Cross-country price and inflation dispersion : retail network or national border?
Date: 2023-02-10
Institutions: European Central Bank
Subjects: Austria, EU statistics, Germany, consumer price, cross-border dimension, economic statistics, euro area, price fluctuation, price index

Teresa Messner, Fabio Rumler, Georg Strasser 

Working Paper Series 

Cross-country price and inflation 
dispersion: retail network or national 
border? 

No 2776 / February 2023 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

 
 
 
Price Micro Setting Analysis Network (PRISMA) 

This paper contains research conducted within the Price Micro Setting Analysis Network (PRISMA). PRISMA consists of economists 
from the ECB and the national central banks (NCBs) of the European System of Central Banks (ESCB). 

PRISMA is coordinated by a team chaired by Luca Dedola (ECB), and consisting of Chiara Osbat (ECB), Peter Karadi (ECB) and Georg 
Strasser (ECB). Fernando Alvarez (University of Chicago), Yuriy Gorodnichenko (University of California Berkeley), Raphael Schoenle 
(Federal Reserve Bank of Cleveland and Brandeis University) and Michael Weber (University of Chicago) act as external consultants. 

PRISMA collects and studies various kinds of price microdata, including data underlying official price indices such as the Consumer 
Price Index (CPI) and the Producer Price Index (PPI), scanner data and online prices to deepen the understanding of price-setting 
behaviour and inflation dynamics in the euro area and EU, with a view to gaining new insights into a key aspect of monetary policy 
transmission (for further information see https://www.ecb.europa.eu/pub/economic-research/research-
networks/html/researcher_prisma.en.html).
The refereeing process of this paper has been co-ordinated by a team composed of Luca Dedola (ECB), Anton Nakov (ECB), Chiara Osbat 
(ECB), Elvira Prades (Banco d’Espana), Sergio Santoro (ECB) and Henning Weber (Bundesbank).
This paper is released in order to make the results of PRISMA research generally available, in preliminary form, to encourage comments 
and suggestions prior to final publication. The views expressed in the paper are the author’s own and do not necessarily reflect those of 
the ESCB. 

ECB Working Paper Series No 2776 / February 20231Abstract

(Why)  do  prices  and  inflation  rates  differ  within  the  euro  area?  We  study  the  relevance  of  a 
national  border  for  grocery  prices  in  the  otherwise  homogenous  and  highly  integrated  border 
region  of  Austria  and  Germany.  Using  transaction  data  on  prices  and  quantities  from  a  large 
household  panel,  we  compare  the  prices  of  identical  products  within  a  narrow  band  along  the 
border.  We find  large  assortment  and  price  differences  between  these  two  regions.  Even  within 
multinational retail chains the prices of identical products on the two sides of the border differ on 
average  by  about  21%.  These  price  differences  are  not  very  persistent  indicating  little  arbitrage 
gain  from  undifferentiated  cross-border  shopping.  Ensuing  product-level  inflation  rates  differ  for 
only  half  of  the  chains.  The  results  highlight  the  importance  of  the  history-dependent  evolution 
of  distribution  networks  and  of  the  structure  of  the  sales  organization  as  a  driver  of  price  and 
inflation  heterogeneity.

Keywords:  Price  discrimination,  goods  market  integration,  border  effect,  cross-border  arbitrage, 
market  power

JEL  classification:  D12,  E31,  D43,  F15,  F4

ECB Working Paper Series No 2776 / February 20232Non-technical summary

The “law of one price” (LOP) states that in a frictionless world the price of a product

traded in two countries is the same. The “frictionless world” refers to a hypothetical,

limiting case – a stylized world free of administrative, cultural, or technical trade

restrictions, with free movement of goods and perfect competition, in which market
participants can exploit any looming arbitrage opportunity right away. But country

pairs with entirely frictionless cross-border markets are rare. Numerous studies have

documented persistent deviations from the LOP, even among neighboring countries.

There is yet no definite answer whether purely administrative national borders can
cause such deviations.

This paper studies the causes of price differences at a border within an almost ideal

setting: a national border dividing an otherwise largely integrated region. The bor-

der region of Austria and Germany is not only characterized by strong economic and

cultural ties, such as a common currency and language and being part of the Euro-
pean Union, but constitutes also an integrated retail market as several retail chains are

active in both countries. To rule out confounding factors as far as possible, we limit

the sample region to a 60 kilometer wide band along both sides of the border. Within

this region, classical trade barriers, exchange rates, and distance should therefore not
matter for the pricing decisions of retailers.

The sample builds on the GfK household panel for Austria and Germany from 2008

to 2018. It covers transaction-level prices of fast-moving consumer goods, which are

primarily food and personal care items sold in supermarkets. The barcode information

ensures a comparison of identical products across multiple locations in both countries.
As six of the eight retail groups in our study operate (not necessarily under the same

name) in both countries, our setting allows for a comparison of prices across the border

even within retail groups.

The cross-border differences in local costs and supply factors, such as taxes, land prices

and retail structure, are minor. Differences in high-level consumption preferences can

also be ruled out as source of price differences because the consumption baskets at the
product-group level (COICOP-4 and 5) are highly correlated across the border between

the regions. Despite the similar preferences, the share of products (identified by their

barcode) available in both countries is relatively small.

But even those products which are sold on both sides of the border are not sold at the

same price. We find substantial differences in prices of identical products across the
border which are significantly larger than within either country. The price differences

ECB Working Paper Series No 2776 / February 20233go in either direction, but on average, prices are about 13% higher on the Austrian

side. The distribution of price differences displays a pronounced mode at zero with

about 14% of all products being equally priced. Disregarding the observations at the

mode, cross-border price differences are centered around a 15% to 18% premium on
the Austrian side, suggesting the existence of an optimal (non-zero) cross-border price

differential. In absolute terms (positive and negative differences taken together), prices

differ on average by 23% between the two countries.

The discrete price jump right at the border is a clear violation of the (absolute) LOP.

For price changes, however, we do not find a strong border effect. As the differences
in price changes across the border are not significantly stronger than within countries,

the LOP seems to hold approximately in its relative version, hinting at common in-

flation drivers. This suggests that the border effect by itself is unlikely to affect the

transmission of monetary policy.

The price differences are not due to differences in retail structure. We trace the bor-
der effect to price differences within retail chains. Even retail chains that operate in

both countries charge different prices on the two sides of the border. Such systematic

differences do not exist for price changes.

We find no evidence that specific household groups (defined by age or income) are dis-

proportionally exposed to price differences. Cross-border price differences are relatively
larger for personal care than for food items. This indicates that whereas consumers

could realize some arbitrage gains by “cherry-picking” specific products, they would not

gain from randomly shopping across the border. Exploiting these arbitrage opportuni-

ties would thus require the acquisition of a lot of information by the consumer.

Retailers differentiate prices always and everywhere, also within countries, but they

do so most extensively across national borders. Their market power allows them to
price discriminate between countries and maximize profits separately on each side. The

resulting pricing strategies might differ by product: uniform pricing (zero price differ-

ence) for some and price differentiation for other products. The choice to differentiate

prices exactly along national borders is most likely due to the retailers’ existing logis-
tics networks. Parts of these networks evolved at a time when regulatory differences

rendered country-specific distribution networks advantageous. An important part of

the border effect might thus be a remainder of the historic evolution of retail markets.

ECB Working Paper Series No 2776 / February 202341

Introduction

The law of one price (LOP) states that in the absence of (physical or administra-

tive) trade barriers and shipping costs, identical goods in different locations should

have the same price. Across countries, however, prices often deviate considerably and

persistently from the LOP even if there are no trade barriers.

This is barely surprising, as trading across national borders is often complicated by a
wide array of trade frictions. National borders often come with differences in regula-

tions, currencies, preferences, market structures and with obstacles to crossing them

such as delays and distance. These can be further exacerbated by differences in taxes,

in monetary and fiscal policies and by restrictions to the movement of goods, services,
and factors of production. Together with potentially higher cross-border search costs,

this can wipe out any potential gain from arbitrage. As a result, prices can differ sub-

stantially across borders (Crucini et al., 2005; Engel and Rogers, 1996; Gorodnichenko

and Tesar, 2009).

Somewhat more recent studies, comparing prices of identical products, provide mixed

evidence on international cross-border price differences, namely differences at the U.S.-
Canadian border. Gopinath et al. (2011) and Burstein and Jaimovich (2012), on the

one hand, document within a single retail chain larger price differences between stores

in different countries than between stores within the same country. Broda and Wein-

stein (2008), on the other hand, looking across multiple retail chains, find for a very
specific set of goods no major difference in the variation of retail prices across versus

within countries. Recent research on Switzerland shows that the exact same prod-

ucts are cheaper in its neighboring countries (Burstein et al., 2022) – but also these

prices are quoted in a different currency, which might complicate the cross-border price
comparison for the consumer.

A takeaway from this literature might be that, indeed, borders matter. They matter

because they separate markets. The well-studied border between Canada and the

USA separates two economic areas with many well-guarded idiosyncratic rules and

regulations. Different tax rates, a different currency, different regulations, the presence
of border controls, and so forth fit the textbook description of border frictions. The

existence of price differences between Canada and the USA is thus not surprising, and

their magnitude might be viewed as a gauge on the severity of these frictions.

Within a monetary union with integrated product markets, such as the euro area,

one might expect that the relevance of borders for price differentiation has largely
diminished. All euro member countries are part of the single market of the European

ECB Working Paper Series No 2776 / February 20235Union, share the same currency, and have – by global standards – similar consumption

tax rates and harmonized regulations. Mejean and Schwellnus (2009) document that

between 1995 to 2004, European economic integration nurtured price convergence as

higher arbitrage pressure restrained the firms’ choice of pricing strategies – but by no
means wiping out price differences completely. But despite a high degree of economic

integration and a common currency, several studies find that borders within the euro

area continue to leave their mark on prices: Reiff and Rumler (2014) find for some

frequently purchased groceries that price variation between 13 euro area countries is
many times larger than the price variation within these countries and show that neither

distance nor tax nor consumption nor income differences fully explain this. Various

other studies highlight large price differences between countries within Europe even for

identical goods, for example TV sets (Imbs et al., 2010) or cars (Dvir and Strasser,
2018). Beck et al. (2020) document median price differences for identical products

between Belgium, Germany and the Netherlands of 15% to 20%.

But what are these frictions? Are the costs of moving people and goods really the cause?

Would a random line marked as “border” entail a similar price difference? Similar to

the paper by Burstein et al. (2022) – who, however, study a border characterised by
classic frictions due to different currencies – we also document that prices do not vary

much with distance to the border within each country, but display a substantial price

gap at the border. We show in this paper that even borders without relevant trade

frictions can entail large price differences and that these price differences are rooted
deeply in deliberate price differentiation of retailers.

Recent papers suggest that borders play less of a role in online markets, because search

and price comparison is easier and geographic hurdles are less relevant there. Cavallo

et al. (2014), for example, analyse online prices from large internationally active re-

tailers, such as IKEA and H&M, and find that their prices within the euro area are
virtually identical, while they differ outside of this monetary union. They differ even if

the currencies are de-facto pegged, as e.g. in the case of Denmark. The prices of multi-

national online retailers seem to largely obey the LOP (Gorodnichenko and Talavera,
2017).1

We complement this literature by studying prices and consumption of identical goods
in a highly integrated region which is divided by a national border. The Bavarian-

Austrian border region is not only integrated in economic but also in cultural terms.

The entire region shares the same language and similar socio-economic characteristics.

It is connected by a tight traffic infrastructure, and, absent border controls, there is a

1Whereas online pricing affects offline pricing (Jo et al., 2019), offline prices in Europe remain more dispersed than

their online counterparts (Strasser and Wittekopf, 2022).

ECB Working Paper Series No 2776 / February 20236considerable number of cross-border commuters. With a number of retailers operating

on both sides of the border, this region constitutes a nearly optimal setting to analyse

international price and inflation differences.

We use data from the GfK2 household panel, which reports barcode-level transactions of
participating households in brick-and-mortar stores. The transactions cover primarily
groceries, household maintenance, and personal care items, commonly dubbed “fast-

moving consumer goods” (FMCG). Somewhat reassuringly, the mode of (crossborder)

price differences is zero. But this mode encompasses only 14% of observations and is
only observed for around 29% of products (barcodes).3 In fact, even in this ideal setting
which eliminates most prominent factors commonly blamed for LOP deviations, the

prices of many products differ substantially in either direction. We show that these

price differences are significantly larger than those within either country. The pervasive

price differences at the product level partly offset each other, so that the overall price
level difference between the two countries is smaller, but nevertheless highly significant.

In contrast, the difference in average inflation rates at the product level between the

two countries is rather small. Overall, we document a widespread failure of the LOP

in its absolute version within this region, whereas the LOP in its relative version (the
postulation that given a common currency, inflation rates at the product level should
be similar in both countries)4 appears to hold approximately.

In order to understand the causes of these cross-border price differences we examine

the pricing within international supermarket retail chains. As noted by Burstein and

Gopinath (2014), the opposing results in Burstein and Jaimovich (2012) and Broda
and Weinstein (2008) might stem from the differences in pricing between retailers. In

contrast to the study of Cavallo et al. (2014), who analyse a specific type of online re-

tailer selling distinct, branded products, i.e. large internationally active companies, our

focus is on supermarkets and discounters selling FMCG. Compared to (semi-)durables,
FMCG have typically a lower price. A customer, pushing the shopping cart from shelf

to shelf, is unlikely to gather and evaluate all information on the products available

– at least not in a reasonable amount of time – and even less so the prices of stores

located in a different country. This opens room for differentiated pricing strategies
and pricing-to-market. Indeed, Nakamura et al. (2011) document vast heterogeneity

in pricing across U.S. retailers even for identical products. In this paper, we explore

whether there are systematic differences between the within-chain and the across-chain

cross-country price differentials. In comparison to Burstein et al. (2022), who provide

2Gesellschaft für Konsumforschung.
3That is, for 29% of barcodes which are available in our cross-border sample, we observe cross-border region pairs

for which the price difference is zero in at least one month.

4See Marsh et al. (2012) or Sarno et al. (2003).

