Datta, Hannes; Van Heerde, H.J.; Dekimpe, Marnik; Steenkamp, J.E.B.M

Tilburg University

Cross-National Differences in Market Response: Line-Length, Price, and Distribution Elasticities in Fourteen Indo-Pacific Rim Economies Datta, Hannes; van Heerde, H.J.; Dekimpe, Marnik; Steenkamp, J.E.B.M.

Publication date: 2019

Document Version Early version, also known as pre-print

Link to publication in Tilburg University Research Portal

Citation for published version (APA): Datta, H., van Heerde, H. J., Dekimpe, M., & Steenkamp, J. E. B. M. (2019). Cross-National Differences in Market Response: Line-Length, Price, and Distribution Elasticities in Fourteen Indo-Pacific Rim Economies.

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Universality or Differences in Marketing Elasticities in Emerging versus Developed Markets? The Moderating Role of Brand Equity

Hannes Datta

Harald J. van Heerde

Marnik G. Dekimpe

Jan-Benedict E.M. Steenkamp

This version: September 13, 2019

Hannes Datta is Associate Professor of Marketing at Tilburg University (e-mail: [email protected]). Harald J. van Heerde is SHARP Research Professor of Marketing at UNSW Business School, Sydney, and CenTER Fellow at Tilburg University, Department of Marketing (e-mail: [email protected]). Marnik G. Dekimpe is Research Professor of Marketing & Head of the Marketing Department, Tilburg University, and Professor of Marketing, KU Leuven (e-mail: [email protected]). Jan-Benedict E.M. Steenkamp is C. Knox Massey Distinguished Professor of Marketing, University of North Carolina at Chapel Hill (e-mail: [email protected]). The authors are indebted to GfK Singapore for making the data available, and thank Yuri Peers as well as participants at the 2018 ANZMAC Conference, the 2019 Research Camp at the University of Hamburg, the 2019 INFORMS Marketing Science conference, and seminar participants at the University of Mannheim for valuable feedback.

Universality or Differences in Marketing Elasticities in Emerging versus Developed Markets? The Moderating Role of Brand Equity

Consumption in emerging markets is growing much faster than in developed markets.

Accordingly, many global brand manufacturers venture into emerging markets, where they battle vigorously for market share. At the same time, emerging-market brands increasingly expand to more developed markets. While most firms have considerable expertise competing in similar markets, they lack insights on how consumers respond to marketing activities in economically dissimilar markets. Therefore, this paper quantifies the market-share elasticities of (i) three key marketing-mix instruments (price, distribution and line length), for (ii) 1,600+ international and domestic brands, across (iii) 14 durable categories (covering more recent categories like smartphones and mature ones like refrigerators), in (iv) 7 emerging and 7 developed markets from Asia and Australasia, for (v) up to 11 years. The findings suggest that differences in marketing elasticities between emerging and developed markets depend not only on the type of marketing variable (strongest difference for distribution), but also on brand equity (which affects elasticities more favorably in emerging markets than in developed markets). The paper discusses implications for marketing theory and practice.

Keywords: Marketing-Mix Elasticities, Emerging Markets, Econometrics, Empirical

Generalizations, Product, Pricing, Sales Promotions, Retailing.

Many U.S. and other firms generate a significant and growing share of their revenues overseas. In the earlier stages of the internationalization of the marketplace, firms expanded primarily to other developed markets (DMs). After all, even a mere fifteen years ago, DMs accounted for 81% of global GDP. The situation has changed dramatically since then; today, emerging markets (EMs) already account for 37% of global GDP,1 a number that many believe will grow further in the coming years.

Firms require knowledge on these highly dissimilar markets, with characteristics many were only modestly aware of just two decades ago. A crucial element in developing a marketing strategy for any given market is knowledge of its market-response parameters, i.e., how consumers in a given country respond to the brands’ marketing mix (Farley and Lehmann 1994).

One could argue that firms could base their strategy upon a wealth of previous research, much of which is summarized in various meta-analyses (e.g., Bijmolt, Van Heerde, and Pieters 2005) and recently compiled in Hanssens (2015). While this body of research is indeed substantial and important, nearly all insights are based on DMs. To date, the marketing literature gives conflicting conceptual perspectives and very little empirical guidance regarding market-response effects in EMs.

In a parallel development, an increasing number of EM brands have started to break out of their traditional market boundaries to also compete in more developed markets (Kumar and

Steenkamp 2013). In both instances, managers have considerably more experience competing in their familiar level of economic development, and wonder to what extent they can expect a similar, or rather a very different, level of responsiveness to their marketing activities when operating in a substantially more (or less) developed economic environment.

1 Based on statistics provided by the World Bank (2019). 3

On the one hand, the theory of global marketing standardization (Yip 2003) and the emergence of global consumer culture (Alden, Steenkamp, and Batra 1999) support the idea that global markets have comparable response parameters. On the other hand, clear differences in languages, cultures, values, income per capita and its distribution, demographics, and consumption levels between DMs and EMs let managers presume there are also large cross- national differences in marketing effectiveness (Farley and Lehmann 1994, Burgess and

Steenkamp 2006). Moreover, few studies offer a direct empirical comparison of marketing elasticities between EMs and DMs.2

