Do MSRPs Decrease Prices?

Transcription

1 Do MSRPs Decrease Prices? Babur De los Santos In Kyung Kim Dmitry Lubensky October 2017 Abstract The nature of manufacturer s suggested retail prices (MSRPs) and whether they increase or decrease prices is poorly understood. We exploit a policy experiment in which a ban on MSRPs was imposed and then lifted a year later, and show that MSRPs decrease prices by 3.6 percent on average. There is no indication that MSRPs lowered prices by acting as binding price ceilings. Instead we find suggestive evidence that MSRPs are aimed at consumers, possibly providing a benchmark by which consumers can evaluate prices. Keywords: recommended retail price, vertical restraints, resale price maintenance JEL Classification Numbers: L110, L400, L810 We are grateful to Ginger Jin, Jeff Prince, Michael Baye, Sergei Koulayev, and participants of the International Industrial Organization Conference in Boston, the Jordan River Conference at Indiana University, the Workshop in Search and Switching Costs in Moscow, CORE@50 Conference in Leuven-la-Neuve for helpful comments. We want to thank Gloria Cox and Kathy Rasmussen at Nielsen and Chunnam Park at Nielsen Korea Retail Measurement Service for their help acquiring the data. We are also grateful to Joowon Kim and Yoojin Lee for excellent research assistance. Department of Economics, Clemson University, babur@clemson.edu. Department of Economics, Nazarbayev University, in.kim@nu.edu.kz. Kelley School of Business, Indiana University, dlubensk@indiana.edu. 1

2 1 Introduction Many consumer products from cars to packaged foods have a manufacturer suggested retail price (MSRP) printed on the label. Why manufacturers make and publicize these recommendations and how they affect actual prices is not well understood. In part this is because a manufacturer s motives for influencing downstream prices are unclear. Competing theories suggest that a manufacturer may wish to increase downstream prices in some circumstances (e.g. to encourage downstream quality provision or foster upstream collusion) and to decrease downstream prices in other circumstances (e.g. to curb double-marginalization). Furthermore, the mechanism by which manufacturer recommendations affect prices is not obvious. A recommendation is non-binding in name, so does it bind in practice? And if not, why do manufacturers make these recommendations and expend resources in publicizing them? In this paper we exploit a unique policy experiment in which a ban on MSRPs was initially imposed and then lifted a year later, to identify the effect of MSRPs on prices, and shed light on the mechanism by which this effect takes hold. A challenge in estimating the effect of MSRPs, as with any vertical instrument in general, is that their use is endogenous to unobserved industry characteristics (Lafontaine and Slade, 2008). In particular, unobserved factors that determine whether manufacturers use MSRPs may also directly influence prices, and thus confound the estimated effect. Here, we address this issue by exploiting an unusual natural policy experiment in South Korea. In Section 2 we outline the policy imposed by the Korean government in July 2010, which banned MSRPs on products in several processed foods categories with the stated purpose of fostering competition and reducing prices. One year later, the government lifted the ban in response to public pressure from consumers. These policies provide the variation in the use of MSRPs necessary to estimate the effect on prices. Furthermore, the unique circumstances of the ban first being implemented and then reversed allow us to identify the effect of the use of MSRPs separately from time trends. Our data comes from the Nielsen Korea Retail Measurement Service and is described in Section 3. This data set contains monthly prices and sales quantities for 253 products, including products subject to the ban and other similarly priced products that remained outside the scope of the regulation. All products subject to the regulation used MSRPs prior to the ban, and after the lift of the ban MSRPs were reinstated on some products but not others. Meanwhile none of the products excluded from the policy had MSRPs throughout the sample. In Section 4 we estimate the effect of MSRPs using several approaches. First, we focus only on the products under the policy which reinstated MSRPs and rely on within-product price variation across periods in which MSRPs were and were not used to identify the effect on prices. Because the ban was first implemented and then reversed we are able to control for a time trend separately from the presence of MSRPs, and because the ban lasted an entire calendar year we can also control for seasonality. We show that MSRPs decreased prices by 3.6 percent. To validate this result, we also separately estimate the effect of MSRPs when they are removed and the effect of MSRPs when they are reinstated. To disentangle the effect of each policy from a 2