ECB Working Paper Series No 2776 / February 20237evidence on the (positive) welfare implication of cross-border shopping amid cross-

border price differences (e.g. due to different currencies), we highlight the potential

arbitrariness of the location of price differences reflecting the retailers’ pricing strate-

gies and market power (documented by e.g. Mejean and Schwellnus, 2009 for an earlier
period of the European Union).

The analysis in this paper proceeds as follows: Section 2 describes the data and region

on which our analysis is based. Section 3 establishes the strong economic integration

of this border region. Section 4 investigates the large cross-border price and inflation

differences. Section 5 traces the origin of these differentials to the pricing strategies of
retail chains. Section 6 examines whether the border effect differs along household or

product characteristics. The paper concludes with a discussion of the implications of

the main results.

2 Data

As other recent studies on international price differences, this paper uses barcode level

transaction data. This allows us to identify and compare identical products across

different locations and over time. The focus on identical products eliminates the het-

erogeneity bias identified by Handbury and Weinstein (2015) in spatial price index
comparisons.

2.1 Transactions

We use the GfK household panel for Austria and Germany for the period from 2008 to

2018. Households in the panel scan and document their everyday purchases of FMCG,

which consist mostly of groceries and personal care products, but contain also some

household maintenance products, pet food, and gardening equipment. The raw sample
comprises over 300,000 different products and about 8.5 million transactions summing

up to sales of about 17.5 million euro. Most products (barcodes), however, were pur-

chased by consumers in only one of the two countries. Once we restrict the sample to

products sold on both sides of the border, we are left with a tenth of products and a
fifth of transactions.5 For each transaction, the data set provides information on the
manufacturer, brand, product type (which we map to the corresponding COICOP6
categories), the product’s price (including tax), and the quantity and units purchased.

Furthermore, it contains the name and the type of the supermarket where the trans-

5This has also been documented in Broda and Weinstein (2008) and Beck et al. (2020). We lose in particular store

brands and other local brand products. See Appendix Table 11.

6The classification of individual consumption by purpose (COICOP) categories, adapted to the needs of harmonized

indices of consumer prices (HICP), is commonly used in inflation statistics.

ECB Working Paper Series No 2776 / February 20238action took place,7 and the home region of the household. In the Austrian data the
lowest regional level is the district area (Bezirk), and in the German data the postal
area.8

2.2 Retailers

The FMCG market in both countries is dominated by a few large chains. Most of the
retailers in each country either belong to a parent company that centralizes procure-

ment or use a common sourcing service provider. For some small retailers we were not

able to identify a parent company or sourcing service provider. Because these stores

seem to be very local in scope, we exclude them from the analysis.

Restricting the sample to transactions at the dominant (in terms of transaction volume)

supermarkets leaves us with one large retail chain per country operating only nationally,
five international retail chains, which operate in both countries, and one sourcing service

provider serving a number of smaller retailers in both countries. For the internationally

operating retailers and those using a common sourcing service provider, we assume

centralized procurement. This implies an identical input price for all outlets of a given
retail chain in both countries. A key hypothesis tested in this paper is that cross-

country price differences within the same retail chain (and among retailers connected

by a common sourcing service provider) are small.

2.3 Regional scope

Apart from the product and retailer differences, the main obstacle to isolating the effect
of an administrative national border is controlling for other, potentially unobservable,

factors that might differ between the two countries. Within the euro area, such fac-

tors may include distance, differences in language, but also income and consumption

preferences. If there was no cost of arbitrage or if preferences were fully homogeneous
across the border, the various retailers should not be able (and not even be trying) to

price to market.

To eliminate such differences, we study a region which is as homogenous as possible.

The Austrian-Bavarian border region gets very close to the ideal of a homogenous
region along many dimensions: It is part of the European Union, the Schengen area,9
and the euro area, and thus free of tariffs, travel restrictions, and currency fluctuations.
But not only the currency is the same, also the language (actually even the dialect). It

7Unfortunately the exact location of the supermarket, and shopping trips across the border are not documented in

the data set.

8See Annex A.2 for details on the data cleaning.
9The Schengen area covers 22 countries of the European Union and the four member states of the European Free

Trade Association. There are no formal border controls between countries in the Schengen area.

ECB Working Paper Series No 2776 / February 20239is connected by a tight road, highway and railway infrastructure. Furthermore, regional

treaties for cross-border labour mobility have been in place for decades, including a

special double-taxation treaty for cross-border commuters.

Focussing on this economically and culturally integrated region eliminates most of the

factors commonly used to explain large price differences at borders. There are very
few border regions in the world where demand and supply conditions on both sides of

a national border are that similar. This motivates our use of a regression discontinuity

design (RDD), which rests on the assumption that unobserved characteristics do not

jump at the discontinuity point.

We implicitly control for distance by restricting the sample to a tight (approximately 60
kilometers on each side of the border) band along the Austrian-German border.10 We
match the information on the region in which the household that reports the transaction

lives with geo-information to calculate several distance measures. We split the border
area in Austria and Bavaria each in 19 roughly equal-sized regions,11 resulting in 703
region pairs (171 pairs within each country and 361 across countries). Based on these,

we analyse differences in consumption as well as in prices and price changes (inflation)

within and across the two countries.

3 The border

There is a multitude of possible reasons for differing prices for an identical product in

two countries. By choosing a highly integrated border region within a monetary and

economic union we rule out or reduce most, but not all of them. A retailer charges

different prices in a given region pair if it is optimal (profit maximizing) and feasible
for him to do so. Separately maximizing profits in each region and thus differentiating

prices across regions for a specific product can be optimal if supply or demand differ

between the two regions. In this section we document that neither taxes nor local costs

nor different market structures nor consumption preferences can explain the observed
price differences. At best, differences in the local economic power as measured by GDP

might be exploited by retailers.

10A large part of the – in total – 120km-wide band has been a territory of the Prince-Archbishopric of Salzburg.
That is, from the 14th until the early 19th century a large part of our sample region was united within a single country.
Other parts of the region (e.g. the Innviertel) have switched their country assignment multiple times until the early
19th century as a fallout of wars and deals between the various royal houses of Europe. Since 1815/1816, however, the
border has been unchanged. Therefore the industrialization and the evolution of mass retail in that region have been
shaped by the borders as they are today.

11See Annex A.3 for details on the definition of these regions.

ECB Working Paper Series No 2776 / February 2023103.1 Differences in local costs and other supply factors

The value-added tax (VAT) rates differ only slightly between Austria and Germany.

With few exceptions, the standard tax rate for food and personal care items is 20% in

Austria and 19% in Germany, while the reduced rate of 10% (7%) applies to most food

items in Austria (and Germany). Given these small differences, all results we report
refer to gross prices including VAT.12

With respect to the cost structure of retailers, the available data allow us to compare
the local price of land on which outlets are built and the corporate tax rates in the two

countries. Unfortunately, there are no harmonized data on local wages in the retail

sector for the two countries.

Table 1: Cost and demand factors potentially affecting price setting of retailers
GDP Popu-
lation
(’000)

Property Corporate Med. house-
hold income
taxes
(’000AC)
(%)

per cap.
(’000AC)

prices
(AC/m2)

Austrian border regions
Northern Upper Austria
Salzburg and Southern Upper Austria
(Part of) Tyrol

German border regions
Lower Bavaria
Eastern Upper Bavaria
Western Upper Bavaria

151
255
514

76
231
538

25.0
25.0
25.0

28.1
27.7
27.8

33
34
34

30
32
34

43
41
41

32
34
31

795
847
573

494
519
568

Note: Property prices refer to land ready for construction derived from transaction data at the county/district
level averaged over the years 2016-2018. Population-weighted averages over counties in Germany and over
districts in Austria. Corporate tax rates for Germany are the sum of federal and municipal corporate tax
rates for 2018; for Austria it is the federal corporate tax rate effective since 2005. Median household income
is the annualized midpoint of the (net) income bracket of the median household covered by the GfK sample.
Population counts refer to the end of 2018. Appendix A.1 provides details on the data sources.

Concerning land prices, we draw on transaction data for properties at the level of
counties (Landkreise) for Germany and at the level of political districts (Bezirke) for

Austria. As property prices are very heterogeneous at the county/district level, we

aggregate them to three big regions on each side of the border and compare the neigh-

boring regions across the border, i.e. Northern Upper Austria with Lower Bavaria,
(part of) Salzburg and Southern Upper Austria with Eastern Upper Bavaria and (part

of) Tyrol with Western Upper Bavaria. Table 1 lists property prices in euro per square

meter, averaged over the years 2016 to 2018 (which is the overlapping period for which

data are available in both countries). With the exception of the region pair Northern
Upper Austria/Lower Bavaria where property prices are higher on the Austrian side,

prices appear to be quite similar in the other cross-border region pairs.

12Excluding VAT does not change the results presented in this paper as evident from comparing the first and second
rows of Tables 3 and 4. The VAT rates in both countries have been constant for the products covered during the entire
sample period 2008-2018.

ECB Working Paper Series No 2776 / February 202311Austria features a uniform corporate tax rate of 25%, while Germany’s corporate tax
rate contains a regional element that can vary across communities.13 But, as can be
seen from Table 1, even though corporate taxes may vary across regions in Germany,

the effective variation considering both federal and local taxes is small. Total corporate
taxes hover around 28% in the German border regions. The difference of about three

percentage points relative to Austria, albeit non-negligible, is unlikely to be a major

driver of price differences – also because corporate taxes are a minor element in the

cost structure of retailers.

Different market structures could be another source of international price differences.
In the structural issues report of the ECB (2011)14 Austria and Germany stick out as
the two countries within the euro area with the highest share of discounters (Chart 5b

ibid.). Outlets appear to be bigger on average in Germany as the share of hypermarkets

(sales area above 2,500 square meters) is higher than in Austria (Chart 4 ibid.). Market
concentration as measured by the Herfindahl-Hirschman index at the retailer level –

called downstream market concentration in the report – is found to be lower in Austria

than in Germany while at the parent company and buying group level – called upstream

concentration – Austria and Germany are quite similar, ranking in the top group of euro
area countries (Tables 5 and 6 ibid.). These findings document that the structure of the

retail markets in Austria and Germany at the national level is broadly similar. Given

that retail markets tend to be highly integrated within countries, we do not expect

large differences in the retail market structure between our border regions either.

Overall, the cost environment and market structure on both sides of the border are
similar. Thus the supply side can be viewed as largely identical across the border.

3.2 Differences in local demand

Another potential driver of international price differences are differences in the local
demand conditions. When income levels differ, international retailers might be able to

increase profits by pricing to market. The net income of the households in our sample,

however, is very similar at this aggregation level in both countries (column 3 of Table

1).

Rural areas are spread throughout all border regions, with somewhat more urban cen-

13Germany has a multi-level corporate tax system: corporations paid during most of the sample period a federal base
corporate tax rate of 15% plus a solidarity contribution (Solidaritätszuschlag) of 0.825% plus a rate of 3.5% times a
local corporate tax multiplier varying between 240% and 400% across the communities in our sample. This implies a
variation of the overall effective corporate tax rate between 24.2% and 29.8% across the communities considered which
is largely levelled out by aggregating to the regional level (second column of Table 1).

14The structural issues report combines a wide range of national data. Although it covers only the early years of our
sample period, it remains the only comprehensive data source for indicators of the retail market structure in all euro
area countries until today.

ECB Working Paper Series No 2776 / February 202312tres on the Austrian side. This might be reflected in the GDP per capita reported in

column 4. It is quite homogenous within each country across the three big regions,

but about one quarter higher on the Austrian side. This is surprising at first sight
as Bavaria as a whole reports a higher income per capita than Austria.15 Within the
border region, however, the situation is reversed. The Austrian regions close to the

border include three major cities with more than 100,000 inhabitants (Linz, Salzburg

and Innsbruck) while in the respective German border regions (which do not include

Munich) the biggest cities (Landshut and Rosenheim) count only around 70,000 in-
habitants. Additionally, Salzburg and Innsbruck are well-known tourist centers which

contribute heavily to local GDP.

The difference in GDP per capita could incentivize retailers to differentiate prices

between the two countries. The direction, however, is not obvious. On the one hand,

it is optimal for a retailer to charge higher prices in less densely populated, rural areas
(Adams and Williams, 2019), and recent research has shown that food prices in the

USA indeed decrease with city size (Handbury and Weinstein, 2015). But as, on the

other hand, even the more remote parts of our sampling regions are well connected

and economically vibrant, the income difference might dominate the demand elasticity,
which would suggest on average higher prices in Austria.

3.3 Differences in preferences

Another key demand-side factor which is in general hard to capture is preferences.

However, given the cultural homogeneity of our sample region – even the local cuisine

in the border region is very similar – it is plausible to assume similar preferences for

food and beverages, which account for the bulk of products in our sample. To establish
this more formally, we compare the consumption baskets on both sides of the border.

Because the actual products (identified by a barcode) might differ on both sides of

the border for marketing reasons (Broda and Weinstein, 2008), we assign all products

to COICOP groups at different levels of granularity. This allows us to verify if, for
example, the chocolate consumption in the Austrian and Bavarian border regions is
comparable, without requiring that both consume the same brands or varieties.16 Based
on this, we calculate the correlation of the consumption baskets for each region pair j

15Over the period 2008-2018, the average GDP per capita amounted to approx. 39,000 euro in Austria and to approx.
41,000 euro in Bavaria. The difference applies to both gross and net income: OECD (2019) reports a similar overall
taxation of labor in the two countries. The overall tax on wages including social security contributions amounts to
48.5% in Austria and to 49.7% in Germany (for single earners of average income, sample period 2008-2018).

16Local varieties might be – besides the number of panelists being small relative to the number of products – one
of the reasons for the small share of common barcodes in Table 2. Furthermore, different package sizes or sometimes
even different vintages of the same product carry different barcodes, and are thus excluded. Matching by barcode is
the strictest possible mapping of products: It requires the identity of products, even in terms of package size. Different
package sizes would require the household to calculate the price per unit and potentially to consume the product at
different rates. The restriction to identical barcodes excludes such frictions to arbitrage.

