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THE FUTURE OF PRIVATE-LABEL MARKETS:
A GLOBAL CONVERGENCE APPROACH
Katrijn Gielens1
Marnik G. Dekimpe2
Anirban Mukherjee3
Kapil Tuli4
1 Katrijn Gielens is Associate Professor of Marketing at the University of North Carolina at Chapel Hill,
Campus Box 3490, McColl Building, Chapel Hill, NC 27599-3490, U.S. Tel: +1-919-962 90 89.
E-mail: [email protected].
2 Marnik G. Dekimpe is Research Professor and head of the marketing department at Tilburg University,
Warandelaan 2, 5000 LE Tilburg, the Netherlands, and Professor of Marketing at KU Leuven, Belgium. Tel: +31-
13-466 3423. E-mail: [email protected].
3 Anirban Mukherjee is Assistant Professor of Marketing at the Lee Kong Chian School of Business, Singapore
Management University, 50 Stamford Road, Singapore 178899. Tel: +65-6828-1932. E-mail:
4 Kapil Tuli is Associate Professor of Marketing at the Lee Kong Chian School of Business, Singapore Management
University, 50 Stamford Road, Singapore 178899. Tel: +65-6828-0434. E-mail: [email protected].
The authors thank participants at the 2015 Leuven EMAC Conference, the 2016 UCLA Marketing Camp, and the
SMU marketing seminar series for several useful comments. Part of the paper was written while the second author
was visiting Singapore Management University.
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THE FUTURE OF PRIVATE-LABEL MARKETS:
A GLOBAL CONVERGENCE APPROACH
ABSTRACT
Private labels (PLs) represent a major opportunity for retailers, and a severe threat to brand
manufacturers. However, considerable heterogeneity can be observed in PL growth rates across
markets, creating ambiguity about their future growth potential. This poses a formidable
challenge to both brands and retailers on how to allocate resources across different markets to
prepare for the future. To offer more insight in the steady-state levels that can be expected for
PLs around the world, the authors take a forward-looking approach by means of a convergence
model. This approach is applied to a unique dataset with a broad geographic scope, spanning
almost 70 - emerging and developed - countries, across up to 60 product categories. The authors
find evidence for a global partial convergence of PL shares, with substantial remaining
heterogeneity across markets. They also find that strategic portfolio and store-format decisions
play a substantial role in shaping these long-term levels, where new products will be key to both
retailers and manufacturers. Moreover, especially for retailers, important differences are found
between emerging and developed markets.
Keywords: Private labels, international marketing, convergence models, time-series models
3
“Where do you see yourself in five years?” is a typical interview question. If you were to ask
private label brands the same question, they would likely say, “everywhere”.
Thompson (2017)
Over the past few decades, private labels (PLs) have increasingly become mainstream in most
CPG (Consumer Packaged Goods) markets, a trend transcending many categories, countries and
retail environments. This growing popularity has become one of the most significant challenges
to national-brand (NB) manufacturers around the world (Steenkamp et al. 2010, p. 1011, italics
added). Given recent growth rates in global PL penetration of 5% per year, versus just 2% for
NBs, one can wonder whether PL strongholds will really be around every corner (Herr 2008), or
whether NBs will prove to be more resilient in certain markets.
Within the CPG market, PLs have already reached a global value share of 14.5% (IRI
2016), corresponding to a sales level of over $350 billion (Euromonitor 2016). Such aggregate
numbers are impressive, and are (not surprisingly) often cited. Still, they conceal considerable
cross-country differences. While previous research has recognized that PL shares in Western
Europe (around 30%) tend to be larger than in the US (around 20%), and vastly exceed the
shares currently observed in most developing countries (Steenkamp et al. 2010), the considerable
heterogeneity within the respective regions, and especially their very different growth rates, is
largely overlooked.
In Western Europe, the 2015 value share for the UK (34.4%) and Germany (34.9%) is
more than twice the share in Italy (15.6%), and in Eastern Europe, the 2015 PL share in Poland
(16.8%) is more than three times larger than in Russia (2.6%). In Asia, the share observed in
Taiwan (13.5%) is many times higher than in Indonesia (2.6%), and in South America, the PL
share in Brazil (1.6%) is much smaller than in Columbia (6.5%). Given these clear level
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differences (which we visualize on a broader scale in Web Appendix A.1), one can question to
what extent there really is a global PL threat, especially since many of the academic insights to
support such a threat-claim have been derived from a very limited set of PL-prone countries. In
addition, we have to keep in mind that while commonalities exist, shares not only vary
substantially between countries, also the categories where PL shares are strongest vary
dramatically by country. Even in the most-developed markets, where one might expect similar
purchasing habits, large differences exist in PL success for each category by country
(Sethuraman 2017). While extant research has demonstrated that PLs are most successful in
highly commoditized categories (Dhar and Hoch 1997), the role and level of commoditization of
categories varies substantially between countries (AC Nielsen 2014). In the remainder of the
paper, when referring to country-category combinations we will use the generic term ‘markets’.
Importantly, there are also substantial growth differences between the countries. In the
UK and Germany, with a growth rate of only .02% and -.05% between 2012 and 2015, PL shares
have clearly stabilized following decades of prolonged growth. In those countries, PLs seem to
have reached a glass ceiling (IRI 2012), which some claim foreshadows the end of the PL party
(AdvertisingAge 2010). In contrast, double-digit growth was observed over that same time span
in other Western European (e.g., Spain and Ireland), most Eastern European (e.g., Czech
Republic and Poland), and several African (e.g., South Africa) and South American (e.g.,
Mexico) countries, as visualized by the darker shaded areas in Web Appendix A.2. At the same
time, countries as Singapore and Costa Rica continued to experience a more moderate (less than
2.5%) growth rate.
Given these level and growth differences, it should come as no surprise that there is little
consensus as to the global PL landscape one can expect in the years to come. Rabobank analysts,
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for example, predict that within 15 years “the main Asian grocery markets, India and China, will
have closed in on the PL share currently seen in Europe” (Rabobank 2013, p. 1). Similarly,
Richard Herbert, Global Insight and Development Director at Europanel, argues that most
Eastern-European countries, which are still lagging in PL acceptance, “will soon converge to the
share levels currently observed in Germany and the UK” (Herbert 2009, p. 5). In sharp contrast,
Euromonitor (2014, p. 6) expects PLs to “only make gradual, if any, inroads into emerging
markets in the years to come”, which Sethuraman (2017) attributes to the fact that in markets like
India and The Philippines, consumers are simply too enamored of brands like Kellogg cereals to
ever care much about PLs in the categories in which these brands operate.
Unfortunately, the rationale for such far-reaching statements, irrespective of the position
taken, is not only anecdotal, but also not very informative as to the specific long-term levels that
can be expected.5 Still, knowing whether and when lagging countries will catch up, and
especially what level they are likely to achieve, is of high strategic relevance to both NB
manufacturers and retailers who want to optimize their geographic and/or product portfolios. We
aim to fill this gap, and contribute to the PL literature along four dimensions. Specifically, our
study differs from prior work in that it (i) takes an explicit forward-looking perspective, (ii)
considerably expands the geographic scope of the insights, (iii) focuses on strategic (portfolio)
rather than tactical (promotional) moderators, and (iv) quantifies to what extent the role of these
moderators changes over time and across developed versus emerging countries.
Forward looking. Most (cross-country) PL research to date has taken a static view, and
therefore does not reflect the very different growth dynamics (also in the moderators) between
different regions in the world, and between more mature and emerging PL markets. However,
5 Indeed, even if the Asian grocery market obtains levels currently seen in Europe (as claimed in Rabobank 2013),
the prospects will be very different depending on whether the Italian or German levels are obtained.
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what holds today, may not hold in the future. To adequately allocate their resources and plan
their international market position, retailers and manufacturers need insights that reflect the
expected future state of affairs, which becomes especially relevant when dealing with countries
(or categories) characterized by double-digit PL growth rates. Looking at the current status quo
does not allow to adequately infer their future situation.
To do so, we draw upon the economic convergence literature (see, for example, Allington
and McCombie 2007 for an overview) to establish an empirical specification that measures
whether lagging (lower-share) PL markets catch up with leading (higher-share) PL markets,
which occurs when the former grow faster than the latter. Moreover, if global convergence can
be established, we can infer for each market its long-term PL market position, and explore
whether certain manufacturer- and/or retailer-controlled drivers can help explain the established
heterogeneity among these long-run steady-state levels.
Geographic scope. Even though an extensive literature on the determinants of PL shares
has developed, prior research has focused almost exclusively on the developed markets of the
United States and Western Europe. This is deplorable, given that (i) emerging markets become
increasingly important to achieve further sales and income growth (Steenkamp 2005; Kumar et
al. 2013), and given that (ii) emerging markets tend to differ from developed markets on several
fundamental dimensions (Bahadir et al. 2015; Sheth 2011). Our study considerably extends the
geographic scope of empirical PL research, and considers the PL evolution in close to 70
countries (including many emerging markets) across all 5 inhabited continents. This more than
doubles the number of countries studied in Steenkamp et al. (2010) and Steenkamp and
Geyskens (2014), and results in the first cross-national study to also include countries from
Africa, the Middle East and Oceania.
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Increasingly, brand manufacturers are refocusing themselves towards developing
economies (Kumar et al. 2013, 2015). Procter & Gamble, for example, recently declared that it
wants to add one billion customers by 2025, a 25% increase, and that emerging markets will be
crucial in achieving those goals (The Economist 2012). These markets not only grow at a faster
pace, which may already be a good reason to focus on them, they also tend to have a much
smaller PL share, resulting in considerably less PL competition. However, is it fair to assume
that what holds in Western Europe will also transcend to the rest of the world? To address this
question, we investigate to what extent different geographic markets converge to similar or
different steady-state scenarios.
Strategic moderators. We extend the set of typically considered (more tactical)
moderators by focusing on a novel set of strategic portfolio decisions that manufacturers/retailers
may use to restrict/nurture further PL development, such as the number of brands/banners to
carry, or how many new products to add to one’s assortment. As manufacturers increasingly
consider PLs as their main competitors, a strategic restructuring of their business operations is
more and more on the table. For example, business analysts have pointed out that the 2015 Kraft-
Heinz merger was partly motivated by the PL threat (Schroeder 2015). With the merger, Kraft
and Heinz hope to regain their influence in the retailscape by better positioning their brands
against PLs through an international distribution platform. From a retailer’s point of view, the
merger of Ahold and Delhaize (the parent company of Food Lion), which both have very strong
PL ranges in Europe, gives the newly formed group the scale, store platform and banner reach to
effectively deploy an international PL strategy (Thompson 2017). Also, with digital and discount
being seismic disruptors in the retailing landscape, retailers more and more wonder what formats
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to emphasize or abandon. Such strategic decisions tend to have longer-term implications and,
importantly, are often hard to reverse without incurring substantial sunk costs.
By focusing on portfolio and format decisions, we study global PL share differences
through a more strategic lens, which aligns with our focus on long-term outcomes. As such, we
add to the extant insights that have focused on more tactical tools, like temporary price cuts
(Steenkamp and Geyskens 2014) or distinctiveness of the packaging (Steenkamp et al. 2010),
which may be better suited to explain short-run fluctuations in PL performance.
