Retail Markups and Discount-Store Entry

Hard-discounters—which are generally defined as retailers that offer limited assortments, high-quality private label brands, and prices that are often 40–60% lower than current “discount” retailers, are an emerging format in the US market (Vroegrijk et al., 2013, 2016; Progressive Grocer, 2019). In fact, two hard discounters, Aldi and Schwarz Group (parent of Lidl)—together accounted for over \(\$100.0\) billion in sales in 2017, and each had nearly three times the compound annual growth rate between 2012–2017 as any other store in the global top-10 (Steenkamp, 2018). Although Aldi has been in the US market for over 20 years, the hard-discount concept has only recently emerged as a clear competitive threat to existing food retailers.

Despite the potentially transformational nature of the hard-discount business model, there is very little empirical research of their impact on existing retailers. In this paper, we provide empirical estimates of the effect of hard-discounter entry on incumbent retailer markups and store profitability.

Estimating the impact of hard-discounter entry on retail markups is not just a matter of curiosity. In recent decades, there have been a number of entry “waves” from retailing formats that seek to capitalize on the relatively large sales volumes that are associated with selling food. For example, in the 1990s, Walmart expanded from its base in Bentonville, Arkansas, to occupy nearly every market in the U.S., and many markets overseas. The impact of Walmart entry has been dramatic, and well-documented (Singh et al., 2006; Basker & Noel, 2009; Zhu & Singh, 2009; Ailawadi et al., 2010; Courtemanche & Carden, 2011; Holmes, 2011; Huang et al., 2012; Iacovone et al., 2015; Arcidiacono et al., 2016; Atkin et al., 2018).

In the 2000s, existing retailers consolidated in the face of Walmart entry, and club stores became the latest competitive threat (Courtemanche & Carden, 2014; Bauner & Wang, 2019). In the 2010s, online shopping emerged, but did not become a force for change in the grocery industry until Amazon acquired Whole Foods in 2017 (Turner, 2017), and the Covid-19 pandemic of 2020 accelerated the move online by some 10 years relative to existing trends (Progressive Grocer, 2020). Currently, European hard-discounters are entering many key markets in the US, and hope to succeed by providing essential items at prices that are even lower than those of Walmart or club stores (Jackson, 2020).

Understanding the effect of entry on incumbent retailers, therefore, is critical both for retail practice, and for developing fundamental knowledge with regard to the forces that shape U.S. retailing.

Researchers generally examine the effect of entry with the use of traditional, demand-side methods, combined with counterfactual simulations. However, estimating the effects of entry using store- and firm-level markup data is arguably more relevant, as the average food retailer carries thousands of products (FMI, 2021). Moreover, consumers often do not recall individual item prices (Dickson & Sawyer, 1990; Loy et al., 2020), but instead basket-level prices (Bell & Lattin, 1998), or firm-level product aggregations (Blonigen & Pierce, 2016). Markups are of central concern to retailers because of their implications for profitability, and to policymakers due to their consequences for price-setting conduct and industry competition. For these reasons, we examine the effect of hard-discounters’ entry into several important U.S. grocery markets with the use of a store-wide, markup-based approach.

We are not the first to consider the competitive effects of hard-discounter entry, and the nature of competitor responses. For example, Vroegrijk et al. (2013) argue that hard-discounters may have a “complementary” effect on existing retailers, as consumers tend to seek out the lowest-cost source for price-sensitive items, while seeking out traditional retailers for categories in which variety and quality may be more important. In a study most similar to ours, Cleeren et al. (2010) examine the inter- and intra-format effects of entry between hard discounters and supermarkets in Germany. Focusing on firm-level outcomes, they find a significant threshold effect for the impact of hard-discounter entry on supermarket profits.

In terms of incumbent responses, Lourenço and Gijsbrechts (2013) suggest that the optimal response by incumbent retailers to hard-discounters’ attempts to take away market share in the national brand market is to introduce only category-leaders, and at prices that reflect favorable value-for-money relative to their traditional-supermarket competitors. Similarly, Vroegrijk et al. (2016) examine whether introducing low-price private labels may be an effective way for supermarkets to beat hard-discounters at their own game, yet these authors find only limited success; while Hökelekli et al. (2017) suggest that price-competition through standard private labels may be the best of a set of ineffective defensive strategies.