A key factor that has been overlooked in the debate on whether marketing-mix responsiveness differs between EMs and DMs is brand equity. This is unfortunate given that the brand is the linchpin around which the firm’s marketing efforts are organized, while brand equity is defined as the differential effect brand knowledge has on consumer response to the marketing of the brand (Keller 1993, p. 2; emphasis added). Regarding the role of brand equity in DMs versus EMs, conflicting conceptual arguments have been put forward. Sheth (2011) argues that the notion of brand equity could be at odds with the realities of emerging markets, given their great number of unbranded products. On the other hand, Roberts et al. (2015) argue that the consumption of high-equity brands is more aspirational in EMs than in DMs. An implication of this line of argumentation is that in EMs, brand equity is expected to have more favorable effects on marketing elasticities (e.g., stronger response to price discounts or expansion of line length for high-equity brands) than in DMs. In this paper, we study a broad cross-section of branded

2 A notable exception is Bahadir, Bharadwaj and Srivastava (2015), who studied four rather similar beverage categories (regular soft drinks, diet soft drinks, energy drinks and juices). It is unclear, however, to what extent any of the observed differences are idiosyncratic to this particular subset of CPG categories. 4

products and develop/test the argument that brand equity will play a different role for brands depending on the country’s development level.

In sum, this paper addresses the following two research questions (RQ): RQ1 Are there systematic differences in marketing-mix elasticities (price, distribution, line length) between DMs and EMs, and if so – what is their magnitude? RQ2 Does brand equity have more favorable effects on marketing-mix elasticities in EMs, compared to DMs?

A major barrier hitherto to study such questions is the lack of suitable data, especially for emerging markets. In order to make a proper comparison, the data need, ideally, be collected in a uniform way and cover the same set of products across the same time period, and span multiple countries, across multiple and diverse product categories and brands, to ensure empirical generalizability. We are in the fortunate position to have access to a unique data set spanning seven DMs (Australia, Hong Kong, Japan, New Zealand, Singapore, South Korea, and Taiwan) and seven EMs (China, India, Indonesia, Malaysia, the Philippines, Thailand, Vietnam), jointly representing nearly half of the world’s population. The global market research agency GfK provided monthly national data on sales and marketing activities for brands in 14 electronics and appliance categories over a time period of up to 11 years (2004-2014). In total, the data include more than 1,600 brands, allowing us to draw broad empirical generalizations on price elasticities, distribution elasticities and line-length elasticities.3 As such, our paper addresses the call to action by Sheth (2011, p. 178): “what we need is comparative empirical research on the actual behavior of customers using marketing analytics.”

3 In the robustness checks, we examine advertising elasticities and whether including advertising spend in our model affects our focal estimates for price, distribution and line length in one developed (Hong Kong) and one emerging market (China) – two countries for which we were able to purchase advertising data.

This paper offers two key contributions to the literature. First, it provides a compelling empirical response to a question that has vexed marketing scholars and marketing managers for many years: Are marketing elasticities really different between EMs and DMs? Second, our paper adds new insights into the crucial, yet differential, role of brand equity in EMs versus

DMs. We develop and test new predictions how brand equity can be leveraged more effectively in emerging markets to foster more favorable marketing-mix responses.

CONCEPTUAL DEVELOPMENT

In this section, we first focus on the distinction between developed and emerging markets.

We offer arguments for the universality of marketing responses across developed and emerging markets, followed by arguments as to why one could expect differences. These arguments hold irrespective of the marketing variable of interest. Next, we zoom in and deliberate on specific marketing variables. Finally, we introduce the differential role of brand equity between developed and emerging markets.

Developed Markets versus Emerging Markets

Our interest is in comparing market-response parameters between DMs and EMs. This country grouping has been recognized as being of particular theoretical and managerial importance. Wallerstein’s (1974, 2004) world-system distinguishes between two types of countries: developed markets, called “core countries” and developing countries, labeled

(somewhat derogatory) “peripheral countries.” Burgess and Steenkamp (2006) compared and contrasted DMs and EMs on three institutional subsystems (socioeconomic, cultural, regulative).

Sheth (2011) identified five dimensions on which EMs are distinctly different from mature markets – inadequate infrastructure, market heterogeneity, socio-political governance, unbranded competition, and chronic shortage of resources. While we recognize that differences in economic

development between countries are gradual, for the sake of clear theoretical arguments, we use the distinction developed versus emerging markets. In the empirical analysis, we will validate this dichotomy with continuous measures of economic development, leading to the same substantive insights.

Universality of Market-Response Parameters

The question to what extent market-response parameters (e.g., price elasticities) are largely the same versus highly variable across countries has been of considerable interest to marketing scientists (Farley and Lehmann 1994; Steenkamp and Geyskens 2014). Marketing theories and models tend to be largely context free, and the main thrust of most marketing- science work on market responsiveness is to estimate marketing-mix elasticities of demand regardless of context (e.g., Ataman, van Heerde, and Mela 2010; Hanssens 2015; van Heerde et al. 2013). The conceptual foundation (or implicit assumption) of this stream of research is

“everything is the same,” i.e., one assumes a universality of parameters, perhaps with some inconsequential random error (Farley and Lehmann 1994, p. 112).

Support for the idea of universality is reflected in various streams of literature that emphasize convergence of world markets. World-systems theory, proposed by Wallerstein

(1974, 2004), emphasizes the world system rather than individual countries as the primary unit of social analysis. Wallerstein argues that economic factors, and in particular the global spread of capitalism, has led to only one world connected by a complex network of economic exchange relationships that transcend national borders. Friedman (2005) offered another narrative for this phenomenon, to which he refers as “The World is Flat”, largely rooted in technological developments. The author identified 10 “flatteners” that are leveling the global playing field, including work-flow software, global information flows, Voice over IP, and smartphones. These

flatteners remove barriers that have kept diverse market actors from exchanging information with one another, and completely and easily interacting.