ECB Working Paper Series No 2776 / February 202313for each year t, i.e. the correlation of annual expenditure shares at the 4- and 5-digit

COICOP levels. We then study the determinants of this correlation with the regression

equation

Yjt = β0 + β11AT (j) + β31B(j)
(cid:125)

(cid:123)(cid:122)
border/country effects

(cid:124)

+ γ1t + γ2t × 1AT (j) + γ3t × 1B(j)
(cid:125)
(cid:123)(cid:122)
border/country trends

(cid:124)

+ϵjt,

(1)

where for now the dependent variable Yjt refers to the correlation of consumption bas-
kets between region pairs and ϵjt to independent and identically distributed residuals.
The effect of the border is captured by the border dummy 1B(j), which assumes a
value of one for cross-border region pairs and zero otherwise. The other independent
variables include a country dummy for Austria 1AT (j), which assumes a value of one
if the region pair j is within Austria, a time trend t, and interactions of the trend
with the two dummies to account for a possible change of country or border effects
over time. The constant β0 captures the correlation of preferences when both regions
of the pair are in Germany (“base” level). The “Austria effect” and the “border ef-

fect” coefficients reflect the additional correlation between within-Austria and between
cross-border region pairs, respectively, relative to the within-Germany region pairs.17

The first two columns of Table 2 show that the consumption baskets are highly corre-
lated between Bavarian and Austrian border regions. The cross-border correlation is

86% (base plus border effect) at COICOP4 granularity, and still 78% at the more de-

tailed COICOP5 granularity. The consumption structure across the border is slightly

less similar than within each country. The correlation shrinks by 0.03 and 0.10, respec-
tively.

A correlation of 78% at the COICP5 level is nevertheless considerable and an indica-

tion that differences in consumption are of minor relevance for cross-border price and

inflation differences. The three lower rows of columns (1) and (2) in Table 2 show how

consumption preferences have evolved over the eleven years in our sample. Whereas
Austria and Germany converged in terms of their within-country heterogeneity, the

similarity across the border did not increase.

4 Border effects

Despite the similar consumption structure, the set of products consumed and available
in each country might differ. Prices and price changes might differ as well. In this

section we show that this is indeed the case, even in a highly integrated region.

17That is, the correlation of consumption baskets of cross-border region pairs is the sum of the estimated border effect
and the base level. Specification (1) is applied analogously to price and price change differences in subsequent sections.

ECB Working Paper Series No 2776 / February 202314Table 2: Border effects

(1)
Basket
correlation
(COICOP4)

(2)
Basket
correlation
(COICOP5)

(3)

(4)
Common Abs. price Abs. price
barcode
difference
share

change
difference

(5)

Constant

Austria

Border

Common trend

Austria trend

Border trend

Frequency
Observations
Adj. R2

0.89***
(0.004)
0.05***
(0.005)
-0.03***
(0.004)
0.004***
(0.001)
-0.003***
(0.001)
-0.003***
(0.001)

year
7,733
0.14

0.88***
(0.003)
0.04***
(0.004)
-0.10***
(0.004)
0.004***
(0.001)
-0.003***
(0.001)
-0.006***
(0.001)

year
7,733
0.49

0.16***
(0.001)
0.08***
(0.001)
-0.14***
(0.001)
0.001***
(<0.001)
-0.005***
(<0.001)
-0.001***
(<0.001)

year
7,733
0.93

8.11***
(0.40)
2.91***
(0.52)
15.31***
(0.70)
0.00
(0.004)
0.01
(0.006)
0.01
(0.008)

bi-month
333,733
0.12

11.21***
(1.14)
2.30
(2.02)
4.64***
(1.41)
0.01
(0.01)
0.04
(0.03)
-0.01
(0.02)

bi-month
44,294
0.07

Note: Sample period 2008–2018. 703 region pairs. Standard errors in parentheses (columns
4 and 5: robust, barcode-clustered standard errors). OLS regressions. Bi-month and retailer
controls in columns 4 and 5 not reported. Dependent variables: (1/2) pairwise correlation
of COICOP4/COICOP5 composition of (annual) baskets of each region pair, (3) common
barcodes in each region pair as share of all barcodes in the region pair, (4) absolute, within-
retailer (log) price difference and (5) absolute y-o-y price change difference of each region pair
at bi-monthly frequency. Germany effect in (1)-(3) is the constant, in (4) and (5) the sum of
constant, avg. coefficient of retailer controls and avg. coefficient of month controls. Asterisks
indicate the level of significance, (*) at the 10%, (**) at the 5%, and (***) at the 1% level.

ECB Working Paper Series No 2776 / February 2023154.1 Basket differences

We start by comparing products, i.e. barcodes, available in both countries. To do
so, we replace the dependent variable Yjt in Equation (1) with the share of common
barcodes in a given region pair among all barcodes in that region pair. Because of the

fine regional grid in our benchmark specification the number of households per region
is limited. As household shopping is very idiosyncratic (Kiss and Strasser, 2022) the

share of common barcodes between any two regions in a given year amounts to only
roughly 10% of all barcodes.18 Across the border this subset of common products
shrinks even further, as shown by column (3) of Table 2. On average only about 2%
of barcodes are available in a given cross-country pair of regions in a given year,19 i.e.
14 percentage points less than between German regions.

Figure 1: Barcodes common across the border

Note: Horizonal axis measures the distance from the Austrian-German border in kilometers, negative distances
refer to Germany and positive ones to Austria. Sample period 2008-2018. Bin width 20 km. Based on random
subsamples of 48,000 barcodes per bin.

The results in column (3) stand in stark contrast to those in column (2). Even though
the households in the two countries share a similar broadly defined consumption basket,

18Consumers in Germany can choose from a larger set of different barcodes, which results in a lower share of barcodes
purchased within a given time interval in two German regions (16%) than of those purchased in two Austrian regions
(24%). According to Neiman and Vavra (2019), the concentration of aggregate spending on the same products has
decreased. Households have increasingly concentrated their spending on a few preferred products, which at the same
time may well be increasingly different products from their neighbors. We do not observe such a trend in our sample.

19Despite this low share, our cross-country price comparisons are based on more than 14,000 products.

ECB Working Paper Series No 2776 / February 202316the actual items in their baskets are rarely identical. There is only very limited evidence

that the items become slightly more similar in the direct proximity of the border

(Figure 1). Metaphorically speaking, the dinner tables look very similar on both sides

of the border, but the packaged food products are labelled differently. The small share
of common barcodes (compared to region pairs within the same country) is a first

indication of market segmentation by product differentiation along the border, which

is not grounded in preference differences.

Figure 2: Products sold in border regions of both countries (percent)

Note: Average over all cross-border region pairs. Share of expenditure and transactions in products (barcodes)
sold in both regions among all purchases in either region of a given cross-border pair, in percent. Share of
barcodes sold in both regions of a given pair relative to all barcodes sold in this region pair. Sample period
2008-2018.

Zooming in on expenditure in common barcodes we see stark differences between prod-

uct categories. Figure 2 shows shares ranging from 9% to 72% of total expenditure

depending on the product type. Expenditure on personal care items (in particular

electric appliances, such as electric toothbrushes), on products for pets, but also on
non-perishable products such as chocolate, coffee, tea and spirits as well as on clean-

ing and household maintenance products is more concentrated on the same products

in both countries than spending on perishable food products and beverages. Fruits

and vegetables, which often are loose, unpackaged goods, display the lowest share of
expenditure in common barcodes, although households on both sides of the border eat

the same types of apples and cucumbers.

58941013312136161711181992020919219202812283182635112735143941163742234448277272020406080FruitsVegetablesBeerSoft drinks, juiceMilk, cheese, eggsOils, fatsOther foodBread, cerealCleaning, householdSpiritsCoffee, tea, cocoaConfectionery, jamProducts for petsPersonal carePersonal care (electric)Products available in both countries (%)expendituretransactionsbarcodesECB Working Paper Series No 2776 / February 2023174.2 Price differences

As local costs and demand factors are largely similar, there appears to be little reason

for strong price differences across this purely administrative border cutting through an

integrated region. The proximity of the regions in our sample – both within and across

countries – suggests that transportation costs are also similar, so that distance might
not matter for cross-border price differences.

The irrelevance of distance close to the border becomes obvious in a RDD setting,20 as
applied previously e.g. by Gopinath et al. (2011) and Beck et al. (2020). Exploiting

the pseudo-experimental characteristics of our data, Figure 3 shows how the price

of product i relative to the products’ average price in both countries at time t (in

logarithmic terms) behaves given a binary treatment, namely being on one side of
the border. The assignment to this treatment is determined by the distance to the

border on the horizontal axis in either direction. Figure 3 shows that relative prices

are largely constant within each country. Even as we approach the border the prices

do not converge. That is, the role of distance to the border for prices within a 100
kilometer band is negligible. This mirrors the finding of Burstein et al. (2022) that

prices in Switzerland are invariant to distance to the border within the country. What

does matter is the side of the border. At the border we observe a large price gap. The

figure suggests that prices on the Austrian side (on the right of the zero line) are at
least 10% higher than those on the German side.

The border leaves its mark on the distribution of prices of individual products. The left

panel of Figure 4 plots the (non-absolute) logarithmic price differences per product in

each cross-border region pair (Austrian minus German prices). Cross-border price dif-

ferences are large and go in either direction. Reassuringly, the mode of price differences
is at zero, but the mode represents only about 14% of all cross-border observations.

The mode for within-country region pairs is far more pronounced. The bar at zero in

the right panel of Figure 4 shows a share of zero price differences of about 48% within
Austria and of about 56% within Germany.21

The right panel of Figure 4 plots the absolute within-country and cross-border price
differences. It highlights the small dispersion in prices within countries compared to

the large cross-border price dispersion, supporting earlier findings by Reiff and Rumler

(2014), Gopinath et al. (2011) and Beck et al. (2020). Table 3 quantifies this observa-

tion: Within Germany, the median absolute price difference across regions is zero and

20See Imbens and Lemieux, 2008. This paper uses the Stata implementation by Calonico et al. (2017).
21See Table 14 in the Appendix for descriptive statistics on barcodes that are always, sometimes or never at the mode
of zero. More uniformly priced products, i.e. products which are observed more often at the mode of price difference
distribution, tend to be on average somewhat cheaper and more frequently purchased.

ECB Working Paper Series No 2776 / February 202318Figure 3: Price gaps at the border

Note: Each dot shows the average log price deviation from the mean for each distance bin, together with the
respective 95% confidence interval. The solid lines are third-order polynomials fitted to these averages. Joint
barcodes only. Horizonal axis measures the distance from the Austrian-German border in kilometers, negative
distances refer to Germany and positive ones to Austria. Number of bins determined by the integrated mean
squared error optimal evenly spaced method, separately for Austria and Germany.

within Austria about 5%; across the border, however, it amounts to almost 20%.

The distribution is not symmetric around the mode. Excluding the probability mass at

the mode, the distribution is largely symmetric around a (cross-border) price difference
of approximately 18% (Table 3), which suggests the existence of an optimal cross-

border price differential. The pronounced mode suggests that the benefits of uniform

pricing imply a tradeoff for firms, which results in the bi-modality of the price difference

distribution. The positive median cross-border price difference of 13% shown as dashed
line in the left panel of Figure 4 reflects the overall higher price level in the Austrian

regions, in line with the evidence of the RDD analysis.

To establish the statistical significance of these price differences, we regress the absolute
log price difference Yirjt (for product i in retailer r in region pair j at time t) at bi-
monthly frequency on the border dummy:

Yirjt = β11AT (j) + β31B(j)
(cid:125)

(cid:123)(cid:122)
border/country effects

(cid:124)

(cid:124)

+ γ1t + γ2t × 1AT (j) + γ3t × 1B(j)
(cid:125)
(cid:123)(cid:122)
border/country trends
λr
θm(t)
+
(cid:124)(cid:123)(cid:122)(cid:125)
(cid:124)(cid:123)(cid:122)(cid:125)
retailer controls
month controls

+ϵirjt

+

(2)

-.1-.050.05.1relative log price-100-50050100distance in kmSample average within binPolynomial fit of order 3ECB Working Paper Series No 2776 / February 202319Figure 4: Cross-country price differences

Note: The left histogram shows the distribution of non-absolute cross-border log price differences in percent
(Austrian minus German prices). The right histogram shows cross-border and within-country absolute log price
differences in percent. The dashed lines refer to the median of the respective distribution. y-axis: Frequency
in percent. Bin width is 4 percentage points, except for the “zero” bin, which only contains zero values.
Observational unit: product × region pair × retail chain × bi-month. 19 regions per country.

The dummies, trend variables and interaction terms are defined as in Equation (1),

augmented by retailer and (seasonal) calendar-month effects.

The results in column (4) of Table 2 confirm the significance of the additional price

gap at the border. It amounts to 15 percentage points, rendering cross-border price

differences on average twice as large as within-country price differences. The test of the
null hypothesis that the border effect equals the Austrian, i.e. largest, country effect is

rejected at any common level of significance (p-value: 0.00). This result is robust over

time, to different regional delimitations and other specifications, including controlling

for distance and income. Both distance and the differences in GDP per capita fail to
explain the price differences between the regions of the two countries.22

22The coefficient on log GDP per capita differences is small (-0.004 percentage points) and insignificant; the coefficient
of distance between each region pair (in kilometers) is also small (0.004 percentage points) but significant. Including
either variable in Equation (1) does not improve the adjusted R2 and leaves the remaining coefficients virtually un-
changed. Appendix B.5 assesses the link between regional characteristics (income, income growth, age, distance to the
border) and the price level directly. Differences in the average income level between the regions do not explain price
level differences between Austrian and German regions beyond what is already captured by the border effect.

051015-100-50050100 price differences0204060020406080100 absolute price differencesacross countrieswithin ATwithin DEmedian across countriesmedian ATmedian DEECB Working Paper Series No 2776 / February 202320Table 3: Moments of price differences between regions

∆p
∆p
med. mean

Cross-border
∆p
std.dev.

Austria

Germany

∆p

|∆p|
normal med. med. mean med. mean

|∆p|

|∆p|

|∆p|

|∆p|

overall
excl. VAT
excl. mode

13.4
10.8
18.3

15.8
13.2
18.0

27.8
28.0
29.0

0.0
0.0
0.0

19.4
18.9
22.6

5.4
5.4
22.6

15.2
15.2
26.6

0.0
0.0
18.3

10.3
10.3
21.6

Note: Medians, means, and standard deviations of price differences between regions by type of
region pair. 19 regions per country. Sample period 2008–2018. ∆p are log price differences.
Cross-border price differences are Austrian minus German prices. The column “normal” reports
the p-value of testing the null hypothesis that the respective price differences are normally dis-
tributed.