Heterogeneity over time and countries. As PLs gain traction and/or mature, the impact of
certain drivers may diminish, while the impact of others may increase (Sethuraman and Gielens
2014, p. 150; Steenkamp et al. 2010, p. 1014), while emerging economies may remain more or
less receptive to certain managerial levers than developed economies (Sheth 2011). To gain
insight in these issues, we will investigate to what extent the impact of the aforementioned
strategic decisions on the future state of the PL market changes over time and differs for
emerging economies.
CONCEPTUAL DEVELOPMENT
To infer the long-run position that PLs will obtain in different markets, we make use of the β-
convergence concept. This concept has originated in the international-economics literature (see,
for example, Barro and Sala-i-Martin 1992), and has since been applied to study convergence in
countries’ productivity (Bernard and Jones 1996), Foreign Direct Investment (Kottardi and
Thomakos 2007), Research & Development (Jungmittag 2006) and prices (Goldberg and
Verboven 2005). More recently, the concept has ventured outside the strict realm of economics,
and has been applied to carbon emissions (Barassi et al. 2008) and consumer preferences (Silver
2010) as well. In the remainder, we refer to β-convergence simply as convergence.
9
Convergence implies that lagging (in our case, with a lower PL share) markets grow
faster than leading (higher-share) markets, causing the difference between them to become
smaller over time. This is illustrated by lagging markets A, B, and C in Figure 1, which are all
growing at a faster rate than the leading market. When the level difference between the lagging
and the leading market disappears completely, a case of absolute convergence is obtained, as
with market A and the leading market in Figure 1. In other instances, the growth rates may
become the same, while a level difference persists between leader and follower, as with markets
B and C in Figure 1. This situation is referred to as partial convergence. Provided that the PL
shares in the leading market have stabilized (and therefore reached a zero growth rate), one can
both infer the long-run PL share to which the other markets will evolve, and explore underlying
reasons for the observed heterogeneity, if any.
Before introducing the formal testing procedures for, respectively, absolute and partial
convergence, we address conceptually the following key issues: (i) why would shares eventually
stabilize, (ii) why can one expect lagging countries to catch up, and (iii) why would countries
differ in their steady-state market shares?
--- Insert Figure 1 about here ---
Why would PL shares eventually stabilize?
Industry observers have repeatedly argued that PL shares in many developed markets, both in
Western Europe and North America, have stabilized, and hit the proverbial “glass ceiling” (IRI
2012; AC Nielsen 2014). In a similar vein, academic research (see, e.g., Ailawadi et al. 2008;
Koschate-Fisher et al. 2014) has pointed out that there exists a certain level beyond which PLs
cannot go without undermining a retailer’s share of wallet and/or profitability. For one, even
though the percentage gross margin may be higher for PLs (ter Braak et al. 2013), the lower
10
retail price may ultimately lead to lower penny profits, especially when all factors, including deal
allowances, warehousing, transportation, and in-store labor are taken into account (Ailawadi and
Harlam 2004). Moreover, heavy PL buyers tend to be less profitable and less loyal. They not
only buy less per shopping trip, they are also less likely to differentiate between the store brands
of different chains, making them less store loyal (Koschate-Fisher et al. 2014). Consumers who
spend a substantial share of their wallet on PLs are mainly drawn to price savings, rather than to
a particular PL, and therefore shop around for the best prices across multiple retailers (Ailawadi
et al. 2008). Given their innate need for variety, consumers may even boycott a banner if it
decides to push its PL program too far, as this may limit consumers’ choice and selection
opportunities (Sethuraman 2017).
Factoring into the equation that brand manufacturers are also deploying better defense
mechanisms by actively creating pull mechanisms from the market to defend their position on
the shelf (Gielens 2012), a natural tendency to stabilize may well be innate to PLs.
Why would one expect lagging countries to catch up?
PL success has often been described as a supply-driven phenomenon (Sethuraman and Gielens
2014), meaning that PL share will be high in markets where retailers are strong. When retailer
power develops, PL share increases. So the underlying question that needs to be addressed is
whether retailer strength and presence will catch up in lagging countries.
For a long time, the retail industry was one of the least internationalized sectors.
However, over the last two decades, more and more retailers have engaged in cross-border
initiatives (Gielens and Dekimpe 2001, 2007), thereby crossing geographic boundaries, political
systems, languages and cultures. This search for new markets has been brought on by retailers’
continued need to grow even when their home markets became saturated.
11
Not surprisingly, retailers increasingly look at emerging markets – the most notable
lagging PL markets – in their search for continued growth (Bronnenberg and Ellickson 2015).
This process has accelerated as markets such as India and China increasingly open up to foreign
retailers (Narayan et al. 2015). As a result, multinationals have rushed to establish a presence in
emerging markets, and benefit from local knowledge by acquiring national and regional players,
allowing them to quickly roll out their PL programs. Japan’s supermarket Aeon, for example,
hopes to considerably grow its business in several Southeast-Asian countries by expanding its
line-up of PL products (Nikkei Asian Review 2015). As the leading international retailers
continue to get bigger, market power becomes more concentrated in the hands of a few global
power players, enabling them to impose their retail and PL strategies more efficiently.
Relatedly, in many of the lagging countries, we witness a transition from a highly
fragmented market with many mom-and-pop stores to an environment where modern retail
formats dominate (Bronnenberg and Ellickson 2015). Modern grocery formats, such as
hypermarkets and hard discounters, are set to further widen their coverage in the world's major
markets (Planet Retail 2016). As hypermarkets like Carrefour grow in China, and discounters
like Lidl continue their international penetration, consumers will have greater access to PLs.
On the consumer side, there is increasing support for the notion that income differences
between countries are becoming smaller. In a seminal study using a panel of 100 countries for
four decades between 1960 and 1989, Barro (1991) found that the speed of convergence across
countries in income per capita is about 2.5% per year, and also sociologists have documented
convergence in income levels across countries, which they attribute to the rapid industrial growth
in China and South Asia (Firebaugh and Goesling 2004). While PLs are often associated with
lower-income households in Western markets, consumers in emerging economies traditionally
12
consider unbranded goods to be of higher social and financial risk, as more status and prestige
can be derived from buying NBs than from PLs (Sethuraman 2017). As income levels in those
countries are catching up, larger fractions of their population become receptive to PLs.
Why would markets differ in their steady-state PL-shares?
PLs become viable for a retailer only when they reach sufficient mass to acquire large amounts
of goods cheaply (Sethuraman and Raju 2012). The extent to which this can be achieved is partly
driven by the future evolution along some key strategic dimensions on the part of both channel
parties, but also by some idiosyncratic characteristics of the market at hand that are beyond their
direct control. In line with our forward-looking perspective, we consider how the steady-state
levels towards which these drivers are expected to evolve (rather than their current or past level)
will impact the long-run PL shares in the different markets.
Strategic Drivers
Retailers and NB manufacturers alike are continuously searching for ways to solidify and
improve their long-term market position. We consider how various dimensions of their
respective portfolio strategies can help in this respect. In addition, we assess to what extent the
(future) popularity of different retail formats matters.
Retailer and Manufacturer Portfolios.
A key strategic lever in building (fighting) PLs revolves around the breadth of the retailers’
(brand manufacturers’) portfolio used to reach the consumer (Sethuraman and Gielens 2014).
From a retailer’s point of view, this is reflected in both the number of banners and the number of
stores the retailer operates (Ailawadi and Farris 2017), and in the number of PL products added
to the assortment (Gielens 2012). From the manufacturers’ side, this is reflected in the number of
brands (Morgan and Rego 2009) and the number of new products (Lamey et al. 2012).
13
Anecdotal evidence suggests that portfolio decisions are indeed used by manufacturers
and retailers to safeguard their long-run positions in the market. For example, merged companies
often hope to deploy their combined brand clout to better guard themselves against further PL
growth (Knowledge@Wharton 2000), as was illustrated by the Kraft-Heinz merger (Schroeder
2015). In contrast, the combined store fleet of the newly merged Ahold-Delhaize group is
believed to offer strong synergies for further PL development (De Jong 2015), while both Tesco
and Carrefour have been adding new banners (such as Tesco Metro and Carrefour Express) to
their portfolio to reach more consumers in different segments, which allows them to deploy their
PL programs at a larger scale.
Conceptually, both parties use their brand/banner portfolio to impact the scale of their
operations, the space allotted on the shelf, and the awareness in the consumers’ mind. A larger
portfolio enables a firm to obtain more power over other channel members (Shocker et al. 1994).
Moreover, given that broader portfolios allow firms to sell across different segments (Steenkamp
et al. 2003), further scale and scope benefits can be generated, while at the same time allowing
for a more tailored positioning of each brand/banner in the mind of the consumer. This, in turn,
can result in greater advertising efficiency (Kumar 2003). In combination, these arguments imply
that larger retailer portfolios benefit PL shares, while larger NB portfolios harm them (we refer
to Morgan and Rego 2009 for a review of the literature on brand portfolios, and to Ailawadi and
Farris 2017 for an in-depth discussion on the benefits of a more extensive distribution breadth).
Formats. Not only the number of new products, brands and/or banners play a role, but
also the channels that retailers embrace to reach their consumers and push their PL program.
Indeed, certain formats are known to be more fertile PL hotbeds than others. Hypermarkets, for
example, offer an assortment that is not only broad, but that is also deep with many NBs. PL-
14
prone buyers therefore use only a limited part of the assortment, even though they still face the
cognitive load of processing the increased amount of information. In countries and categories
where hypermarkets are highly successful, more consumers value the extensive NB variety that
this retail format offers (Koschate-Fisher et al. 2014, p. 13). An emphasis on such big-box
formats may therefore hamper long-term PL success. A stronger reliance on discounters, in
contrast, is more likely to stimulate PLs, as the hard-discount concept critically depends on PLs
to keep their cost of doing business at rock-bottom levels (Lourenço and Gijsbrechts 2013).6
Moreover, capitalizing on their increasing consumer trust, online retailers are increasingly
stepping up their PL investments as a means of differentiation, and as a way to buffer their
bottom line (Planet Retail 2017). For example, Amazon has been ramping up its PL capabilities,
launching PLs in CPG categories such as coffee, laundry detergents and snacks.
Combined, these evolutions suggest that as certain formats become more important
gateways to consumers in certain markets, PL levels may converge to a higher (lower) level.
Market Idiosyncrasies
Particular market contexts, beyond the direct sphere of influence of the retailers and/or
manufacturers can be more or less conducive to PL success. More specifically, the efficiency of
production, transaction and distribution of products and services that retailers and manufacturers
can potentially achieve are of utmost importance.
First, to generate the required economies of scales, the size of the market matters. Not
only the sheer potential of the market as reflected in typical GDP-like measures matters, but also
the distribution of the population within markets. Only in more urbanized markets, economies of
scale can be fully taken advantage of. Moreover, size does not only come to play at the
6 For example, the growing discounter presence has been seen as a major contributing factor to the increased PL
acceptance in Turkey (McKinsey.com 2015).
