Creating and Assessing Candidate Food Service and Retail Beef Demand Indices. Prepared for the Cattlemen s Beef Board

Transcription

1 Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Prepared for the Cattlemen s Beef Board Glynn T. Tonsor Kansas State University and Ted. C. Schroeder Kansas State University February 3, 2017 Acknowledgements: We gratefully acknowledge the Cattlemen s Beef Board for providing funding to support this project. Thanks to Courtney Kalous, Cattlemen s Beef Board, for coordinating this study. We also thank Alison Krebs, National Cattlemen s Beef Association, for assisting in providing data and information as requested. We further appreciate the Technomic, Inc. and Information Resources, Inc. employees who took time to visit with us and provide valuable information. All opinions presented in this study are solely those of the authors.

2 Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Glynn T. Tonsor and Ted. C. Schroeder Table of Contents I. Situation and Project Objectives... 4 Figure 1. Existing All-Fresh Beef, Disappearance Based Index... 4 Figure 2. Position of Examined New Food Service and Retail Indices... 5 II. Procedural Overview... 6 III. Food Service Indices... 6 Figure 3. Synthesis of Core Data for a Potential New Food Service, Beef Index... 8 Figure 4. Share of Commercial Beef Production, Food Service Beef Volume... 9 Figure 5. Food Service Total Beef Demand Index (2003=100) Figure 6. Food Service Ground Beef Per Capita Volume and Real Prices Figure 7. Food Service Steak Per Capita Volume and Real Prices Figure 8. Food Service Other Beef Per Capita Volume and Real Prices Figure 9. Food Service Ground Beef, Steak, and Other Beef Demand Indices (2007=100) Figure 10. Stock Prices of Publically Traded Hamburger Serving Companies Figure 11. Stock Prices of Publically Traded Pizza Companies Figure 12. Stock Prices of Publically Traded Steakhouse Companies IV. Retail Indices Table 1. Key Holiday Effects in IRI Data Table 2. Summary Statistics of Grocery-Store, US Class-Level Non Seasonally-Adjusted Beef Demand Index: Jan-2011 to Nov Table 3. OLS Results Grocery-Store, US Class-Level Non Seasonally-Adjusted Beef Demand Index, Jan to Nov Figure 13. Grocery-Store, US Class-Level, Non Seasonally-Adjusted Beef Demand Index Table 4. Summary Statistics of Grocery-Store, US Category-Level Non Seasonally-Adjusted Beef Demand Indices: Jan-2011 to Nov Figure 14. Grocery-Store, US Category-Level, Non Seasonally-Adjusted Beef Demand Indices Table 5. OLS Results Grocery-Store, US Category-Level Non Seasonally-Adjusted Beef Demand Indices, Jan to Nov Table 6. Summary Statistics of Grocery-Store, US SubCategory-Level Non Seasonally-Adjusted Ground Beef Demand Indices: Jan-2011 to Nov Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 2

3 Figure 15. Grocery-Store, US SubCategory-Level, Non-Seasonally-Adjusted Ground Beef Demand Indices Regional Assessment Figure 16. IRI Regions Table 7. Summary Statistics of Grocery-Store, Regional Class-Level Non Seasonally-Adjusted Beef Demand Indices: Jan-2011 to Nov Figure 17. Grocery-Store, Regional Class-Level, Non Seasonally-Adjusted Beef Demand Indices Table 8. OLS Results Grocery-Store, Regional Class-Level Non Seasonally-Adjusted Beef Demand Indices, Jan to Nov V. Comparison of Retail Price Data Figure 18. Comparison of Retail Price Series Spreads, Jan Nov Figure 19. Comparison of Retail Price Series Ratios, Jan Nov VI. Recommendations VII. Appendix Grocery-Store, Non Seasonally-Adjusted Demand Index Values Grocery-Store, Seasonally-Adjusted Demand Index Values Index Calculation and Process Synthesis Class-Level Demand Indices of Competing Meats Table 9. Grocery-Store, US Class, Category, and SubCategory Non Seasonally-Adjusted Demand Index Values: Jan-2011 to Nov Table 9. Grocery-Store, US Class, Category, and SubCategory Non Seasonally-Adjusted Demand Index Values: Jan-2011 to Nov-2016, continued Table 10. Grocery-Store, US Class, Category, and SubCategory Seasonally-Adjusted Demand Index Values: Jan-2011 to Nov-2016, continued Figure 20. Grocery-Store, US Class-Level, Non-Seasonally-Adjusted Chicken and Pork Demand Indices References Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 3