4.3

Inflation differences

The inflation rates of Austria and Germany are not necessarily synchronized despite the

regions’ proximity and similarity. The evidence on price differences indicates a failure of
the LOP in its absolute form, suggesting that arbitrage is impaired by some frictions.

The cost of arbitrage entailed by these frictions bounds the range of arbitrage-free

international price differences. If the price differences are already at the no-arbitrage

if that constraint is binding, the prices of these products will not
constraint, i.e.
diverge further. This one-sided bound on relative prices might entail synchronized

price changes between countries. In this section, we explore to what extent this weaker

condition – LOP in its relative version (e.g. Sarno et al., 2003) – holds between the

two countries.

Our dataset allows us to calculate sample inflation rates based on a common basket of
identical FMCG products. During the period from 2008 to 2018 these annual inflation

rates have been approximately one percentage point higher in the Austrian border

region. By comparison, over the same period the official HICP inflation rate of food

and beverages is virtually the same in both countries, while the overall HICP inflation
rate has been only slightly higher in Austria.

Looking at aggregate figures conceals, however, the heterogeneity of the underlying

price change patterns between the two countries. Figure 5 compares the annual rate of

price change for each barcode i and retailer r for each region pair j, both as absolute

and non-absolute difference in a two-month period t.

The mode of cross-border price change differences is again zero, which can be seen from
the left panel. With 19% of price changes being identical, the mode of price change

differences is more pronounced than that of prices. In contrast to price differentials,

the price change differentials are symmetric around zero, which suggests that the two

countries share a common price trend (or no trend at all) during the sample period.

ECB Working Paper Series No 2776 / February 202321Figure 5: Cross-country price change differences

Note: The left histogram shows (non-absolute) cross-border y-o-y price change differences in percent (Austrian
minus German price changes). The right histogram shows cross-border and within-country, absolute log price
differences in percent. The dashed lines refer to the median of the respective distribution. y-axis: Frequency
in percent. Bin width is 4 percentage points, except for the “zero” bin, which only contains zero values.
Observational unit: product × region pair × retail chain × bi-month. 19 regions per country.

That is, when comparing price changes of common barcodes in our sample at a more

disaggregate level, i.e. within region and retailer, we find that prices increased roughly

at the same pace in German and Austrian border regions (mean difference 0.3 per-
centage points p.a., median 0). This small average, however, conceals price change

differences of a median size of 13 percentage points in either direction at the product

level (light blue line in right panel). Such large differences may easily occur in a panel

when irregularly purchased products are occasionally on sale, so that prices vary in
both directions both over time and between regions.

The medians, shown as dashed lines in the right panel of Figure 5 and explicitly given
in Table 4 suggest furthermore that price changes do not differ more across than within

countries. A regression analogous to Equation (2), with absolute price change differ-

ences as dependent variable and controlling for retailers and time, shows that price

changes are more dispersed across the border than within Germany (rightmost column
in Table 2), but this is not significantly different from within Austria (p-value: 0.23).

Overall, we cannot reject the relative version of the LOP.

05101520-100-50050100 price change differences0510152025020406080100 absolute price change differencesacross countrieswithin ATwithin DEmedian across countriesmedian ATmedian DEECB Working Paper Series No 2776 / February 202322Table 4: Moments of price change differences between regions

∆π
∆π
med. mean

Cross-border
∆π
std.dev.

Austria

Germany

∆π

|∆π|
normal med. med. mean med. mean

|∆π|

|∆π|

|∆π|

|∆π|

overall
excl. VAT
excl. mode

0.0
0.0
0.5

0.3
0.2
0.3

26.3
26.4
27.7

0.0
0.0
0.0

13.2
13.3
15.7

15.1
15.1
17.6

20.6
20.6
22.9

10.3
10.4
14.1

15.8
15.9
19.0

Note: Medians, means, and standard deviations of price change differences between regions by
type of region pair. 19 regions per country. Sample period 2008–2018. ∆π are price change dif-
ferences in percentage points. Cross-border price change differences are Austrian minus German
prices. The column “normal” reports the p-value of testing the null hypothesis that the respective
price change differences are normally distributed.

4.4 Persistence of price differences

Can consumers in our sample actually benefit from these price differences? In the case
of Switzerland, where a price gap to neighboring euro-countries is sustained by different

currencies, Burstein et al. (2022) document sizeable welfare effects, particularly for

households living close to the border who are more likely to cross it for shopping. The

price differences across the border in their study appear to be sufficiently persistent,
i.e. not due to short-term sales, so that consumers can be certain to benefit from cross-

border shopping. Within a currency area, however, this may be less clear. Kaplan and

Menzio (2015) argue that U.S. households seem unable to time their purchases to fully

benefit from temporary sales (in a given store), but that some U.S. households are very
good at assigning their purchase to stores with overall lower-than-average prices. From

a retailer perspective, however, to discourage cross-border arbitrage, price differences

for a given product should not be too persistent.

Estimating the persistence of prices at the product level requires price observations for

a given product in a region pair in many periods. To increase the number of products in
this analysis we aggregate the households to three regions on each side of the border23
and transactions to a bi-monthly frequency. We regress the price difference between

the resulting region pairs separately on selected lagged differences, interacting with the

regional dummies, and month and retailer controls.

Yirjt = β11AT (j) + β31B(j)
(cid:125)

(cid:123)(cid:122)
border/ country effects

(cid:124)

+ γ1Yirj,t−τ + γ2Yirj,t−τ × 1AT (j) + γ3Yirj,t−τ × 1B(j)
(cid:125)
(cid:123)(cid:122)
border/ country autoregressive coeff.

(cid:124)

+

λr
(cid:124)(cid:123)(cid:122)(cid:125)
retailer controls

+

θm(t)
(cid:124)(cid:123)(cid:122)(cid:125)
month controls

+ϵirjt

(3)

In this equation Yirjt is the absolute log price difference or absolute price change dif-

23For comparison, we report the results with 19 regions in each country in Table 22 of Appendix B.

ECB Working Paper Series No 2776 / February 202323ference (for product i, in retailer r, in region pair j during the two-month period t).
Yirj,t−τ refers to the lagged difference at offsets of τ = 1, 2, 3, 6 periods, corresponding
to 2, 4, 6 and 12 months.24

The upper panel of Table 5 shows the autoregressive coefficients on price differences

within and across countries for each offset. Overall, price differences are only weakly
correlated over time within countries (coefficient on first lag for Germany is 0.24),

but significantly more so across countries (additional 0.28). The overall autoregressive

coefficient of 0.52 over two months remains relatively stable thereafter, which indicates

some persistence in cross-border price level differences. This could indeed provide
arbitrage opportunities, but they might – in line with Kaplan and Menzio (2015) – still

be too small to be recognized and too small to justify a cross-border shopping trip.

Table 5: Persistence of price and price change differences

Offset

2 months

4 months

6 months

1 year

Germany (base)
Austria (additional)
Border (additional)

Observations
R2

Price differences

0.24***
-0.07***
0.28***

57,127
0.27

0.21***
-0.05**
0.28***

50,956
0.25

Price change differences

Germany (base)
Austria (additional)
Border (additional)

Observations
R2

0.25***
-0.10**
-0.06

12,779
0.09

0.19***
-0.03
-0.03*

11,446
0.08

0.19***
-0.03
0.29***

47,396
0.25

0.21***
-0.02
-0.05

10,835
0.08

0.18***
-0.03
0.25***

44,801
0.23

0.35***
-0.01
-0.04

16,903
0.15

Note: Sample period 2008–2018. 15 region pairs. Bimonthly frequency.
The table shows the within- and cross-country autoregressive coefficients
of price differences by length of lag from an OLS regression (with robust,
barcode-clustered standard errors, not reported). Explanatory variables: in-
teraction of first, second, third and sixth lag of absolute log price differ-
ence (columns) with regional dummy (rows). Trend, bi-month and retailer
controls not reported. Dependent variable, upper panel: absolute, within-
retailer (log) price difference. Dependent variable, bottom panel: absolute,
within-retailer y-o-y price change difference. Asterisks indicate the level of
significance, (*) at the 10%, (**) at the 5%, and (***) at the 1% level.

In contrast, differences in price changes are not more persistent across the border than

within a country. This reflects that spells of price changes in the same direction are

constrained by the arbitrage bounds on price differences. The positive sign of the base

coefficient in the lower panel of Table 5 suggests that we observe persistent genuine,
i.e. non-sale, price changes. If price changes were largely due to sales, we would expect

price changes to reverse. This would imply a negative autoregressive coefficient, which

24The dummies and interaction terms are defined as in Equation (1). Including multiple lags in a single equation

leads to small-sample problems. For this reason, we run separate regressions for each lag here.

ECB Working Paper Series No 2776 / February 202324is rejected by the data.

5 Retail network versus national border

We have documented that even though GDP per capita is slightly different across the

border, preferences in the two countries appear to be similar. The prices and price
changes of many products, however, differ considerably between the two countries – in

either direction. This suggests that consumers can gain only little from blindly shopping

across the border, but more from product-by-product arbitrage. Such cherry-picking

requires a careful price comparison. The cost of obtaining the necessary information,
e.g. comparing prices and keeping up with promotions across the border, might render

cross-border arbitrage so unattractive (Reis, 2006; Nevo and Wong, 2019) that retailers

can maximize their margins separately on each side of the border.

There is no plausible reason on the cost side for systematic differences in the wholesale

price of goods between retailers. The price difference can hardly be justified by dif-
ferences in local costs, because prices deviate in both directions across the border and

because most of the retailers operate in both countries (e.g., Aldi, Rewe/Billa, Lidl,

Penny) or use a common sourcing service provider (Markant). Therefore, Burstein and

Gopinath (2014) argue that the differing results in Burstein and Jaimovich (2012) and
Broda and Weinstein (2008) might stem from the differences in pricing across retailers.

According to Nakamura et al. (2011) price setting varies strongly across retailers. In our

sample the presence of two large retailers operating only in either country might then

suggest that the two retail markets are not perfectly integrated. Is the border merely a
result of different retailer composition of the respective market? Are there systematic

differences between within-chain and across-chain cross-country price and inflation dif-

ferentials? To investigate the role of retail chains further, we restrict the data set to

only those supermarket chains which either exist on both sides of the border or use
the same sourcing service provider, implying similar input costs for the participating

supermarkets. This results in a set of six retailers active in both countries.

We first look at price gaps at the border for each retailer separately in Figure 6 using

the same RDD approach as in the previous sections. Within retailers the within-

country distance does not seem to matter as price differences remain constant on either
side of the border. But at the border the prices of each retailer display a striking,

discrete discontinuity. Interestingly, price jumps at the border appear to be larger for

supermarkets than for most discounters.

In order to quantify the within-retailer border effects, we interact the country and

ECB Working Paper Series No 2776 / February 202325Figure 6: Price gaps at the border by retailer

Note: Dots show the average log price deviation from the mean for each distance bin. Solid line is an (un-
weighted) second-order polynomial fitted to these averages. Joint barcodes only, percentage deviation from
mean. Horizonal axis measures the distance from the Austrian-German border in kilometers, negative dis-
tances refer to Germany and positive ones to Austria. Number of bins determined by the integrated mean
squared error optimal evenly spaced method, separately for Austria and Germany.

Retailer

Table 6: Within-retailer border effects: Prices
within
Austria
(additional)

within
Germany

(additional)

Cross-ctry

Test cross-ctry
= max. within
(p-value)

9.85***
Supermarket A
Supermarket B 11.63***
Discounter C
Discounter D
Discounter E
Discounter F

0.47
6.21***
2.97***
7.46***

3.25***
4.69***
0.97
1.91**
3.18**
2.83**

16.43***
16.46***
18.13***
15.10***
8.72***
12.96***

0.00
0.00
0.00
0.00
0.00
0.00

This table presents the within-retailer country and border effect coefficients from the
OLS estimation of Equation (4) with time trends and barcode-clustered standard er-
rors. Sample period 2008–2018. Dependent variable: absolute, within-retailer (log)
price difference of each region pair at bi-monthly frequency. 333,733 observations.
Adjusted R2 = 0.46. Last column H0: border effect = largest country effect. Aster-
isks indicate the level of significance, (*) at the 10%, (**) at the 5%, and (***) at
the 1% level.

-.1-.050.05.1relative log price-100-50050100Supermarket A-.1-.050.05.1-100-50050100Supermarket B-.1-.050.05.1-100-50050100Discounter C-.1-.050.05.1relative log price-100-50050100distance in kmDiscounter D-.1-.050.05.1-100-50050100distance in kmDiscounter E-.1-.050.05.1-100-50050100distance in kmDiscounter FSample average within binPolynomial fit of order 2ECB Working Paper Series No 2776 / February 202326border dummies with the retailer. These interactions capture the within- and across-

country effect for each retailer.

Yirjt =

λr
(cid:124)(cid:123)(cid:122)(cid:125)
within-retailer DE effects
γrt + γAT
(cid:124)

r × 1AT (j)
+ λAT
(cid:123)(cid:122)
(cid:124)
(cid:125)
additional AT effects
r t × 1B(j)
t × 1AT (j) + γB
(cid:125)
(cid:123)(cid:122)
border/ country trends

r × 1B(j)
(cid:125)
(cid:123)(cid:122)
additional border effects
θm(t)
+
(cid:124)(cid:123)(cid:122)(cid:125)
month controls

+ λB

(cid:124)

r

+ϵirjt

+

(4)

A look at the individual chains in Table 6 reveals that the border effect is significant

in all six chains, no matter whether they are discounters or supermarkets. Within

Austria, the basic dispersion is about 2.8 percentage points higher than in Germany (on
average 6.4%), but this is dwarfed by the border: On average, prices differ additionally

by around 15 percentage points across the border. That is, for identical products the

price differences across the border are almost twice as large as within a country. For

all retailers, the additional difference at the border is sizeable and significant, ranging
from 9% to more than 18%. Interestingly, discounters appear to have a smaller within-

country or “basic” dispersion than the two supermarkets (columns 1 and 2), resulting in

a border effect that is larger relative to the within-country dispersion. Nevertheless, in

absolute terms cross-border price differences somewhat larger for the two supermarkets,
in line with the RDD analysis.