Ritzer (2007) proposed grobalization theory to explain the increasing similarities across markets. In this theory, growth in global influences (hence grobalization) is fueled by the growth of three sub-processes – capitalism, McDonaldization, and Americanization.4 The growing importance of these three sub-processes is reflected in the parallel growth in global marketing strategies of multinational companies, and the cultural hegemony exerted by the United States and its institutions, values, and ideologies (Ritzer 2007, pp. 15-30). Grobalization theory is informed by the notion that the cultural world is growing increasingly similar. Consistent with this notion, Ritzer (2007, p. 15) defines grobalization “as a form of transnational expansion of common codes and practices (homogeneity).”

In marketing, the idea of global market convergence underlies the theory of global marketing standardization (Fatt 1967; Levitt 1983; Yip 2003). As one of the most prominent advocates of this view, Levitt (1983) implores companies to operate as if the world is one large market. In a similar vein, Yip (2003, p. 95) noted that “in many situations standardization can actually increase preference.” This does not mean, however, that all consumers are the same.

Different groups of consumers may be looking for different levels of price and quality, but these segments can be found around the globe and hence constitute global segments (Hassan and

Katsanis 1994; Steenkamp and ter Hofstede 2002). In support for the standardization idea, a number of studies have found that global marketing standardization is associated with better firm

4 In Ritzer’s (2004, 2007) work, McDonaldization refers to an economic model based on efficiency, predictability, calculability and control, and the replacement of human by nonhuman technology, which has allowed such systems to spread around the world. McDonald’s is used as exemplar of such systems.

performance (Özsomer and Simonin 2004; Schilke, Reimann, and Thomas 2009; Szymanski,

Bharadwaj, and Varadarajan 1993; Zou and Cavusgil 2002).

Support for the notion of global convergence can also be found in the emergence of the global consumer culture, which reflects people’s desire to associate themselves with global citizenship and the desire to participate in the “global village” (Alden, Steenkamp, and Batra

1999; Steenkamp and de Jong 2010; Strizhakova, Coulter, and Price 2008). Steenkamp, Batra, and Alden (2003) found that consumers’ preferences for globally-branded products are positively related to the degree to which they believe that these products are sold around the world rather than being available only on a local basis. Consumers often equate consumption of global products and brands with modernity, progress, consumerism, efficiency, and a promise of abundance (Holton 2000).

In sum, from a global convergence point of view, we would expect that the differences in market response parameters between developed markets and emerging markets, if present, are small.

Contingency View

The hypothesis of global convergence is not without its critics. Clear differences in languages, cultures, values, income per capita, demographics, and consumption levels between countries support the idea that there are substantial cross-national differences in market response parameters. Managers working in local markets often embrace this view, and for understandable reasons – after all, differences between countries are the raison d’être for their jobs. The idea of heterogeneity in market response parameters is also used to explain why the market share of any given brand varies substantially between countries.

The convergence hypothesis has also been criticized by academics. Ghemawat (2009) called Friedman’s (2005) assertions “exaggerated”, and argued that the world is not nearly as

economically, culturally, or technologically integrated as globalists claim. Wallerstein’s world- system theory has been criticized as being too focused on economic processes (Bergesen 1990), and to ignore the role of culture (Boyne 1990). Further, local consumer culture remains a powerful force in the marketplace (Steenkamp 2019). A number of marketing scholars have maintained that persistent institutional (economic, legal, cultural) differences between various country markets render standardization strategies ineffective, if not detrimental, to firm performance (Boddewyn, Soehl, and Picard 1986; Douglas and Wind 1987).

The hypothesis that market-response parameters are likely to vary between countries is consistent with contingency theory (Katsikeas, Samiee, and Theodosiou 2006; Zeithaml,

Varadarajan, and Zeithaml 1988). Contingency theory suggests that the relation between marketing effects and outcomes is conditional on context, and hence there is no universal set of market-response effects across countries. Contingency theory proposes to categorize environmental settings and to analyze and compare market-response parameters according to these factors.

Hence, from a contingency point of view, we would expect that there are differences in marketing responsiveness between emerging and developed markets, even though the direction of these differences may neither be a priori obvious nor uniform across marketing-mix instruments. We elaborate on these potential differences for specific marketing variables next.

Differences in Marketing Effectiveness Between EMs and DMs

EMs and DMs differ on a number of dimensions that are relevant for how demand responds to marketing activities. First, the average income per capita tends to be much lower in

EMs than in DMs, restricting consumer purchasing power. At the same time, EMs are characterized by strong growth in incomes. For many consumers in EMs, being able to buy leading brands is highly aspirational (Roberts et al. 2015), both for internal reasons (e.g.,

personal reward for income lift) and for external reasons (e.g., displaying status to others). Third, the infrastructure in EMs tends to be much less developed than in DMs (Burgess and Steenkamp

2006; Sheth 2011). In particular, roads and public transport tend to be at a lower standard than in

DMs. As we argue next, these three factors play different roles for the responsiveness of demand to price, distribution and product line.

Price. Given lower average incomes in EMs, higher prices represent a higher relative sacrifice. As a result, we can expect EMs to be more price sensitive (Dawar and Chattopadhyay

2002; see also Zielke and Komor 2015), and to show a more negative price elasticity than DMs.