4 I. Situation and Project Objectives Beef demand is critical to understand and monitor because it directly influences overall beef industry prosperity. As the industry has transitioned from historically tight supplies to herd expansion, the need for deeper understanding and monitoring of beef demand has become clear (Tonsor and Schroeder, 2015). This situation underlies the substantial economic value in accurate and informative beef demand indicators. 1 One way to synthesize beef demand is through construction of an index that measures and tracks changes in demand over time. 2 An index is appealing because it provides an easy to understand, single-measure indicator of beef demand change over time. A demand index can be created by inferring the price one would expect to observe if demand was unchanged with that experienced in a base year (Tonsor, 2010). The inferred constant-demand price is compared to the beef price actually transpiring in the marketplace to indicate changes in underlying demand. If the realized beef price is higher (lower) than what is expected if demand were constant, economists say demand has increased (decreased) by the percentage difference detected. Applying this approach to publically available annual USDA aggregate beef disappearance and BLS retail price data provides information such as contained in Figure 1 indicating notable demand growth between 2010 and 2015 based upon existing indices currently maintained at Kansas State University. Figure 1. Existing All-Fresh Beef, Disappearance Based Index 1 Separate resources are available outlining demand concepts for interested readers (Schroeder, Tonsor, and Mintert, 2013). 2 The Appendix contains additional detail and documentation of how demand indices are calculated. Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 4

5 Despite the usefulness of existing demand indices for monitoring demand strength, they suffer from a number of important limitations such as: 1) use of total beef disappearance volume aggregated across food service and retail market channels, 2) use of simple average in-store product labeled retail prices rather than volume-adjusted, actual grocery-store transaction prices, 3) one-quarter delays in demand index updating given frequency available data is reported, and 4) they reveal nothing about heterogeneity in demand response across market channel, US region, or product type. Having beef demand indices that measure actual consumer purchases by market channel and use volume-weighted prices actually paid by consumers are more accurate and precise than existing beef demand indexes. Furthermore, having indexes that are available to update more frequently and can be dissected by product or market region can be especially informative about where demand is changing most. Armed with such information, the beef industry could better adjust, target, and monitor progress of product marketing strategies. Information available on different market channels and beef product types has evolved immensely since Purcell built the now widely recognized retail beef demand index (Purcell, 1998a). Figure 2 provides a simple depiction of how expanding beef market data availability facilitated more informative beef demand index assessments provided in this project. The three market channels of product movement from packers, processors, and wholesale importers are delineated in Figure 2 as food service, grocery store, and exports. This contrasts with the current disappearance based indices that aggregates per capita volumes across all of these market channels into a single index. The two green boxes in Figure 2 highlight areas of potential new demand indices of focus in this project. First, ongoing shifts towards food-away-from-home consumption and the lack of any corresponding food service specific beef demand indices are addressed by including Technomic data as a source of information on food service operator, wholesale purchasing behavior. Similarly, inclusion of Information Resources, Inc. (IRI) data reflects industry investment in recent years in grocery-store scanner data and the prospect for that information to be used in providing more delineated retail demand indices. Figure 2. Position of Examined New Food Service and Retail Indices Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 5

6 The project s overriding objective was to assess feasibility of developing new food service and grocery-store beef demand indices and to provide industry direct guidance on the merits of these indices for ongoing use. The remainder of this report is organized as follows. A broad overview of the procedures employed is followed by separate sections on development and assessment of beef demand indices specific to the food service and grocery-store market channels. Subsequently recommendations are provided and followed by an Appendix containing additional details. II. Procedural Overview The process followed in this project can be summarized in four main steps: 1. First we utilized available data to build new demand indices. Given data available this was conducted both for each market channel considering beef broadly and by product type (e.g., ground beef specific). Where available, we also considered region-specific indices. 2. Next we assessed the strengths and weaknesses of each candidate index. This included qualitatively assessing whether each index makes sense and quantitatively examining each index with available external data. This second step is essential, though admittedly somewhat ad hoc and subjective. The indexes we are creating here use data that have not been used for this purpose in the past and each has unique weaknesses that might result in an index that is not useful if unreliable. 3. The third step was to draw conclusions on net viability of each index to provide the industry with recommendations regarding which indices to maintain. 4. Lastly, starting with presentations at the Cattle Industry Annual Convention, we will collaborate with CBB staff to widely disseminate findings and implications to industry stakeholders. III. Food Service Indices Development of food service (FS) demand indices followed the well-documented process outlined by Purcell (1998a,b) and currently followed by Tonsor (2016) in construction of the aggregated beef disappearance and retail price demand index discussed above and presented in Figure 1. Throughout this new index development process, multiple data sources were used. Core underlying data comes from reports provided annually since 2003 by Technomic to NCBA, a contractor to the beef checkoff, to reflect the volume and expenditure of beef purchased by the FS sector. Estimates of total volume (MM lbs) purchased by FS operations was used to measure the quantity of beef transacting through this market channel. Technomic s estimate of total FS purchases ($ MM) was used along with total volume to derive an inferred average beef price paid by FS operators. In Technomic data annual numbers reflect the October 1 st September 31 st period and as such comparisons to other annual information should be made realizing this Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 6