3 time trend we employ a difference-in-differences approach. For the ban, we show that products not under the policy provide a suitable control group, and in estimating the model find that MSRPs decreased prices by 2.0 percent, consistent with our previous result. For the lift, we can use either products under the policy that did not reinstate or products not under the policy as a control group, but unlike with the estimate of the ban the suitability of each of these groups is more tenuous. 1 When products that did not reinstate are the control group, MSRPs lower prices by 3.8 percent, while when products excluded from the policy are the control group the effect is not significant. In Section 5, we perform two robustness checks. First, we separately estimate the effects of the ban and the lift using synthetic control methods. Second, we use an auxiliary dataset from the Korea Consumer Agency consisting of weekly store-level prices for a somewhat smaller set of products, and conduct the same analyses as above. We obtain estimates similar to those in the original specifications. Having found evidence that MSRPs decrease prices, in Section 6 we explore the mechanism by which this occurs. We consider four possible roles of MSRPs proposed in the literature: (i) as informative signals to retailers, (ii) as price ceilings, (iii) as a nudge to induce consumer search, and (iv) as a benchmark for purchase decisions. For explanation (i), proposed by Buehler and Gartner (2013), in which a manufacturer uses the MSRP to communicate cost and demand information to retailers, with our data it is difficult to find either supporting or contradicting evidence. First, when MSRPs are banned the manufacturer can plausibly find other channels to communicate, thus it is not clear whether any effect of the ban should be observed at all. Second, even if retailers do face extra uncertainty about market conditions as a result of the ban, the effect of this uncertainty on prices is ambiguous. For explanation (ii), if MSRPs acted as price ceilings then indeed their removal would result in higher prices. However, a closer inspection of the distributions of retail prices casts doubt on this explanation. An overwhelming majority of retailers charged prices substantially below the recommended price throughout the sample period, both during times when MSRPs were present and when they were banned. Furthermore, during the times that MSRPs were present, there was no evidence of bunching around the recommended price, and during the ban there was no evidence of an increase in the number of prices above the old recommended price. Although price recommendations impacted prices overall, they did not act as binding constraints. For explanation (iii), if a higher proportion of consumers are nudged into comparison shopping rather than simply buying at the current seller, then sellers in turn compete more fiercely and prices are lower in equilibrium (e.g. Varian (1980)). To test this theory, we check whether when facing a distribution of prices consumers were more likely to pay a lower price when MSRPs were present, but find no evidence of this. Finally, for explanation (iv), the conjecture is that an MSRP helps form a reference price above 1 We find that MSRPs were less likely to be reinstated on products for which prices rose substantially during the ban period. If this previous price increase is predictive of future price changes, then this creates a confounding factor in interpreting difference-in-differences estimates. 3

4 which consumers are disproportionately less likely to purchase. 2 Although our data is not wellsuited for checking directly whether the demand curve is kinked at some reference price, there are several pieces of evidence that indirectly support this theory. First, if MSRPs did induce a kink in demand then prices indeed would be lower when MSRPs are present. Second, when the government requested that MSRPs remain at the old levels after the lift, sellers of products whose prices rose disproportionately during the ban were more reluctant to reinstate. Potentially, this was because for these products prices rose from below the reference price induced by the original MSRP to above it by the time of the lift. 3 Third, MSRPs lowered prices the most at large retailers, which typically had the lowest prices throughout the sample. If we expect that more price sensitive consumers react more strongly (i.e. demand is more kinked) to MSRPs, then the effect of MSRPs would be most pronounced at the lowest priced retailers, whose customers are on average more price sensitive. We conclude that MSRPs likely affect prices by altering consumer behavior, and that while our findings are consistent with consumers using the MSRP as a benchmark for evaluating prices, more evidence is needed to establish this mechanism conclusively. Our work is one of the first empirical studies of the effect of manufacturer price recommendations. The other is Faber and Janssen (2017), which shows that variation in suggested prices in the Dutch gasoline market explains variation in retail prices beyond that explained by the spot price of oil, and therefore can be evidence of price recommendations coordinating retailers. Our focus is somewhat different because we consider the effect of the presence of MSRPs rather than their levels. 4 We also find that MSRPs lower prices, which is in contrast to the finding in Faber and Janssen (2017) that recommendations support collusion. However, we consider the findings of the two papers complimentary given the differences in the markets studied. 5 More broadly we contribute to the literature on whether manufacturers use vertical instruments pro- or anticompetitively. 6 With the unusual nature of the ban followed by the reversal, we have the unique opportunity to identify the effect of an otherwise endogenous choice by the manufacturer, and show that here manufacturers wish to exert downward pressure on retail prices. 2 Background In this section we describe the main aspects of MSRP regulation and the retail setting in Korea. The regulation of MSRPs in Korea has a long history and encompasses a wide range of products. Initially, the Korean government viewed MSRPs as a useful device for informing consumers of 2 The reference price can be a behavioral rule of thumb as in Puppe and Rosenkranz (2011) or a rationally formed search threshold as in Lubensky (2017). 3 The reference price need not equal the MSRP, and for a given MSRP can vary across consumers. In this case, the conjecture is that the actual prices rose within the distribution of reference prices. 4 In our setting there little within-product variation in MSRP levels, therefore the analysis in Faber and Janssen (2017) is not feasible. 5 Gasoline is a homogeneous product sold at low markups, thus the scope for collusive behavior is more salient relative to the market we consider. 6 See Lafontaine and Slade (2008) for a summary. 4