While these studies provide valuable insight as to the reasons why hard-discounters may co-exist with traditional retailers in the same markets, these studies do not quantify the ultimate effect that hard-discounter entry is likely to have on incumbent markups, and the studies focus on empirical evidence from only a limited number of product categories.

Beyond the specific example of hard-discounter entry, and the evident importance of firm-level profit, surprisingly little is known about retailing markups in general. Conventional wisdom holds that the retailing sector is very competitive (Beresteanu et al., 2010; Ellickson, 2016), with net margins averaging 2.0% according to industry “stylized facts” (Campbell, 2020). However, estimating markups at the store level is a difficult empirical problem. The typical approach to estimating market power in retailing relies on demand-side methods (Berry et al., 1995; Nevo, 2001; Chintagunta, 2002), wherein the researcher estimates a large matrix of own- and cross-price demand elasticities, which condition the retailers’ ability to achieve an equilibrium price under some assumed form of an oligopolistic pricing game.

However, more recently, others recognize that if the goal is to estimate firm-level markups, then starting from a highly disaggregate set of products is not necessarily the most efficient way to begin, and tends to be very restrictive as well. De Loecker (2011a, 2011b); De Loecker and Warzynski (2012), Traina (2018), and many others, approach the problem of markup estimation instead from the production side: They apply the insight of Hall (1988) that markups can be estimated from a simple condition on the output elasticity of a variable input, and input-expenditure shares of that input. While most applications are in trade (De Loecker, 2007; Klette, 1999) and macroeconomic markup estimation (De Loecker et al., 2020; Traina, 2018), this approach is also useful in uncovering markup patterns among food retailers.

We provide new evidence on the relative competitiveness of food retailing, and examine the specific case of how market-entry by a discount-retail chain affects markups of incumbent retailers, by estimating store-level markups from a production-side perspective.

Our conceptual approach is well-understood: extending the growth model of Solow (1957) and Hall (1988) shows that “[U]nder competition and constant returns, the observed share of labor is an exact measure of the elasticity of the production function...”consequently, any departure between these two measures—if we assume constant returns to scale—is interpreted as a measure of imperfect competition (p. 923). De Loecker (2007), De Loecker (2011a) and De Loecker (2011b), and De Loecker and Warzynski (2012) develop the econometric details of how this conceptual model can be used to test for departures from competition in firm-level data, but the underlying logic is the same: with only limited firm-level production data, we can infer market power from changes in observed levels of output, and the employment of a variable input, if we assume no adjustment costs. This approach is particularly well-suited to estimating market power in a retailing context because it makes no assumptions with regard to the nature of demand relationships among individual products that are typical of other empirical studies in this literature (Berry et al., 1995; Nevo, 2001). This approach also “scales” well, so that it is able to recover markup estimates—even when the firms involved sell thousands of items, across a wide range of potentially-unrelated categories (Gelper et al., 2016). In this sense, the production-side approach avoids the curse of dimensionality that is common to all methods of estimating multi-product seller markups.

We develop a variation on the production-side markup estimation approach that was developed by De Loecker (2011a, 2011b) and De Loecker and Warzynski (2012), and apply our approach to store-level, food-retailer data. While the production approach to markup estimation is typically applied to Census of Manufacturers (CoM) data in the US (Foster et al., 2008; Asker et al., 2014; De Loecker et al., 2020), the equivalent data—from the Census of Retail Trade (CRT)—are insufficient for our purposes (Foster et al., 2006). Because the retailing industry tends to be more concentrated in local areas, the public CRT data are available only on an aggregated basis for reasons of confidentiality, and the establishment-level micro data do not contain the physical input measures we require.¹

Therefore, we use the TDLinx establishment-level data set from Nielsen, Inc. TDLinx is a census-type data set that aims to describe the locations and fundamental operating characteristics of all food retail locations in the U.S. (Cho et al., 2019). With these data, we are able to conduct our analysis at the firm level, and to explain company-wide markups that vary with changes in the firm’s competitive environment with the use of physical measures of key input quantities, and dollar-sales output values.² We address issues that remain with our store-level data below in the description of our identification strategy; but our production-side approach remains far less data-intensive than the equivalent demand-side approach for the same objectives.