7 difference in coverage period. For instance, the 2016 year reflects three-fourths of calendar year 2016 by spanning from October 1, 2015 to September 31, Recognize any demand index built using Technomic s data reflect survey-based FS operator purchases and prices paid for wholesale beef and do not directly measure prices consumers paid or the amount they consumed in food service establishments (Figure 2). While FS operator demand is derived demand reflecting primary demand of patrons visiting FS establishments, having information one-level removed from the consumer should be noted. This is noteworthy because changes in a demand index calculated using Technomic FS purchase data can occur without a change in underlying consumer demand for beef in this channel. For example, changes in FS costs such as labor, transportation, promotion, or other non-beef food ingredients shift FS derived demand even without a change in underlying primary consumer beef demand. The implication is that changes in the FS demand index calculated using data supplied by Technomic occur not only from consumer primary demand. Given base estimates of product flow and nominal prices it is important to account for population and inflation effects over time to avoid confounding those effects in inferences on changes in demand. Total FS volume was used along with estimates of the US resident population from the US Census Bureau to derive per capita FS beef volume. The nominal average price paid by FS operators was deflated using the Food CPI provided by the US Department of Labor, Bureau of Labor Statistics yielding an inflation-adjusted price series. Figure 3 is a scatterplot showing estimates of per capita purchase volumes and wholesale prices for beef purchased by food service establishments over the period. A preliminary assessment of Figure 3 suggests that an aggregate beef index specific to the food service channel may be reasonable to use on an annual basis. The individual points on the scatterplot fall on multiple demand curves that shift around over time. However, some rough indications of potential usefulness of these data for demand index construction can be derived from this Figure. The data in the scatterplot reflect the expected downward sloping relationship between volume and prices. To highlight demand concepts evident even before a formal index is created, note Figure 3 suggests food service beef demand declined in 2008, 2009, and 2015 as BOTH volume and price declined in those years relative to the prior year. Conversely, both volumes and prices increased in 2004, 2006, and 2012 signaling years of demand growth. Construction of a demand index using these price/quantity changes over time can reveal specific demand shift magnitudes for each year. Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 7

8 Figure 3. Synthesis of Core Data for a Potential New Food Service, Beef Index To further assess the underlying raw data from Technomic, we identified the portion of total commercial beef production represented by the reported food service volume estimates. As shown in Figure 4, this calculation reveals Technomic estimates suggests a substantial increase in the portion of total beef transacting through food service over the period, a pullback and stagnant pattern during and following the Great Recession ( ), and increases since with the notable exception of We are unaware of any additional estimates specific to meat, much less beef, to make corresponding cross-checks against. However, USDA-ERS provides annual estimates of consumer expenditures on food-away-from-home as a share of total food dollars. While this series is only available thru 2014, it is worth noting it has a 0.76 correlation with the share of commercial beef implied by Technomic to transact through food service. Furthermore, this ERS series (Figure 4) also indicates increasing predominance of food away from home over the period, a slight reduction during , and increases over the period. While indirect and aggregated, this cross-check is loosely supportive of the Technomic beef purchase data being consistent with broad trends in the food service sector. Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 8

9 Figure 4. Share of Commercial Beef Production, Food Service Beef Volume To derive demand indices, the constant demand expected inflation-adjusted price is calculated utilizing a flexibility estimate and changes in volume in the current period relative to the prior period. 3 The specific flexibility estimate used is % which was derived from the own-price elasticity estimate of -0.70% provided by USDA ERS. 4 The new FS beef demand index is presented in Figure 5. All index values can be interpreted relative to 2003 as the base year (index=100%). As an example, the 2016 index value of 92 suggests demand declined by 8% between 2003 and In other words, if demand had not changed, real beef prices paid by FS operators for beef would have been 8% higher in 2016 than what they actually paid. The index suggests substantial demand decline occurred over the period. Given corresponding large reductions in cattle prices observed over this period, this type of updated and refined demand insight helps further explain market events. By design the FS beef demand index increases and decrease when it obviously should. That is, when both price and quantity increase (decrease) economists know demand expands (contracts). This property of demand underlies construction of all demand indices in this report. 5 As shown in Figure 3, in years where both price and quantity increased (2004, 2006 and 2012) the FS beef 3 Own-price flexibility measures the percentage change in price given a 1% percentage change in quantity and is approximately the reciprocal of own-price elasticity measures. 4 This estimate is provided in table 6 of this 2012 report: as the unconditional elasticity estimate of beef demand at home. This report contains information on food away from home elasticities but not specific to beef. For related context, the average of limited-service, full-service, and other FAFH own-price elasticities is As a cross-check on data quality all derived indices were coded to flag if any violations of these obvious demand changes occur. In this study, none of the examined indices incurred such violations. Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 9