5 a proper price level and left the use of MSRPs at the discretion of the manufacturer. 7 As a result, MSRPs were included on a variety of products, including consumer electronics, clothes, and processed foods. However, by the late 1990 s, the Korean government s view of MSRPs had changed. In 1997, the Ministry of Health and Welfare banned MSRPs on cosmetics, hygiene items, and prescription drugs. In 1999, MSRPs were banned from consumer electronics and durable goods, including TVs, VCRs, corded telephones, stereos, and washing machines, and several clothing categories, including suits, children s wear, and sportswear. Open Price Regulation In July 2009, the Ministry of Commerce, Industry, and Energy (MCIE) announced the open price regulation with the objective of fostering competition, banning MSRPs from several processed foods categories including biscuits and pies, ice cream, ramen, and snacks. In order to give manufacturers time to comply, the ban was set to start one year after the announcement, in July A surprising aspect of this regulation is that it was reversed after only one year. In June of 2011, the MCIE announced that it would allow the reinstatement of MSRPs as early as August Two factors might have influenced the government s decision to lift the ban for these product categories. First, there was a general perception that prices for these food items increased after the ban. 8 Second, a March 2011 survey by the Korean Consumer Agency a public institution that defends consumer interests in Korea found that retailers did not consistently display prices of several products that were subject to the open price regulation (e.g percent of retailers did not display prices for ice cream, 48.8 percent for ramen, and 61.2 percent for snacks). In addition, 93.4 percent of consumers surveyed responded that they felt uncomfortable without MSRP information. For the ensuing lift of the MSRP ban one year later, two key aspects should be noted. First, producers decided not to reintroduce MSRPs for some products and second, the government pressured sellers to set MSRPs at the same level as before the ban. 9 At the government s request, producers declared the products for which they intended to reintroduce the price recommendations and the prices they would recommend, listed in Table A-2. Decreases in MSRPs were found for 1 out of 12 products in the biscuit and pie category, 5 out of 18 products in the ice cream category, and 1 out of 9 products in the snack category. Only 1 product out of 18 in the ice cream category increased their recommended prices One exception is prescription drugs, for which Article 58 of the Pharmaceutical Affairs Law required pharmaceutical companies to provide an MSRP. 8 ChosunBiz.com reported on June 15, 2011 that prices of snacks, ramen, and ice cream increased sharply after the open price regulation. They reported the price of saewookkang (a snack) increased 16 percent at hypermarkets, 12.5 percent at convenience stores, and 10.4 percent at chain supermarkets. Similarly, HyundaiCapital.com reported on July 7, 2011 that negative publicity over the open price regulation was mainly due to soaring prices and confusion from the absence of MSRPs. They reported that prices in the past year had grown well above the CPI increase of 3.8 percent; in particular, prices increased 7.8 percent for snacks, 10.8 percent for ice cream, and 13.7 percent for biscuits. 9 fwnews.com reported on August 2, 2011 on this issue in detail. 10 This information is not available for the ramen category. 5

6 Retail Landscape in Korea In order to put in context the data, we briefly describe the Korean processed food retail landscape. Table 1 presents the composition and classification of grocery retailers in Korea. The newer distribution channel includes similar retailers to what one would find in a U.S. city, small convenience store chains, supermarket chains where food represents a larger proportion of sales, and large hypermarkets (or mega stores) with a larger product variety. The older distribution channel is composed of supermarkets (bodegas or grocery stores) which are independently owned, and small corner shops. Traditionally, processed foods were largely sold through the old distribution channel, but in the past decade as the number of supermarket chains, hypermarkets and convenience stores have increased sharply, food sales at the new distribution channel are increasingly important. 3 Data The data come from Nielsen Korea Retail Measurement Service and cover 253 processed food products from January 2010 to January The data contain monthly sales figures and average unit prices across five retailer categories for biscuits and pies, ramen, ice cream, and snacks, which are subject to the open price regulation, and products in cereal and yogurt categories, which are not. Cereal and yogurt products were allowed to use MSRPs throughout the sample but did not do so. The sample consists of 24,837 product-retailer category-month observations after dropping 7,363 observations due to missing prices or sales data. 11 Table 2 presents descriptive statistics of the sample organized by product category. Price levels and dispersion for products subject to the open price regulation (Panel A) are comparable to those products not subject to it (Panel B). Average prices range from $5.53 for ramen to $13.80 for products in the snack category. Average prices were $5.73 for products in the yogurt category, and $10.55 for products in the cereal category. Price dispersion is higher for categories under the policy. The coefficient of variation ranges from 23 to 45 percent for products under the policy and 14 to 21 percent for products not under the policy. Table 3 describes the market structure for each product category. There are between two and five producers in each category, and prices within each category are similar across products and producers. The cereal and yogurt markets are more concentrated than categories affected by the open price regulation. In both categories, two firms control more than 96 percent of the market, as compared to the market share of the two largest firms for categories subject to the regulation, which range from 62 to 86 percent. Also note that these manufacturers operate across multiple food categories, and in some cases the same manufacturer is subject to the regulation in one category but not another. For example, Lotte produces biscuits, ice cream, and snacks; Nongsim produces ramen, snacks, and cereal; and Bingrae produces yogurt and ice cream. Because cereal and yogurt belong to the processed foods industry and have similar prices and market structure, they may 11 A product is a brand, and for each brand there could be several different SKUs (for instance packages of different size). The price of a product is a sales-weighted average across all that product s SKUs. 6