15
aggregate, country level but also at the product-category level. Consumers may be more likely to
look for a good value and low price, and hence be more receptive to PLs, if the category
accounts for a greater share of their budget (Sethuraman and Gielens 2014).
Another important factor affecting the success of PLs in a country has been the
consolidation of retailers. If a small number of retailers accounts for most of total sales, these
retailers will be in a more powerful position when dealing with NB manufacturers and in
developing their own strong PL program (Kumar and Steenkamp 2007). Moreover, as observed
by Dhar and Hoch (1997), in less concentrated environments retailers realize that they need NBs
as traffic builders, and are therefore less inclined to push their PL program to the limit. On the
flipside, brand power is greater if a few brands account for most NB sales (Steenkamp et al.
2005), which will be reflected in a wider distribution and more and better shelf space (Ailawadi
and Farris 2017). Moreover, a retailer may find it more difficult to find PL manufacturers in
markets with higher NB concentration.
Indeed, few retailers have the capabilities to manufacture PLs themselves. Rather, they
source them from independent suppliers. In order for retailers to produce high-quality PLs, they
may need to enter relationships with local manufacturers, and/or acquire their own production
facilities (ter Braak et al. 2013). Only in efficient markets with low transaction costs can retailers
compete effectively against NB manufacturers to secure their supply at lower costs (Steenkamp
and Geyskens 2014). As such, these costs affect the country’s PL success and attractiveness to
invest to retailers, and thereby affect the retail production function (Baily and Solow 2001;
Bronnenberg and Ellickson 2015). Finally, advertising intensity in a country has been shown to
be a strong impediment to PL growth (Deleersnyder et al. 2009).
Cross-country heterogeneity in responsiveness
16
Past research has often emphasized that retailing remains one of the biggest challenges of doing
business in emerging markets. The unstructured retail markets in emerging markets are believed
to increase the complexity of marketing-mix decisions, and to decrease the information available
to make appropriate distribution decisions and align brands, products and store formats (Kumar,
et al. 2015). In such an environment, it is not only harder to create the required scale to build
PLs, but it is also harder for both retailers and brand manufacturers to develop and assess the
most appropriate mix of strategies to address various PL challenges. At this point in time, little
information is available on how distributional and portfolio-related success recipes that may
work in more mature PL markets translate to emerging settings. As such, we will test for
potential heterogeneity in the responsiveness to our focal drivers in an exploratory way.7
MODELING FRAMEWORK
To quantify to what extent countries lagging in PL development will eventually catch up with
their more developed counterparts, we use the notion of β-convergence (Barro and Sala-i-Martin
1992). In statistical terms, β-convergence requires any remaining share differences to be mean-
reverting or stationary (Lau 2010), so that idiosyncratic (market-specific) shocks only have
temporary effects on the category’s PL share in a market relative to a reference market. In case
of absolute convergence, this mean is zero. In case of partial convergence, the difference relative
to the reference country does not fully disappear, but becomes (apart from temporary
disturbances) constant over time. Without stationarity, idiosyncratic shocks have a continuing
impact, and lead to diverging growth paths (Powers et al. 1991). Because of this underlying
stationarity requirement, convergence can be formally tested in a unit-root framework.
One possibility would be to examine one by one whether each market’s market-share
7 A similar approach was recently followed by Ailawadi et al. (2014).
17
series (relative to the PL share of the category in the reference country) presents a unit root.
However, univariate unit-root tests of the type pioneered by Dickey and Fuller (1979) are known
to have low power, especially when the time series is rather short, making it difficult to reject the
unit-root null hypothesis when it is false. One way to address this issue is to exploit the panel
dimension of the data (Cecchetti et al. 2002). Panel unit-root tests allow for group-wise (in our
case, global), rather than bivariate, tests of convergence, and have the advantage of providing
considerably higher power by pooling information across the different countries. Following
Goldberg and Verboven (2005), we adopt the Levin et al. (2002) panel unit-root test:
(1) ∆𝑙𝑜𝑔 (𝑃𝐿𝑝,𝑐,𝑡
𝑃𝐿𝑝,𝑅𝑒𝑓,𝑡) = 𝛼𝑝,𝑐 + 𝛽𝑝𝑙𝑜𝑔 (
𝑃𝐿𝑝,𝑐,𝑡−1
𝑃𝐿𝑝,𝑅𝑒𝑓,𝑡−1) + ∑ 𝜂𝑙∆𝑙𝑜𝑔 (
𝑃𝐿𝑝,𝑐,𝑡−𝑙
𝑃𝐿𝑝,𝑅𝑒𝑓,𝑡−𝑙)𝐿
𝑙=1 + 𝜀𝑝,𝑐,𝑡.
The dependent variable is the first difference (∆) in the log of the PL share of country c
(for a given product category p) in year t relative to the corresponding PL share of that category
in the reference country (Ref). The main parameter of interest is 𝛽𝑝, which denotes the global
speed of convergence in product category p. If 𝛽𝑝 is significantly lower than zero, we reject the
unit-root or divergence hypothesis, and conclude that global share differences become smaller
over time. We apply this test separately to each of the different categories to allow for the fact
that global convergence might occur for some categories but not for others, or occur at a faster
speed in some categories than others.
The inclusion of the country-specific fixed effects allows us to distinguish absolute from
partial convergence, while we include the lag of the dependent variable to account for potential
serial correlation (cf. Dekimpe and Hanssens 2004). Panel UR tests can, however, lead one to
spuriously support the convergence hypothesis if there is a significant degree of positive cross-
sectional error dependence that is ignored (O’Connell 1998). Such cross-sectional dependence
18
could, in our setting, arise between countries that are geographically close to one another, or
which are culturally and/or economically similar. Still, if the cross-sectional dependence is not
sufficiently high, a loss of power may result if tests that allow for cross-sectional dependence are
used (Pesaran 2007). Following Pesaran (2007), we therefore apply in a first step the cross-
sectional dependence (CD) test of Pesaran (2004). Based on the outcome of that test, we apply
the “original” Levin et al. (2002) test, which assumes i.i.d. disturbances, or the “cross-sectionally
de-meaned” version, which controls for this correlation (Pesaran 2007).8
Derivation of long-run differentials & speed of convergence
Once convergence is established, we use the estimated values of 𝛽𝑝 and 𝛼𝑝,𝑐 to derive (i) the
asymptotic differentials relative to the reference country, and (ii) the speed of convergence
towards this steady-state differential. In case the unit-root (or divergence) hypothesis is rejected,
the difference between category p’s long-run PL share in country c and the reference country,
which we call the long-run share differential, is given by (Goldberg and Verboven 2005):
(2) ∆∞𝑃𝐿𝑝,𝑐 = 𝑒−
𝛼𝑝,𝑐
𝛽𝑝 .
It expresses to what percentage of the reference country’s PL share the focal country will
converge in that category. If the reference country’s equilibrium share is 40% in a given
category, and the long-term differential (∆∞𝑃𝐿) is estimated to be .75, the focal country’s PL
share is expected to converge to 30% (= .75 * 40%). When 𝛼𝑝,𝑐 is not significantly different
from zero, the focal country converges to 100% of the reference country, and absolute
8 We opt for the Levin, et al. test (2002), rather than the Im et al. (2003) specification for three reasons. First,
allowing for heterogeneity in the growth parameter, as implied in the Im et al. (2003) approach, has been argued
conceptually to make the notion of convergence a rather trivial construct (Islam 1988). Moreover, the Levin et al.
approach is more conservative. With the Levin et al. procedure, it is less likely that the behavior of one or two
countries leads one to reject the unit-root null hypothesis (Goldberg and Verboven 2005). Finally, unlike the Im et
al. procedure, the Levin et al. test gives a direct panel estimate of 𝛽𝑝 (Cechetti et al. 2002).
19
convergence is obtained.
𝛽𝑝 reflects the rate (speed) at which the PL shares in category p approach their steady-
state values. Based on this value, the so-called 90% duration interval, i.e., the number of years it
will take to cover 90% of the distance separating the current PL differential from the asymptotic
differential, can be computed (Cechetti et al. 2002):
(3) 𝑡90%,𝑝 = −ln (
1
1−.9)
ln (𝛽𝑝+1) .
A value for βp of -.25, for example, implies a 90% duration interval of 8 years.
If the data only covers a finite number of time periods, estimates of 𝛽𝑝 are known to be
biased downward. To this extent we implement Nickell’s (1981) small sample adjustment to all
parameters in Eq. (1) (formal equations are provided in Web Appendix B). Imputing the adjusted
parameters, 𝛽𝑝𝑎𝑑𝑗
and 𝛼𝑝,𝑐𝑎𝑑𝑗
, into Eq. (2) and (3) gives the adjusted long-run differentials
Δ∞𝑎𝑑𝑗
𝑃𝐿𝑝,𝑐 and duration intervals (𝑡90%,𝑝 𝑎𝑑𝑗
).
Explaining variation in long-term differentials
We subsequently test whether, and to what extent, the long-term share differentials are
systematically and predictably linked to future manufacturer and retailer conduct in a given
market. For each country (c) - product category (p) combination, we relate Δ∞𝑎𝑑𝑗
PLp,c to a set of
variables that reflect long-run portfolio decisions of both channel parties, while accounting for
the anticipated long-term importance of different retail formats in that market.
(4) Δ∞𝑎𝑑𝑗
𝑃𝐿𝑝,𝑐 = 𝛾0 + 𝛾1Δ∞𝑎𝑑𝑗
𝐵𝑅𝐴𝑁𝐷𝑆𝑝,𝑐 + 𝛾2Δ∞𝑎𝑑𝑗
𝑁𝑃_𝑁𝐵𝑝,𝑐 + 𝛾3Δ∞𝑎𝑑𝑗
𝐵𝐴𝑁𝑁𝐸𝑅𝑆𝑐 +
𝛾4Δ∞𝑎𝑑𝑗
𝑁𝑃_𝑃𝐿𝑝,𝑐 + 𝛾5Δ∞𝑎𝑑𝑗
𝑆𝑇𝑂𝑅𝐸𝑆𝑐 + 𝛾6Δ∞𝑎𝑑𝑗
𝐻𝑌𝑃𝐸𝑅𝑝,𝑐 + 𝛾7Δ∞
𝑎𝑑𝑗𝐷𝐼𝑆𝐶𝑝,𝑐+
𝛾8Δ∞𝑎𝑑𝑗
𝑂𝑁𝐿𝐼𝑁𝐸𝑝,𝑐 + 𝛾9Δ∞𝑎𝑑𝑗
𝐼𝑁𝐷𝐸𝑃𝑝,𝑐 + 𝛾10Δ∞𝑎𝑑𝑗
𝐶5_𝑁𝐵𝑝,𝑐 + 𝛾11Δ∞𝑎𝑑𝑗
𝐶5_𝑅𝐸𝑇𝑐 +
𝛾12Δ∞𝑎𝑑𝑗
𝐺𝐷𝑃𝑐+ 𝛾13Δ∞𝑎𝑑𝑗
𝑈𝑅𝐵𝐴𝑁𝑐 + 𝛾14Δ∞𝑎𝑑𝑗
𝑆𝑂𝑊𝑝,𝑐 + 𝛾15Δ∞𝑎𝑑𝑗
𝑇𝑅𝐴𝑁𝑆_𝐶𝑂𝑆𝑇1 𝑐 +
20
𝛾16Δ∞𝑎𝑑𝑗
𝑇𝑅𝐴𝑁𝑆_𝐶𝑂𝑆𝑇2𝑐 + 𝛾17Δ∞𝑎𝑑𝑗
𝐴𝐷𝑉𝑐 + ∑ 𝛾𝑟′
𝑟 𝑅𝐸𝐺𝑟 + ∑ 𝛾𝑗′′
𝑗 𝐶𝐴𝑇𝑗 + 𝜀𝑝,𝑐
On the manufacturer portfolio side, Δ∞𝑎𝑑𝑗
BRANDSp,c captures the number of brands held by the
top global players in the market, while Δ∞𝑎𝑑𝑗
NP_NBp,c reflects the new SKUs they launch. On the
retailer-portfolio side, Δ∞𝑎𝑑𝑗
BANNERSc represents the banners operated by the leading global
retailers in the market, Δ∞𝑎𝑑𝑗
NP_PLp,c represents the new PL introductions, and Δ∞𝑎𝑑𝑗
STORESc
signals the evolution in their number of outlets. To capture the importance of different formats in
the market, we account for the importance of hypermarkets (Δ∞𝑎𝑑𝑗
HYPERp,c), hard discounters
(Δ∞𝑎𝑑𝑗
DISCOUNTp,c) and online shopping (Δ∞𝑎𝑑𝑗
ONLINEp,c), and also capture the impact of
independent retailers (Δ∞𝑎𝑑𝑗
INDEPp,c) in each category-country combination. As such, apart from
the number of banners and number of stores (which are by definition not category-specific), we
operationalize all strategic drivers at the market (country-category) level.