10 demand index indicates demand growth while in years of price and volume declines (2008, 2009, and 2015) the index contracts. Observing the FS index to expand significantly in 2004 is consistent with the Atkins effect (Tonsor, Mintert, and Schroeder, 2010). The index contracting significantly in 2008 and 2009 is consistent with expectations given the substantial economic recession. It is also useful to compare these results to the current KSU All-Fresh Beef Demand Index (Figure 1). We see consistency in both approaches suggesting year-over-year demand growth in 2004, 2011, 2012, and 2014 and consistency in both indicating demand reductions in 2005, 2008, and We do however see five of the 12 overlapping years of inconsistent demand patterns (2006, 2007, 2010, 2013, and 2015). This is not however a clear indictment as by design the FS index captures data on one specific market channel while the KSU AFBDI is much broader and each year in the FS index aligns with three of four quarters underlying each annual KSU AFBDI estimate. A reasonableness judgment of reliability of this index includes questioning revealed variation. For example, the stark decline in demand from an index value of 121 in 2004 down to 101 in 2005, then huge jump back up in 2006 to 127 raises concern. This is a large enough shift in demand that it begs asking the question of what happened to cause this large of a shift, if it actually occurred? We know for example, from Figure 1 that the aggregate beef disappearance and retail demand index declined from 91 to 88 (base year 1990), but it also went down even further to 85 in 2006, contrasting the FS index. Considering other data, there was a decline in the National Restaurant Association Restaurant Performance Index 6 (RPI) from a monthly high of about down to about 101 during the period and it jumped back up to 102 during 2006, which is a consistent pattern with the Technomic-based FS index. The RPI also dropped all the way down to 97 by 2009, which again corroborates with the FS index in Figure 5 dropping to 98 by Thus, the pattern of change is consistent with RPI. However, the magnitudes of shifts in the FS demand index are not entirely credible. Such dramatic upward and downward shifts in demand year-to-year, as suggested in the FS index, are much larger than we would expect in such a mature national market without any major market disruptions or expansions during the time. 6 Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 10

11 Figure 5. Food Service Total Beef Demand Index (2003=100) Using the same process described above, FS indices specific to ground beef, pre-cut steak, and other beef categories were derived to further examine categorical areas of relative FS beef demand strength. Data for this category-level analysis is only available from Technomic since To derive the other beef index, both volume and purchase estimates were derived by taking the total FS estimates and subtracting out ground beef and steak categories. This results in the other beef category as defined here to capture Roasts, Pre-Cooked Roast Beef, Ribs, and Other Beef categories as identified by Technomic. It is useful to note the general price and volume patterns of these three categories which are captured in Figures 6-8. Figure 6 suggests food service ground beef demand clearly declined in 2013, 2015, and 2016 while demand increased in The reduction in per capita ground beef volumes stands out given aggregate beef production increases that occurred in the October September 2016 period. Figure 7 indicates steak demand increased in 2010 and 2012 but declined in Figure 8 indicates other beef demand increased in yet declined in Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 11

12 Figure 6. Food Service Ground Beef Per Capita Volume and Real Prices Figure 7. Food Service Steak Per Capita Volume and Real Prices Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 12

13 Figure 8. Food Service Other Beef Per Capita Volume and Real Prices On average over the period, ground beef, steak, and other beef comprise 64%, 14%, and 22% of the total volume and 36%, 36%, and 28% of the total value, respectively. The relatively large volume share held by ground beef warrants appreciation as this category will particularly have a larger impact on the aggregated, total FS beef demand index. Figure 9 presents the resulting product-category level FS indices. Figure 9. Food Service Ground Beef, Steak, and Other Beef Demand Indices (2007=100) Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 13

14 The suggested substantial increase in ground beef demand is consistent with well-documented growth in gourmet or made-to-order burger offering establishments (In-and-Out Burger, Five Guys Burger, Fries, Smashburger, etc.). Three publically traded burger companies that offer stock prices can be used for additional assessment: Red Robin Gourmet Burger (RRGB), Habit Burger (HABT), and Shake Shack (SHAK). RRGB has been trading since While comparing any demand index, particularly given our limited number of annual observations, to a given company s stock can easily be misleading, we note annual returns on RRGB have a 0.37 correlation with the FS Ground Beef Demand Index. While HABT has only been trading since November 2014, shares have fallen from about $30/share in early 2015 to the $14-$17 range since October of Similarly, SHAK has only been trading since February SHAK shares initially increased to a peak of $90 in May of 2015, yet similar to HABT they proceeded to decline to about $32 in October of Both SHAK and HABT have increased since October 1 st but that period would lie outside of the 2016 period covered by Technomic and will appear in their 2017 estimates. Since stock market comparisons for these gourmet burger establishments are limited to recent years, we proceeded to also look to lower-priced burger offering restaurants including McDonald s, Wendy s, Jack in the Box, and Sonic (Figure 10) and major pizza companies including Yum! Brands, Inc. (includes Pizza Hut), Papa John s, and Domino s (Figure 11). Overall these stock prices increased between 2008 and 2014 similar to the FS Ground Beef Demand Index. On balance, the pattern of these stocks is generally consistent with the FS Ground Beef Demand index over the period. However, reconciling differences in stock price and FS Ground Beef Demand index patterns in is challenging. Figure 10. Stock Prices of Publically Traded Hamburger Serving Companies Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 14