7 constitute a suitable control group. Table 4 presents the market shares and average unit prices of the five retailer categories in each product category. For products under the policy, the market share of each retailer category ranges from 10 to 40 percent with the exception of ice cream category, where the market share of corner shops is approximately 80 percent. The average unit price is lowest at hypermarkets in five out of six product categories and highest at convenience stores in all product categories. In addition, using the MCIE press release of October 2011 described in the previous section, we identify products under the regulation that included an MSRP after the ban was lifted and which products in the same categories did not reinstate MSRPs. Table 5 presents the number of products in each product category that reinstated MSRPs after the reversal of the ban. This information was released by the MCIE on October 18, 2011, two and a half months after the ban was lifted, and indicates producers intentions to include MSRPs on products. By this date, producers reinstated MSRPs on 12 out of 49 biscuit and pie products, 18 out of 94 ice cream products, and 9 out of 40 snack products. The MCIE did not provide information on ramen products. We consider products that did not reinstate MSRPs as a control group to estimate the effect of the lift of the ban (see Table A-2 for a list of products). We complement this data with the monthly producer price index (PPI) from the Bank of Korea for each of the 6 product categories Empirical Analysis Manufacturers have the option but not the obligation to include MSRPs on their products, and the variation we observe is in their ability to exercise this option. We are interested in the price effect of MSRPs when they are used, 13 and estimate this using two approaches. First, we focus only on products under the policy, exploiting within product price variation across periods in which MSRPs were and were not used. Due to the the unusual nature of the policies, in which the ban was imposed and then lifted one year later, we are able to control for seasonal effects and a secular time trend without defining any control group. Second, to individually estimate the effects of removing MSRPs and the effects of adding MSRPs, we employ a differencein-differences methodology first for the ban and then for the lift a year later. For the estimate of the ban, the econometric specification exploits the differential impact on products under the policy with comparable products that were not included in the regulation. For estimates of the ban s reversal, we consider both the products not under the policy along with products for which MSRPs were not reinstated as control groups. 12 PPIs for pie and frozen dessert products are separately available from biscuit and ice cream products, respectively. 13 Alternatively one may investigate the effect of the option to use MSRPs, which averages over those that do and do not have this option. 7

8 The Effect of MSRPs We begin by estimating the effect of the presence of MSRPs on prices, imposing that the effects of adding and removing MSRPs are symmetric. We use observations of all periods from January 2010 to January 2012 for the products in categories under the policy change, that is, biscuit and pie, ice cream, ramen, and snack. Our observations are for product i at time (month) t and for a given retailer category; however, for notational simplicity we omit the retailer index in the following specification: ln P it = α + δmsrp it + x it λ + u it, (1) where the dependent variable ln P it is the log of unit price. A dummy MSRP it is equal to 1 if MSRP is used for product i at time t. The parameter δ measures the effect of MSRP on price. The x it vector includes PPI and its lag PPI 1 for each product category, which capture supply shocks from input prices, a linear and square time index, and a set of controls for retailer category, month, and product. Lastly, u it are product, month, and retailer-specific errors. 14 The specification in model (1) essentially compares prices for the same product between periods in which MSRPs were and were not used. A typical concern in this type of specification is that the difference in prices before and after a policy can be due to other factors, such as a secular time trend or seasonality. However, the unique nature of these particular policies allows us to alleviate both concerns. Because the ban was in place for a full calendar year we can control for seasonality with month fixed effects, and because MSRPs were present early and late in the sample but not in the middle, the effects of an upward or downward trend in prices can be controlled for separately from the effect of MSRPs. Estimation results in the first column of Table 6 suggest that MSRPs lower prices by 3.6 percent on average. The effect of MSRPs is negative and significant in all five retailer categories, strongest at large sellers such as supermarket chains (7.4 percent) and hypermarkets (3.4 percent) and weaker at smaller sellers such as independent supermarkets (2.7 percent), convenience stores (2.3 percent), and corner shops (1.9 percent). These findings indicate that MSRPs reduce prices. An important identifying assumption in model (1) is that the removal and reinstatement of MSRPs have symmetric effects. In principle, the result that MSRPs reduce prices could be driven only by the ban or only by the lift, and to validate our findings we wish to estimate the effect of MSRPs first using the ban and then the lift separately. As per the preceding discussion, in studying only one policy at a time we cannot identify the effect of MSRPs by focusing only on the products affected by the policy, and instead we introduce additional products as a control group for a difference-in-differences specification. 14 To alleviate concerns about the serial correlation of u it, we follow Bertrand, Duflo, and Mullainathan (2004) by estimating the model with clustered robust standard errors, considering a product in each regime (that is, pre-ban, during the ban, and after-reversal) as a cluster allowing an unrestricted variance-covariance structure (see also Donald and Lang, 2007). 8