To factor in market idiosyncrasies we include GDP per Capita (Δ∞𝑎𝑑𝑗
GDPc) and level of
Urbanization (Δ∞𝑎𝑑𝑗
URBANc), and capture the size of the category by share of wallet
(Δ∞𝑎𝑑𝑗
SOWp,c). We consider retailer and manufacturer concentration by the combined share of the
leading manufacturers (Δ∞𝑎𝑑𝑗
C5_NBp,c) and retailers (Δ∞𝑎𝑑𝑗
C5_RETc). We incorporate the ease of
closing contracts and the ease of setting up a new business to reflect transaction costs
(Δ∞𝑎𝑑𝑗
TRANS_COST1c, Δ∞𝑎𝑑𝑗
TRANS_COST2c). Finally, we include the country’s total advertising
spending (Δ∞𝑎𝑑𝑗
ADVc), and region (REGr) and category-type (CATj) fixed effects.
Importantly, we link the bias-adjusted long-run share differentials to the bias-adjusted
long-run differentials in the various drivers, rather than to the currently-observed differences. For
example, hard discounters, with their almost exclusive focus on PLs (Steenkamp and Kumar
2009), may still have a limited presence in certain countries, but are one of the fastest-growing
21
grocery formats. When explaining the long-run PL potential, we therefore take the expected
evolution in each retail format into account.
To account for the possible endogeneity of the manufacturer- and retailer-related variables
in Eq. (4), we implement Gaussian copulas (see, e.g., Burmester et al. 2015, or Datta et al. 2015,
2017 for recent marketing applications). In contrast to classical methods to correct for
endogeneity, this approach does not require instrumental variables to partial out the exogenous
variation in the endogenous regressors (Park and Gupta 2012). Specifically, for each potentially
endogeneous variable (IV) we add the following regressors to Equation (4): 𝐼𝑉∗ =
∅−1[(H𝐼𝑉(𝐼𝑉𝑖)], where ∅−1 is the inverse of the cumulative normal distribution function, and
where H𝐼𝑉(. ) denotes the empirical distribution function. For identification, the potentially
endogenous regressors must be non-normally distributed, which was confirmed through the
Shapiro-Wilk tests (p < .05 for each of the variables).
Finally, to account for the fact that the long-run differentials are estimated quantities, we
bootstrap the parameter’s standard errors (similar to Lamey et al. 2012), and apply 10,000
repetitions to ensure the stability and reliability of our standard-error estimates. We first estimate
a model where we include a copula regressor for each potentially endogenous variable.
Following Mathys et al. (2016), we retain the copula terms that are statistically significant (p <
.05), and then re-estimate the model.
DATA
Sample composition
We use data from Euromonitor International’s Passport GMID to obtain PL value-share
22
information for 13 years (2003-2015). 9 Annual PL information is available across a wide range
of CPG categories and countries in five continents. Interestingly, of the 2,798 country/category
combinations represented in our data, more than 60% come from outside the often-studied
Western-European and North-American markets, and depict the PL situation in Africa (115
combinations), Eastern Europe (663), Latin America (386), the Far East (392), the Middle East
(160), and Oceania (106). The geographic coverage across the five different continents is
depicted in Table 1. In combination, these 68 countries represent about 75% of the world’s
population in 2015, and over 90% of the world’s GDP. So far, very few studies have considered
more than one country, and three of them only considered a very limited set of Western countries
(see, e.g., Erdem et al. 2004 (3 countries), Erdem and Chang 2012 (3 countries) and Lamey et al.
2007 (4 countries)). While Steenkamp et al. (2010) and Steenkamp and Geyskens (2014)
included a more extensive set of 23 countries, we consider more than twice that number, and are
the first to also include countries from Africa (e.g., Cameroon, Egypt, and South Africa), the
Middle East (e.g., Israel, Saudi Arabia and Turkey) and Oceania. As such, we are the first to
cover all five inhabited continents. Apart from this geographic coverage, our data also cover a
wide range of categories, including nine Beauty & Personal Care (e.g., bath & shower products
and deodorants), two Hot (coffee and tea) and four Cold (e.g., bottled water and soft drinks)
Beverages, 25 Processed Food (e.g., cereals and soup), eight Home Care (e.g., dishwashing and
laundry care), two Pet Food (cat and dog food) and nine Tissue & Hygiene (e.g., kitchen towels
and sanitary protection) categories, as summarized in Web Appendix C.
--- Insert Table 1 about here ---
9 Euromonitor data have been used repeatedly in durable-goods studies (see, e.g., Tellis et al. 2003). Within a CPG
setting, Euromonitor data were recently used in Bronnenberg and Ellickson (2015). Importantly, the Passport data
base is said to be fully cross-country comparable.
23
To evaluate convergence, the UK is chosen as benchmark, for several reasons. First, it is
widely seen as one of the most sophisticated retail markets (Gielens 2012). It also has a PL
market that is among the most mature in the world, and which has been very stable over the last
few years, which we formally test using a unit-root test (please see below for a more detailed
discussion). Importantly, UK data are available for all 59 categories covered in the sample.
Model-free evidence
Looking at the PL share growth trajectories between 2003 and 2015, some first light is shed on
the likeliness of a global convergence proposition. Using the breakfast-cereal category as an
example, we present in Figure 2, for some representative countries, the evolution in, respectively,
the share levels (Panel A) and the share differentials (Panel B). Both figures visually support the
notion of a partial, but not of an absolute, convergence. A similar picture emerges when looking
at the average PL shares curves across all categories (Figure 3, Panel A), and for the Home Care
(Panel B), Beauty & Personal Care (Panel C) and Packaged Good categories (Panel D). We
depict the average PL share trajectory in the UK and in eight different regions in the world (i.e.
Africa, Western and Eastern Europe, Latin and North America, the Middle and Far East, and
Oceania). Clearly, substantial heterogeneity in the long-run differentials is observed.
---Insert Figures 2 and 3 about here---
Data on the underlying drivers
To better understand this heterogeneity, we collected data that reflect some key portfolio and
format decisions, along with a range of category and country characteristics from several data
sources, including Euromonitor’s International Passport GMID, Planet Retail, GlobalData’s
Product Launch Analytics, and the Worldbank. To measure retailers’ and manufacturers’
portfolio decisions, we focus on the combined decisions of the leading (top five) global brand
24
manufacturers and retailers in a specific market. For the manufacturers, we track how many
brands they carry in their portfolio, and how many new SKUs are introduced in a specific
country and product category. In a similar vein, to reflect retailers’ portfolio decisions, we track
with how many banners they operate, how they change their number of stores, and how many PL
SKUs are introduced in each product category in a given country. The importance of each store
format is captured by the share of grocery purchases realized through that particular format in
each category and country. A detailed description on all variables is provided in Table 2. The
time-varying information allowed us to derive long-term differentials (using Eq. (1)), where we
again use the UK as reference market for each of these measures. As with the PL share
differentials, the long-term asymptotes are corrected to control for a potential small-observation-
window bias. We provide descriptives on all measures (prior to the differential calculations) in
Web Appendix D.
---Insert Table 2 about here---
The descriptives show that, on average, global NB manufacturers carry more brands in their
portfolios in North America than in, say, the Far East and Eastern Europe. They also launch
considerably most new products in North America, followed by Western Europe and Oceania.
Global retailers operate more banners in North America and more new PLs are introduced in
that region. More new stores, on the other hand, are opened in the Far East, and far less in North
America. Hypermarkets are least represented in Oceania, while discounters are clearly most
popular in Western Europe and the Middle East. Online grocery retail penetration is still fairly
low across the globe, but most advanced in Western Europe. Not surprisingly, the share of
independent retailers is lowest in Western Europe and North America.
EMPIRICAL FINDINGS
25
First, we test whether convergence will be achieved in the 59 product categories tracked in this
study. If so, we explore whether there is evidence of absolute or relative convergence, and how
quickly the resulting steady-state shares will be reached in each category. Next, we consider to
what extent these steady-state levels differ from the ones obtained in the UK, our reference
country, and assess why these differences vary across countries and/or categories.
Global convergence: fact or fiction? We applied the global convergence test described in
Eq. (1) to each of the 59 categories. The number of countries reflected in each test ranged from
13 (Meal Replacement) to 62 (Toilet Paper), with a median value of 48 countries. In 56 out of
the 59 cases, the test statistic firmly rejected the unit-root null hypothesis (with p-values ranging
between .000 and .024). For three categories, i.e. Milk, Hair Care and Oral Care, no statistical
evidence of global convergence was found (p -value > .05). The combined evidence across 95%
of all cases offers the clear empirical generalization that PL shares are globally converging.
Absolute or partial convergence? No evidence of absolute convergence was obtained in
the vast majority of the categories, as the fixed-effect terms, i.e. the 𝛼𝑝,𝑐 in Eq. (1), were jointly
significant. Hence, while the long-run differential relative to the (stabilized) UK value is each
time mean-reverting, it is different from zero for at least a subset of countries. As shown in Table
3, the size of the long-run differential index varies considerably across both regions and product
classes. The average differential (across all categories) ranges from low values of .23 and .25 in,
Latin America and the Far East to 1.58 in Western Europe. Averaging across the different
regions, the long-run differential varies between .59 (.60) for Home Care (Hot Beverages) and
.95 for Non Alcoholic Beverages to 1.72 for the Beauty & Personal-Care categories.