15 Figure 11. Stock Prices of Publically Traded Pizza Companies A similar cross-check can be made of the FS Steak Demand Index. First, the substantial demand reduction suggested for 2008 and 2009 is consistent with expectations as more expensive items are anticipated to experience larger demand hits in a recession. While steak demand is suggested to have improved over the period, what is worrisome is indication of substantial ongoing demand decline since Based on our reasonableness judgment, we simply do not believe the FS steak demand index. We do not believe FS steak demand has declined over 40% over the time frame. We cannot discern the apparent reason for this observed decline, but we suspect variation in data collection and inclusion or exclusion of participating companies or products is part of the problem. The stock prices of four publically traded major steak restaurant companies [Ruth s Hospitality Group, Inc. (RUTH, Ruth s Chris Steakhouse), Bloomin Brands, Inc. (BLMN, Outback Steakhouse), Darden Restaurants (DRI, LongHorn Steakhouse) 7, and Texas Roadhouse, Inc. (TXRH)] are presented in Figure 12 starting in October of 2006 to match the period covered by the FS Steak Demand Index. 7 Darden Restaurants and Bloomin Brands, Inc. have a broader portfolio of brands and arguably are less tied to beef steak than Ruth s Hospitality Group, Inc. and Texas Roadhouse, Inc. Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 15

16 Figure 12. Stock Prices of Publically Traded Steakhouse Companies The significant declines in RUTH, DRI, and TXRH between October 2006 and October 2008 is consistent with the FS Steak Demand Index declining from 2007 to However, the FS Steak Demand Index continuing to declining into 2009 is inconsistent with these stock prices. Moreover, post-recession each stock price proceeded to increase substantially thru October of 2015 yet the FS Steak Demand Index peaked out in Our understanding is that Technomic employs a build up approach rather than a random sampling of food service operations. It also appears Technomic purposely varies over time whether certain industry segments are included in their data collection effort. 8 While Technomic certainly would prefer to retain consistency in the representativeness of firms captured, we are concerned about how entry and exit of firms (or entire industry segments) from their data collection impacts any demand index built from Technomic s annual estimates. For demonstrative purposes consider the case of a set of firms being omitted that have similar price sensitivity to firms retained in a prior annual report. In this situation our indices would indicate a year-over-year demand decline (reflecting reduction in the volume estimate without a price change) that would not be accurate. These concerns elevate as finer-level assessments are attempted because they illuminate single product variation. As an example, we are suspicious that the extent of inconsistencies in the steak demand index and relevant company stock prices is partial evidence of this. Though some of this unexplained noise in the data and indexes might dissipate through aggregation to the FS Beef Demand index, we caution that aggregation is not a cure all for data problems in this situation. For example, if restaurant sampling of firms entering 8 As an example, the 2014 report notes segments of Bars & Taverns, Recreation, Military, Corrections, and Continuous Care Retirement Centers were not surveyed for this 2014 research wave. Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 16

17 or dropping from the sample is part of the problem with using these data to develop demand indexes, as we suspect it is, this will not be resolved through product aggregation. Another concern we have is that price information provided by Technomic is more of a snapshot for a given period, roughly the current September price each year, rather than a volume-weighted representative average for the year. This is an issue as annual volumes are merged with a snapshot price to provide the annual reports our analysis builds from. When prices are more variable, as has been the case for several recent years, this situation may not accurately reflect price sensitivity of the FS segment. Furthermore, with known seasonality in both beef demand and supply, using only the September price each year to value beef volume purchased throughout the year is a substantial weakness of the Technomic data for building demand indexes. Combined, the Technomic data has the advantage, compared for instance to current KSU demand indices that rely on total per capita disappearance and BLS prices, in representing activity in the food service channel. We are aware of no other sources of this information. This itself underlies the potential value of retaining new food service demand indices using Technomic data and more broadly the value these Technomic reports may provide the beef industry in other ways. An argument could be made that while not perfect any additional information on the food service segment is better than the current situation offered by existing indices (e.g., KSU All-Fresh Beef Demand). That said, if food service demand indices built and explored here are maintained going forward, we would particularly encourage a focus on more aggregated (FS Beef Demand) indices. We also stress we would not recommend use of the FS Beef Demand index in isolation. That is, in addition to the FS Index we recommend simultaneously monitoring other restaurant performance measures such as stock prices, RPI, and USDA purchase shares tandem to this index to repeatedly assess reasonableness. IV. Retail Indices Three separate data sets are regularly procured from IRI with grocery-store, retail level price and quantity information with substantial additional data processing by Meat Solutions, Inc. (VMMEAT System). 9 Retail Meat Class data provides estimates of total dollars, pounds, and average retail prices for beef in aggregate for the retail (grocery-store) segment. This data is used here to build Grocery-Store Beef Demand indices. Furthermore, class-level (e.g. species delineated) data was available for chicken and pork. While demand indices for these competing meats were not the focus of this project, as one method of assessing overall data quality we built these indices and include them in the Appendix. Category Total US and Region Report Whole Muscle data provides estimates of total dollars and pounds for beef by muscle or perhaps more accurately described as by primal. Five categories (Ground, Loin, Round, Chuck, and Rib) with 5% or more of total value or pounds 9 For industry simplicity we refer to this IRI data as grocery-store data. Strictly speaking, this MULO or multioutlet data capturing additional retail outlets. Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 17