On the other hand, since brands tend to be more aspirational in EMs, consumers in EMs may be less price sensitive and reject cheap options suspected to be of lower quality (price as quality indicator, Roberts et al. 2015), especially since the credibility of a brand is more important to consumers in EMs (Erdem, Swait, and Valenzuela 2006). This should reduce price sensitivity, and lead to a less negative price elasticity in EMs than in DMs. In sum, there are arguments for both a more or less negative price elasticity in EMs compared to DMs.

Distribution. Distribution coverage is the percentage of distribution points (e.g., stores) that carry the brand. As discussed, infrastructure tends to be less developed in EMs than in DMs.

On the one hand, this means it is harder (e.g., in terms of time and effort) for consumers to transport themselves to a store, such that an increase in distribution will not be as effective as in

DMs where transportation is less cumbersome (Sharma, Kumar, and Cosguner 2019). On the other hand, by offering more selling points, an increase in distribution can overcome the very infrastructure problem that EMs face by making the average distance to the store shorter for consumers (Kumar, Sunder, and Sharma 2015; Reinartz et al. 2011). Hence, distribution elasticities could be either weaker or stronger in EMs than in DMs.

Product line. A product line is the set of SKUs that a brand carries in a product category.

For example, within smartphones, Apple offers the iPhone XS (with either 64GB, 256GB, or 512

GB of storage space, and a screen size of 5.8 inch), the iPhone XR (with either 64GB, 128GB, or

256GB of storage space and a screen size of 5.8 inch), and the iPhone XS Max (with either

64GB, 256GB, or 512GB of storage, and a screen size of 6.5 inch), etc. We characterize a brand’s product line through line length, which is the number of SKUs a brand offers in the category. The corresponding line-length elasticity is the percentage change in demand due to 1% more SKUs. Product-line proliferation, which is particularly prevalent in technologically dynamic categories, allows brands to better satisfy the needs and wants of a heterogeneous customer base (Bayus and Putsis 1999).

As discussed, many consumers in EMs aspire to buy branded products, some of them for the first time. Compared to DMs, we expect that demand in EMs is still less differentiated. As a result, offering more products in the product line will have a weaker effect in EMs compared to

DMs, i.e., EMs will have a lower line-length elasticity.

The Moderating Role of Brand Equity in Shaping Marketing-Mix Effectiveness in EMs and

DMs

Direct effect of brand equity on marketing-mix effectiveness. Brand equity is the value of a branded product compared to the same product without the brand name. Strong brand equity means that the brand has strong appeal to consumers that goes beyond its observable attributes and marketing variables. In other words, a high brand-equity brand will experience higher demand than a low-equity brand, keeping everything else constant.

Brand equity not only implies differential preferences and demand due to carrying the brand name, but also differential responses to marketing variables (Keller 1998). While brand equity means that a brand has a strong intrinsic appeal to customers, this does not necessarily

mean that its effect on marketing-mix effectiveness is uniform. For price elasticity, Datta et al.

(2017) find that high-equity brands experience stronger response to price promotions. The rationale is that for many consumers high-equity brands are out of (financial) reach, but when these brands are discounted, consumers seize the opportunity to purchase them. Thus, we expect that price-discount effects are stronger for higher-equity brands (more negative price elasticity).

For distribution elasticity, in line with Datta et al. (2017), our argument is that high- equity brands are de facto sought after, and consumers are willing to make an extra effort to find a point of purchase. Hence, the marginal return on more distribution is expected to be lower for high-equity brands. Conversely, low-equity brands benefit strongly from additional distribution points, as this is where “the rubber meets the road” (i.e., consumers are exposed to the brand), which is necessary to overcome a low-equity brand’s lack of intrinsic appeal. Hence, we expect a negative effect of brand equity on distribution elasticity.

The intrinsic appeal of high-equity brands also means that we expect their demand is driven less by product-line length (lower line-length elasticity). The rationale is that high-equity brands do not have to satisfy every niche in the market (through line length) in order to attract customers. Conversely, to overcome their low intrinsic appeal, low-equity brands have to offer an extensive product line. Thus, the effect of brand equity on line-length elasticity is expected to be negative.

Differential impact of brand equity on marketing-mix effectiveness in EM versus DMs. As consumers in EMs tend to aspire consuming branded products more than consumers in DMs

(Roberts et al. 2015), we expect that this holds in particular for high-equity brands. The latter tend to be associated with higher quality and esteem, which makes them more desirable, especially for consumers in EMs. As a result, we expect that brand equity will have more favorable effects on marketing-mix effectiveness in EMs than in DMs.

Specifically, we expect that the strengthening effect of brand equity on price-discount effectiveness is stronger in EMs than in DMs. In other words, we expect a negative interaction between brand equity and an indicator for EMs (versus DMs) on price elasticity. Similarly, we expect that the response of distribution elasticity to brand equity is more favorable in EMs than in DMs. Thus, for the distribution elasticity, we anticipate a positive interaction between brand equity and an indicator for EM. In a similar vein, we also expect that line-length elasticity will respond more favorably to brand equity in EMs. Consequently, also for this elasticity, we expect a positive interaction effect between brand equity and an EM indicator. Table 1 summarizes our expectations, while Figure 1 provides a conceptual framework for the most important variables in this research.

[Insert Table 1 and Figure 1 about here]

DATA AND MEASURES

Data Sources

The global market-research agency GfK provided us with national monthly data from their retail panel across 14 countries, 14 product categories and 1,600+ brands over a time period of up to 11 years (2004-2014).

The 14 countries in the sample are from Asia and Australasia, and cover the world’s two emerging economic giants China and India, more established Asian powerhouses such as Japan and South Korea, rapidly developing EMs (e.g., Vietnam, Malaysia), and smaller highly developed markets (e.g., Singapore). The data also includes two established Western countries

(Australia and New Zealand). In Table 2, we provide an overview of the countries in the sample.