--- Insert Table 3 about here ---
26
Speed of convergence. Having established (partial) global PL convergence, we turn to
the speed of this process. Specifically, we compute for each of the 56 converging categories, the
number of years it will take to cover 90% of the distance separating the current (2015) PL
differential from the steady-state differential. In Table 4, we report estimates for these 90%
duration intervals, averaged per category type and across all categories.
Even though global convergence will take place, it will not happen any time soon, given a
median duration interval of 19.73 years. However, considerable heterogeneity is observed both
across product classes and within a given product class. For example, the median duration for the
Beauty& Personal Care categories (23.88) is twice as large as for the pet-food categories (11.79),
and in the Processed-Food class, the 90%-duration interval varies from a low of one decade
(Soups) to almost three decades (Snack Bars). Hence, while 56 (out of 59) categories will
eventually reach a long-run equilibrium, the time given to NB manufacturers to prepare for the
new competitive reality will vary considerably depending on their product portfolio.
---Insert Table 4 about here---
Cross-region heterogeneity in differentials and growth potential. Table 3 not only
summarizes the estimated long-run share differentials, but also contrasts them with the currently
observed (2015) differential.10 In 2015, the PL shares were smaller, on average, than in the UK.
For example, in 2015 the average PL share in Eastern Europe was only 60% of the UK level, and
was even smaller in Latin America (19%) and Africa (42%). The 2015 share in North America,
in turn, amounted to 77% of the UK level. In each of these regions, the long-run gap is smaller
than the current gap. For North America and Eastern Europe, the currently-observed differences
10 Our long-run estimates are very robust to the length of the observation period. When dropping the last observation
(see Dekimpe et al. 1998 for a similar practice) very similar results are obtained; the median absolute deviation
relative to the earlier estimates is about .04 and the correlation between both sets of estimates is a high .90.
27
are expected to almost disappear (as its long-run PL share is estimated at, respectively, 84% and
83% of the UK’s level). For Latin America and the Far East, the estimated long-run shares are
expected to be higher as well, even though the gap will remain substantial at, respectively, 23%
and 25% of the UK level. In Australia & New Zealand, PL markets are expected to settle
between the two former scenarios, viz. at 55% of the UK level. Interestingly, this is also the case
for the African and Middle Eastern PL markets. In Europe, PLs will continue to exceed the UK
benchmark. In addition, while PL shares in the UK had already stabilized in 2015, as confirmed
by the formal unit-root test of Eq. (1) across the 59 categories (LLC test-statistic: -4.09, p < .01),
substantial growth potential remains also in the Western-European realm, as the average PL
differential is expected to grow further from 1.42 in 2015 to 1.58 in equilibrium.
Globally, it is fair to state that the end of PL growth is not in sight. Yet, global
convergence clearly does not imply a uniform UK scenario across the globe, as only two of the
seven regions are expected to reach levels comparable to the UK’s. Interestingly, the remaining
geographic divide will not follow the classic lines between developing and developed markets.
Whereas the Middle East and Eastern Europe (and to lesser extent Africa) are identified as fertile
PL seedbeds, Latin America and the Far East will remain lukewarm to PL acceptance.
Cross-category heterogeneity in differentials. This geographic divide does not tell the
entire story, however, as considerable variation exists between and within product classes. For
example, within the Processed Food product class, where long-term differentials are expected to
stabilize on average at the .79 level, marked differences can be observed when looking at the
distribution patterns of the differentials across countries within individual product categories.
Figure 4 illustrates the observed heterogeneity in differentials for four food product categories.
---Insert Figure 4 about here---
28
In the Soup category, a strong left-skewed pattern can be observed, with 70% of all countries
reaching PL differentials below .70 in the long run, and less than 10% of all countries exceeding
parity with the UK. These notable exceptions are all situated in Western Europe. For Breakfast
Cereals, we again find a left-skewed distribution, albeit with a more pronounced right tail: about
35% of all countries’ PL differentials are expected to exceed the UK’s level, several of them
with even more than 30%. Interestingly, the latter set of countries is not exclusively found
among the Western European countries, as also Hungary and Cameroon figure in this set. In
contrast, the US and Canada will reach long-run PL differentials well below .70. Moving to the
Oils and Fats category, we notice a more smoothed, fairly uniform distribution with less than
50% of the differentials remaining below .70 in the long-run. Finally, in some categories, like
Yogurt, the distribution is almost reversed with a dominant right tail, with about 50% of all
countries settling 30% above the UK PL level. Among the strongest performers in this set of
countries, we find Latvia, South-Africa, and Turkey. Combined, these patterns lend support for
the notion that structural differences between product and geographic markets may impact the
heterogeneity in long-term PL differentials. The question therefore arises to what extent some of
these differences can be systematically and predictably linked to strategic retailer and/or
manufacturer decisions.
Explaining the heterogeneity in long-run differentials. Table 5 reports the parameter
estimates of Eq. (4). Combined, all variables explain 49% of the variation. 11 Interestingly, we
find that the combined contribution of only the portfolio constructs and format variables amounts
to 33% of the variance versus 24% when only including the combined market idiosyncrasies
11 For all variables in Eq. (4) we tested whether the constructs converged relative to the UK, before constructing the
differentials. All statistics firmly rejected the unit-root null hypothesis (all p-values < .05).
29
(see, e.g., Dhar and Hoch 1997 for a similar approach), thereby underscoring the importance of
the considered strategic decisions in shaping long-run PL success.
The maximum VIF value is 6.81, well below 10, indicating that multi-collinearity is a not
a concern. Four variables were found to be endogenous, i.e. the brand and banner portfolios, the
store-fleet evolution, and the share of discounters. We included the Gaussian copulas for each of
these constructs, and all four turned out to be significant (p < .05)
--- Insert Table 5 about here ---
As expected, the size of the manufacturers’ portfolio helps to keep PLs at bay: the higher
the long-run differential in NB brands (γ1 = -.065, p < .01) and new products (γ2 = -.093, p <
.001), the lower the long-run PL differential.12 Hence, to counter future PL growth, it is not only
the mere number of NBs present in a market that matters, but also the number of new products
introduced by these brands (as also suggested in Lamey et al. 2012 and Gielens 2012 ).
Retailer portfolios, in contrast, have a positive impact on PL shares, as reflected by the
positive and significant impact of the number of banners (γ3 = .147, p < .01), the number of new
PL products (γ4 = .181, p < .001), and the evolution in the number of stores (γ5 = .195, p < .001).
Combined, this suggests that retailers can leverage their scale, shelf dominance and awareness in
the consumers’ mind to further hone their PL presence, and underscores the importance of
retailers’ distribution intensity.
As for the importance of retail formats, we find that because of their almost exclusive
focus on PLs, a growing discounter share contributes to a higher long-run differential (γ7 = .031,
p < .01). A larger hypermarket share, however, has a negative impact (γ6 = -.033, p < .01).
Indeed, when hypermarkets are successful, consumers value the extensive NB variety that the
12 As indicated before, this differential is expressed as an index relative to the UK level in a given category. As
such, a lower differential implies that a smaller fraction of the UK level will be obtained.
30
format offers (Koschate-Fisher et al. 2014).
Turning to the market idiosyncrasies, we find that the long-term PL potential is enhanced
in categories with a higher share of wallet (γ12 =.350, p < .01) in the country, in countries with
higher retail concentration (γ11 =.186, p < .001) and higher transaction cost efficiencies (γ15
=.802, p < .01; γ16 =.304, p < .05). In contrast, in markets with higher NB concentration (γ10 = -
.186, p < .001) and advertising spending, which is predominantly NB driven (Lamey et al. 2012)
(γ17 = -.029, p < .01), the potential PLs can achieve is limited.
To summarize, both retailers and brand manufacturers play an active role in shaping the
PL share that can be expected. Strong NB portfolios tend to lower the long-run differential,
while strong retailer portfolios have an opposite effect. Moreover, emphasizing the right mix of
retail formats is also of crucial importance.
Cross-time heterogeneity in responsiveness. A natural follow-up question is whether
decisions that may have shaped the PL scene in the past and/or present will remain equally
important in the future. To address this question, we re-estimated Eq. (4) using a stacked data set
that includes not only the asymptotic, long-term information but also the observed values at the
start (2003) and end (2015) of the observation window. This allows us to estimate to what extent
the effectiveness of strategic tools as observed at the end of the observation window changed
relative to the previous years, and how it will change in the years to come. In Table 6, we
summarize the results for our focal strategic variables.
---Insert Table 6 about here---
For three drivers (number of brands, the share of both hypermarkets and independent
retailers), no over-time variation in impact is observed (Panel A). Whereas one portfolio driver,
store fleet expansion, lost some impact relative to the early 2000s, the number of banners and the
31
innovation-based drivers gained in effectiveness. Within the manufacturer portfolio, especially
the role of new products as antidote to limit PL growth has changed considerably over time. In
the past, new NB products may actually have created (probably unintentionally) a contrast effect
allowing PLs to excel along the dimension at which they were best, price (see Gielens 2012, p.
411, for a similar observation). This appears to no longer be the case in the future, as PLs are
moving towards equal-quality alternatives with a more blurred price image (ter Braak et al.
2013). Outpacing PLs along the quality and innovation dimension will therefore become highly
relevant in the years to come. Interestingly, we notice that also the role of new PL products has
gained considerable traction over time, which shows that also retailers will increasingly have to
play the innovation game. Consistent with this, a recent IRI (2016) report argues that the next
wave of private-label growth will be fed primarily by premiumization through targeted
innovations, which should allow retailers to differentiate themselves also on the quality
dimension from their NB competitors. In terms of store format, especially the role of the hard
discounters, with its increasing penetration in more and more countries, will become more
prominent, while the disincentive effect of online channels will disappear over time.
Geographic heterogeneity in responsiveness. Finally, we explore to what extent strategic
decisions have the same effect in developed and emerging markets. To do so, we followed the
procedure of Burgess and Steenkamp (2006) to classify all countries as either emerging or
developed. We then tested whether a construct has a different effect in emerging versus
developed countries. The homogeneity assumption was not rejected (p > .05) for four constructs:
the number of brands, the number of new NB and PL products, and the share of discounters
(Panel B). While the effect of both NB portfolio decisions is the same in shaping long-run PL
potential in emerging and developing countries, we find two noteworthy differences on the
32
retailer side. Distribution breadth, as reflected in the number of banners and the number of stores,
is found to have a substantially smaller impact in emerging countries. This may help explain why
emerging-market retailers often consider to leapfrog their physical operations in favor of digital
operations (Gill 2016). However, this leapfrogging may not give the expected effect (at least in
terms of PL penetration/success) given that a higher share for the online channel dampens PL
success in emerging markets, while it does not affect PL development in the developed markets.
Moreover, we notice that the previously-observed negative effect for hypermarkets is primarily
driven by emerging economies, and that independent retailers will remain a major roadblock in
those PL markets. In combination, these findings support the notion that also in the years to
come, retailing and distribution will remain one of the most complex challenges when doing
business in emerging markets (Kumar et al. 2015).
DISCUSSION
Industry experts agree that PLs are here to stay, and expect that they will continue to grow.