The basic dispersion within supermarkets and countries may be stemming from sales,
which often follow a specific cycle but are not necessarily synchronized across retailers.25
The basic dispersion is small (0-11%) for discounters but reaches 10-16% for supermar-

kets. This could be due to the fact that, in contrast to supermarkets, discounters

who have lower prices on average (“everyday low prices”) employ less promotional pric-
ing, e.g. fewer weekly sales, which explains a smaller within-country price dispersion.

Furthermore, price margins of discounters are smaller and thus store prices may de-

viate less from wholesale prices, which should be similar across countries within our

internationally operating chains. Finally, the prices of the independent retailers using
a common sourcing service provider are not more dispersed than those of the other

supermarkets.

It is evident from Table 6 that the national retail subsidiaries set their prices (and

promotions) rather independently. Recalling that the border in this example does not

reflect major differences in preferences, this suggests that the arbitrage cost must be

25If customers arrive at stores at random times, the ones arriving earlier in the week (or month) might obtain a
different price than those arriving later. This, combined with ad-personam offers (rebate cards, discounts), generates a
basic price dispersion within a chain-country even if prices are compared within shorter time intervals. In our case it is
further elevated because we do not distinguish different supermarkets within the parent company (e.g. Billa vs. Merkur
within Austrian Rewe), but only store types (e.g. discounter Penny vs. Rewe supermarkets) within a chain.

ECB Working Paper Series No 2776 / February 202327high. Crossing the border between Austria and Bavaria, however, imposes virtually no

(additional) cost on the shopper. This implies that for a given product this national

border features the same cost of arbitrage as the within-country “border” between any

pair of same-country regions. The key difference appears to be that the national border
has been chosen by retailers for differentiating prices. Retailers could differentiate along

any other line on the map, but – likely due to their existing logistics network – they

chose to follow the national border.

Uniform pricing within countries is very common. DellaVigna and Gentzkow (2019)

find substantial unexploited variation in price elasticities across stores within a chain.
Uniform pricing entails higher prices in more densely populated (due to more outside

options) and in poorer regions (due to tighter budget) than under more granular region-

specific pricing, because uniform pricing cannot account for the more elastic demand

in these regions. Adams and Williams (2019) show that uniform pricing emerges natu-
rally from strategic complementarities. Competition between chains might reduce the

possible extra profit from more price differentiation, and a complete decentralization

of pricing might trigger even competition between stores of the same chain. If con-

sumers expect a certain standard of fairness and are able to at least partially monitor
compliance, e.g. by easy access to online price information of other stores of the same

chain (Ater and Rigbi, 2022), then retailers might adjust their pricing behavior along

the lines of Kahneman et al. (1986). Hitsch et al. (2021) even argue that proper store-

by-store price differentiation might not be feasible given the estimation uncertainty at
such fine granularity, an argument which might extend to price differentiation between

very granular regions as well.

Given this within-country evidence and the tight integration of our sample region, why

do chains refrain from extending uniform pricing also across the border? There are at

least three reason for this, which differ in persistence.

Eizenberg et al. (2021) document that household demand is spatially segmented. They
present an example where chains differentiate prices within a city, exploiting the het-

erogenous mobility of the population. Despite the integration of our sampling region,

despite busses and trains connecting towns on both sides in short time intervals, it is

likely that mobility across the border is still weaker than within borders. This differ-
ence in mobility might make price differentiation profitable, but over time the remaining

mobility difference might disappear and with it the basis for price differentiation.

Another cause of spatial segmentation are heterogeneous information sets. Ater and

Rigbi (2022) stress how informative advertising can stimulate competition. Cross-

border advertising, however, seems to be still uncommon. The information flow across

ECB Working Paper Series No 2776 / February 202328borders requires more than just broadband cables (as in Akerman et al., 2022), but

rather an easy information exchange. International price comparison websites, which

are not well-established at this point, could reduce information frictions and give bite

to the fairness considerations which appear to influence domestic pricing.

Most persistent, however, is the incentive to differentiate prices “somewhere”. A country
(or relevant part thereof) is large enough to estimate demand elasticities at sufficient
precision.26 National borders might act like pre-set boundaries of marketing regions,
which are common knowledge, and can thus simplify coordination between retailers on

less granular pricing (see, e.g., Dobson and Waterson, 2005).

Today, the national border between Austria and Bavaria in itself has little economic
substance. In the past, however, it did matter for economic activity. It is well known

that small variations in historical conditions can affect economic geography down to

the present day (Allen and Donaldson, 2020). In their survey, Lin and Rauch (2022)

point out that the substantial (historical) sunk costs of creating a hub – in our case the
cost of setting up of a national distribution network – can dominate current economic

conditions. Legacy (distribution) networks might not be optimal any more, but can
remain in place for a very long time and influence economic activity today.27

To put this all in a nutshell: Retailers differentiate prices – always and everywhere,

also within-country – but most extensively across national borders. The subsidiaries
of the same retailer charge different prices for the same product in two countries. This

indicates, in line with Mejean and Schwellnus (2009), that the ability of individual

firms to differentiate prices across countries and to segment markets is at the core of

(the lack of) price convergence between countries.

What implications can be drawn from this for price change differences and inflation

differences? The previous section has shown that, on average, price changes in Austria
In
and Germany are comparable, and less dispersed across the border than prices.

order to test whether retailers change prices systematically differently on the two sides

of the border, we rerun regression (4) with differences in annual price changes as the

dependent variable. Table 7 shows that the additional dispersion across the border is
small compared to the basic dispersion (in particular in Austria), and significant in

only two of the discounters.

The results suggest that (sample) inflation is less dispersed across the Austrian-German

26In each country, the regions with uniform prices are larger than the border regions studied here. Obviously, the
retailer will pick the optimal uniform price in each country based on the demand characteristics of the entire region.
The demand characteristics along the border subregion might well differ from those in the rest of the region (with
the same uniform price) and might in fact – as in our example – resemble more those right on the other side of the
border. From this it follows that the incentive for retailers to price to market is not necessarily evident from the demand
characteristics of the narrow border regions.

27Michaels and Rauch (2018) describe this in the context of town locations and traffic routes in France and the UK.

ECB Working Paper Series No 2776 / February 202329Retailer

Table 7: Within-retailer border effects: price changes
within
Austria
(additional)

Test cross-ctry
= max. within
(p-value)

within
Germany

(additional)

Cross-ctry

Supermarket A 11.33***
Supermarket B 18.90***
Discounter C
Discounter D
Discounter E
Discounter F

0.97
11.32***
6.23**
10.52***

5.21*
1.00
0.48
-1.16
-3.82
1.22

5.63**
2.12
6.10**
3.50*
-0.69
5.12**

0.87
0.52
0.04
0.05
0.20
0.15

Note: The table shows within-retailer country and border effect coefficients from the
OLS estimation of Equation (4) with time trends (barcode-clustered standard errors).
Period 2008–2018. Dependent variable: absolute, within-retailer y-o-y price change
difference at a bi-monthly frequency. 44,294 observations. Adjusted R2 = 0.47.
Last column H0: border effect = largest country effect. Asterisks indicate the level
of significance, (*) at the 10%, (**) at the 5%, and (***) at the 1% level.

border than the prices themselves. While retailers maintain different price levels, we

find no evidence that they systematically deviate from a common price trend. Both
countries appear to share important inflation drivers. Price differences within a given

retailer are only weakly correlated over time and more persistent across the border
than within a country.28 Differences across the border in the price of specific products
decay rather quickly. Recalling that the correlation of the absolute cross-border price
differentials over two months is 0.52, large arbitrage opportunities for consumers for

individual products persist not for very long. The price changes in either direction can

offset each other, leading to a less dispersed aggregate inflation.

6 Border effect along household and product characteristics

One might wonder whether the border effect is equally strong for all households, that is,

whether household characteristics matter for within- or cross-country price differences.

Likewise, market segmentation might be limited to a few product categories. The first

part of this section studies the relevance of household characteristics. The second part
looks for differences between product categories and studies if the origin of the product

matters.

6.1 Household-specific price differentiation

It is well known that the elasticity of substitution varies by income (Auer et al., 2022;

Burstein et al., 2022). One might expect an analogous heterogeneity in the households’

efforts in arbitrage, within and across countries. Establishing a causal relationship

28The degree of persistence within and across countries differs somewhat across retailers. See Table 24 in the Appendix.

ECB Working Paper Series No 2776 / February 202330between household characteristics, such as income or age, and location-specific prices

paid (i.e. the border effect) is in general non-trivial, as any differences in cross-border

price gaps across households could also reflect differences in their preferences or product

availability. Handbury (2021) documents that grocery costs vary with income and
location: low-income households face less advantageous grocery offers in wealthier cities

because the product assortment there matches the consumption habits of wealthier

households better. Most of this variation is driven by cross-city differences in product

availability, not by differences in prices. We have shown in the preceding analysis that
the border effect is relatively larger for discounters. As such it might be that we observe

a larger border effect for households shopping more frequently in discounters, and if

these were low income households, it would reflect both an assortment and a price

effect of the border concentrated at low income groups.

To explore this, we calculate price differences within income groups. We use the abso-
lute log price difference Yirjyt (for product i at in retailer r in region pair j for income
group y at time t) at bi-monthly frequency to re-estimate – analogous to Equation

(4) – the border effect by interacting the region pair dummy with the income group
variable.29

Table 8: Within income group border effect: price differences

Product
group

within
Germany

within
Austria
(additional)

Cross-ctry

(additional)

Test cross-ctry Test border
effect diff.
= max. within
(p-value)
(p-value)

Income group 1
Income group 2
Income group 3
Income group 4

9.22***
9.03***
9.14***
10.34***

6.39***
6.70***
6.35***
4.63***

15.71***
15.06***
15.64***
14.80***

0.00
0.00
0.00
0.00

(base)
0.22
0.90
0.14

Note: The table shows country and border effect coefficients of the OLS estimation of Equation (4),
where the income variable replaces the shop group variable in the interaction term. Barcode-clustered
standard errors not reported. Period 2008-2018. Dependent variable: absolute within-retailer and
income group y-o-y price difference at a bi-monthly frequency. 196,914 observations. Adjusted R2 =
0.38. Second last column H0: border effect = largest country effect. Last column: H0: product group
border effect = border effect for income group 1. Asterisks indicate the level of significance, (*) at
the 10%, (**) at the 5%, and (***) at the 1% level.

The results in Table 8 show that the border effect does not vary significantly across the

four income groups. In line with Handbury (2021), the difference in prices of an identi-

cal product in two locations across the border (i.e. the border effect) is largely invariant

to income. Also the different frequency of shopping at discounters does not seem to
matter, which might be due to the growing upscale assortment in many Austrian and

German discounters. Another household characteristic, age, might be somewhat more

relevant. For households aged 60 and above the border effect is marginally larger than

29See Table 18 in the Appendix for details on the variable definition. To ensure a sufficient number of observations per
subgroup we loosen in the following the restriction of a product being available in both countries and in the same month,
before calculating price differences on a bimonthly basis. This increases price variation due to sales and promotions.

ECB Working Paper Series No 2776 / February 202331for younger households.30 Most variation in the price differences across households may
nevertheless be explained by the variation in retailers, where households buy their gro-

ceries. The subsequent section examines whether the border effect varies with product

characteristics.

6.2 Product-specific price differentiation

Given the significant border effect within retailers, one might wonder if the border is

visible in the entire assortment or limited to a few categories or products. Do retailers
employ product- or product-group-specific pricing strategies? Do they maintain a

(persistent) price gap across the border only for specific product groups, including

products of a certain origin, despite being sourced at the same cost?

Table 9: Within COICOP border effect: price differences

Product
group

within
Germany

within
Austria
(additional)

Cross-ctry

(additional)

Test cross-ctry Test border
effect diff.
= max. within
(p-value)
(p-value)

Food (11)
Non-alc. beverages (12)
Alc. beverages (21)
Household maint. (56)
Garden and pets (93)
Personal care (121)

10.7***
6.1***
9.8***
3.8***
9.4***
4.2***

4.9***
10.7***
3.8
6.6***
3.4***
7.1***

13.9***
11.7***
14.7***
15.2***
16.3***
21.1***

0.00
0.39
0.00
0.00
0.00
0.00

(base)
0.17
0.75
0.31
0.01
0.00

Note: The table shows country and border effect coefficients of the OLS estimation of Equation (4), where
the COICOP 3-digit variable replaces the shop group variable in the interaction term. Barcode-clustered
standard errors not reported. Period 2008-2018. Dependent variable: absolute within-retailer y-o-y price
difference at a bi-monthly frequency. 333,733 observations. Adjusted R2 = 0.44. Second last column H0:
border effect = largest country effect. Last column: H0: product group border effect = border effect for food.
Asterisks indicate the level of significance, (*) at the 10%, (**) at the 5%, and (***) at the 1% level.

We first look at differences across product categories and replace the explanatory re-
tailer variable in Equation (4) with a product-category variable and interact it with

the border dummy. As Table 9 shows, the (total) cross-border price differences are

similar in all product groups. The additional price difference at the border is, however,

significantly larger for personal care, garden and pet products than for food products.

For retailers to profit from such price-differentiation, i.e.

for consumers across the

border not to detect and exploit the wedge, the differences should not be too persistent.
When looking at the first-order autoregressive coefficient for broader product groups,31
however, we find that personal care items appear to exhibit above-average persistence of
price differences.32 Price differences of food and beverage products, conversely, which

30The definition of the age group variable and regression results can be found in Tables 18 and 19 in the Appendix.
31Due to small number of observations in COICOP-groups 12, 21, 56 and 93 we merge them into broader groups:
“Food & beverages” including alcoholic beverages, “Household & garden” including items for household maintenance,
gardening equipment and pet food. For comparison, Appendix Table 20 repeats the product category regressions of
Table 9 for these groups.

32Table 21 in the Appendix reports an additional autoregressive coefficient at lag one across the border of 0.45, in

ECB Working Paper Series No 2776 / February 202332exhibit a smaller price gap at the border, are also slightly less persistent across the

border. Consumers might indeed gain from “cherry-picking” personal care products.