9 MSRP Ban To estimate the effect of MSRPs using only the ban we employ a difference-in-differences approach. The treatment group includes the products under the open price regulation policy: those in the biscuit and pie, ice cream, ramen, and snack categories. 15 The control group includes products in the cereal and yogurt categories, which did not use MSRPs prior to the ban and were not included in the ban. To determine whether the control group is suitable, we first study product characteristics as described in Table 2. While the products in the control group have lower overall sales than those in the treatment group, average unit prices, price dispersion, and the number of producers are comparable across the two categories. In addition, to check for a pre-existing trend in the relative prices of the two groups, we run the following specification ln P it = α + t δ t t T i + x it λ + u it, (2) in which T i is an indicator of whether product i is in the treatment group, t is an indicator for each month from January to December 2010, and x it is a set of controls that includes PPI and PPI 1 for each product category and fixed effects for retailer category, month, and product. Each δ t captures the effect of being in the treatment group at time t, and the estimates are reported in Figure A-2. There appears to be no visible relative trend between the treatment and control groups prior to the ban except for January Given the similarities of the products in the control and treatment groups and the lack of a relative price trend prior to the policy, we proceed to estimate the effect of MSRPs on prices using the following specification ln P it = α + δafterban t T i + x it λ + u it, (3) in which AfterBan t is an indicator of whether month t occurs after the ban and δ is the estimated effect of MSRPs. We run the analysis for a window of six months before and after, and report the results in Table 7. Overall, MSRPs decrease prices by 2.0 percent. We also show that after the ban of MSRPs, prices rose at larger sellers, by 3.7 percent at supermarket chains and 2.6 percent at hypermarkets, and remained largely unchanged at smaller sellers, with a 0.8 percent increase at independent supermarkets and no significant change at convenience stores and corner shops. To see the effect of the ban over time, we use different time windows around the official date of the ban using the specification in Table 7. Figure 1 presents coefficients of the effect of the ban, starting with a one-month window and increasing the window up to six months before and six months after the ban. For the one-month and two-month windows, the estimated effect of the ban of MSRPs is above 1 percent and statistically significant. For longer time windows, the estimated effects on prices are larger than 2 percent. These findings demonstrate that the sign of the effect 15 Recall that every product in these categories had an MSRP prior to the ban. 9

10 is not driven by the selection of the time window, and suggest that the effect of MSRPs on prices strengthened as time elapsed and consumers and sellers adjusted their behavior. Coefficients and 95% confidence intervals Time window (months) Figure 1: Coefficients of the Effect of the MSRP Ban We also decompose the effect of MSRPs by product category. In Table A-3 we show that prices rose by 1.6 percent for biscuits, 5.4 percent for ice cream, and 5.9 percent for snacks. The effect on the price of ramen is not significant, but the sign and magnitude of the point estimate are consistent with the other findings. Overall, we conclude that the removal of MSRPs due to the ban increased prices, and by an amount similar to that estimated in model (1) using both the ban and the lift. MSRP Ban Reversal In this section we present estimates of the effect of the lift of the MSRP ban. As mentioned above, this policy reversal was announced in June 2011, allowing price recommendations back on products as early as August The reinstatement of MSRPs was optional, but not all manufacturers reintroduced MSRPs on their products. Because we wish to study how MSRPs affect prices when they are used, the treated group includes only products that reinstated. Meanwhile, for the control group we can consider using those products that did not reinstate MSRPs as well as cereal and yogurt products which did not use MSRPs even prior to the ban. For products in the treatment group manufacturers chose to reinstate MSRPs, and before proceeding to the analysis we wish to understand how this choice was made. 16 In particular, why did producers choose not to reinstate MSRPs on some products that had them prior to the ban? One explanation stems from the fact the authority pressured those that reinstated to do so at the old MSRP levels. As we later demonstrate using the KCA data, most prices are lower than the 16 By contrast, every product that had an MSRP prior to the ban had to be treated. 10