We find that there are two effects on incumbent retailers that are due to the entry of a hard-discounter that remain after accounting for the endogeneity of both entry and shocks to labor productivity: First, sales increase for retailers in the proximity of the entering retailer by approximately 2.0% (within 3 or 5 km), which we interpret as a positive traffic-effect (from outside this proximate area) that is due to the limited assortment that is offered by the entering retailer and/or lower retail prices as a result of more intense competition. In this regard, our findings are consistent with Vroegrijk et al. (2013, p. 609), who find that “...losses to HDs are not necessarily most severe for incumbents in close proximity”Ċonsumers are attracted to the hard-discounter as they search for lower prices on either staple items, or items that are unique to the retailer, but then finish their shopping at other, local stores when they cannot find the range of items they are looking for at the hard discounter.

Second, markups are lower (by roughly 7.0%) for incumbent retailers as they reduce prices to compete with the entering retailer, or lose sales on previously high-margin sales that have been taken by the entering hard-discounter. Regardless of the specific mechanism, store-level markups are lower for stores in the proximity off an entering hard-discounter.

Aggregating the positive effect on store-sales, and the negative effect on markups, we find that the net effect on incumbent retailers is unambiguously negative. In general, therefore, we find that there is a net negative effect on incumbent performance due to hard-discounter entry, even without allowing for the potential dynamic effects that would be associated with potential non-price competitive effects of rivals: e.g., additional variety, low-price services, enhanced private-label strategy, or online delivery options.

We examine the potential response to hard-discounter entry from incumbent retailers through a series of counterfactual simulations. Retailers have been successfully improving productivity through the adoption of labor-saving technologies such as barcodes and barcode scanners (Basker, 2012), automated self-checkout systems (Litfin & Wolfram, 2006), digital price tags (Inman & Nikolova, 2017), or automated warehouses, robotic in-store fulfillment, and autonomous floor cleaning robots (Begley et al., 2019). Each of these advances can be interpreted in our context as labor-productivity enhancing investments that may help incumbent retailers compete with hard discounters on a cost basis.

Alternatively, retailers can adopt demand-side strategies by making enhancements to private-label offerings to attract market share from hard-discount retailers, as in Vroegrijk et al. (2016) or Hökelekli et al. (2017). We examine one example of each of these strategies, and show that a productivity-enhancing investment is able to raise store-level profit by 9.3% in competition with a hard discounter—relative to a do-nothing scenario—while an output-improving investment of the same magnitude may increase profit by 13.5%. Therefore, our hypothetical scenarios suggest that managers would be well advised to consider not competing directly against an entering hard-discounter, but by making the most of the market opportunities that are provided by the discounter, and exploiting the part of the market that is not well served by the hard-discount format, in general.

Our empirical model, and our findings, contribute to the methodological literature on estimating markups in food retailing, the substantive literature on the profit-effects of retailer entry, and the managerial literature on retailers’ response to new-format entry.

In terms of our methodological contribution: We are the first to apply a store-level, production-side approach to estimating market power in the food-retailing sector—and specifically to address the problem of how market entry affects the market power of existing firms. There are a number of reasons why a store-level approach is valuable in estimating the effect of entry: First, and most important, retailers sell thousands of items—items that can be either complements or substitutes in demand. While others examine the implications of complementarity among retail products for store-level market power (Thomassen et al., 2017; Richards et al., 2018), their analyses are necessarily restricted to a limited set of products in the store, and do not attempt to study the profitability of the store in general. On the other hand, our production-side approach, by definition, at least implicitly takes into account the firm-level profit implications of a wide variety of product-level interactions.

Second, our approach is sufficiently flexible to account for heterogeneous effects among the retailers in our sample: The retailers in our sample range from very small, limited-assortment retailers (approximately 3000 stock-keeping units, or SKUs) to large club stores and supercenters (upwards of 50,000 SKUs). A demand-side approach, which is dependent upon consistently estimating interactions among all possible products, cannot possibly yield comparable estimates across stores that differ in assortment to this extent.

Third, when the unit of observation is the establishment itself, there is a wider range of variables that can serve as instruments for endogenous labor input and entry decisions. Entry-endogeneity is a clear barrier to identification in any study of this type (Cleeren et al., 2010), so having access to suitable instruments is both important, and necessary.

Fourth, in the entry literature we are first to apply a production-side markup estimation approach to estimate the effect of entry on incumbent retailer profit-performance. Our approach is appropriate for this purpose as retailers are more likely to use firm-level measures of profitability to evaluate either the feasibility of entering a new market, or the extent of competitive harm that is inflicted by an entering rival. In the empirical model, we disaggregate the effects of entry into aggregate, store-level impacts on both sales and markups. Our simulation model shows that the net effect of entry—after accounting for higher sales due to positive traffic-effects in the local market and negative effects on markups due to price competition—reduces the overall level of store profitability.

