A single espresso, please! Rationalizing espresso price dispersion across Italian cities

Delbono, Flavio; Dipoppa, Gemma; Lambertini, Luca; Reggiani, Carlo Working Paper A single espresso, please! Rationalizing espresso price dispersion across Italian cities Quaderni - Working Paper DSE, No. 1017 Provided in Cooperation with: University of Bologna, Department of Economics Suggested Citation: Delbono, Flavio; Dipoppa, Gemma; Lambertini, Luca; Reggiani, Carlo (2015) : A single espresso, please! Rationalizing espresso price dispersion across Italian cities, Quaderni - Working Paper DSE, No. 1017, Alma Mater Studiorum - Università di Bologna, Dipartimento di Scienze Economiche (DSE), Bologna, https://doi.org/10.6092/unibo/amsacta/4311 This Version is available at: https://hdl.handle.net/10419/159855 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/3.0/ ISSN 2282-6483 A single espresso, please! Rationalizing espresso price dispersion across Italian cities Flavio Delbono Gemma Dipoppa Luca Lambertini Carlo Reggiani Quaderni - Working Paper DSE N°1017 A single espresso, please! Rationalizing espresso price dispersion across Italian cities Flavio Delbonox, Gemma Dipoppa#, Luca Lambertinixand Carlo Reggiani § Department of Economics, University of Bologna, Italy; ‡[email protected]; [email protected]. # Department of Comparative Politics, University of Pennsylvania, US. * School of Social Sciences-Economics, University of Manchester, UK. Abstract This paper aims at providing an explanation of the observed espresso price dispersion across major Italian cities. The empirical evidence suggests a positive relationships between the average espresso price in a city and the number of co¤ee shops (normalized for the adult population) operating in that city. This …nding is shown to be robust after controlling for GDP per capita and consumers’price index. We provide an interpretation of the empirical …ndings relying on a model of price competition delivering a continuum of Nash equilibria, where …rms adjust the mark-up to o¤set the negative e¤ect of any increase in their number. Keywords: price dispersion, espresso prices, market competition. JEL Classi…cation: L11, L13, L66. 1 1 Introduction For the average Italian adult, drinking an espresso is not only a ritual, but it is most often a repeated ritual during the day. According to anecdotical evidence, espresso is the second most drunk beverage in Italy (water being …rst), dozens million cups being consumed daily. A discerning consumer travelling from Northern to Southern Italian cities would probably notice a non-negligible fall in espresso prices. Actually, in major Italian cities espresso cups are priced in bars very similarly within cities and very di¤erently across cities.1For instance, in 2001 the average price2charged in Milano was 0.78 euros, it declines to 0.62 in Roma and even more in Palermo, 0.56. In 2011, the last year in our sample, the dispersion across cities did not change much as it ranged from 1.01 euros in Bologna to 0.79 in Roma and 0.71 in Bari. Since the standard espresso drunk at the bar is a fairly homogeneous good, except for location, broad price di¤erences look surprising. Moreover, descriptives suggest a large variance in the average number of consumers per bar across cities. In particular, it turns out that higher prices are associated to lower number of clients (proxied by adult inhabitants) per bar. These facts give rise to interesting questions: why prices are so high in some cities and not in others? do consumers per bar play a role in explaining these large 1In what follows we refer to “bar”to indicate a plethora of establishments selling co¤ee (co¤ee shops, cafeterias, pubs. . . ), excluding restaurants. More details are provided in Appendix A.1 describing the dataset. 2By average price we mean the price charged for a cup of single espresso drunk standing at the counter. Unlike many other countries, this is the standard way Italians consume espresso. Hence, the sample excludes the service surcharge applied by cafeterias to the price of espresso when customers sit down. 2 di¤erences in prices? High levels of price are compatible with several di¤erent explanations. The …rst and well known relies upon explicit collusion.3A second interpretation may be based on tacit collusion in a repeated game. However, there are at least two characteristics of the espresso market that are hardly compatible with the two above classical explanations. First, the number of bars/…rms is large in all cities. Second, in many cities demand is seasonally ‡uctuating because of tourists’‡ows and the discontinuous presence of students. Both factors are known as obstacles to implement and sustain collusive agreements. However, there is a third approach that