Nevertheless, considerable ambiguity exists about their future growth potential. This poses a
formidable challenge to both NB manufacturers and retailers on how to best allocate resources in
different markets to best prepare for the future. To do so, a forward-looking approach is called
for, which we achieve by adopting a convergence modelling approach to benchmark how fast a
country’s PL market is growing relative to an established (and stabilized) PL leader, viz. the UK.
This approach was applied to a unique dataset with a broad geographic scope that allowed us to
glean insights in important, yet typically overlooked, emerging markets. Moreover, by covering
59 product categories we could track differences in PL success not just across geographic
boundaries, and acknowledge the different roles categories tend to play in different countries.
Indeed, while commonalities exist, the categories where PLs will reach their highest acceptance
33
level vary dramatically by both country and region. Based upon our results, we propose the
following key takeaways.
Global catching up is taking place. In many countries and categories, the proverbial glass
ceiling has not yet been reached. Also, PL growth rates are higher in lagging than in more
established nations. As a status quo has not yet been obtained in the global PL market, both NB
manufacturers and retailers may want to prepare for the upcoming long-term reality.
The time lines are highly divergent. The speed of this convergence process varies
considerably across product categories, ranging from 10 to more than 30 years, and averaging
approximately 20 years. As such, we find no support for the popular contention that global PL
convergence will already be obtained within less than 15 years. Interestingly, even within the
same product group, substantial variation can be found. Manufacturers carrying different
products in their portfolio may want to use these insights when prioritizing strategic adjustments.
A uniform PL market remains unlikely. Global convergence does not imply that the UK’s PL
scene, with an average share of over 30% will be replicated everywhere. This will not even be
the case within Western Europe, in spite of EU policies to stimulate the development of
European nations into the same direction. Even though in Eastern Europe and North America,
the PL gap is expected to gradually disappear, in other regions, viz. Africa, Latin America, the
Far East and Oceania, the overall PL share will remain considerably lower. Especially Latin
America and the Far East will continue to resist the long-term appeal of PLs, as shares are
expected to stagnate at approximately one fourth of the UK level. Importantly, we find no
support for the contention of some business analysts that countries like India and China will soon
reach the PL shares currently observed in Western Europe. NB losses will therefore be relatively
modest in the most important emerging markets in those regions. Moreover, substantial variation
34
exists in long-run potential among the currently lagging PL countries. For example, in one of the
most important gateways to the African continent, South Africa, PL shares are predicted to
gravitate towards a higher level (80% of the UK level) than the rest of the region (46%).
Strategic weapons are available. Even though preparing for a fairly distant future is
difficult, based on the identified relationships between the long-run share differentials and
various portfolio and format drivers, we can offer some interesting guidelines. Foremost,
innovation is key! While NB manufacturers may be hesitant to keep on investing in R&D for
fear of quickly being copied by their PL rivals (Ezrachi and Bernitz 2009), NB innovations
remain a powerful antidote to further PL growth. To retailers, adding new PL SKUs is even more
effective, as these can create a unique draw for shoppers of that category (IRI 2016).
What holds today may not necessarily hold over time. To determine an optimal mix of
strategies, a keen understanding of future market dynamics is necessary. Overly relying on past
and current recipes for success may be dangerous, given that the effectiveness of several drivers
changes substantially over time, as does the sensitivity to certain retail formats.
One-size-fits-all strategies are not on the table. What works in developed markets may not
necessarily do so in emerging markets. Interestingly, while NB activities appear equally effective
in developed and emerging markets, retailers have to be careful not to automatically transfer
their developed-market heuristics and decision rules, especially in terms of the number of
banners and stores to operate.
Limitations
Our research has a number of limitations that offer worthwhile avenues for further research.
First, from a methodological point of view, one may want to extend the currently-used panel
unit-root tests to immediately include potential covariates (in the spirit of Wang and Zhang 2008)
35
to come to a one-step estimation. Second, even though our geographic coverage, with 67
countries from 5 continents, was much more extensive than in previous studies, several countries
(especially from Western, Central and Eastern Africa) are not yet included. Third, it would be
interesting to complement the current global analysis with in-depth studies of individual markets
to identify further factors that may impede or accelerate further PL growth. Similarly, the
category coverage in some important countries as China, India and Russia was less extensive
than in some other markets. A fully-balanced panel would offer a more complete picture of the
global PL situation. In addition, it would be useful to consider a wider set of performance,
including profit, metrics to paint a more complete picture of how different strategies ultimately
affect the retailer’s and manufacturer’s bottom line. Finally, explicitly accounting for long-run
differentials in consumers’ quality perceptions and countries’ cultural values would be
interesting. Not only would it allow us to gain insight in how these perceptions and values shape
long-term PL asymptotes but also how they alter the effectiveness of portfolio and format
strategies. As more time-varying data become available along these dimensions, these effect will
be able to be quantified in future research.
36
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FIGURE 1: ABSOLUTE VERSUS PARTIAL CONVERGENCE a
Figure notes:
(a) PL = Private Label
A
Leading Market
PL share
time
B
C
42
FIGURE 2: ILLUSTRATION OF THE EVOLUTION IN
PL SHARES AND PL SHARE DIFFERENTIALS
Figure notes:
(a) The vertical axis is the private label (PL) share (in percentage) in breakfast cereals in the country and year.
(b) The vertical axis is the private label (PL) share differential in breakfast cereals in the country and year with the
UK as the reference country.
0
5
10
15
20
25
2003 2005 2007 2009 2011 2013 2015
Panel A: PL Shares of Breakfast Cereals a
UK US South Africa Dominican Republic Czech Republic
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
2003 2005 2007 2009 2011 2013 2015
Panel B: PL Share Differentials of Breakfast Cereals b
US South Africa Dominican Republic Czech Republic
43
FIGURE 3: MODEL-FREE EVIDENCE: THE EVOLUTION IN PL SHARES a
Figure notes: (a) The vertical axis is the average private label (PL) share (in percentage) in the category type, region, and year.
0
5
10
15
20
25
2003 2005 2007 2009 2011 2013 2015
Panel A: All Categories
0
5
10
15
20
25
2003 2005 2007 2009 2011 2013 2015
Panel B: Home Care
0
2
4
6
8
2003 2005 2007 2009 2011 2013 2015
Panel C: Beauty and Personal Care
0
5
10
15
20
25
30
2003 2005 2007 2009 2011 2013 2015
Panel D: Packaged Food
44
FIGURE 4: ILLUSTRATION OF THE HETEROGENEITY IN LONG-RUN SHARE DIFFERENTIALS a, b
Figure notes:
(a) The vertical axis is the percentage of observations that falls into each interval.
(b) The horizontal axis is the average (small-sample adjusted) private label (PL) share differential in the category and country, grouped into five intervals.
0%
10%
20%
30%
40%
50%
60%
70%
< 0.7 0.7 - 0.9 0.9 - 1.1 1.1 - 1.3 > 1.3
Panel A: Breakfast Cereals
0%
10%
20%
30%
40%
50%
60%
70%
< 0.7 0.7 - 0.9 0.9 - 1.1 1.1 - 1.3 > 1.3
Panel B: Oils and Fats
0%
10%
20%
30%
40%
50%
60%
70%
< 0.7 0.7 - 0.9 0.9 - 1.1 1.1 - 1.3 > 1.3
Panel C: Soup
0%
10%
20%
30%
40%
50%
60%
70%
< 0.7 0.7 - 0.9 0.9 - 1.1 1.1 - 1.3 > 1.3
Panel D: Yoghurt and Sour Milk Products
45
TABLE 1: GEOGRAPHIC COVERAGE
Region a Countries b
Africa (4) Egypt (9), Cameroon (27), Morocco (25), South Africa (54)
America
Latin America (12) Argentina (46), Brazil (53), Chile (43), Colombia (46), Costa Rica (15), Dominican Republic (27), Ecuador
(18), Guatemala (22), Mexico (47), Peru (30), Uruguay (19), Venezuela (20)
North America (2) Canada (58), USA (59)
Asia
Far East Asia (12) China (13), Hong Kong (48), India (24), Indonesia (20), Japan (52), Malaysia (36), Philippines (25), Singapore
(49), South Korea (32), Taiwan (39), Thailand (44), Vietnam (10)
Middle East Asia (4) Israel (53), Saudi Arabia (31), Turkey (51), United Arab Emirates (25)
Europe
Eastern Europe (16) Bosnia Herzegovina (176), Bulgaria (50), Croatia (47), Czech Republic (56), Estonia (37), Hungary (56),
Latvia (42), Lithuania (38), Macedonia (18), Poland (56), Romania (27), Russia (35), Serbia (33), Slovakia
(57), Slovenia (56), Ukraine (39)
Western Europe (16) Austria (57), Belgium (59), Denmark (56), Finland (57), France (59), Germany (59), Greece (56), Ireland (58),
Italy (55), Netherlands (59), Norway (52), Portugal (58), Spain (58), Sweden (57), Switzerland (59), United
Kingdom (59)
Oceania (2) Australia (57), New Zealand (49)
Table notes:
(a): The number in parenthesis is the number of countries on which data are available in the region.
(b): The number in parenthesis is the number of categories on which data are available in the country.
46
TABLE 2: OPERATIONALIZATION OF VARIABLES
Variable Source Operationalization
Focal Variable
Private label (PL)
value share
Euromonitor-
PassportGMID
Value share of private label in category p and country c.
Manufacturer Portfolio
Brands Euromonitor-
PassportGMID
Weighted number of brands in category p and country c sold by the top 5 global manufacturers.
Ranks and weights are determined based on revenue shares. Revenue share is the worldwide
revenue share per category, averaged over the observation window (2003 to 2015).
New products Euromonitor-
PassportGMID
Number of new SKUs launched in category p and country c by the top 5 global manufacturers.
Ranks are determined based on revenue shares.Revenue share is the worldwide revenue share per
category, averaged over the observation window (2003 to 2015).
Retailer Portfolio
Banners Planet Retail Weighted number of banners in country c operated by the top 5 global retailers. Ranks and
weights are determined based on revenue shares. Revenue share is the worldwide revenue share,
averaged over the observation window (2003 to 2015).
New PL products Global Data-Product
Launch Analytics
Number of new Private Label (PL) SKUs launched in category p and country c.
Store-fleet
expansion
Planet Retail Number of new outlets opened in country c by the top 5 global retailers, expressed as an index
relative to the number of stores operated in the previous year. Global retailers are defined based
on average revenues shares worldwide over the observation window (2003 to 2015).
Format Importance
Hypermarket share Euromonitor-
PassportGMID
Share of revenue of hypermarkets in category p and country c. Hypermarkets are retail outlets
selling groceries and non-food merchandise, with a selling space of over 2,500 square meters.
They are frequently located on out-of-town sites or used as an anchor store in a shopping center.
Discounter share Euromonitor-
PassportGMID
Share of revenue of discounters (hard and soft) in category p and country c. Hard discounters are
retail outlets that are typically 300-900 square meters and stock fewer than 1,000 product lines.
Soft discounters are retail outlets that are typically slightly larger than hard discounters and stock
1,000-4,000 product lines. The discounter-share metric excludes mass merchandisers and
warehouse clubs.