We differentiate between seven DMs (Australia, Hong Kong, Japan, New Zealand, Singapore,

South Korea, and Taiwan) and seven EMs (China, India, Indonesia, Malaysia, the Philippines,

Thailand, and Vietnam). Collectively, these countries have 3.4 billion inhabitants, representing

47% of the world population in 2014, the end of our data collection (World Bank 2019). The region is also leading the world in terms of consumer spending and growth in most of the categories we study (PlanetRetail 2019).

Table 2 lists two additional development indicators. The Global Competitiveness Index

(which was recently used in Steenkamp and Geykens 2014) measures each country’s micro- and macro-economic foundations of national competitiveness, and tends to be higher for DMs than for EMs. The same applies for the Human Development Index, which captures aspects such as healthcare quality and education outcomes, and was recently used in Sudhir and Talukdar

(2015). Even though there is a clear gap in GDP per capita between the DMs (top seven countries in Table 2) and EMs (bottom seven countries in Table 2), a dichotomous classification is obviously somewhat coarse. Therefore, later in the paper, we will provide a series of robustness checks using the three continuous metrics (GDP per capita, Global Competitiveness

Index, and Human Development Index) to measure a country’s state of development.

[Insert Table 2 about here]

Sample

Category selection. The product categories in the data cover a wide range of electronic goods, i.e., compact cameras (“point-and-shoot”), SLR cameras (catering to more professional photographers), desktop computers, laptop computers, DVD players and recorders, LCD TV,

Plasma TV, CRT TV (old, “bulky” TVs), tablets, smartphones, and regular mobile phones, as well as microwave ovens, refrigerators, and washing machines. These categories cover both novel products, such as tablets and smartphones, as well as more mature products, such as washing machines and microwaves.

In total, there are 196 potential markets defined by the combination of 14 countries and

14 product categories. Data in four markets were unavailable to us (microwaves in Japan, refrigerators, desktop PCs, and washing machines in New Zealand). In four more markets, we did not have sufficient data (e.g., less than four years of data, or only one brand remaining) and we excluded these four markets: tablets, compact cameras, and SLR cameras in India, and CRT

TV in Japan. Finally, we exclude a further four more markets, because an inspection of the category’s sales volume revealed implausible breaks between two consecutive calendar years

(laptops and desktop PCs in India and China), probably due to a change in data collection methodology. Thus, in total we have 196-4-4-4 = 184 category-country combinations, or 184 markets for short.

Brand selection. Many of the markets we study are highly fragmented, i.e., have many brands with a small to negligible market share. For example, there are 101 brands selling tablets in Hong Kong, while the top two brands Apple and Samsung account for 91% of the market. To arrive at a feasible set of brands for estimation that also guarantees a sufficient number of monthly observations with non-zero sales and non-missing marketing-mix instruments, we select all brands in a market that obtain at least 1% unit share over a consecutive period of 5 years (4 years for tablets, which is the newest category in our sample). On average, we capture 90% of total unit sales using this selection rule, covering 1,701 category-country-brand time series, or

1,701 brands for short. Since the same brand may operate in different countries and categories

(e.g., Samsung operates in all 14 countries and 14 categories in the sample), the total number of unique brands is lower, 343 to be precise. The other, smaller brands (with less than 1% share) are included as a composite rest brand in the analysis. Hence we estimate models with data on 1,701

+ 184 composite brands = 1,885 brands. In the second stage regression, we analyze how elasticities vary across the actual (non-composite) brands using brand-specific covariates. As we

cannot link some of the 1,701 brands to brand-specific covariates, we report results on those

1,687 brands for which these characteristics are available.5 Table A1 in the Web Appendix provides a list with all brands, along with information on the brands’ country-of-origin. Most brands in our sample are from China (49), Japan (42) and the US (37).

Table 3 provides an overview of the categories, along with their coverage across countries, the number of brands, and the top 5 selling brands. The top brands include well-known giants such as Apple, Canon, HP, LG, and Samsung. In Table A2 in the Web Appendix, we cross-tabulate countries with categories, and give details about the number of included brands per category/country combination.

[Insert Table 3 about here]

Variable Operationalization and Summary Statistics

The GfK data includes monthly SKU sales for all brands in each market. To keep the analysis tractable, we aggregate the SKU-level data to the brand level. In line with Reibstein and

Wittink (2005), we use a brand’s market share as dependent variable. Reibstein and Wittink advocate the use of measures relative to competition by arguing that the important question is how one is doing relative to competitors operating under the same economic conditions (a similar reasoning was applied in Lamey et al. (2012), who study drivers of brand success in times of recession and expansion). By focusing on market share, we also filter out the autonomous category growth that may vary across categories and countries.

For each brand in a market, we calculate monthly volume (i.e., unit) shares. We also capture three marketing-mix variables: unit price, weighted distribution, and line length (i.e.,

5 Specifically, across the markets where they operate, we exclude a brand named “unbranded” from the desktop PC and laptop categories, and two brands from the DVD player category (Super and Amazon; we have verified that the latter is not US-based Amazon.com).

number of SKUs). Table 4 (Panel A) provides details about the operationalization of these variables. In addition, we include as independent variables a wide range of category-specific physical search attributes of the brands. In Web Appendix A3, we give details about these attributes.

We note that pricing for electronics and appliances tends to follow a common pattern.