does not need to invoke either implicit or explicit collusion. Such an approach is based upon Dastidar’s (1995, 2001) model of one-shot price competition with homogeneous goods under convex costs. According to the Dastidar model a price equilibrium outcome mimicking joint pro…t maximization may indeed emerge as a noncooperative equilibrium without repetition. We will show that our empirical …ndings …t better Dastidar’s conclusion than the two traditional approaches mentioned above. The empirical evidence suggests a positive relationships between the average espresso price in a city and the number of bars, or conversely, higher prices tend to prevail in cities with a lower number of clients per bar, which is consistent with the comparative statics properties of Dastidar’s model. We perform a simple empirical analysis on the distribution of espresso prices in the 20 Italian regional capitals, a sample including the most populated Italian cities, between 2001 and 2011. A glance to the basic descriptive 3Italian bars are usually members of professional associations. It is not rare for these associations to suggest "focal" price levels for espresso. This could be a factor facilitating collusion or, at least, coordination of price levels across bars. 3 statistics reveals a negative correlation between espresso prices and the average number of customers per bar. For instance, in 2011, the upper limit of one euro is the price approached when the number of clients falls below 400. Using data available for four years (2001, 2005, 2009, 2011) within the period, we estimate a Linear Probability Model Fixed E¤ects Regression and show that increasing the average number of customers per bar (or decreasing the number of bars with respect to the population) has a negative and sig- ni…cant impact on espresso price. The result is obtained controlling for both GDP per capita and city price indices. This evidence is then rationalized within Dastidar’s model, by means of some comparative statics. The paper is organized as follows. In section 2 we illustrate the statistical evidence and develop our simple empirical analysis. The detailed description of data set is in the Appendix. Section 3 illustrates how the basic empirical …ndings may be derived within Dastidar model of price competition. Section 4 concludes by bridging the empirical analysis and theoretical predictions. 2 Empirical analysis 2.1 Data We aim to perform an empirical analysis on the distribution of espresso prices in Italy. The lack of data available on the sector studied constitutes an important constraint to our analysis. In Appendix A.1 we provide a detailed account of how we constructed the sample. Generally, our analysis focuses on the capital cities of the 20 Italian regions in the years 2001, 2005, 2009 and 2011, for a total of about 80 observations. The espresso prices are collected by Italian National Institute of Statistics (henceforth ISTAT). For 4 each city, we also have information about the number of bars, the total adult population,4the consumers price index (ISTAT) and the GDP per capita (Eurostat). From the previous sources, we compute the average number of clients per bar in each city. The descriptives of the sample are summarized in Table 1. Table 1. Descriptive statistics Obs Mean Std Dev Min Max Co¤ee Price 78 0:78075 0:11517 0:56 1:012 Clients per bar 80 537:777 188:702 294:529 1239:579 GDP per capita 80 25400 7035:102 13800 46600 Price Index 77 113:6266 9:4284 101:8 130:9 2.2 Empirical results Observing the raw data we detect the presence of a negative relationship between the price of the espresso and the average number of clients per bar. The scatter plot of clients per bar and espresso prices displays a downward pattern that approaches the average price of 1 euro when clients per bar are less than 400. The relationship is con…rmed by the correlation coe¢ cient of 0:6292. 4The total adult population is o¢ cially de…ned by ISTAT as residents of 15 years of age or above. 5 Fig. 1 Clients per bar and espresso prices. The previous descriptive evidence is not su¢ cient to explain the observed pattern of espresso prices in Italy. For a better understanding of the price dispersion, we need at least to control for a number of other factors. In particular, price dispersion is likely to be explained by variables like cities’GDP per capita and general consumers’price indices. Common sense would suggest that richer cities should experience higher price levels, including higher espresso prices.5 Given the panel structure of our dataset, we estimate a Linear Probability Model with Fixed E¤ects using the following speci…cation: Espresso priceit =it +Clientsit +GDPpc +PriceIndexit +"it (1) where tindicates the year, ithe city and it represents the city …xed e¤ects. For ease of interpretation of the following results, the variable Clients is measured in hundred clients per bar and GDPpc in thousands of euros. 