thirds of markets generate as much, if not more, profits for the supplier after entry even

when excluding contributions from Wal-Mart. Contrary to both Chen (2003) and Dukes

et al. (2006) our results clearly show that changes in wholesale prices are not the main driver

of post-entry supplier profit changes; market expansion is. We observe a significant increase

in shipments to 45% of markets studied. Surprisingly, as post-entry incumbent shipments

drop less than 3%, on average, cannibalization by Wal-Mart is limited. Furthermore, our

analysis demonstrates that both supplier shipments and profits increases are highest for

markets in which incumbents o!er a wider array of products and carry items that Wal-Mart

does not sell.

Our study’s managerial implications are threefold. First, in addition to other benefits

that partnering with Wal-Mart may o!er, e.g., improved distribution processes and inventory

management systems, (e.g.,Bergdahl 2004, Vedder and Cox 2006) our study shows that

selling to Wal-Mart can directly boost suppliers’ bottom line. Even though news stories

threaded with manufacturer complaints about Wal-Mart resonate with critics and general

public alike, we argue that some complaints about selling to Wal-Mart may be overstated as

the supplier studied is clearly better o! post-entry.

20

Second, we show that while suppliers benefit from the additional volume Wal-Mart gen-

erates they perform best when post-entry shipments to incumbents increase or remain un-

changed. In addition, the strong link between assortment characteristics and shipments to

incumbents suggests suppliers should encourage them to carry larger and non-overlapping

assortments, which could have important implications for retail competition. Wal-Mart is

known for its aggressive competitive strategy, as David Glass (CEO Wal-Mart Stores Inc.

1988-2000) explained: “We want everybody to be selling the same stu!, and we want to

compete on a price basis, and they will go broke 5 percent before we will.”(Fishman 2006,

p. 48, 68). Trying to beat Wal-Mart at the pricing game is infeasible (Bergdahl 2004)

but, as one grocer put it, “They can’t beat our price on items they don’t have.” (O’Keefe

2002). Retailers selling the same goods as Wal-Mart not only put themselves in jeopardy

(e.g., Stone 1995, Gielens et al. 2008), our results demonstrate they also bring down supplier

shipments and profits. Increased retail di!erentiation will not only boost supplier profits

from incumbents but, surprisingly, also from Wal-Mart. Therefore, we suggest that sup-

pliers motivate incumbents to diversify, for example, by o!ering them specialty products,

slotting allowances, services, or training that Wal-Mart does not need or want.

Third, to enhance incumbents assortment options manufacturers should maintain prod-

uct lines. Wal-Mart has become the primary customer for many suppliers, who devote a

large chunk of their marketing resources to serve the retailer (Useem 2003, Fishman 2006).

Some manufacturers even prune their product lines to better target Wal-Mart’s customers

(Fishman 2006), o!ering incumbents little room to diversify. They are setting up a “lose-

lose” situation, as retailers cannot but carry the same products Wal-Mart does. Just as

Wal-Mart continually improves itself by studying its customers buying habits (Huey 1998),

manufacturers should not only develop and market products for Wal-Mart, but also learn

from and cater to incumbent retailers’ consumers.

Future research could extend our findings in several ways. For example, while we focus on

one major manufacturer in large number of geographical areas in the U.S., researchers could

21

expand our findings to additional industries and countries. Moreover, whereas our study

quantifies Wal-Mart entry impact on supplier profits, wholesale prices, and shipments, its

implications on incumbent retailer profits have yet to be addressed. Since our results clearly

indicate limited cannibalization from incumbents, it would be interesting to investigate the

underlying causes of the Wal-Mart entry shipment boost. Is it an income e!ect? Is the

increase driven by a disproportionate change in the demand for products in a specific quality

tier? Are incumbent retailers lowering prices for certain products? Furthermore, retailers,

suppliers, and consumers may benefit if future studies could determine how retailers can best

di!erentiate their assortments from Wal-Mart.

Wal-Mart’s influence on today’s global economy is unlike that of any other retailer. Our

study provides several new and important insights into Wal-Mart’s impact on its suppliers.

We hope that, with manufacturer and retailer cooperation, researchers will be able to paint

a comprehensive picture of the costs and benefits for all constituents when Wal-Mart comes

to town.