When a new SKU is released, it comes with a recommended retailer price, which is often heavily discounted around certain times of the year, such as Divali in India, Chinese New Year, or

Boxing Day in Australia and New Zealand, or depending on the product category, around certain specific events (like mother’s day or father’s day).6 Similarly, discounts are often used to clear inventories of certain items. Hence, the price variation observed in these data tends to be predominantly based on temporary price discounting. As such, we interpret the price elasticity as a measure for the effects of price discounting.

[Insert Table 4 about here]

Further inspection reveals that some observations are missing (e.g., a brand’s average price), and we linearly interpolate up to two months of missing intermediate observations in a given data series. If brands have more than two subsequent missing observations (e.g., minor brands with few sales that can easily “fall off the radar”), we select those time periods for which most observations for the focal variables are available consecutively. We visually inspected all time series for all variables for all brands, and there were no further irregularities. This data set constitutes our final data set, and we provide summary statistics in Web Appendix A4.

Control variables. As we explain next, we use an econometric model to estimate marketing elasticities for all 1,885 brands (i.e., including the composite brands and some brands

6 See, for example, https://www.consumerreports.org/discounts-rebates/when-to-get-the-best-deals-on-appliances/ or https://www.consumerreports.org/discounts-rebates/products-on-deep-discount-in-march/. 18

for which we miss some covariates in the second-stage analysis). In this model, we control for seasonality through brand-specific quarterly dummies. We further include a brand-specific trend.

Once the marketing elasticities have been estimated, our second-stage analysis on 1,687 brands7 studies (i) to what extent they differ systematically and predictably between EMs and DMs, and

(ii) how they are driven by brand equity and the interaction between brand equity and an EM indicator. We explain in detail how we measure brand equity in the next section. This second- stage analysis also controls for other drivers of marketing elasticities. In particular, besides an indicator for EM (versus DM), we control for a country’s income inequality (Van den Bulte and

Stremersch 2004). As category characteristics, we control for category growth, category concentration, and category type (appliance or electronics). We also control for whether the brand is a domestic or foreign brand. Table 4 (Panel B) offers details.

METHOD

Market Share Model Specification

We want to estimate marketing-mix elasticities in a uniform way across a wide set of markets that differ in the number of brands. For that purpose, we use a market-share attraction model, estimated for each category for each country (e.g., Cooper and Nakanishi 1988; Fok,

Franses, and Paap 2002; Datta et al. 2017). The model has a number of desirable features. It is linearizable; it scales well to data with many brands; it is logically consistent with market shares between 0 and 1 and adding up to 1; and it naturally captures cross-effects between brands. We use the multiplicative competitive interaction (MCI) specification with heterogeneous brand-

7 I.e., excluding the composite brands, and brands for which we cannot verify their country-of-origin. See also footnote 5.

specific response coefficients. To allow for dynamic (carry-over) effects, we include a brand’s lagged market share. This allows us to also calculate the corresponding long-term elasticities, as the short-term elasticity divided by one minus the carry-over coefficient (Fok et al. 2002).8

Including lagged market share also deals with potential auto-correlation in market share that may otherwise affect standard errors. While market shares tend to be less sensitive to seasonality than absolute sales levels, we control for any remaining seasonality with brand-specific parameters for the quarterly dummy variables. Last, a brand-specific trend captures a brand’s growth or decline during the observation period.

The model for brand b (out of in total Mlc brands) in each country l and category c is:

A MS = lcb,t (1) lcb,t Mlc ∑j=1 Alcj,t if brand b is present in country l and category c in period t, with

A = βlcb1 βlcb2 βlcb3 lcb,t exp (αlcb) ∗ pricelcb,t ∗ distributionlcb,t ∗ linelengthlcb,t ∗

Quarter (2) 4 q,t φlcb Rlc θlcr γlc ∏q=2(κlcbq) ∗ trendt ∗ ∏r=1 (Attributelcbr,t) ∗ MSlcb,t−1 ∗

exp(ϵlcb,t), and

MSlcb,t = Market share (= brand unit sales / category unit sales) for brand b in country l and category c in month t;

Alcb,t = Attraction of brand b in country l and category c in month t;

αlcb = Brand-specific intercept for brand b in country l and category c; pricelcb,t = Average price of brand b in country l and category c in month t; expressed in local currency and deflated by a country-specific Consumer Price Index to account for country-wide price changes;

8 Inspired by the work of Köhler et al. (2017), we have verified that the analytically derived elasticities from the market-share attraction model are virtually the same as those obtained through dynamic simulations. The robustness- check section provides details.

distributionlcb,t = Distribution of brand b in country l and category c in month t (0 = no market coverage, 100 = full market coverage); linelengthlcb,t = Number of unique SKUs sold by brand b in country l and category c in month t;

Quarterq,t = Quarterly dummy for quarter q (Quarterq,t = 1 if month t is in quarter q, and 0 otherwise); trendt = Counter (trendt = 1, 2, …, T) to account for the time trend;

Attributelcbr,t = Attribute r (r = 1, …, Rlc) of brand b in country l and category c in month t. If attribute r is an indicator variable at the SKU level (e.g., availability of a

touchscreen), then Attributelcbr,t (which is defined at the brand-level) captures the share of SKUs of brand b in country l and category c in month t that carry that particular attribute, multiplied by 100. If attribute r is not an

indicator variable (e.g., memory), then Attributelcbr,t captures the average attribute level across all of brand b’s SKUs. As such, all attributes are continuous at the brand level by construction.

εlcb,t = Normally distributed error term for brand b in country l and category c in month t.

The exact operationalizations are reported in Table 4.