5Intuition may suggest that richer areas entail higher espresso prices. However, this maybe misleading: within the same city, for example Bologna, espresso prices are basically ‡at across districts, despite very large di¤erences in income per capita across city districts (Bologna City Council, 2014). 6 Table 2 presents our main results. Table 2. Espresso price dispersion: a linear probability model Espresso price Clients 0:06275*** (0:01333) GDPpc 0:020878*** (0:006672) Price Index 0:0003536 (0:0003424) Const 0:618089** (0:239975) R20:4916 Obs 75 First and fairly surprisingly, city consumers’price levels are not statistically signi…cant in explaining espresso prices.6Whereas we could a priori think that the price index may capture, for example, the impact of tourism on the price of an espresso cup, our regression analysis suggests instead no role for this regressor. Second, the GDP per capita has a statistically sig- ni…cant role in explaining the espresso price dispersion. A thousand euros increase in the GDP per capita has an estimated positive impact of 2 cents on the price of a cup. Last but not least, controlling for …xed e¤ects, evidence shows that increasing the average number of clients per bar has a signi…cant and negative e¤ect on the price of espresso. In particular, an extra hundred clients per each bar is estimated to lead to an average decrease of 6.2 cents in 6We included both GDP per capita and consumers’price levels among the regressors as they are not highly collinear. 7 bar over the total number of eating and drinking establishments in 2011. For the remaining years, 2005 and 2009, we calculate the number of bars by interpolating the original data on establishments in 2001 and 2011 and then using again the 2011 proportion to estimate the number of bars in those years. The underlying assumption is that the proportion of bars over the total number of establishments has not changed throughout the 2001-2011 period. In other words, the data-generation process relies upon the conjecture that the number of bars were not subject to any technological, demand or supply shock in the ten years time span considered: given the characteristics of the sector (very homogeneous product, very low technology, very customary clients) our conjecture and the resulting constant rate appears justi…ed. Second, we need to estimate the price of espresso for years 2001 and 2011. A very similar reasoning was adopted. Given the original 2005 and 2009 data, we interpolate them linearly to calculate the estimated espresso prices for years 2001 and 2011. A.2 Robustness checks In the light of the di¢ culties faced in constructing the dataset for the analysis, it is extremely important to check the robustness of them. To do so, in this section we test two alternative speci…cations. The …rst speci…cation includes only the years for which we have original information on the number of eating and drinking establishments, i.e. 2001 and 2011. The second speci…cation includes only the years for which original data on espresso prices are available, i.e. 2005 and 2009. Table A.1 reports the results for these two speci…cations in columns (1) and (2) respectively. Column (1) shows that our results are extremely robust to the …rst of our new, reduced speci…cations. 14 Table A1. Espresso price: robustness checks (1) (2) Clients 0:061702*** 0:005125 (0:013277) (0:021477) GDPpc 0:022716*** 0:034596 (0:006977) (0:0050634) PriceIndex 0:000632 0:009313*** (0:000342) (0:001617) Const 0:599450** 0:264686 (0:213104) (0:276885) 2001-2011 only 2005-2009 only R20:4916 0:0058 Obs 38 37 The signs and signi…cance are comparable and even the magnitude of the e¤ects is almost una¤ected. The results are less encouraging when focusing on Column (2). In that speci…cation, covering years 2005 and 2009, the number of clients per bar has a positive e¤ect on espresso prices. The e¤ect, however, has a very small magnitude and it is not statistically signi…cant (p-value'0:81). GDP per capita is also not signi…cant and all the e¤ects seem to be captured by the consumers’price level, that is highly signi…cant. A look at the descriptives for years 2005 and 2009, however, seems to con- …rm the evidence provided in the main text. Figure 2 shows a weaker but negative relationship between the price of espresso and the clients per bar. On top of that the correlation coe¢ cient between these two variables is still 0:6262. The regression results in (2), instead, might be a¤ected by the reduced number of observations when focusing only on two years. 15 Fig. 2 Clients per bar and espresso prices, 2005-2009. 16 