Finally, our findings provide valuable information to retail managers who seek to stave off the worst impacts of hard-discounter entry. While intuition may suggest that incumbent retailers can survive by doing what hard-discounters do—but better—our findings show that total store sales are actually higher in proximity to hard discounter entrants. Therefore, increasing variety and providing high-quality store brands may be a more appropriate response. This result is reminiscent of Arcidiacono et al. (2016), who show why mimicking Walmart in the 2000s was likely a bad idea as the retailers who were different from Walmart survived, while direct competitors did not. Our findings in this regard align with Vroegrijk et al. (2013), who find that consumers are likely to “trade up and trade down” across retail formats due to hard-discounter entry, taking advantage of low prices in price-sensitive categories by hard discounters, while buying more price-sensitive items from high-variety traditional retailers. Our ultimate conclusions are the same as theirs: Hard discounter entry need not mean the end of retailers with store formats that appeal to other consumer segments.

This paper is structured as follows: in the next section, we describe a conceptual model of retail markups that is based on the markup-estimation framework of Hall (1988) and De Loecker and Warzynski (2012), and motivate our primary hypotheses. In the third section, we describe our data and identification strategy, while we derive an empirical model that is able to recover markups of the form that is required in our conceptual model in Sect. 4. We present and interpret our empirical findings in Sect. 5, and interpret our results in terms of the implications for retailer performance, and likely response strategies. We conclude and describe the limitations of our research in the Sect. 6.

In this section, we frame our theoretical expectations with regard to the effect of hard-discounter entry based on the existing literature on retail-format entry, explain our core hypotheses, and provide a theoretically-consistent framework for testing each hypothesis in an empirical production context.

We examine the problem of hard-discounter entry from both a “naive” non-structural perspective and from a structural perspective: we first examine the data with the use of relatively simple, non-structural models (that ignore the likely endogeneity of entry and employment) to determine whether there are any patterns in the data that are consistent with our expectations with regard to the potential competitive effects of hard-discounter entry on both the sales volumes and the prices (via mark-ups) of nearby incumbent retailers.

We then estimate structural production functions that account for entry and for the well-understood endogeneity of variable inputs (Olley & Pakes, 1996; Levinsohn & Petrin, 2003; Ackerberg et al., 2015) using the approach that was developed for repeated-cross sectional data by Lowrey et al. (2021). We use this approach to test the effect of entry and of labor productivity on sales volumes, and markups, more formally. We then use the estimates from this model to conduct a set of counterfactual simulations that compare sale volumes and markups with and without hard-discounter entry. We interpret the results from these simulations as the likely impact of entry on incumbent retailers in our sample markets,⁶

In this section, we first present results from a non-structural procedure for estimating store output (measured as annual store sales revenue), and then the structural estimates of our markup-and-entry model of store output.

Our non-structural estimates control for state, chain, and year fixed effects, as well as the full set of production inputs and store-attribute values. These findings are in Table 4. We estimate several versions of the model in order to examine the sensitivity of the core model parameters—the labor elasticity of output—to changing the set of covariates. We estimate: a basic model with only fixed effects and production inputs (Model 1); a model that includes store attributes (defined as whether the store sells gas or liquor, Model 2); a model that adds the distance from an entering hard-discounter as an output-shifting variable (Model 3); one that allows entry to affect output and the labor-elasticity (Model 4); and a model that defines entry as a discrete variable that takes a value of 1 if a hard-discounter enters within 5 km (Model 5).

From the results in this table, we see that the core output-elasticity estimates are relatively stable across the different specifications, and that the implied returns to scale (the sum of the elasticities) is not statistically different from 1.0.¹⁵ Further, these reduced-form results show that output is greater for stores that sell both gas and liquor, all else constant, so there are clear opportunities for cross-selling services or alternative products in food retailing.

Most important for our objectives, however, Model 3 shows that sales appear to be significantly lower for stores that are nearer to an entering discounter than otherwise. But, controlling for the dual effects of entry on store output and labor efficiency (Model 4), the results in this table show that the main effect of entry on output is felt through the efficiency of labor, and not directly on gross output per se: In the presence of entry each worker generates less dollar sales than in the absence of entry—which we interpret as implying that average prices are lower across the store as management meets the new competitive pressure from the entering hard discounter. In Model 4, however, the direct effect of entry on store sales is not statistically significant, while it is in Model 5.