11 recommended price, and therefore producers may have been reluctant to reinstate MSRPs at the old levels on products whose prices had risen during the ban. This explanation is supported in Figure A-1, depicting the price trends of products that reinstated and did not reinstate by product category. It can be seen that products that did not reinstate experienced a substantial price increase in the six months leading up to the lift, while the prices for products that reinstated MSRPs grew only modestly. Formally, we consider the following binary dependent variable model: MSRP i = { 1 if x i γ + u i 0 0 if x i γ + u i < 0, where for each product i the vector x i includes the price change during the 6 month period before the lift of the ban, the market share during that period, and product category and producer fixed effects, and u i has a standard logistic distribution. Table A-4 then shows that the price increase between the ban and the lift strongly predicts the reinstatement decisions. Specifically, the likelihood of producer s recommending retail price decreases by 0.8 percent in response to 1 percent point increases in the price change during the 6 months period before the lift of the ban. Meanwhile no factor other than the previous price change predicts reinstatement. The fact that the reinstatement decision is affected by the price trend leading up to the decision complicates the inference one can draw from a difference-in-differences analysis. Consider first products that did not reinstate MSRPs as a control group. Applying the specification in (2) for a window of six month before and after the lift, Figure A-3 confirms that there is a clear downward trend in relative prices prior to the lift, suggesting a downward bias when estimating the effect of the policy. Alternatively, if the control group is cereal and yogurt products which never used MSRPs, Figure A-4 shows no apparent relative trend in prices prior to the lift. However there is still an implicit concern because the treatment group consists of products whose prices increased more slowly than other products in the same category. If the treatment products are simply those that respond more slowly to a category-specific shock, then their prices could rise after the lift due to this latent shock and not the lift itself, and therefore create an upward bias for the estimated effect. With these caveats in mind, we estimate the difference-in-differences specification below ln P it = α + δafterreversal t T i + x it λ + u it. (4) where the indicator T i equals 0 before the reversal on August 2011 and equals 1 afterward, and the effect of MSRPs is captured by δ. Table 8 presents estimates of the policy reversal coefficient when the control group includes (i) only products that reinstated, (ii) only cereal and yogurt, and (iii) products that reinstated and cereal and yogurt. In (i) the estimated effect -3.8 percent, but as discussed above there is concern that this estimate is biased downward and the true effect is weaker. In (ii) the point estimate is positive 1.4 percent but the effect is not significant, and as above there 11

12 is concern that the estimate can be biased upward. Finally, using both products that reinstated and cereal and yogurt as a control group yields a significant negative effect of -2.8 percent, and given the previous two results, this is due to the fact that there are many more products that did not reinstate than in the cereal and yogurt category, thus the average effect is weighted toward the former group. Overall, we conclude that due to the issues in finding a suitable control group it is difficult to identify the effect of MSRPs using only the lift of the ban. We do not find evidence to contradict the earlier results that MSRPs reduce prices, but also do not find strong evidence in support. 5 Robustness In this section we run two additional analyses to check the validity of the results found in the main section. First, rather than selecting a control group and arguing its suitability, we instead use synthetic control methods to create a control group and then run the analysis. Then, we use a separate smaller data set collected by the Korea Consumer Agency (KCA) and run the main analysis from the preceding section. Synthetic Control Method Given the difficulty in finding a proper control group for the estimation of the effect of the lift of the ban, we apply the synthetic control method (Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010)). Specifically, we construct a synthetic control group and compare its price change with the average price change of products in the treatment group. To do this, we first compute normalized prices by dividing prices by the price in the first month of the 6 month window for both the pre-ban and the pre-reversal periods. Next, for each month we average normalized prices of products in the treatment group. Finally, we find the convex combination of products in the control group that reproduces the average normalized price path for the treatment group before the policy change. This is our synthetic control group. The left panel of Figure 2 shows that in the six months after the ban the price of the treatment group rises by approximately 3.5 percent and the price of the control group falls by approximately 2.5 percent. The right panel shows that the two groups have a similar price trend for the first two months after the lift of the ban. This can be attributed to the fact that producers did not immediately include MSRPs on all products in the treatment group. However, by January of 2012, six months after the lift of the ban, the price of the treatment group increases approximately by 1.5 percent and the price of the synthetic control group rises by more than 6.5 percent. Figure A-5 presents similar patterns for each product category. Estimates from the ban and the lift are consistent with MSRPs reducing prices by a magnitude similar to that found in the original analysis. 12

13 MSRP Ban MSRP Ban Reversal Jan 2010 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Feb 2011 Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 2012 Normalized Unit Price Normalized Unit Price Treatment Group Synthetic Control Group Figure 2: Trends in Price: Treatment Group versus Synthetic Control Group KCA data Starting from March 2010, KCA collected weekly unit prices from 170 stores, with the set of products described in Table 9. The data is skewed towards large sellers, with approximately 90 percent of observations from stores of 10 big retail chains. 17 There are 72 products in total, 25 are under the policy and 47 are not under the policy, the latter group including 21 processed grain products and 26 dairy products. 18 Among the 25 products under the policy, 10 products reinstated MSRPs after the lift of the ban, 9 products did not reinstate, and information is not available for the remaining 6 products. Average unit prices in the KCA data are slightly different from those in the Nielsen data. For instance, the average unit price of the ramen products, $4.2, is lower than that of the Nielsen data, $5.53. This may be due to the fact that KCA collects prices mostly from stores of large retailer chains, in which prices are usually lower than in other smaller independent stores, or simply a function of the KCA data containing fewer products. Using the KCA data we run the three main analyses outlined in Section 4. First, as in (1), we exploit within product price variation across the entire time from before the ban to after the lift, using all 25 products under the policy. 19 Next, for the effect of only the MSRP ban we use the 25 products under the policy as the treatment group and the following three control groups, one by one: (i) processed grain products, (ii) processed grain products and dairy products (i.e. all products not under the policy), (iii) cereal and yogurt only. 20 Lastly, for the estimation of the effect of the lift of the ban, we use the 10 products that reinstated MSRPs after the lift of the ban 17 According to Table 1, large sellers like hypermarkets and chain supermarkets account for roughly percent of total retail space, and a similar percentage of total retail space devoted to food items. 18 By contrast the Nielsen data includes 253 total products, with 228 under the policy and 25 not under the policy, and the two data sets share 20 products under the policy and 8 products not under the policy. 19 Retailer category fixed effects in model (1) are replaced with store fixed effects. 20 Retailer category and month fixed effects in model (3) are replaced with store and week fixed effects, respectively. 13