Model Estimation

The attraction model for a given market can be written as a system of Mlc equations.

Because market shares sum to 1, the equations depend on each other, and the rank of the system is Mlc-1. We normalize the system by subtracting the attraction of a base brand (Bronnenberg,

Mahajan, and Vanhonacker 2000), which has been shown to be mathematically equivalent to geometric mean-centering (Fok et al. 2002).

To linearize the model (1), we take its logarithm on both sides for each of the Mlc brands.

Next, we subtract a base brand B from both sides of the remaining Mlc-1 equations. The base brand is selected as one of the brands that is consistently present in the market. We estimate this

system of seemingly unrelated equations (see equation 3a) for each market using feasible generalized least squares:

MS α̃ + β log (price ) − β log (price ) log ( lcb,t) = lcb lcb1 lcb,t lcB1 lcB,t MSlcB,t + βlcb2log (distributionlcb,t) − βlcB2log (distributionlcB,t)

+ β log(linelength ) − β log(linelength ) (3a) lcb3 lcb,t lcB3 lcB,t

4 + ∑q=2 log(κ̃lcbq) ∗ Quarterq,t + φ̃lcblog (trendt)

Rlc Attributelcbr,t MSlcb,t−1 + ∑r=1 θlcr log ( ) + γlc log ( ) + ε̃lcb,t . AttributelcBr,t MSlcB,t−1

We note that the model only identifies the differences in αlcb and αlcB, defined as α̃lcb =

αlcb − αlcB (in other words, we set αlcB to zero for model identification). Similarly, ϵ̃lcb,t =

ϵlcb,t − ϵlcB,t, κ̃lcbq = κlcbq − κlcBq, and φ̃lcb = φlcb − φlcB. Because the model specification requires us to take logarithms, we add 1 to all variables that can take on values of zero (in particular, distribution, line length, as well as several product attributes).

Controlling for endogeneity. To account for the potential endogeneity of the marketing- mix variables, we augment Equation (3a) with Gaussian copula terms that absorb the correlation between potentially endogenous marketing-mix instruments and the normally distributed error term (Park and Gupta 2012). The copula method, which was recently used in, among others,

Datta, Foubert and van Heerde (2015) and Gielens, Geyskens, Deleersnyder and Nohe (2018), does not require instrumental variables, and hence is particularly useful when valid instruments are hard to find (Rossi 2014):

MS α̃ + β log (price ) − β log (price ) log ( lcb,t) = lcb lcb1 lcb,t lcB1 lcB,t MSlcB,t (3b) + βlcb2log (distributionlcb,t) − βlcB2log (distributionlcB,t)

+ βlcb3 log(linelengthlcb,t) − βlcB3 log(linelengthlcB,t)

4 + ∑q=2 log(κ̃lcbq) ∗ Quarterq,t

+ φ̃lcb log(trendt) + ∑k(ωklcbCopulaklcb,t − ωklcBCopulaklcB,t)

Rlc Attributelcbr,t MSlcb,t−1 + ∑r=1 θlcr log ( ) + γlc log ( ) + ε̃lcb,t AttributelcBr,t MSlcB,t−1 where Copulaklcb,t is the Gaussian copula (control-function term) for the log-transformed marketing-mix variable k of brand b in country l and category c in month t, to control for potential endogeneity of the variable.9 Rather than including all copula terms at once, we follow

Mathys, Burmester and Clement (2016) and Gim, Tuli and Dekimpe (2018), and first test for the presence of endogeneity per instrument. Specifically, we first estimate the model including all of the brands’ Gaussian copula correction terms for only one particular marketing-mix variable, for each of the three marketing-mix variables separately. We subsequently retain those copula terms that were significant at p < .10 in the first step (28.65% of all copula terms), and estimate the entire model. We note that in one of the 184 markets (tablets in New Zealand), the carry-over coefficient was initially found to be negative, which implies an implausible oscillating pattern.

We re-estimate that model by restricting the carry-over parameter to be zero. For the other 183 markets, the carry-over coefficient was between 0 and 1, with an average value of .487. After controlling for lagged market share, the remaining auto-correlation is low (mean of absolute auto-correlations = .148).

Marketing elasticities. We next derive long-term marketing-mix elasticities (Cooper and

Nakanishi 1988, p. 34), and obtain the associated standard errors using the Delta Method

9 With a normally-distributed error term, an identification requirement for the Gaussian copula method is that the endogenous regressors are not normally distributed. In our application, Shapiro-Wilk tests at p < .10 confirm this for 94% of the cases. 23

(Greene 2003). Specifically, the long-term elasticity for marketing-mix variable k of brand b in country l and category c is given by:

βlcbk (4) ηlcbk = (1 − MS̅̅̅̅lcb), 1−γlc with MS̅̅̅̅lcb the average market share over the period that brand b was present in country l and category c. In the discussion of the results, we will focus on long-term elasticities as they capture the full market-share impact of a 1% change in the focal marketing variable.

Brand equity. We adopt sales-based brand equity as our measure of brand equity, which captures the attraction of brand b net of its marketing mix and physical attributes. The model provides us with direct estimates of this net attraction in the form of brand-specific intercepts,

α̃lcb. Using that the net attraction for base brand B, αlcB, is set to 0 for model identification, brand equity of the remaining brands b  B equals α̃lcb. We standardize this metric in each market to obtain comparable measures across countries l and categories c. For a similar practice, see Datta et al. (2017).