22

A Estimation algorithm

A system of regression equations (see equations 1-3) is estimated for each pair of matched

markets, where the errors are assumed to follow a normal distribution with mean 0 and

covariance matrix !i. In the second stage the %i coe"cients from the first stage are linked

to a set of cross-sectional characteristics Zi. Natural conjugate priors are specified for the

hyper prior parameters: V! " IW (w0, W0), vec(")|V! " N(vec("̄), V! $ A#1) where %̄ = 0

, A = 0.01I, w0 = nz + 3, W0 = w0I. The prior for the first stage error structure is

!i " IW ((0, V0) where (0 = 6 and V0 = (0I. Thus, we constructed a Gibbs sampler for this

model by 1) drawing the regression parameters %i|!i and !i|%i given the parameters of the

first stage prior, " and V! 2) drawing " and V! conditional on %i, !i using a multivariate

regression. The sampler is run for a total of 20,000 iterations to simulate the posterior

distributions of the parameters. Every 5th draw of the last 10,000 is selected, leaving 2,000

draws for inference. Since we use a standard linear hierarchical Bayesian model, we refer to

Rossi et al. (2005) for further details.

23

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Figure 1: Location of Wal-Mart entries

29

0.0 0.2 0.4 0.6 0.8 1.0

020

4060

80

Pr[Entry=1|X]

Dens

ity

Control MarketExperimental Market

Figure 2: Propensity score distribution for experimental and control markets

30

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

05

1015

20

Pr[Entry=1|X]

Dens

ity

Control MarketExperimental Market

Figure 3: Propensity score distribution for experimental and control markets on commonsupport of P (X)

31

0.0 0.1 0.2 0.3 0.4 0.5

01

23

4

Control Market

Pr[Entry=1|X]

Dens

ity

0.0 0.1 0.2 0.3 0.4 0.5

01

23

4

Experimental Market

Pr[Entry=1|X]

Dens

ity

Figure 4: Propensity score distribution for matched markets

32

Supplier Profits (Total Market)

Freq

uenc

y

− 0+

020

4060

8010

012

014

0

Supplier Profits (Incumbents)

Freq

uenc

y

− 0+

050

100

150

Figure 5: Wal-Mart entry impact on supplier profits

33

Wholesale Prices (Total Market)

Freq

uenc

y

− 0+

020

4060

80

Wholesale Prices (Incumbents)

Freq

uenc

y

− 0 +

020

4060

80

Figure 6: Wal-Mart entry impact on wholesale prices charged

34

Shipments (Total Market)

Freq

uenc

y

− 0+

020

4060

8010

012

0

Shipments (Incumbents)

Freq

uenc

y

− 0+

020

4060

8010

012

0

Figure 7: Wal-Mart entry impact on supplier shipments

35

Profits from Wal−Mart

Prof

its fr

om in

cum

bent

s

0 +

−0+

Profits from Wal−Mart

Prof

its fr

om to

tal m

arke

t

0 +

−0+

Profits from incumbents

Prof

its fr

om to

tal m

arke

t

− 0 +

−0

+

Figure 8: Profits from Wal-Mart, Incumbents, and Total market

36

Shipments to Wal−Mart

Ship

men

ts to

incu

mbe

nts

0 +

−0+

Shipments to Wal−Mart

Ship

men

ts to

tota

l mar

ket

0 +

−0+

Shipments to incumbents

Ship

men

ts to

tota

l mar

ket

− 0 +

−0+

Figure 9: Shipments to from Wal-Mart, Incumbents, and Total market

37

Supplier Profits

Who

lesa

le P

rices

− 0 +

−0+

Figure 10: Wal-Mart entry impact on supplier profits versus Wal-Mart entry impact onwholesale prices charged to incumbents

38

Supplier Profits

Ship

men

ts to

Tot

al M

arke

t

− 0+

−0+

Supplier Profits

Ship

men

ts to

Incu

mbe

nts

− 0+

−0+

Figure 11: Wal-Mart entry impact on supplier profits versus Wal-Mart entry impact onshipments to total market (left panel) and incumbents only (right panel)

39

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Assortment Overlap

Dens

ity pre−entrypost−entry

Figure 12: Density of assortment overlap

40

2000 2001 2002 2003 2004 2005

5060

7080

9010

011

0

Qua

rterly

Ass

ortm

ent I

ndex

Product line length is indexed to December 1999 for confidentiality reasons.

Figure 13: Change in length of supplier product line

41

Wal-Mart’s impact on supplier proﬁts - Semantic Scholar€¦ · Wal-Mart’s impact on supplier proﬁts Qingyi Huang1 Vincent Nijs2 Karsten Hansen3 Eric T. Anderson4 September

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