14 as the treatment group and four possible control groups: each of the three control groups used for the ban and the 9 products that did not reinstate MSRPs. Table 10 reports robustness estimates of the effect of the ban and the lift of the ban on prices for different control group specifications. The estimated effect of the ban ranges from a price increase of 1.5 to 1.8 percent, consistent with the estimated effect of a 2.2 percent increase found using the Nielsen data. The estimated effect of the lift is insignificant when products that did not reinstate or all products not under the policy are the control group, is -2.5 percent if the control group is only processed grain products, and is -3.1 percent if the control group is only cereal and yogurt. The estimates thus suggest that the lift of the ban decreased prices, but this is subject to the previous concerns about the suitability of control groups. These results provide additional support for our main findings that MSRPs decrease prices. Endogeneity A potential concern in the analysis of the lift of the ban since the use of MSRPs by producers may be related to unobserved factors that might in turn affect prices. To alleviate this concern we present estimates of a bias-corrected matching estimator in the appendix. The estimates provide evidence that the lift of the ban decreases prices. The lift of the ban causes prices to drop by 3.2 percent on average and by 3.4 percent when restricted only to the products in the treatment group. Across different retailer categories, average treatment effect estimates range from -0.8 to -6.0 percent. 6 The Mechanism behind MSRPs We have shown evidence that MSRPs lower retail prices, but what is the mechanism by which this happens? Here we consider three explanations proposed in the literature: that MSRPs are a way for a manufacturer to communicate with retailers, that MSRPs act as binding price ceilings for retailers, and that MSRPs are a way for a manufacturer to communicate with consumers. Buehler and Gartner (2013) proposes that a manufacturer uses MSRPs to convey information about demand and costs to a retailer. The authors demonstrate that such communication is credible in an infinitely repeated game because there exists an equilibrium in which both firms have a shared objective of maximizing joint profits. A ban of MSRPs cuts this communication channel, but the ensuing effect on prices is unclear. First, printing recommendations on product packaging is one of many ways for a manufacturer to communicate with its retailers, and it is possible that communication continues but simply takes a different form. Second, even if the amount of information communicated to retailers is reduced, it is not clear whether retailers would set higher or lower prices when facing this additional uncertainty about market conditions. It is thus difficult to say that the theory of MSRPs as informative signals to retailers explains our empirical results. It may instead be that manufacturers use recommendations to control retailers rather than 14

15 inform them. Although price recommendations do not bind in name, they may bind in practice if, for instance, a manufacturer punishes retailers for exceeding the recommended price by withholding the product in the future or terminating the relationship altogether. In this case a recommendation acts as a binding price ceiling, lowering retail prices as our results suggest. To find further support for this theory we take a closer look at the distributions of prices using the KCA data. 21 Density Density Price / MSRP Price / MSRP (a) Six Months Before the Ban (b) Six Months After the Ban Density Price / MSRP (c) Six Months After the Ban Reversal Figure 3: Relative Price Distributions of Products under the MSRP Regulation Figure 3 depicts distributions of prices for products that had used MSRPs prior to the ban, taken over three different periods: six months prior to the ban, six months after the ban beginning, and then six months after the reversal. The figures lead us to question whether MSRPs acted as binding ceilings. First, note that at each of the three times most prices are far below the MSRP, thus the recommended price is not a binding constraint for a vast majority of retailers. Across the three snapshots, the proportion of prices more than 10 percent below MSRP is 96 percent prior to the ban, 97 percent during the ban, and 98 percent after the reversal. Second, there is no substantial increase in the number of stores charging prices above the recommendation after the ban, and no substantial decrease after the ban reversal. Thus, it seems that the MSRPs do not bind even for retailers that set prices near the recommendation. We conclude that it is not likely that MSRPs acted as price ceilings. Alternatively, recommendations may be aimed at consumers rather than retailers, explaining 21 The Nielsen data set is not suited for this exercise while it covers a comprehensive sample of products, for any particular product it only provides the average sales-weighted price across retailers and not the distribution of prices at the store level. 15