47
TABLE 2 (continued)
Variable Source Operationalization
Internet-retailing
share
Euromonitor-
PassportGMID
Share of revenue of internet retailing in category p and country c. Internet channels sell consumer
goods to the general public via the Internet.
Independent-retailer
share
Euromonitor-
PassportGMID
Share of revenue of independent retailers in category p and country c. Independent retailers are
retail outlets that are owned by an entrepreneur owning and operating one or more, but fewer than
ten, retail outlets.
Market Idiosyncrasies
National brand
concentration
Euromonitor-
PassportGMID
Sum of value shares of top 5 national brands in category p and country c. Value shares are based on
country-specific grocery revenue shares and include both global and local players.
Retailer
concentration
PlanetRetail Sum of value shares of the top 5 retailers in country c. Value shares are based on country-specific
grocery revenue shares and include global and local players.
GDP per capita
PlanetRetail Total value of an economy's annual domestic output of goods and services (in US$), per inhabitant.
Urbanization Euromonitor-
PassportGMID
Proportion of population living in areas defined as urban in country c, as reported by the United
Nations.
Share of wallet Euromonitor-
PassportGMID
Share of grocery spending spent on category p in country c.
Transaction costs Worldbank The transaction costs measures are captured as a distance of country c’s economy to a frontier
(details on the derivation of the measure is provided on the Worldbank’s ‘Doing Business’ website:
http://www.doingbusiness.org/data/distance-to-frontier). Two measures are used in this study:
1. Starting a business: Procedures that are officially required for an entrepreneur to start up and
formally operate an industrial or commercial business.
2. Enforcing a contract: The efficiency of the judicial system in resolving a commercial dispute.
Advertising
spending
Euromonitor-
PassportGMID
The amount spent on advertising (US $ mn.) in country c.
48
TABLE 3: CURRENT AND LONG-RUN SHARE DIFFERENTIALS a
Africa America Asia Europe
Oceania All Latin North Far Middle East West
Beauty & Personal Care Δ∞𝑎𝑑𝑗
𝑃𝐿 1.19 .32 1.29 .36 1.50 2.12 2.58 .75 1.72
Δ2015𝑃𝐿 .62 .26 1.13 .32 .70 1.45 2.09 .60 1.30
∆∞𝑎𝑑𝑗
𝑃𝐿 − Δ2015𝑃𝐿 .57 .06 .16 .04 .80 .67 .50 .15 .42
Home Care ∆∞𝑎𝑑𝑗
𝑃𝐿 .27 .23 .41 .18 .24 .52 1.20 .48 .59
Δ2015𝑃𝐿 .26 .21 .38 .15 .23 .42 1.12 .46 .54
∆∞𝑎𝑑𝑗
𝑃𝐿 − Δ2015𝑃𝐿 .01 .02 .02 .02 .02 .11 .08 .02 .06
Hot Beverages ∆∞𝑎𝑑𝑗
𝑃𝐿 .21 .15 .36 .23 .36 .46 1.14 .30 .60
Δ2015𝑃𝐿 .18 .13 .37 .20 .37 .38 1.05 .28 .54
∆∞𝑎𝑑𝑗
𝑃𝐿 − Δ2015𝑃𝐿 .04 .02 -.01 .03 -.01 .08 .10 .02 .06
Non-Alcoholic Beverages ∆∞𝑎𝑑𝑗
𝑃𝐿 .27 .11 .51 .17 .19 .72 1.69 .37 .95
Δ2015𝑃𝐿 .32 .10 .61 .17 .14 .66 1.72 .45 .95
∆∞𝑎𝑑𝑗
𝑃𝐿 − Δ2015𝑃𝐿 -.05 .01 -.09 .00 .04 .07 -.03 -.09 .00
Packaged Food ∆∞𝑎𝑑𝑗
𝑃𝐿 .64 .21 .87 .20 .70 .73 1.38 .52 .79
Δ2015𝑃𝐿 .57 .17 .80 .17 .33 .50 1.32 .53 .68
∆∞𝑎𝑑𝑗
𝑃𝐿 − Δ2015𝑃𝐿 .07 .03 .07 .03 .38 .22 .05 -.01 .10
Pet Food ∆∞𝑎𝑑𝑗
𝑃𝐿 .13 .19 .62 .19 .29 1.03 1.29 .47 .75
Δ2015𝑃𝐿 .12 .11 .59 .12 .14 .58 1.21 .46 .62
Δ∞𝑃𝐿 − Δ2015𝑃𝐿 .01 .07 .02 .07 .16 .45 .08 .01 .13
Tissue & Hygiene ∆∞𝑎𝑑𝑗
𝑃𝐿 .33 .30 1.13 .38 .70 .72 1.78 .68 .90
Δ2015𝑃𝐿 .29 .23 .99 .32 .62 .54 1.45 .56 .73
∆∞𝑎𝑑𝑗
𝑃𝐿 − Δ2015𝑃𝐿 .04 .07 .14 .05 .08 .18 .33 .12 .17
All ∆∞𝑎𝑑𝑗
𝑃𝐿 .51 .23 .84 .25 .66 .83 1.58 .55 .88
Δ2015𝑃𝐿 .42 .19 .77 .21 .39 .60 1.42 .52 .74
∆∞𝑎𝑑𝑗
𝑃𝐿 − Δ2015𝑃𝐿 .09 .04 .07 .04 .27 .23 .15 .04 .14
Table notes: (a): ∆∞𝑎𝑑𝑗
PL is the (small-sample adjusted) long run private label (PL) share differential; Δ2015 PL is the PL share differential in 2015.
49
TABLE 4: 90% DURATION INTERVALS a
N
𝜷𝒑𝒂𝒅𝒋
90% duration
interval
Beauty & Personal Care 7 Median -.09 23.88
Minimum -.12 18.49
Maximum -.08 27.88
Home Care 8 Median -.12 18.11
Minimum -.15 14.02
Maximum -.10 21.93
Hot Beverages 2 Median -.09 23.41
Minimum -.10 21.54
Maximum -.09 25.28
Non-Alcoholic Beverages 4 Median -.11 19.90
Minimum -.20 10.27
Maximum -.07 31.64
Packaged Food 24 Median -.11 20.46
Minimum -.20 10.13
Maximum -.07 31.41
Pet Food 2 Median -.18 11.79
Minimum -.13 15.94
Maximum -.09 24.97
Tissue & Hygiene 9 Median -.11 19.54
Minimum -.13 16.32
Maximum -.07 30.56
All 56 Median -.11 19.73
Minimum -.20 10.13
Maximum -.07 31.64
Table notes:
(a): N = Number of observations; 𝛽𝑝𝑎𝑑𝑗
= Estimated rate (speed) of convergence of private label shares to their long-
run values; 90% duration interval = Number of years required to cover 90% of the distance separating the current
(2015) private label share differential from the long-run private label share differential.
50
TABLE 5: DETERMINANTS OF LONG-RUN SHARE DIFFERENTIALS a, b
Coefficient t-statistic
Manufacturer Portfolio
∆∞𝑎𝑑𝑗
Brands (γ1) -.065** -2.79
Δ∞𝑎𝑑𝑗
New products (γ2) -.093*** -4.77
Retailer Portfolio
Δ∞𝑎𝑑𝑗
Banners (γ3) .147** 2.70
Δ∞𝑎𝑑𝑗
New PL products (γ4) .181*** 7.34
Δ∞𝑎𝑑𝑗
Store-fleet expansion (γ5) .195*** 5.43
Format Importance
Δ∞𝑎𝑑𝑗
Hypermarket share (γ6) -.033** -2.66
Δ∞𝑎𝑑𝑗
Discounter share (γ7) .031** 3.13
Δ∞𝑎𝑑𝑗
Internet-retailing share (γ8) .013 1.00
Δ∞𝑎𝑑𝑗
Independent-retailer share (γ9) -.005 -.83
Market Idiosyncrasies
Δ∞𝑎𝑑𝑗
National brand concentration (γ10) -.186*** -6.76
Δ∞𝑎𝑑𝑗
Retailer concentration (γ11) .186*** 6.80
Δ∞𝑎𝑑𝑗
GDP per capita (γ12) .058 .91
Δ∞𝑎𝑑𝑗
Urbanization (γ13) .074* 1.99
Δ∞𝑎𝑑𝑗
Share of wallet (γ14) .350** 2.74
Δ∞𝑎𝑑𝑗
Tr. costs – Starting a business (γ15) c .802** 3.15
Δ∞𝑎𝑑𝑗
Tr. costs – Enforcing a contract (γ16) c .304* 2.18
Δ∞𝑎𝑑𝑗
Advertising spending (γ17) -.029** -2.65
Regional dummies
Africa -.966*** -5.46
Latin America -.989*** -9.04
North America -.730*** -7.01
Far East -.924*** -8.52
Middle East -.548*** -4.23
Eastern Europe -.116 -1.22
Oceania -.358* -2.44
Category-type dummies
Beauty & Personal Care .473*** 5.25
Home Care -.314*** -4.65
Hot Beverages .110 .83
Non-Alcoholic Beverages -.240* -2.51
Pet Food -.249* -2.23
Tissue & Hygiene -.083 -1.15
Copulas
Δ∞𝑎𝑑𝑗
Brands .155*** 3.32
Δ∞𝑎𝑑𝑗
Banners -.264*** -2.93
Δ∞𝑎𝑑𝑗
Store-fleet expansion -.115*** -2.25
Δ∞𝑎𝑑𝑗
Discounter share .129*** 2.72
Intercept .629*** 3.52
Table notes:
(a): Δ∞is the (small-sample adjusted) long run differential. The baseline category-type is “Packaged Foods” and the
baseline region is “Western Europe”.
(b): Number of observations = 2,655; * p < .05 (two-sided); ** p < .01 (two-sided); *** 𝑝 < .001 (two-sided).
(c) Tr. costs = Transaction costs.
51
TABLE 6a: DETERMINANTS OF LONG RUN, 2003, AND 2015 SHARE
DIFFERENTIALS a, b
Reference =
Long-Run
2003 c 2015 c
Coefficient Coefficient Coefficient
Manufacturer Portfolio
∆∞|03|15 Brands -.032 d - -
∆∞|03|15 New products -.085*** .144*** .048*
Retailer Portfolio
∆∞|03|15 Banners .097** .006 -.086*
∆∞|03|15 New PL products .112*** -.084*** -.062**
∆∞|03|15 Store-fleet expansion .133*** .051* .039
Format Importance
∆∞|03|15 Hypermarket share -.040*** - -
∆∞|03|15 Discounter share .025*** -.022*** -.003
∆∞|03|15 Internet-retailing share .023* -.032 e .003
∆∞|03|15 Independent-retailer share -.004 - -
TABLE 6b: REGION-SPECIFIC DETERMINANTS OF LONG-RUN SHARE
DIFFERENTIALS a, f
Reference =
Developed markets
Emerging markets c
Manufacturer Portfolio
Δ∞𝑎𝑑𝑗
Brands -.058** -
Δ∞𝑎𝑑𝑗
New products -.089*** -
Retailer Portfolio
Δ∞𝑎𝑑𝑗
Banners .110*** -.117**
Δ∞𝑎𝑑𝑗
New PL products .158*** -
Δ∞𝑎𝑑𝑗
Store-fleet expansion .327*** -.189***
Format Importance
Δ∞𝑎𝑑𝑗
Hypermarket share .180*** -.168***
Δ∞𝑎𝑑𝑗
Discounter share .021* -
Δ∞𝑎𝑑𝑗
Internet-retailing share .050 -.053*
Δ∞𝑎𝑑𝑗
Independent-retailer share .008 -.037***
Table notes:
(a): ∆∞|03|15 is the (small-sample adjusted) long-run differential, the differential in 2003, and the differential in
2015, in the model of long-run private label (PL) shares, PL shares in 2003, and PL shares in 2015 respectively.