They will, however, likely benefit less (or not at all) if they also buy products from
other product groups, e.g. food products.33

As we have shown, distances do not play any role in explaining the price gap at the
border. In presence of home bias, however, the origin of the product might. Unfor-

tunately, the location of the producer is not part of the data set, but a link to the

trademark owner can be established via the product’s barcode. We match the barcode

with the online GTIN database, the GS1 GEPIR (Global Electronic Party Informa-
tion Registry).34 We then translate the retrieved postcodes into the geo-location of
where the product comes from. For simplicity, we distinguish only Austrian, German

and third-country products. We furthermore use the geo-location within Austria and

Germany to identify products originating from within the border region, i.e. regional
products.35 The resulting product origin variable takes five values and distinguishes
Austrian and German products originating from the border region, Austrian and Ger-

man products from outside the border region, and products that originate from a third

country. In regression (4) we now interact the border and country dummies with this
product origin variable instead of a retailer or product group variable.

Table 10: Border effect by product origin: price differences

Product
origin

within
Germany

within
Austria
(additional)

Cross-
country
(additional)

Test cross-ctry Test border
effect diff.
= max. within
(p-value)
(p-value)

AT, non-border region
AT, border region
DE, non-border region
DE, border region
Third country

8.80***
10.51***
9.76***
10.29***
9.98***

8.38**
2.62*
4.40***
5.46***
4.81***

13.52***
9.71***
15.18***
17.58***
14.93***

0.00
0.00
0.00
0.00
0.00

0.15
0.01
0.76
0.36
(base)

Note: The table shows country and border effect coefficients of the OLS estimation of Equation (4) with
product origin dummy entering the interaction term instead of the shop groups, and time trends. Barcode-
clustered standard errors not reported. Period 2008–2018. Dependent variable: absolute within-retailer
y-o-y price difference at a bi-monthly frequency. 333,327 observations. Adjusted R2 = 0.44. Second last
column H0: border effect = largest country effect. Last column: H0: group border effect = “third country”
border effect. Asterisks indicate the level of significance, (*) at the 10%, (**) at the 5%, and (***) at the
1% level.

As evident from Table 10, the border effect remains significant no matter where to

total 0.71.

33According to anecdotal evidence from Austrians living in the border region, personal care items are indeed most

often named as products with the largest price gap.

34A barcode or GTIN (Global Trade Item Number) consists of two parts: a company-specific prefix and several
product-specific digits. The company prefix has between 7 and 11 digits and reveals the location of the company in
terms of its trademark ownership.

35We visually inspect cross-border price differences for different product origins and product categories by looking
at the distribution of cross-border price differences for Austrian, German and third-country products. In line with the
previous results, we find that prices for all products, regardless of origin and type, are more expensive in Austria. See
Figure 8 in Appendix B.4.

ECB Working Paper Series No 2776 / February 202333product comes from. It does not vary significantly by product origin with the exception

of Austrian products from the border region itself which are marginally different from
other groups.36 That is, Austrian products produced close to the border are more
uniformly priced in Austria and Germany, than products produced in Germany or in
third countries.

While this particular (slightly asymmetric) variation is puzzling and requires further
analysis,37 it might result from bimodal pricing, i.e.
(different) mix of uniform pricing and price differentiation to those products. It might

that retailers apply a certain

be that some companies in this region negotiate uniform prices (and sales prices) with
It might also be that companies serve the retailers in both countries
the retailers.

from a single distribution centre. If both countries are served by the same cross-border

delivery journey, it might be inefficient (e.g. in terms of additional accounting effort) to

charge different prices on the two sides of a border – of a border without fundamental
relevance for their business.

7 Conclusion

This paper examines the country dimension of price and inflation differences in the euro

area. Using a large household panel we compare transaction prices on both sides of
the Austrian-German border. The focus on a narrow band along the border combined

with the cultural homogeneity and strong economic integration of the region eliminates

the most prominent explanations of price differences at borders, such as currencies,

preferences, distance, and language.

While many products are priced identically on both sides of the border, most prices
differ. These prices differ in either direction, and many by a significant amount. As the

paper shows, neither differences in local costs nor in the composition of retail markets

can explain this wedge.

We show that this “border effect” is present even within retailers operating in both

countries. That is, households pay significantly different prices for identical products

at the same retailer depending on the side of the border. Ceteris paribus, the border
effect might therefore be the effect of price discrimination. Whereas retailers price-

discriminate within countries as well, they do so most extensively across the border.

The price differences of specific products are not very persistent and their high varia-

tion suggests that marketing, i.e. non-fundamental, factors dominate the price setting

36I.e. third country or German products. If we did not distinguish regional and non-regional products, the border
effect of Austrian products overall would still be significantly smaller than those of German and third country products.
37Eliminating international brands from the German border sample, for example, reduces the border effect of products

from the German border region as well, but it remains significant.

ECB Working Paper Series No 2776 / February 202334process.

The similar expenditure shares by product category point towards similar preferences

on both sides of the border. This stands in stark contrast to the small share of identi-

cally labelled products consumed in both countries. Thus, on top of price differentiation

for identical products, the border also entails vast product differentiation.

The significance of the border effect points towards considerable market power of re-
tailers vis-à-vis consumers. Retailers appear to maximise margins separately on either

side of the border. The resulting pricing strategies might differ by product: uniform

pricing (zero price difference) for some, and price discrimination with some (optimal)

price difference for other products. Given the similarity of preferences, the latter pric-
ing choice points to a high cost of arbitrage. A relevant cost factor might be the time

variation in price differences at the product level. Consumers would gain little from

randomly shopping cross-border. Exploiting the price differences would entail the ef-

fort of price comparisons at each point in time, and might be feasible only for certain
product categories.

The evidence puts the relevance of national borders into perspective. Ceteris paribus,

national borders might not matter that much. The cost of spatial arbitrage is non-

negligible even within a country, reflected in small price differences between same-

country regions. Crossing the Austrian-Bavarian border imposes virtually no additional
cost on the shopper, implying a similar cost of arbitrage between cross-border as be-

tween same-country region pairs. We argue that given the cost of spatial arbitrage,

retailers can freely choose where to differentiate prices, if they intend to do so. Their

choice to differentiate prices exactly along the national border – as opposed to some
other random line on the map – is most likely due to their existing logistics network

and thus history-dependent.

Unlike the LOP in its absolute version, which fails to hold within our border region, the

LOP in its relative version appears to hold approximately. As a result, price changes are

less dispersed across the Austrian-German border than price levels. Similar aggregate

inflation rates in both countries conceal large, asynchronous price changes in the two
countries. Common cost shocks, e.g. to the price of energy or other commodities, move

prices in both countries in the same way. At the product level, this common factor is

dwarfed by product-specific pricing. Two countries can therefore share similar price-

setting and a similar inflation process despite a large “border effect” in price levels.
This suggests that the border effect by itself is unlikely to affect the transmission of

monetary policy.

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ECB Working Paper Series No 2776 / February 202339A Data appendix

A.1 Data sources

A.1.1 Regional statistics

Real estate prices for Austria are based on the real estate price statistics by Statistik

Austria. For Germany, they are based on the regional statistics database GENESIS.

The data source for municipal corporate tax multipliers in Germany is Destatis. GDP
per capita is obtained from Eurostat (harmonized definition) at the NUTS3 level and

mapped to our regions.

A.1.2 Distances

We assign the regional information of each household to geographical coordinates (lat-

itude and longitude). In Austria, the coordinates of the geographical center point of
each political district are computed from the official shape file of Austrian provinces,

augmented by information on OpenStreetMaps.

In Germany, the geographical cen-

ter point coordinates are taken from the “list of municipalities information system” of

Destatis. Unlisted postal codes are manually translated into the name of the munici-
pality using information from OpenStreetMaps and other public sources.

The distance to the border is calculated as the linear distance – more precisely the

length of the shortest curve along the surface of the earth – to the nearest region in

the other respective country.

A.2 Data preparation

The household panel for Germany is available for a longer time period than for Austria.

In this paper, we restrict the data to the common sample period from January 2008 until
December 2018. Products without a barcode are excluded. Where necessary, we align
the Austrian and German price reporting based on the volume per unit information.38
If the reported price per unit for a given barcode differs between the two countries by

more than a factor of four, while the reported volume per unit differs only little, we
consider these two as different products and therefore exclude that barcode from the

cross-country analysis.

The 60km sample contains German households in Bavaria (postal areas starting with

82, 83, 84, 85, and 94) and Austrian households in parts of Upper Austria (political

38For identical products, i.e. products with the same barcode, the two countries might differ in their reporting. One

country might report the price per multipack, whereas the other might report the price per individual item.

ECB Working Paper Series No 2776 / February 202340districts Braunau am Inn, Eferding, Grieskirchen, Linz (Stadt und Land), Ried im

Innkreis, Rohrbach, Schärding, Urfahr (Umgebung), Vöcklabruck, Wels (Stadt und

Land)), part of Salzburg (districts Hallein, Salzburg (Stadt und Umgebung), St. Jo-

hann im Pongau, Zell am See) and part of Tyrol (districts Innsbruck (Stadt und Land),
Kitzbühel, Kufstein, Schwaz).39 We exclude an observation if the reported postal code
(in Germany) or the reported political district (in Austria) does not fit to the reported

federal state.

A.2.1 Product categories

To ensure consistency of the Austrian and the German dataset, we manually align the

classification of products into COICOP categories between Austria and Germany down

to the five-digit level. The analysis uses only the COICOPs which are well represented

in the sample, that is, the COICOPs 1.1 (food), 1.2 (non-alcoholic beverages), 2.1
(alcoholic beverages), 5.6 (household maintenance), 9.3 (recreational items/equipment

and pet food) and 12.1 (personal care). We exclude meat and fish (COICOPs 1.1.2 and

1.1.3), because these categories are not part of the Austrian sample. Furthermore, we

exclude 9.3.3 (garden, plants and flowers) and 12.1.1 (hairdressing salons and personal
grooming establishments) because of very few products in the sample.40

A.2.2 Retailers

Our focus is on stationary retailers present in both countries. Accordingly, we do not

consider transactions in speciality stores, such as bakeries, gas stations, or hardware
stores. Likewise, we exclude a transaction if the store name is unknown – typically

because the store does not belong to a national chain. We do not consider the (small

amount of) sales via self-service (vending machines) and home delivery (door-to-door).

To augment our sample of international retail chains, we keep the largest single-country
retailer in each country for the within-country statistics.

The restrictions on key COICOPs and key retailers reduce the number of barcodes by

about one third in Austria and by about one forth in Germany, as shown in the upper

part of Table 11. The upper part also shows that the number of transaction shrinks

less, reflecting that the excluded items and stores are indeed somewhat exotic. The
relatively strong decline of expenditure in Germany is due to outliers.

39In Austria we exclude political districts if their driving distance to the border is disproportionately larger than the
linear distance. The 100 km sample considered in Figures 1 and 3 for Austria covers 38 political districts (including
those listed in the main text), and the 100 km sample for Germany covers parts of the postal areas 80, 81, 86, 87, and
93 (in addition to those listed in the main text).

40When assessing cross-border product or category shares, we exclude also 2.1.2 (wine), because this category appears

underreported in the Austrian sample.

ECB Working Paper Series No 2776 / February 202341Table 11: Data preparation

Border
region

Product
subset
(after ...
selection)

100 km

all
main COICOPs
+ main retailers

60 km

main retailers
+ in both countries
+ within same month
+ within same retailer
+ cross-border

trans-
actions
(count,
’000)

Austria
expen-
diture
(euro,
’000)

49.4
47.2
42.3

28.5
5.6
2.5
2.1
0.9

124.4
118.1
98.6

66.4
15.7
6.4
5.2
2.1

bar-
codes
(count)

184,591
175,641
131,623

120,077
34,110
18,479
14,469
12,546

Germany
expen-
diture
(euro,
’000)

trans-
actions
(count,
’000)

113.1
98.8
90.1

29.8
7.5
2.5
2.0
0.9

228.8
196.9
170.4

56.1
16.6
5.4
4.3
1.9

bar-
codes
(count)

331,655
296,694
260,705

196,732
34,110
18,479
14,449
12,546

Note: Sample period 2008–2018. 19 regions per country within a 60 km band. Only products with
a barcode. The columns “transactions” and “expenditure” report the average per month. The column
“barcodes” reports the number of unique barcodes during the entire sample period. Product selections
are incremental from row to row. For example, the first column of row “60 km + in both countries”
reports the number of transactions in the 60km-wide border region of Austria of products (belonging to
one of the COICOPs listed in section A.2) sold in both the Austrian and the German 60km-wide border
region. The selection criterion “within same retailer” applies to the respective country only. The final
selection criterion “cross-border” requires the product to be purchased at the same retailer in the same
month in both countries.

The lower part of the table restricts the sample to the 60 km band along both sides of

the border, which we use in the main analysis. Thereafter, the sample shrinks already

by a non negligible amount. However, once we restrict the sample to those products
that are available on both sides of the border, the sample in terms of unique products

decreases to not even a third in the case of Austria and to less than a fifth in the

case of Germany. Restricting the sample to occur in both countries in a given month

almost halves the sample.
In order to obtain the final dataset and calculate price
and price change differences, products need to occur at least twice within the same

retailer and twice in any two different regions (e.g. in two German regions). A further

specification, which is printed in the last row of Table 11, is that a given product needs

to be purchased in two regions that lie on each side of the border.

A.3 Regions

We distinguish 38 border regions, 19 in Germany and 19 in Austria. In Austria we use
as regions the political districts (“Bezirke”), in Germany the two-digit postal areas. To

obtain regions of similar size and compact shape we combine and split some of these

districts respectively postal areas. Except in the regression discontinuity graphs we

include only households which reside less than 60 kilometers away from the border.

