The Effects of Mergers on Prices:

Transcription

1 The Effects of Mergers on Prices: Evidence from the German Retail Sector D. Rickert 1, J. P. Schain 2, J. Stiebale 3 Cresse 2017 June 29, TSE, INRA, DICE 2 DICE 3 DICE 1/20

2 Research question What are the effects of a merger between two German retailers on grocery prices? Identification Strategy We exploit the fact that retailers set prices at the local market level DiD estimator to compare markets with pre-merger overlap of acquirer and target to a control group of unaffected markets Results Prices sign. increase, on average, after the merger by 0.5%. Prices increase is larger for a larger change in concentration (up to 6% in highly concentrated markets) Innovative identification strategy allows us to disentangle concentration effects (upward pressure), efficiency effects (downward pressure) and the net effect. Robust: prop reweighting, eliminating neighboring markets, placebo tests 2/20

3 3/20 The direction of the net effect of mergers on prices is not clear Prices may increase (e.g., von Ungern-Sternberg 1996) or decrease (Williamson 1968) The effect of M&A even more difficult to predict in vertical market structures Size-related discounts (e.g., Katz 1987) and reduction in the number of suppliers alternatives (e.g., Chipty and Snyder) Mergers allow shift from secret to public contracting within the merged entity, which may have an upward pressure on prices (Caprice and Rey 2015) European Commission and US: Expected welfare-enhancing effects from buyer power increase In Germany, concerns about consumer surplus and producer profits

4 4/20 Related Literature Hosken, Olson, and Smith (2012) analyze 14 retail mergers and find that prices decrease after some mergers while they increased or remained unchanged in other cases. Allain, Chambolle, Turolla, and Villas-Boas (2015) report a significant price increase after a merger among French retailers Argentesi, Buccirossi, Cervone, Duso, and Marrazzo (2016) find no significant price changes after mergers in the Dutch retail market, but a reduction in retail variety Closely related: Ashenfelter, Hosken, and Weinberg (2015) a proxy for merger-specific efficiencies (the reduction in distance between the retailer and the nearest Coors brewery)

5 5/20 The Merger We consider the merger of R1 and R2 with pre-merger market shares of 25% and 5% Outsiders O1-O3 split the remainder rather equally (20%,15%,15%) The merger was proposed in first half of 2008 and approved at the end of (of 2700) stores had to be sold to competitor O1, 1800 were converted to R1 D No major retail mergers outside the Safe-Harbor between 2006 and 2009

6 6/20 Data Set Representative GFK home-scan consumer panel data from of consumers Our empirical analysis focuses on 5 product categories: milk, jogurts, coffee, diapers, and toilet paper. Individual level data: purchasing time, product characteristics, retailer, actual transaction price (including promotions), and quantities Information on income, children, age and education However, markets are defined at the regional level and we do not have information on the retail location

7 7/20 Market Definition at the Retail Level Unlike in the UK (Dobson and Waterson 2005), in the Netherlands (Argentesi et al. 2016) or in France (Allain et al. 2015), German retailers adopt a local pricing strategy Local merchants with degrees of freedom in their price and variety decision Retail prices vary wrt local market characteristics We define local markets at the municipality-level, (which is finer than Nielsen s definition): 11,000 local markets mean of km 2, median of km 2, and variance of km 2

8 Regression of Prices on Regional Characteristics Pop density 0.107*** *** *** *** (0.0103) (0.0089) (0.0017) (0.0012) Income *** *** (0.0095) (0.0081) (0.0016) (0.0015) Age *** *** *** *** (0.0008) (0.0007) (0.0001) (0.0001) HH with children 0.165*** 0.119*** ** * (0.0226) (0.0192) (0.0039) (0.0034) Unemployment rate *** (0.0016) (0.0014) (0.0003) (0.0002) HHI 0.261*** 0.279*** (0.0359) (0.0315) (0.0064) (0.0056) Time FE yes yes yes yes Retailer FE no yes yes yes Brand FE no no yes yes Product Type-Time FE no no no yes Retailer-Brand-Time FE no no no yes N Standard errors in parentheses. Clustered at regional level. Significant at 1% ***, Significant at 5% **, Significant at 10% * 8/20

9 9/20 Decomposition of Variance in Local and National Component 4 4 Regression of prices on retailer-brand FE. 1 R 2 gives variance which can be explained by local pricing

10 10/20 Identification Strategy Three types of markets: Type Players Concentration Efficiency A R1 and R2, O X X B R1 or R2, O - X C O - - P A P B = Concentration Effect P B P C = Efficiency Effect P A P C = Net Effect Comparing market A to B and C gives an average effect which is our baseline specification

11 11/20 Estimation Equation Our baseline equation for prices is specified as ln(p igjt ) = α igj + θ post t MA g + δ t + [x gtβ + η ilt + ω kt ] + ε igjt i denotes a retail chain g indexes local markets j refers to a brand t indexes time periods (quarter) k indexes a product category l indexes private label and national brands MA g is a dummy variable indicating retailers in regions affected by the merger x contains demographics (age, income, pop density, HH with children)

12 12/20 Results In the baseline specification on average, retailers raise prices by 0.47% the increase is entirely driven by supermarkets, 0.9% possibility to raise prices for discounters is very limited as competition is fierce by two large hard discounters. the price increase is larger in markets with a higher expected change in concentration with a maximum increase of 6.25%. Also interaction only relevant for supermarkets.

13 13/20 DiD Regression Results with Log Prices: Baseline Specification Model 1 Model 2 Model 3 Model 4 Model 5 Treat * ** ** *** *** (0.002) (0.002) (0.002) (0.0025) (0.0026) Treat DC *** ** (0.0033) (0.0034) Treat HHI 0.111** (0.0549) Treat DC HHI (0.0722) Retailer-PL-Time FE no yes yes yes yes Regional Controls no no yes yes yes FEs for Region-Retailer-Brand, Category-Time N Standard errors in parentheses. Clustered at regional level. Significant at 1% ***, Significant at 5% **, Significant at 10% *

14 14/20 More Results Disentangling price effects on average, retailers raise prices by 0.57% in the specification where we expect the largest increase in prices. Net Effect is non significant and small. Specification with downward pressure average negative price effect of -0.7% The effect depends on the market concentration. The higher the HHI is (low market concentration) the smaller is the negative effect. Pass through of efficiency gains depends on market concentration

15 15/20 Concentration Effects, Efficiency Gains, and Net Effect Model 6 Model 7 Model 8 Model 9 Effect Concentration Net Efficiency Efficiency Treat *** * *** ( ) ( ) (0.0041) (0.0044) Treat HHI *** (0.0051) FEs for Region-Retailer-Brand, Category-Time, Retailer-PL-Time + Regional Controls N Standard errors in parentheses. Clustered at regional level. Significant at 1% ***, Significant at 5% **, Significant at 10% *

16 16/20 Additional specifications In additional specifications, we show that outsiders also raise prices, but to a smaller extent the price increase is of the same magnitude for PL and NB the positive price effect is equally prevalent in remedy regions

17 17/20 Robustness Checks Propensity Reweighting Estimator We exclude neighboring municipalities which are located less than 15 kilometers (ca 20min driving time) from our control group Placebo tests: Other treat times and exclude the actual treat time. Real treat time but random assignment of treat group.

18 18/20 Thank you for your attention!

19 19/20

20 20/20