18 collectively comprise over 85% of total value and pounds in the IRI data. Accordingly, we built five new category-level indices. Ground Beef Category data provides estimates of total dollars, pounds, and average retail prices separated across sub-categories of the broader ground beef category. Given this finer dissection of ground beef grocery-store channel information, and the explicit interest in this segment noted in the call for project proposals, we explored ground beef sub-category specific indices to compare with insights offered by more aggregated indices. IRI data is available starting in January of 2011 for the country in aggregate as well as for eight separate regions. 10 Accordingly, the feasibility of national and region-specific indices was explored. IRI annually issues restatements of the firms covered in their data resulting in roughly five- or six-year periods of IRI data representing a consistent set of retail firms. On one hand the consistency in firm representation is highly desirable; on the other hand a main implication is that indices for only the most recent five or six years are viable. 11 Core procedural steps involved in building grocery-store indices include: 1. The initial year, currently 2011, is identified as this changes with each IRI re-statement. 2. Population and CPI information is used to generate per capita and inflation-adjusted estimates. 3. Final indices are built to not signal demand changes simply because of underlying differences in days embedded in raw IRI data. a. Adjustments are made to put everything on a days/month (~30-day month) basis. This reflects one leap year every four years. b. Original IRI data contains 5 weeks of data for March, June, September, and December and 4 weeks for all other months. 4. Comparing months of IRI data over years can result in key holidays falling in different months (Table 1). As an example, Easter falls in March in 2016 yet in April in 2015 and In this example, a holiday loss effect would apply in April of 2016 (since the prior year contained a holiday) while a holiday gain would apply in March of a. Rather than attempting to back out these effects in an ad hoc way, interpretation of index changes should be mindful of holiday effects. 5. A beef own-price elasticity estimate of (Taylor and Tonsor, 2013) is used as it was derived from an earlier period using IRI data. 10 Earlier data omits Wal-Mart information which reflects a substantial change in overall industry representativeness. 11 Internally we considered scaling data from earlier years to reflect changes in ACV in an effort to merge with more recent observations and build indices using data covering a longer, multi-restatement period. Following discussions with IRI we do not suggest this approach be used going forward due to changes in stores that would underlie such an approach. Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 18

19 6. Two alternative indices are derived varying in how seasonality is addressed. 12 a. One approach follows current KSU and Purcell procedures by making year-over-year comparisons. As an example, data from November 2016 is compared to November of 2015 to infer demand strength. This is a seasonally-adjusted approach in that it compares demand for the same period the year before. Using this approach, the first 12 observations are all 100 by definition. b. The second approach is not seasonally adjusted and makes month-over-month comparisons. As an example, data from November 2016 is compared to October of 2016 to infer demand strength. This approach embeds seasonality such that demand index changes are in-part reflecting seasonal effects. Only the first observation (January 2011 currently) has an index value of 100. i. Throughout this report we present non-seasonally adjusted, month-to-month indices as they provide the most useful near-term assessments of current demand patterns that likely are of highest industry interest. The seasonallyadjusted, year-over-year indices are shown in the appendix. Table 1. Key Holiday Effects in IRI Data Key Holidays Year Easter Memorial Day Independence Day Labor Day Thanksgiving Christmas /24 5/30 7/4 9/5 11/24 12/ /8 5/28 7/4 9/3 11/22 12/ /31 5/27 7/4 9/2 11/28 12/ /20 5/26 7/4 9/1 11/27 12/ /5 5/25 7/4 9/7 11/26 12/ /27 5/30 7/4 9/5 11/24 12/ /16 5/29 7/4 9/4 11/23 12/25 IRI Data Month Including Each Holiday Year Easter Memorial Day Independence Day Labor Day Thanksgiving Christmas 2011 April June July September November December 2012 April June July September November December 2013 March June July September December December 2014 April June July September December December 2015 April June July September December December 2016 March June July September December December 2017 April May July September December December The resulting grocery-store, US class-level beef demand index for January 2011-November 2016 is summarized in Table 2 and Figure 13. The current non-seasonally adjusted index, where 12 This distinction is not applicable to the FS indices as only annual data is available. Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 19