16 why MSRPs are public, visibly printed on product packaging, and often promoted through costly advertising. One theory is that MSRPs simply remind consumers to consider whether the current price is a good deal, or whether it is best to buy this product elsewhere or to buy a substitute product. When the proportion of consumers that shop for the best deal increases when an MSRP is present, then as in Varian (1980) prices will decrease. If indeed MSRPs nudge consumers into searching, then an implication is that in the presence of MSRPs consumers take better advantage of available savings. To verify whether this is the case we estimate the following model: Share irt = α + βmarkup irt + δmsrp it Markup irt + ψ i + ψ r + u irt, where subscripts i, r, and t represent product, retailer category, and time (month), respectively. The dependent variable Share irt is the quantity share of retailer category r for product i at time t. The variable Markup irt denotes the difference between price charged at retailer category r and the lowest price charged across all retailer categories for product i and time t, and is the maximal available savings for a consumer about to purchase from r. ψ i and ψ r are product and retailer category fixed effects, respectively. The variable δ measures for a given markup the change in market share when MSRPs are present. The conjecture that MSRPs nudge more consumers into searching implies that δ < 0, however as shown in Table A-5 in the Appendix, δ is not significant. Put differently, the proportion of sales at retailers that charge higher prices than their competitors does not fall when MSRPs are present. Therefore, we do not find support for MSRPs nudging consumers into search. Instead of serving as a reminder to search, Puppe and Rosenkranz (2011) and Lubensky (2017) propose that an MSRP can provide consumers with a benchmark for the purchase decision. In Puppe and Rosenkranz (2011) the recommended price induces a kink in demand (not necessarily at the recommended price) above which the probability of purchase falls off disproportionately, while in Lubensky (2017) the recommendation is an informative cheap-talk signal that helps consumers determine whether the current price is a good deal relative to other sellers. In either case the presence of an MSRP imposes downward pressure on prices because retailers avoid prices above the benchmark. The sharpest prediction of the benchmark theories is a kinked demand curve in the presence of an MSRP and no kink in its absence. Our data does not allow for this test directly the Nielsen data has prices and quantities but only at the aggregate retailer-category level, while the KCA data has store-level prices but no quantities. However, one piece of indirect evidence is the manufacturers reluctance to reinstate MSRPs at the old levels for products whose prices had risen substantially during the period of the ban. If consumers expect a discount below MSRP as a benchmark, then sellers would lose these consumers by reinstating the same MSRP at the now higher prices. Furthermore, we conjecture that a consumer is more likely to pay attention to the MSRP if they are otherwise more price sensitive. If this is the case then sellers whose customers are on average more price sensitive should drop prices by more in the presence of MSRPs. Indeed this is what we observe in the data large sellers such as chain supermarkets and hypermarkets that charge the 16

17 lowest prices for most products (Table 4) are likely to have a more price-sensitive customer base, and it is at exactly these sellers that prices drop the most in the presence of MSRPs (Tables 6 and 7). To conclude, MSRPs are likely aimed at consumers rather than retailers. From the fact that most prices are substantially below the recommended price and that manufacturers were reluctant to reinstate MSRPs at the old levels for products whose prices rose substantially during the ban period, we infer that consumers may use an expected discount below the MSRP as a reference point. In addition, the higher sensitivity to the presence of MSRPs by low-priced sellers suggests that price-sensitive consumers are more likely to incorporate MSRPs in their purchasing decisions. 7 Conclusion The use of manufacturer suggested retail prices is ubiquitous but poorly understood. In this paper we exploit a natural experiment in which a ban of MSRPs was initially imposed and then reversed a year later. Using products that used MSRPs whenever the opportunity was available, we show that average prices are lowered by 3.6 percent when MSRPs are used. We then estimate the effects of MSRPs separately for the ban and the lift using various control groups, and also perform the analysis on an auxiliary data set. Overall the results appear robust across the different specifications. In estimating the effect we help address an existing puzzle of how, if at all, MSRPs impact actual prices. We find no evidence that MSRPs act as a binding restraint like a price ceiling, and instead find suggestive evidence that MSRPs affect prices indirectly by altering consumer behavior. Although consumers are not any more likely to take advantage of low prices when MSRPs are present, the fact that manufacturers were reluctant to reinstate old MRSPs for products whose prices had risen during the ban suggests that they expect consumers use MSRPs for guidance on whether prices are too high. In addition, that MSRPs lower prices the most at the lowest priced sellers suggests that price sensitivity and MSRP sensitivity are positively correlated for consumers. Beyond the effects of recommended prices, our results also shed light on the motives of the manufacturer in using vertical instruments to affect downstream prices. Competing theories predict that the manufacturer may either want to increase or decrease downstream prices, and empirical evidence is sparse due to the endogeneity of the use of these instruments. In this paper the policies of the ban followed by the lift provide the rare opportunity to observe plausibly exogenous variation in the use of MSRPs and to identify the effects of the policy separately from that of time. In finding that MSRPs reduced prices we find evidence that a manufacturer s motives for affecting downstream prices could be pro-competitive. 17