We interpret the positive effect of entry on store sales—controlling for the effect on labor productivity—as meaning that the entering hard-discounter drives incremental traffic to the area around the store, but forces prices down in equilibrium. Customers from outside the area who travel to the hard discounter for its low prices may not be able to find at the hard discounter the products that they want in every category, however—in particular national brands (Hökelekli et al., 2017)—so they shop at the nearest full-service supermarket in order to top off their baskets.

While the non-structural model results are suggestive of deeper patterns in the data, they are likely to be biased for the reasons cited above. After controlling for the endogeneity of entry and of labor inputs, we obtain structural estimates of labor-productivity on a store-level basis, and use these estimates to infer values for the markup over marginal cost. These estimates are in Table 5. In this table, we again report estimates from models that consider various definitions of entry: the distance to an entering hard discounter (Model 1); a binary indicator of a discounter within 3 km (Model 2); 5 km (Model 3); and 10 km (Model 4). According to the goodness-of-fit statistics reported in this table, it appears that Model 4 provides the best fit to the data among the “binary” models, although each fit the data slightly worse than the continuous definition in Model 1.

Because of the similarity in fit, and the magnitudes of the estimated parameters, we rely on the parameters from Model 4, simply due to the fact that we regard the binary definition of entry as the most consistent with how entry likely impacts retailers’ decisions in practice: the retailers are not likely to be affected by a discounter outside of what they regard as their market area, but will respond to what they perceive as a direct competitor.

Relative to the non-structural estimates in Table 4, we see that controlling for both labor and entry endogeneity leads to somewhat larger estimates for the output-elasticity with respect to labor. According to the theoretical model of markup determination (Hall, 1988), this implies higher markups after controlling for endogeneity. In Model 4, the results also show a negative marginal value of selling liquor, all else constant. Although liquor is notoriously a high-volume, high-margin category, it may be the case that retailers have to employ additional labor to manage the category, so controlling for labor-endogeneity removes any economic benefits to selling liquor.

Most important, however, the entry effects from Table 4 are confirmed for this structural model: We find a similar pattern of entry effects on incumbent retailers: Store output rises for stores that are near an entering hard-discounter—by an average of 1.9%, when we use the data in Table 1—but margins fall. Relative to the non-structural evidence, our structural model shows an attenuated store-output effect, but an accentuated impact on labor-efficiency. As in the previous table, we interpret this result as implying that entering discounters increase customers for surrounding stores that provide more variety; but the hard discounters increase pricing pressure on nearby retailers, which reduces the latter’s store-level markups.¹⁶

Our estimates imply non-trivial changes in profitability for stores that are exposed to hard-discounter competition. In our preferred model (Model 4), the difference in markups that is implied by these estimates represents a 7.2% reduction in the price–cost margin. While this may seem to be a small impact, retail food net margins, which also take into account fixed costs of operation, tend to be roughly 2% (Campbell, 2020). A 7% reduction in the amount of margin available to pay fixed costs, therefore, can be substantial.

For example, suppose that a 2% margin results from a 50 pound bundle of groceries that sell for \(\$2.00\) per pound, with a cost-of-goods-sold of \(\$1.00\) per pound, and a fixed cost of \(\$48.00\) for the store. Net profit is \(\$2.00\) on sales of \(\$100.00\). If the price–cost markup falls from \(\$1.00\) to \(\$0.93\) per pound, then this 2% net margin becomes a loss of 1.5%. Over the longer term, bankruptcy ensues—despite the total sales effect that is implied by our model.

The simulation exercise provides important managerial insights into the type of defensive strategies that are likely to be successful (or not). Our first experiment, which uses a hypothetical expansion of sales volume (which could follow from a change in store-assortments among traditional retailers as a means of attracting additional customers) is successful in generating additional profit throughout the full range of the simulated store-preference parameter (Table 6). However, this effect shows clear diminishing marginal returns as the maringal gains disappear after a 10% increase in store-demand.

In the second experiment—an increase in the productivity of store labor—we find that the initial gains are maximized through a 3% gain in productivity, but the interaction with discounter-entry dominates after that point. Because markups are diminished by hard-discounter entry, and depend critically on labor productivity in our theoretical framework, higher productivity increases store profit through the direct effect, but reduces store profit due to the entry interaction. Intuitively, if a retailer becomes too profitable due to labor-related innovations, a hard-discounter will interpret this profitability as a market opportunity, enter, and reverse any gains in profit that were available.