Δ∞𝑎𝑑𝑗
is the (small-sample adjusted) long-run differential. The baseline category-type is “Packaged Foods” and the
baseline region is “Western Europe”.
(b): Number of observations = 7,965; * p < .05 (two-sided); ** p < .01 (two-sided); *** 𝑝 < .001 (two-sided).
(c): The coefficient is the extent to which the time- or emerging-market-specific effect deviates from the reference
case. ‘-’ indicates that in a model with time-specific or emerging-market-specific effects for all variables, the Wald
test for time- or emerging-market-specific effects did not jointly reject the null for this variable (p > 0.05).
(d): t-statistic = -1.82; p < 0.1 (two-sided).
(e): t-statistic = -1.96; p < 0.1 (two-sided).
(f): Number of observations = 2,655; * p < .05 (two-sided); ** p < .01 (two-sided); *** 𝑝 < .001 (two-sided).
W-1
WEB APPENDIX A.1: PRIVATE LABEL SHARE IN 2015a
: Between 0 and 5%.
: Between 5 and 10%.
: Between 10 and 20%.
: Greater than 20%.
: Countries not covered in our data.
Figure notes: (a) Data source: Euromonitor-PassportGMID: numbers reflect the cross-category average.
W-2
WEB APPENDIX A.2: GROWTH RATE OF PL SHARE FROM 2012 TO 2015a
: Between 0 and 2.5%.
: Between 2.5% and 5%.
: Between 5% and 10%.
: Greater than 10%.
: Countries not covered in our data.
Figure notes: (a) Data source: Euromonitor-PassportGMID: numbers reflect the cross-category average.
W-3
WEB APPENDIX B: NICKELL’S BIAS
Nickell’s correction is required in dynamic panel models if the data are observed for a
finite number of periods, and if the specification includes a cross-section specific fixed
effect. Estimating the model requires taking out the fixed effect. As the data is only observed
for a finite number of periods, this induces a dependence between the transformed lagged
term (on the right-hand side of the estimation equation) and the transformed error term (for
example, see equation (6) in Nickell (1981)). This dependence causes the bias.
Nickell (1981) developed a formula to correct for the bias, which we use to obtain
𝛽𝑝𝑎𝑑𝑗
and 𝜂𝑝𝑎𝑑𝑗
the bias-corrected coefficients for the model given in Eq. (1) of the main text
(we refer to p. 1089 in Cecchetti et al. 2002 for a detailed derivation) as follows:
(1) 𝛽𝑝𝑎𝑑𝑗
= 𝜌𝑝𝑎𝑑𝑗
− 1, and
(2) 𝜌𝑝𝑎𝑑𝑗
= 𝜌𝑝 +−(1+𝜌𝑝)
𝑇−1∗ {1 −
1
𝑇
(1−𝜌𝑝𝑇)
1−𝜌𝑝} ∗ {1 −
2𝜌𝑝
(1−𝜌𝑝)(𝑇−1)[1 −
1
𝑇
(1−𝜌𝑝𝑇)
1−𝜌𝑝]}
−1
,
where 𝜌𝑝 = �̂�𝑝 + 1, T is the number of time observations, and �̂�𝑝 is the parameter
estimate. 𝜂𝑝𝑎𝑑𝑗
is derived similar to 𝛽𝑝𝑎𝑑𝑗
.
Given 𝛽𝑝𝑎𝑑𝑗
and 𝜂𝑝𝑎𝑑𝑗
, bias-adjusted values of 𝛼𝑝,𝑐 (𝛼𝑝,𝑐𝑎𝑑𝑗
) are obtained using the
procedure described in Gaulier et al. (1999):
(3) 𝛼𝑝,𝑐𝑎𝑑𝑗
= ∆̅𝑙𝑜𝑔 (𝑃𝐿
𝑝,𝑐, 𝑡
𝑃𝐿𝑝,𝑅𝑒𝑓,𝑡.) − 𝛽𝑝
𝑎𝑑𝑗𝑙𝑜𝑔 (
𝑃𝐿𝑝,𝑐, 𝑡
𝑃𝐿𝑝,𝑅𝑒𝑓, 𝑡
)̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅
− 𝜂𝑝𝑎𝑑𝑗
∆̅𝑙𝑜𝑔 (𝑃𝐿
𝑝,𝑐, 𝑡−1
𝑃𝐿𝑝,𝑅𝑒𝑓, 𝑡−1
),
where the overbar refers to the over-time average and ∆ to the first difference.
W-4
WEB APPENDIX C: PRODUCT CATEGORY COVERAGE
Category Typea Categoriesb
Beauty & Personal
Care (7)
Bath and Shower (54), Color Cosmetics (32), Deodorants (36),
Depilatories (38), Men's Grooming (46), Skin Care (47), Sun Care (29)
Home Care (8) Air Care (45), Bleach (56), Dishwashing (58), Home Insecticides (34),
Laundry Care (60), Polishes (51), Surface Care (62), Toilet Care (48)
Hot Beverages (2) Coffee (47), Tea (47)
Non-Alcoholic
Beverages (4)
Bottled Water (40), Carbonates (42), Fruit/Vegetable Juice (43), Sports
and Energy Drinks (30)
Processed Food
(24)
Baby Food (25), Baked Goods (56), Biscuits (55), Breakfast Cereals
(51), Canned/Preserved Food (55), Cheese (48), Chilled Processed
Food (55), Chocolate Confectionery (39), Dried Processed Food (62),
Frozen Processed Food (57), Gum (23), Ice Cream (46), Meal
Replacement (13), Noodles (42), Oils and Fats (53), Pasta (59), Ready
Meals (56), Sauces, Dressings and Condiments (63), Snack Bars (30),
Soup (43), Spreads (56), Sugar Confectionery (40), Sweet and Savory
Snacks (57), Yoghurt and Sour Milk Products (41)
Pet Food (2) Cat Food (43), Dog Food (45)
Tissue & Hygiene
(9)
Cotton Wool/Buds/Pads (57), Incontinence (39), Kitchen Towels (61),
Nappies/Diapers/Pants (54), Paper Tableware (58), Sanitary Protection
(49), Tissues (58), Toilet Paper (62), Wipes (58)
Table notes:
(a) The number between brackets refers to the number of product categories within the category type.
(b) The number between brackets refers to the number of countries for which data are available for the category.
W-5
WEB APPENDIX D.1: DESCRIPTIVE STATISTICS (MEANS) OF THE EXPLANATORY VARIABLES
Africa Western
Europe
Eastern
Europe
North
America
Latin
America
Middle
East
Far
East
Oceaniaa
Manufacturer Portfolio
Brands 3.72 3.40 2.32 4.34 2.92 2.60 2.37 3.42
New products 4.96 5.92 3.32 45.59 3.52 3.01 3.68 5.38
Retailer Portfolio
Banners 1.81 2.33 .90 6.06 2.82 1.58 1.69 .00
New PL products 2.34 2.58 .65 14.21 .83 .59 .92 1.13
Store-fleet expansion 1.49 1.23 1.53 1.08 1.17 1.50 2.23 .00
Format Importance
Hypermarket share 7.82 16.48 17.63 14.86 18.19 17.39 16.10 .03
Discounter share .34 11.45 5.02 4.89 3.20 1.14 .01 .56
Internet-retailing share .03 .82 .26 .53 .13 .23 .67 .33
Independent-retailer share 24.95 5.92 22.69 6.55 29.28 19.62 19.45 3.22
Market Idiosyncrasies
National brand concentration .60 .54 .57 .53 .66 .64 .62 .63
Retailer concentration .36 .64 .39 .39 .25 .19 .21 .78
GDP per capita 2790 31065 8253 34209 5205 16278 14223 29367
Urbanization .56 .76 .63 .81 .78 .81 .71 .87
Share of wallet 1.27 1.18 1.19 1.17 1.25 1.25 1.17 1.16
Tr. C.: Starting a business b 74.23 84.58 8.18 94.06 68.22 8.32 77.20 96.82
Tr. C.: Enforcing a contract b 55.11 69.27 63.48 68.56 53.67 55.22 67.79 77.22
Advertising spending 1746.05 4728.30 799.44 83213.19 2276.82 947.64 7001.38 5912.16
Table notes:
(a) There were no top 5 global retailers present in Oceania during the observation window. The asymptote of store-fleet evolution is therefore set at zero for the
corresponding observations.
(b) Tr. C. = Transaction Costs.
W-6
WEB APPENDIX D.2: CORRELATION TABLE OF THE EXPLANATORY VARIABLES
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17)
∆∞ Brands (1) 1.00
∆∞ New products (2) .02 1.00
∆∞ Banners (3) .12 .13 1.00
∆∞ New PL products (4) .03 .41 .27 1.00
∆∞ Store-fleet expansion (5) .09 .02 .61 .13 1.00
∆∞ Hypermarket share (6) -.01 -.01 .26 .06 .40 1.00
∆∞ Discounter share (7) .03 .01 .21 .10 .24 .25 1.00
∆∞ Internet-retailing share (8) .08 -.05 .15 .14 .15 .07 .28 1.00
∆∞ Independent-retailer share (9) -.03 .09 .04 -.02 .02 .08 -.10 -.14 1.00
∆∞ National brand concentration (10) .05 .03 -.07 -.11 -.06 .01 -.05 -.08 .08 1.00
∆∞ Retailer concentration (11) -.06 -.02 -.21 -.01 -.21 -.21 -.09 -.05 .03 -.01 1.00
∆∞ GDP per capita (12) .11 -.02 .09 .07 .18 -.10 .43 .37 -.21 -.10 -.29 1.00
∆∞ Urbanization (13) -.02 .04 .07 -.01 .13 .04 -.35 -.19 .13 .06 .02 -.49 1.00
∆∞ Share of wallet (14) .09 .06 .24 .11 .17 .03 .34 .25 -.07 -.10 -.52 .66 -.36 1.00
∆∞ Tr. C.: Starting a business (15) a .03 -.06 -.13 -.04 .02 -.11 .19 .18 -.15 -.04 -.07 .47 -.23 .23 1.00
∆∞ Tr. C.: Enforcing a contract (16) a .06 -.03 .03 .00 .00 -.13 .18 .27 -.15 -.08 -.18 .57 -.18 .47 .49 1.00
∆∞ Advertising spending (17) -.02 .06 .21 .01 .07 .01 -.26 -.22 .15 .04 .02 -.45 .37 -.24 -.31 -.18 1.00
Table notes:
(a) Tr. C. = Transaction Costs.
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