20 comparisons are the prior month, value of indicates that national demand is 0.64% stronger in November of 2016 than in January Stated differently, this suggests US beef prices in grocery stores would have been 0.64% lower in November had demand not improved over this period. This index peaked in July of 2015 and declined in November of 2016 by 6.02% from October levels. Table 2. Summary Statistics of Grocery-Store, US Class-Level Non Seasonally-Adjusted Beef Demand Index: Jan-2011 to Nov-2016 N 71 Mean Standard Deviation 5.02 Minimum Maximum Min Date Max Date Nov-11 Jul-15 Current Month Last Month Last Year Percent Change in Demand Last Month Last Year Note: All estimates and analyses based upon IRI data are by the author and not by IRI. 13 Figure 13 indicates grocery-store beef demand largely grew between 2012 and 2015 before declining substantially in This pattern is consistent with the annual (and first three quarters of 2016) demand patterns reflected in the current KSU demand index (Figure 1). More narrowly, the KSU All-Fresh Beef Demand Index was increasing through the third quarter of 2015 and has declined since then through the third quarter of One outcome that is inconsistent across indices is changes between 2011 and KSU (and the earlier Technomic based food service) indices indicate demand growth in 2012 while the national grocery-store indices indicate demand contraction. This divergence is plausible given the different market segments and underlying data being used in each index. To further examine viability of the national grocery-store beef demand index, we conducted a host of regression analyses where the demand index was regressed against variables including holiday effects, the Consumer Sentiment index tracked by the University of Michigan, the 13 All requests for IRI data can be directed to: INFORMATION RESOURCES, INC. 150 NORTH CLINTON STREET, CHICAGO, ILLINOIS Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 20

21 average pork price (inflation adjusted) within the IRI data, and monthly dummy variables (relative to December). Table 3 presents the results of the model best explaining observed variation in the new index. This regression indicates that nearly two-thirds of observed variation in the index can be explained by these basic variables. Each statistically significant variable is consistent with expectations. Narrowly, these results reveal the expected positive relationship between the index and consumer sentiment and pork price holds. For each $1/lb increase in pork price, beef demand is estimated to increase 5.44%. While pork prices do not change by a $1/lb in a single month, there are times of sizeable market movement. As an example, in May 2014 pork prices increased by $0.149/lb, which according to Table 3 increased beef demand that month by 0.81%. Weaker Beef demand in April and November, and stronger in May-July is also consistent with Figure 13 and our expectations of grilling impacts on beef demand. Table 3. OLS Results Grocery-Store, US Class-Level Non Seasonally-Adjusted Beef Demand Index, Jan to Nov Intercept Holiday Gain Holiday Loss Consumer Sentiment Pork Price Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Adjusted R-Square MAE Note: All estimates and analyses based upon IRI data are by the author and not by IRI. Bold coefficient estimates indicate statistical significance at the.10 level (or more). Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 21

22 Figure 13. Grocery-Store, US Class-Level, Non Seasonally-Adjusted Beef Demand Index Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 22

23 Category-level indices are summarized in Table 4 and presented in Figure 14. These indices were built for Chuck, Ground, Loin, Rib, and Round. Several things immediately standout when examining category-level indices relative to aggregated beef, class-level indices (Table 2, Figure 13). The standard deviations and ranges between minimum and maximum points of most category-level indices are larger. This increased variability is consistent with what we would expect as less aggregated levels of assessment are conducted. However, this variability can become extreme and make certain indices of limited pragmatic value. As an example, the seasonal Rib index has nonsensical summary statistics. 14 Examining patterns in Figure 14 reveals that thru 2015, Ground beef demand grew in the grocery-store segment before falling substantially thru November At the other extreme, assessment of the Round indices indicates this category was a drag on demand in 2011 and has yet to substantially recover. The Loin index reveals a pattern consistent with grilling based expectations grocery-store demand strength in May-July and weakness in November-December. Table 5 summarizes results of regression analyses where each category-level demand index was regressed against holiday effects, Consumer Sentiment, monthly dummy variables, and a one-month lagged index variable. The seasonal Rib index regression confirms concerns noted earlier indicating this index is not viable. Collectively the other regressions indicate that 80-90% of observed variation in each index is explained by these basic variables. On balance, each statistically significant variable is consistent with expectations. When significant, the lagged index effects are between 0 and 1 indicting general stability and persistence in demand patterns suggested by those indices. Key seasonal examples include Ground beef demand being lowest in February and December while Loin demand is highest in May-July consistent with grilling and holiday effects. 14 Potentially this reflects holiday effects noted earlier. Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 23

24 Table 4. Summary Statistics of Grocery-Store, US Category-Level Non Seasonally-Adjusted Beef Demand Indices: Jan-2011 to Nov-2016 Max Current Last Last Last Last Variable Mean StdDev Min Max Min Date Date Month Month Year Month Year Nov- Percent Change 2016 Oct-16 Nov-15 Chuck Jul-11 Oct Ground Nov-11 Jul Loin Nov-12 Jun Rib , , , Dec-11 Dec Round Jun-13 Jan Note: All estimates and analyses based upon Information Resources, Inc. data are by the authors and not by Information Resources, Inc. Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 24

25 Figure 14. Grocery-Store, US Category-Level, Non Seasonally-Adjusted Beef Demand Indices Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 25