Our findings are important for understanding both the broader, economic impacts of hard-discounter entry, and potential managerial responses: First, although we do not calculate the implied welfare effects on competing firms and consumers that buy from these retailers, it is clear from our results that producer surplus is likely to fall due to lower markups, but consumer surplus will rise for the same reason. To the extent that incumbent retailers may also reduce their SKU count in order to compete with hard discounters, consumers may also experience a reduction in welfare from a loss of variety. In general, however, lower prices tend to be net positive for consumers.

Second, retail managers need to be aware of the potential consequences due to the entry of paradigm-changing retailers—such as hard discounters—and draft responses that are least likely to be self-destructive. For instance, Arcidiacono et al. (2016) describe the impact on local markets due to Walmart’s entry into retail markets throughout the 1990s and 2000s. While the common perception was that “main street” would suffer most, it was instead the direct competitors to Walmart—the supermarkets that competed most directly with Walmart’s primary line of business—that suffered. In fact, retailers that were sufficiently differentiated from Walmart were less affected, if at all.

Our findings make a similar case: Responding by reducing prices and SKUs in order to match the hard discounters’ business models will likely end in failure; but differentiating by emphasizing variety, quality, and sharply differentiated store brands (Hökelekli et al., 2017) is more likely to be successful.

In this paper, we examine the impact of hard-discounter entry on incumbent retailer markups. Because retailers tend to sell thousands of items, we argue that the traditional demand-side approach to entry is intractable for a problem such this; consequently, we adopt the production-side markup-estimation approach of De Loecker and Warzynski (2012) for this purpose. Although this approach has been used extensively to estimate the effect of trade status on manufacturing margins (De Loecker, 2007) or corporate markups in general (De Loecker et al., 2020), we are the first to use this approach to examine retail-store performance.

We find that, averaged over all incumbent retailers and markets, markups fall by over 7.0% in response to hard-discounter entry, although total store sales rise by nearly 2.0%. There are two mechanisms that explain these effects: first, competitive pressure from entering hard discounters causes retail prices, and hence markups, to fall; and, second, customers who are newly attracted to a geographic area to shop at the hard discounter also shop in nearby stores in order to fill their baskets with items that are not available at the hard-discounter. This finding is remarkably consistent with those of others who document this same inter-store complementarity effect with the use of entirely different methods and data (Vroegrijk et al., 2013). Moderately higher sales, however, are likely irrelevant if the lower markup causes already-low-margin retailers to sell at a long-run loss.

Our empirical model has both macroeconomic and managerial significance: If the spread of hard discounters leads to lower retail prices for food, then consumers will almost certainly benefit in terms of total welfare. However, if hard-discounter entry leads to the exit of grocery stores in nearby markets—which were perhaps already marginally suited to traditional grocery retailers—then the “food desert” problem may be accentuated in these markets (Allcott et al., 2019).

Finding that store sales rise in response to new entry suggests a way out of the implied competitive trap for existing retailers: Rather than respond to entering hard discounters by restructuring in order to “beat them at their own game,” our findings suggest instead that incumbent retailers are better off pivoting away from these new formats, and offering instead what hard discounters do not. For example, this means offering a greater variety of national brands, and perhaps store brands that offer more favorable price-quality perceptions than those that are offered by the hard discounters (Cleeren et al., 2010; Lourenço & Gijsbrechts, 2013).

Our research has some limitation: first, the TDLinx data that forms the core of our empirical model includes only estimates of the output for each sample store. However, Nielsen, the provider of TDLinx, has a commercial stake in ensuring the quality and accuracy of these estimates, so our data are likely as good as any available alternative. Second, we estimate a relatively simple production surface for our sample retailers. While others in this literature estimate translog models in order to capture interactions between inputs, and other sources of non-linearity, we take a more parsimonious approach, and introduce flexibility through the use of a random-parameter production specification. To the extent that our approach does not capture the same underlying structure, our estimates will be in error. Third, the retailer that forms the focus of our paper entered only a limited number of markets (nine states) over a three-year period. A longer time-series of entry events would have likely provided sharper identification conditions for our core model. However, the effects we describe appear to be precisely estimated, so it may be the case that more data were not needed.