Table 12 shows how the typical sample size per region in 2018 shrinks as we require
purchases in both countries, in a narrower time interval, within the same retailer and

ECB Working Paper Series No 2776 / February 202342Table 12: Region summary statistics (average per region, 2018)

Product
subset
(after ...
selection)

trans-
actions
(count)

Austria
expen-
diture
(euro)

bar-
codes
(count)

trans-
actions
(count)

Germany
expen-
diture
(euro)

bar-
codes
(count)

main retailers
+ in both AT+DE
+ within 1 month
+ within same retailer
+ cross-border

1490
302
142
116
49

3756
874
381
305
128

7159
1696
806
623
333

1727
435
155
125
51

3397
976
335
266
107

9319
2257
851
638
348

Note: Main COICOPs and retailers during 2018. 19 regions per country within a 60 km
band. The columns “transactions” and “expenditure” report the average per month during
2018. The column “barcodes” reports the number of unique barcodes among the transactions
during the year 2018. Product selections are incremental from row to row. Quantities are
averages per region in the respective country. See notes to Table 11 for more details.

in two different locations. The average transaction (i.e. the purchases of some amount

of a given barcode at a given retailer in a given month) amounts to about 2.5 euros in
Austria and to about two euros in Germany.

As a robustness check, we use a coarser regional split, distinguishing only 6 regions (3
in Germany, 3 in Austria). These are also the regions for which we show the regional

statistics in Table 1. For Austria we use “Northern Upper Austria”, “(part of) Salzburg

and Southern Upper Austria”, and a “(part of) Tyrol”. Likewise we use in Germany

“Eastern Upper Bavaria”, “Western Upper Bavaria”, and “Lower Bavaria”. Again we
restrict the sample in most analyses to regions which are less than 60 kilometers away

from the border.

A.4 Barcodes

Table 13 shows the composition of the sample of products sold in both the Austrian

and the German border region during two-month intervals at least twice within the

same retailer and available in at least two regions (see restrictions of Table 12) by
COICOP 3-digit group. Expenditure is dominated by food, followed by personal care.

The transactions in food amount to approximately only two euros each, and are thus

much smaller than in beverages, where a single transaction amounts to about 4.5 euros

(non-alcoholic) and about nine euros (alcoholic). With more than 4,500 barcodes,
personal care offers the largest variety (and thus heterogeneity between households)

relative to the number of transactions.

Table 14 shows key characteristics of barcodes grouped by their proximity to uniform

pricing. We define these groups based on the share of within-country or cross-country

price differences (at the mode) of zero, ranging from never observed to observed in
all instances. Products that are more uniformly priced across the border tend to be

ECB Working Paper Series No 2776 / February 202343Table 13: Barcode summary statistics (joint barcodes)
Germany

Austria

product
category

bar-
codes
(count)

trans-
actions
(count)

expen-
diture
(euro)

trans-
actions
(count)

expen-
diture
(euro)

Food
Non-alcoholic beverages
Alcoholic beverages
Household maintenance
Hobbies and pet food
Personal care

6,647
683
149
1,194
850
4,612

1,489
86
16
65
123
171

3,287
386
141
203
377
524

1,393
81
17
81
136
198

2,479
372
156
189
275
506

rather low in price (in particular in comparison to the respective within country group).
Products which are sometimes (but not always) sold at the same price in two regions

tend to be frequently purchased products.

region pair

cross-border

within Austria

within Germany

Table 14: Barcodes with zero price difference
share of zero
price
differences
ψ = P (∆p = 0)

expendi-
ture

trans-
actions

bar-
codes

(count)

(count)

(euro)

expendi-
ture per
barcode
(euro)

always
66% ≤ ψ < 100%
33% ≤ ψ < 66%
0% < ψ < 33%
never

always
66% ≤ ψ < 100%
33% ≤ ψ < 66%
0% < ψ < 33%
never

always
66% ≤ ψ < 100%
33% ≤ ψ < 66%
0% < ψ < 33%
never

520
257
370
703
8,714

830
754
1,267
339
1,171

1,181
803
1,302
248
1,303

33,897
47,305
74,145
369,365
581,866

25,123
94,764
311,378
81,457
22,569

95,697
179,473
537,658
78,256
97,295

59,447
82,493
156,966
820,155
1,497,731

62,043
187,156
722,694
205,538
58,820

206,626
382,968
1,172,488
191,255
264,281

1.8
1.7
2.1
2.2
2.6

2.5
2.0
2.3
2.5
2.6

2.2
2.1
2.2
2.4
2.7

transact.
per
barcode
(count)

65
184
200
525
67

30
126
246
240
19

81
224
413
316
75

Note: Sample period 2008–2018. 19 regions per country within a 60 km band. Only products with a
barcode. The column ’zero price difference’ refers to the share of price differences of a given barcode at
the mode in percent (0). The first four groups are restricted to at least two observations at zero. The
columns ’barcodes’ refers to the total count of unique barcodes within a group, ’transaction’ refers to the
total count of transactions in barcodes within the respective group, ’expenditure’ refers to the total sum
of expenditure in barcodes in the respective group in euro, the next column is the share of the latter two
in euro, and the last column is the number of transactions per barcode.

ECB Working Paper Series No 2776 / February 202344B Robustness and further results

B.1 Common barcodes by retailer type

Figure 7 shows that drug stores have the highest share of products sold in both coun-
tries, most likely due to the large share of internationally branded items in their as-

sortment. Discounters mark the opposite end of the scale. Their store brands are often

country-specific, and therefore available in only one of the two countries.

Figure 7: Common barcodes by retailer type (percent)

Note: Share of expenditure and transactions in products sold in both regions among all purchases in either
region for a given cross-border pair, in percent. Share of barcodes sold in both regions for a given pair relative
to all barcodes sold in this region pair. Sample period 2008-2018. Average over all cross-border region pairs.

At all types of retailers expenditure and transaction shares are roughly equal, which

means that products available in both countries have a similar price distribution as
the remaining products. The common products in drugstores attract an even larger

share of shopping expenditure, i.e. they are high-turnover products. This applies to

common products in the other store types as well, but is there less pronounced than

for drugstores.

B.2 Coarser regions

The more finely we break up the border region, i.e. the more regions we distinguish,

the more homogeneous are the resulting regions.

In the main text of the paper we

1212142128312129323363610204060Discount storesSupermarketsHypermarketsDrugstoresProducts available in both countries (%)expendituretransactionsbarcodesECB Working Paper Series No 2776 / February 202345distinguish 38 border regions, 19 in Germany and 19 in Austria. The homogeneity of

the spatial strata comes at the cost of fewer transactions within a given time period,

and therefore fewer contemporaneous cross-region price pairs. In this section we verify

the robustness of our results to a coarser regional split, which distinguishes only three
regions on each side of the border, but on the upside allows comparing prices within a

narrow time window. Six regions allow nine pairwise cross-country comparisons, plus

three within each country.

Table 15 shows that despite the different aggregation the magnitude of the border

effect is similar as in the main specification (Table 2). The higher aggregation entails
very high within-country basket correlations (columns 1 and 2) and higher common

barcodes shares (column 3). The border effect in baskets changes relatively little, but

remains significant. In common barcodes, however, it is now twice as big as in the less

aggregated setup.

Table 15: Border effects (15 region pairs)

(1)

(2)

Basket correlation
COICOP COICOP

4

5

(3)
Common
barcode
share

(4)
(5)
Absolute price
difference

(6)
(7)
Absolute price
change
difference

Constant
(Germany)
Austria

Border

Common trend
(Germany)
Austria trend

Border trend

Frequency
Observations
Adj. R2

0.98***
(0.006)
0.01*
(0.008)
-0.06***
(0.006)
0.00
(0.001)
-0.00
(0.001)
-0.01***
(0.001)

year
165
0.92

0.97***
(0.006)
0.01
(0.008)
-0.17***
(0.001)
0.00
(0.001)
-0.00
(0.001)
-0.01***
(0.001)

year
165
0.98

0.40***
(0.006)
0.08***
(0.008)
-0.34***
(0.007)
0.002**
(0.001)
-0.002**
(0.001)
0.00
(0.00)

year
165
0.99

5.74***
(0.38)
3.00***
(0.53)
16.23***
(1.04)
-0.003***
(0.001)
0.01***
(0.001)
0.01***
(0.003)

week
101,518
0.17

7.22***
(0.34)
2.12***
(0.43)
14.71***
(0.59)
0.01**
(0.003)
0.01*
(0.004)
0.01
(0.007)

month
215,565
0.14

12.16***
(1.19)
-0.59
(2.02)
5.53***
(1.51)
0.02
(0.01)
0.04
(0.02)
-0.04
(0.02)

month
13,161
0.07

12.28***
(0.75)
1.80
(1.02)
4.31***
(0.79)
0.03**
(0.01)
0.02
(0.01)
0.00
(0.01)

bi-month
44,696
0.04

Note: Sample period 2008–2018. 15 region pairs. Standard errors in parentheses (columns 4-6 robust,
barcode-clustered standard errors). OLS regressions. Time and retailer controls in columns 4-6 not reported.
Dependent variables: (1/2) pairwise correlation of COICOP4/COICOP5 composition of (annual) baskets
of each region pair, (3) common barcodes in each region pair as share of all barcodes in the region pair,
(4-5) absolute, within-retailer (log) price difference of each region pair at weekly and monthly frequency, (6-
7) absolute, within-retailer y-o-y price change difference at a monthly and bi-monthly frequency. Germany
effect in (1)-(3) is the constant, in (4)-(7) the sum of constant, avg. coefficient of retailer controls and avg.
coefficient of month controls. Asterisks indicate the level of significance, (*) at the 10%, (**) at the 5%, and
(***) at the 1% level.

ECB Working Paper Series No 2776 / February 202346B.3 Controlling for distance

Table 16 replicates Table 2, now including the distance between regions as control

variable in the regression.

Table 16: Border effects

(1)
Basket
correlation
(COICOP4)

(2)
Basket
correlation
(COICOP5)

(3)

(4)
Common Abs. price Abs. price
barcode
difference
share

change
difference

(5)

Constant
(Germany)
Austria

Border

Common trend
(Germany)
Austria trend

Border trend

Distance

Frequency
Observations
Adj. R2

0.89***
(0.004)
0.05***
(0.005)
-0.03***
(0.004)
0.004***
(0.001)
-0.003***
(0.001)
-0.003***
(0.001)
0.004***
(0.002)

year
7,733
0.14

0.88***
(0.003)
0.04***
(0.004)
-0.10***
(0.004)
0.004***
(0.001)
-0.003***
(0.001)
-0.006***
(0.001)
0.002
(0.001)

year
7,733
0.49

0.16***
(0.001)
0.08***
(0.001)
-0.14***
(0.001)
0.001***
(<0.001)
-0.005***
(<0.001)
-0.001***
(<0.001)
-0.004***
(0.001)

7.78***
(0.40)
2.82***
(0.52)
15.16***
(0.70)
0.00
(0.008)
0.01
(0.006)
0.01
(0.008)
0.4***
(0.1)

11.08***
(1.16)
2.25
(2.01)
4.57***
(1.41)
0.01
(0.012)
0.04
(0.027)
-0.01
(0.018)
0.2
(0.2)

year
7,733
0.93

bi-month
333,733
0.12

bi-month
44,294
0.07

Note: Sample period 2008–2018. 703 region pairs. Standard errors in parentheses (columns
4 and 5: robust, barcode-clustered standard errors). Estimation by ordinary least squares.
Bi-month and retailer controls in columns 4 and 5 not reported. Dependent variables: (1/2)
pairwise correlation of COICOP4/COICOP5 composition of (annual) baskets of each region
pair, (3) common barcodes in each region pair as share of all barcodes in the region pair, (4)
absolute, within-retailer (log) price difference and (5) absolute y-o-y price change difference
of each region pair at bi-monthly frequency. Germany effect in (1)-(3) is the constant, in
(4) and (5) the sum of constant, avg. coefficient of retailer controls and avg. coefficient of
month controls. “Distance” refers to the distance between two regions of a region pair in 100
kilometers. Asterisks indicate the level of significance, (*) at the 10%, (**) at the 5%, and
(***) at the 1% level.

The results shows that whereas several quantities vary with distance, the magnitude of

the distance effect within the sample region is negligible. The estimates of the border
effect remain largely unchanged, and the maximum distance effect within the sample

region is one order of magnitude (or more) smaller than the border effect.

B.4 Price differences by product origin

In this appendix we distinguish products by their origin (as in section 6.2). In line

with the previous results, we find that the prices for all products – regardless of origin

and type – are more expensive in Austria. Furthermore, the median (non-absolute)

price differences (solid blue lines in Figure 8) are largely similar across product groups.

ECB Working Paper Series No 2776 / February 202347Figure 8: Price differences by product origin

Note: The histograms show the (non-absolute) cross-border log price differences in percent (Austrian minus
German prices) for Austrian, German and third-country products overall and by product category. The dashed
lines refer to the median of the respective distribution

The distributions of overall and food price differences exhibit a more pronounced bi-

modal distribution for products originating from Austria, with one mode at zero and

a second one at the median, i.e. at a – potentially – optimal value in terms of price

discrimination. This pattern could indicate that for certain products and under certain
circumstances, both pricing strategies, i.e. uniform pricing and price differentiation can

be optimal. Overall, cross-border price differences seem to be somewhat smaller for

products originating from Austria. This result is driven by food products, while for

personal care, household and garden items the price differences are larger for products
originating from Austria.

B.5 Relation between region characteristics and prices

In the paper we establish that the 19 regions in each country are very similar. But as

they are obviously not identical, we explore in this appendix to what extent differences

in regional characteristics can explain price differences both within each country and

across the border. To do so, we study the price level differences between and within
Austria and Germany directly, i.e. Yirjt are now non-absolute differences. Cross-border

0.01.02.03.04.05-100-50050100 Overall0.01.02.03.04.05-100-50050100 Food (incl. alcoholic beverages)0.02.04.06-100-50050100 Household maintenance, garden & pet equip.0.01.02.03.04.05-100-50050100 Personal careAustrian productsGerman productsthird country productsmedian AT productsmedian DE productsmedian third country productsmedian overallECB Working Paper Series No 2776 / February 202348region pairs subtract the German from the Austrian value. (Within-country region

pairs are randomly ordered.) The regression specification is

Yirjt = β0 + β11AT (j) + β31B(j)
(cid:125)

(cid:123)(cid:122)
border/country effects

(cid:124)

+ Xj × 1R(j)
(cid:125)

(cid:123)(cid:122)
∆ explanatory variables

(cid:124)

+ γ1t × 1R(j)
(cid:125)

(cid:123)(cid:122)
border/country trends

(cid:124)

+

λr
(cid:124)(cid:123)(cid:122)(cid:125)
retailer controls

+

θm(t)
(cid:124)(cid:123)(cid:122)(cid:125)
month controls

+ϵirjt,

(5)

where the explanatory variables Xj ×1R(j) are the (non-absolute) difference in average
(logarithmic) regional household income (in percent), in 2008-2018 income growth (in

percent), in the age of the household head (in years) and in the distance to the border

(in kilometers) for each of the 703 region pairs.