26 Table 5. OLS Results Grocery-Store, US Category-Level Non Seasonally-Adjusted Beef Demand Indices, Jan to Nov Chuck Ground Loin Rib Round Intercept , Holiday Gain Holiday Loss Consumer Sentiment Jan , Feb , Mar , Apr , May , Jun , Jul , Aug , Sep , Oct , Nov , Lagged Index R-Square MAE Note: All estimates and analyses based upon IRI data are by the author and not by IRI. Bold coefficient estimates indicate statistical significance at the.10 level (or more). SubCategory-level, national ground beef demand indices are summarized in Table 6 and presented in Figure 15. These indices were built for 70-77% Lean, 78-84% Lean, 85-89% Lean, 90-95% Lean, and Chuck. We utilized elasticity estimates from Schulz, Schroeder, and Xia (2012) in building these indices. Specifically, own-price elasticities of , , , and were used for 70-77%, 78-84%, 85-89%, and 90-95% Lean, respectively. The estimate was used for Chuck as Schulz, Schroeder, and Xia (2012) did not provide a separate Chuck estimate. These different elasticity estimates reflect consumer variation in price sensitivity across ground beef product lean percentage. Consistent with the point made earlier, proceeding to further dissect the ground beef category into these five sub-category-level indices results in measures that are also more variable. In fact, the two leanest subcategories (70-77% and 78-84%) scale out of control with a substantial demand improvement in 2015 leading to index values exceeding 450. While demand indeed may have been particularly strong, the magnitude of these index changes is concerning especially since combined they reflect roughly 50% of both total value and volume in the ground beef category. The only sub-category ground beef demand index not peaking in 2015 or 2016 is the Chuck index which peaked in January of The exact month that each of these indices peaked and the magnitude of demand decline that has followed thru November of 2016 varies notably. Another important underlying point specific to these ground beef sub-category insights is critical to appreciate. Over time the price and volume of both aggregate and sub-category measurements of ground beef transactions vary for a host of reasons. Beyond the changes in core demand by US consumers of key interest here, the endogenous decisions involved in selecting what beef products to grind, what percent lean to produce, etc. must be appreciated. In short, these endogenous packer and processor decisions are more complex in consideration Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 26

27 of ground beef sub-category demand indices than likely are required for more aggregated indices. This likely is part of the observed increase in variability of these indices and makes us hesitant to recommend them for use. Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 27

28 Table 6. Summary Statistics of Grocery-Store, US SubCategory-Level Non Seasonally-Adjusted Ground Beef Demand Indices: Jan-2011 to Nov-2016 Current Last Last Last Last Variable Mean StdDev Min Max Min Date Max Date Month Month Year Month Year Nov-2016 Oct-16 Nov-15 Percent Change 70-77% Lean Jan-11 Feb % Lean Apr-12 Oct % Lean Dec-11 Jan % Lean Dec-13 Jan Chuck Aug-16 Jan Note: All estimates and analyses based upon Information Resources, Inc. data are by the authors and not by Information Resources, Inc. Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 28

29 Figure 15. Grocery-Store, US SubCategory-Level, Non-Seasonally-Adjusted Ground Beef Demand Indices Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 29

30 Regional Assessment As shown in Figure 16, IRI data is available for eight separate regions comprised of different combinations of US states. Given diversity in current populations across regions and a history over time of having specific geographic areas be targeted for marketing or new product efforts, we explored viability of new region-specific grocery-store beef demand indices. Unfortunately we were unable to procure any estimates of how market coverage (ACV) varies across these regions. The eight IRI regions were collapsed to four regions such that corresponding US Census population and BLS CPI regional information can be used to derive per-capita, inflation-adjusted estimates specific to each region. 15 Ultimately, we built class-level beef demand indices for four regions: West (IRI s West region and CA), Midwest (IRI s Plains and Great Lakes regions), South (IRI s South Central, Southeast, and Mid South regions), and Northeast (IRI s Northeast region). This results in indices summarized in Table 7 and Figure The US Census provides annual population estimates as of July 1 st for each year so we linearly extrapolated to derive monthly estimates. Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 30

31 Figure 16. IRI Regions Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 31

32 Table 7. Summary Statistics of Grocery-Store, Regional Class-Level Non Seasonally-Adjusted Beef Demand Indices: Jan-2011 to Nov-2016 Variable N Mean SD Minimum Maximum Min Date Max Date Current Month Last Month Last Year Last Month Nov-2016 Oct-16 Nov-15 Percent Change in Demand Northeast Nov-11 Jun South Feb-13 Nov Midwest Nov-11 Oct West Nov-12 Jul Note: All estimates and analyses based upon Information Resources, Inc. data are by the authors and not by Information Resources, Inc. Last Year Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 32

33 Figure 17. Grocery-Store, Regional Class-Level, Non Seasonally-Adjusted Beef Demand Indices Creating and Assessing Candidate Food Service and Retail Beef Demand Indices Pg 33