Table 17: Price level and regional characteristics
cross-
within
within
country Germany Austria

explanatory
variable

Region effect
∆ avg. income
∆ avg. income growth
∆ avg. age
∆ distance to border

15.20***
-0.02
-0.02**
0.35***
0.06***

0.58
-0.04***
-0.01*
0.08
0.00

-0.29
0.07***
-0.06***
0.00
0.03***

Note: The table shows country and border coefficients of the OLS
estimation of Equation (5). Period 2008–2018. Dependent vari-
able: (non-absolute) within-retailer y-o-y price difference at a bi-
monthly frequency. Explanatory variables: (non-absolute) differ-
ence in average (logarithmic) regional household income (in per-
cent), income growth (in percent), age (years) of household head
and distance to the border (km) for each of the 703 region pairs,
as well as time trends and retailer and month controls, which
are not reported. In case of cross-border pairs it is the Austrian
minus the German price. 333,327 observations. Adjusted R2 =
0.11. Barcode-clustered standard errors not reported, asterisks
indicate the level of significance, (*) at the 10%, (**) at the 5%,
and (***) at the 1% level.

The results in Table 17 confirm the significantly higher price level in Austria (15%)

already discussed in the main text. This is large compared to the income difference and
even compared to the GDP per capita difference. For a Spanish apparel manufacturer

selling via the internet, for example, Simonovska (2015) estimates that countries with

twice the income per capita pay on average 18% higher prices – among very different

and very distant countries. The income difference between the Austrian and Bavarian
border region studied here is much smaller. As shown in the third column of Table 1,

Austrian GDP per capita in the border region is on average only about 30% higher than

on the German side. A back-of-the-envelope calculation based on these numbers gives

a five percentage point price difference as upper bound to what might be explained by
the average income difference. That is, income alone cannot explain the border effect.

The income between the regions within each country differs even less. We find that

ECB Working Paper Series No 2776 / February 202349these small income variations do not explain economically meaningful price differences

between regions of the same country. Likewise, besides the country-income effect,

regional income does not capture cross-border regional price variation, i.e. nothing

in addition to what is already captured by the border effect. This obtains because
retailers practice more or less uniform pricing on each side of the border.

Instead, the average age has some explanatory power, with regions that are older on
average facing (slightly) higher prices.41

B.6 Border effect along household and product characteristics

Table 18 shows the definition of age and income groups of the households in our sample
used to compute log price differences, Yirjyt, within income and age groups. The groups
were chosen according to quartiles. The age variable is defined as age of household head
in years, which is a continuous variable in the Austrian, but grouped in age brackets

in the German dataset. The income variable is defined as the monthly net income in

euro of all members of the household from all sources of income. The income brackets

provided in the raw data differ between the two countries and are therefore combined
in such a way that they roughly align across the two countries. Because the raw income

ranges of the bottom and top groups are given as half-open intervals, we approximate

the income of these groups with a best-guess income median consistent with the overall

shape of the country’s income distribution.

Table 19 suggests a marginally larger border effect in the purchases of older shoppers.

Tables 20 and 21 show the most permanent border effect within personal care items.

B.7 More granular regions (price difference regression)

Table 22 repeats the persistence analysis, distinguishing 19 instead of three regions per

country.

41This stands in contrast to Aguiar and Hurst (2007), who find that older U.S. households realize lower prices as they
spend more time on comparing prices. Elderly households and retirees in Austria and Germany might differ from those
in the USA, as they are not necessarily poorer than the rest of the population in terms of disposable income.

ECB Working Paper Series No 2776 / February 202350Table 18: Age and income groups in Austria and Germany

Bracket

1 ≤ p25
2 > p25 ∧ ≤ p50
3 > p50 ∧ ≤ p75
4 > p75

1 ≤ p25
2 > p25 ∧ ≤ p50
3 > p50 ∧ ≤ p75
4 > p75

Bracket

1 ≤ p25
2 > p25 ∧ ≤ p50
3 > p50 ∧ ≤ p75
4 > p75

1 ≤ p25
2 > p25 ∧ ≤ p50
3 > p50 ∧ ≤ p75
4 > p75

AT

DE

AT

DE

Age (of household head, in years)
Mean Min

Obs.

Max

190,838
185,645
178,274
182,531

288,253
241,342
241,316
215,168

32
42
53
66

33
45
54
67

16
38
48
59

18
42
52
62

37
47
58
94

37
47
57
77

Income (of household, in euro)
Max
Mean Min
Obs.

225,741
151,223
207,175
153,149

280,964
277,278
230,491
197,346

1,551
2,384
3,132
5,000

1,459
2,366
3,089
4,571

400
2,175
2,850
5,000

300
2,125
2,875
3,625

2,025
2,550
3,450
5,000

1,875
2,625
3,375
6,250

Table 19: Within age group border effect: price differences

Product
group

within
Germany

within
Austria
(additional)

Cross-ctry

(additional)

Test cross-ctry Test border
effect diff.
= max. within
(p-value)
(p-value)

Age group 1
Age group 2
Age group 3
Age group 4

8.88
9.33
9.77
8.22

5.69
6.20
6.47
6.36

15.11
14.83
15.26
16.71

0.00
0.00
0.00
0.00

(base)
0.62
0.83
0.09

Note: The table shows country and border effect coefficients of the OLS estimation of Equation
(4), where the age variable replaces the shop group variable in the interaction term. Barcode-
clustered standard errors not reported. Period 2008-2018. Dependent variable: absolute within-
retailer and income group y-o-y price difference at a bi-monthly frequency. 206,320 observations.
Adjusted R2 = 0.38. Second last column H0: border effect = country effect. Last column: H0:
product group border effect = border effect for food. Asterisks indicate the level of significance,
(*) at the 10%, (**) at the 5%, and (***) at the 1% level.

ECB Working Paper Series No 2776 / February 202351Table 20: Within broader COICOP border effect: prices

Product
group

within
Germany

within
Austria
(additional)

Cross-ctry

(additional)

Test cross-ctry Test border
effect diff.
= max. within
(p-value)
(p-value)

Food & beverages
Household & garden
Personal care

10.5***
6.4***
4.2***

5.2***
5.4***
7.1***

13.8***
15.6***
21.1***

0.00
0.00
0.00

0.00
0.00
(base)

Note: The table shows country and border effect coefficients of the OLS regression as in Equation (4)
with time trends (barcode-clustered standard errors) by product group, where “food & beverages” refers to
COICOP groups 11, 12 and 21, “household & garden” to the COICOPs 56 and 93 and “personal care”
to COICOP 121. 703 region pairs. Period 2008–2018. Dependent variable: absolute, within-retailer
y-o-y price difference at a bi-monthly frequency. 333,733 observations. Adjusted R2 = 0.44. Second last
column H0: - country effect + border effect = 0. Last column: H0: - base group border effect + other
group border effect = 0. Asterisks indicate the level of significance, (*) at the 10%, (**) at the 5%, and
(***) at the 1% level.

Table 21: Within COICOP first lag autoregressive coeff. of price differences
Test cross-ctry Test border
Product
effect diff.
= max. within
group
(p-value)
(p-value)

within
Austria
(additional)

within
Germany

(additional)

Cross-ctry

Overall
Food & beverages
Household & garden
Personal care

0.24***
0.19***
0.33***
0.26***

-0.07***
-0.05**
-0.08
0.003

0.28***
0.25***
0.30***
0.45***

0.00
0.00
0.00
0.00

0.00
0.00
0.01
(base)

Note: The table shows the within- and cross-country autoregressive coefficients of price differences be-
tween the 15 region pairs from an OLS regression by product group. “Food & beverages” refers to COICOP
groups 11, 12 and 21 (46,212 observations), “household & garden” to the COICOPs 56 and 93 (5,828
observations) and “personal care” to COICOP 121 (5,087 observations). Dependent variable: absolute
log price differences. Explanatory variables: interaction of first lag of absolute log price difference with
regional dummy. Sample period 2008–2018. Trend, bi-month and retailer controls not reported. Second
last column H0: - country effect + border effect = 0. Last column: H0: - base group border effect + other
group border effect = 0. Robust standard errors (not reported). Asterisks indicate the level of significance,
(*) at the 10%, (**) at the 5%, and (***) at the 1% level. Bi-monthly frequency.

ECB Working Paper Series No 2776 / February 202352Table 22: Persistence of price and price change differences

Offset

2 months

4 months

6 months

1 year

Germany (basis)
Austria (additional)
Border (additional)

Observations
R2

Price differences

0.24***
-0.06
0.35***

10,883
0.24

0.19***
-0.01
0.38***

9,070
0.22

Price change differences

Germany (basis)
Austria (additional)
Border (additional)

0.25***
-0.17***
0.01

Observations
R2

5,917
0.12

0.24***
-0.08
-0.08

5,315
0.12

0.23***
-0.08*
0.33***

8,095
0.21

0.24***
-0.11
-0.04

4,834
0.13

0.17***
-0.03
0.36***

7,614
0.13

0.38***
-0.05
-0.06

9,247
0.17

Note: Sample period 2008-2018. 703 region pairs. Bimonthly frequency.
The table shows the within- and cross-country autoregressive coefficients of
price differences by length of lag from an OLS regression. Explanatory vari-
ables: interaction of first, second, third and sixth lag of absolute log price
difference (columns) with regional dummy (rows). Trend, bi-month and
retailer controls not reported. Dependent variable, upper panel: absolute,
within-retailer (log) price difference. Dependent variable, bottom panel:
absolute, within-retailer y-o-y price change difference. Robust, barcode-
clustered standard errors (not reported). Asterisks indicate the level of sig-
nificance, (*) at the 10%, (**) at the 5%, and (***) at the 1% level.

Table 23: Within COICOP first lag autoregressive coeff. of price differences

Product
group

within
Germany

within
Austria
(additional)

Cross-ctry

(additional)

Test cross-ctry
= max. within-ctry
(p-value)

Test border
effect diff.
(p-value)

Overall
Food & beverages
Household & garden
Personal care

0.24***
0.21***
0.30***
0.21*

-0.06*
-0.03
-0.07
0.33*

0.35***
0.34***
0.33***
0.48***

0.00
0.00
0.00
0.44

0.30
0.33
0.26
(base)

Note: The table shows the within- and cross-country autoregressive coefficients of price differences between
the 703 region pairs from OLS regression by product group, where “food & beverages” refers to COICOP groups
11, 12 and 21 (8,864 observations), “household & garden” to the COICOPs 56 and 93 (985 observations)
and “personal care” to COICOP 121 (1,034 observations). Dependent variable: absolute log price differences.
Explanatory variables: interaction of first lag of absolute log price difference with regional dummy. Sample
period 2008–2018. Trend, bi-month and retailer controls not reported. Second last column H0: - country effect
+ border effect = 0. Last column: H0: - base group border effect + other group border effect = 0. Robust
standard errors (not reported). Asterisks indicate the level of significance, (*) at the 10%, (**) at the 5%, and
(***) at the 1% level. Bi-monthly frequency.

ECB Working Paper Series No 2776 / February 202353Table 24: Within-retailer first lag autoregressive coeff. of price differences

Product
group

within
Germany

within
Austria
(additional)

Cross-ctry

(additional)

Test cross-ctry Test border
effect diff.
= max. within
(p-value)
(p-value)

Overall
Supermarket A
Supermarket B
Discounter C
Discounter D
Discounter E
Discounter F

0.24***
0.29***
0.19***
0.03
0.28***
0.01
0.28***

-0.07***
0.00
-0.11
0.03
-0.15***
0.00
-0.13

0.28***
0.23***
0.20***
0.78***
0.30***
0.38***
0.26***

0.00
0.00
0.00
0.00
0.00
0.00
0.00

0.00
(base)
0.41
0.00
0.06
0.14
0.68

Note: The table shows the within- and cross-country autoregressive coefficients of price differences
between the 15 region pairs from OLS regression by retailer. Dependent variable: absolute log
price differences. Explanatory variables: interaction of first lag of absolute log price difference with
regional dummy. Sample period 2008–2018. Trend, bi-month and retailer controls not reported.
Second last column H0: - country effect + border effect = 0. Last column: H0: - base group border
effect + other group border effect = 0. Robust standard errors (not reported). Asterisks indicate
the level of significance, (*) at the 10%, (**) at the 5%, and (***) at the 1% level. Bi-monthly
frequency.

ECB Working Paper Series No 2776 / February 202354Acknowledgements 

We would like to thank the members of the PRISMA team, Mirco Balatti, Luca Dedola, Oleksiy Kryvtsov, and the participants of the 2022 
“Inflation: Drivers and Dynamics Conference”, the “IWH-CIREQ-GW Macroeconometric Workshop on Inflation”, and the “conference on 
the occasion of the 30th Anniversary of the Establishment of the Single Market” for their helpful comments and suggestions. We also 
thank Lukas Henkel for his help with adding the GS1 data, and Lorenz Eichberger and Nico Pintar for excellent research assistance. 
The views expressed are those of the authors and do not necessarily reflect those of the European Central Bank, the Oesterreichische 
Nationalbank, or the Eurosystem. 

Teresa Messner 
Oesterreichische Nationalbank, Vienna, Austria; email: teresa.messner@oenb.at 

Fabio Rumler 
Oesterreichische Nationalbank, Vienna, Austria; email: fabio.rumler@oenb.at 

Georg Strasser (corresponding author) 
European Central Bank, Frankfurt am Main, Germany; email: georg.strasser@ecb.europa.eu 

© European Central Bank, 2023 

Postal address  60640 Frankfurt am Main, Germany 
Telephone 
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All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced 
electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.  

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

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ISSN 1725-2806 

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