Second-stage regression. Our conceptual framework specifies that price, distribution, and line-length elasticities vary predictably in function of country (EM versus DM), equity of the brand and the interaction between the two. To arrive at valid empirical results, besides the included control variables, we need to account for unobserved brand, country and category characteristics, which we do through random effects for each of these three dimensions. Thus, we estimate the following regression model for each of the three long-term marketing elasticities,

ηlcbk for marketing variable k, country l, category c and brand b:

(5) ηlcbk = δ0k + δ1kEMl + δ2kBrandEquitylcb + δ3kBrandEquitylcb × EMl +

δ4k ln CatConclc + δ5k ln CatGrowthlc + δ6kAppliancesc +

δ7k ln IncomeInequalityl + δ8kDomesticBrandlb + μlk + νck + ξbk +

ulcbk.

where EM is an indicator that is 1 for an Emerging Market (and 0 for a Developed Market) and

BrandEquity is the sales-based brand equity discussed before. The variables CatConc (category concentration), CatGrowth (category growth), Appliances, IncomeInequality and DomesticBrand are defined in Table 4B. The terms μlk, νck, and ξbk are random effects for country l, category c and brand b, respectively. We estimate model (5) with weights equal to the inverse standard errors of the elasticity estimates.

RESULTS

Descriptive Results

The market share models fit well, with an average R2 of 88.2%. The correlation between the independent variables in equation (2) is modest (see Table A4 in the Web Appendix; the maximum is .434). Table 5 shows the mean, median and 90% interval of the long-term marketing elasticities. For each elasticity, the mean and median are fairly similar, suggesting fairly symmetrical distributions. Given their similarity, we focus on means rather than medians from now on.

Table 6 offers a first, relatively simple, analysis of differences in marketing elasticities between EMs and DMs. The analysis accounts for the uncertainty in the elasticity estimates by using weighted least squares in a regression of the marketing elasticity on an indicator for EM, while using the inverse standard error of the estimate as weight. The mean price elasticity across

DMs is -.559, which suggests that demand is relatively inelastic for these durables (cf. Bijmolt et al. 2005). The mean distribution elasticity in DMs is .826, similar to what Ataman et al. (2010) reported in their large-scale analysis across consumer packaged goods brands. The line length

elasticity is .689, again in line with Ataman et al. (2010). Thus, the face validity of the elasticities in DMs is good.

Turning to a comparison between DMs and EMs, Table 6 shows that, on average, EMs have a significantly weaker distribution elasticity (.756) than DMs (.826, p < .05). This preliminary finding is in line with the argument that the infrastructure in EMs may decrease the effectiveness of distribution. The average price elasticity is stronger (i.e., more negative; -.689) in EMs, compared to DMs (-.559, p < .05), consistent with the idea that consumers in EMs, often with more constrained budgets, are more price-sensitive than consumers in DMs. The average line-length elasticity, in contrast, does not differ significantly between EMs and DMs.

[Insert Table 6 about here]

Table 7 shows the mean long-term elasticities for the three marketing variables for each of the 14 countries in the sample. All have the expected sign: negative for price elasticity, and positive for the distribution and line-length elasticity. It is noteworthy, however, that the magnitude of the elasticities is not perfectly aligned with the EM versus DM classification. For example, the most price sensitive countries are the emerging markets China (elasticity = -.928),

Vietnam (elasticity = -.881) and Thailand (-.740), but the fourth most price sensitive country is the developed market New Zealand (elasticity = -.701). Similarly, while the three countries with the highest average distribution elasticity are all developed markets (Australia, New Zealand and

Japan), the distribution elasticity of several EMs (such as Thailand and Malaysia) is considerably larger than in South Korea.

[Insert Table 7 about here]

Second-Stage Regression

We now move to a more complete analysis of the elasticities, using the second-stage regression model (5). Table 8 shows that price elasticities in EMs are stronger (i.e., more

negative) than in DMs (δ1 =-.132, marginally significant at p < .10), in line with the initial finding (Table 6). A stronger brand equity is associated with a stronger price elasticity (more negative; δ2 =-.935, p < .01), consistent with the argument that stronger brands will benefit more from price discounting. This effect is further enhanced in EMs (δ3=-.125, p < .01), showing that strong brands especially benefit from price discounting in EMs. These findings are consistent with our expectations (Table 1).

The second column of Table 8 shows that distribution elasticities are lower in EMs than in DMs (δ1 =-.154, p < .05), which is also in line with the initial finding (Table 6). Higher- equity brands have lower distribution elasticity (δ2 =-.152, p < .01). However, this negative effect is attenuated in EMs (δ3 =.101, p < .01), which is congruent with the idea that brand equity is associated with a relatively more favorable marketing response in EMs than in DMs.

These findings are again consistent with Table 1.

Contrary to expectations, there is no significant direct effect of EM on line-length elasticity. However, stronger brand equity is associated with a lower line-length elasticity (Table

8, column (3), δ2 =-.135, p < .01). This finding supports our prior reasoning that stronger brands are less dependent on carrying a long product line. There is no significant interaction between

EM and brand equity.

Covariates. While not the focus of this research, the effects of the control variables on the elasticities uncover some interesting insights. Categories with more concentrated demand are associated with less negative price elasticities (δ4 =.113, p <.1), in line with the notion that such categories are less competitive. Strong category growth is associated with more negative price elasticities (δ5 = = -.273, p < .05), consistent with the meta-analytical finding of Bijmolt et al.

(2005). Compared to foreign brands, domestic brands enjoy stronger response to price discounts

(i.e., more negative price elasticities: δ8 = = -.189, p < .01), stronger distribution elasticities (δ8

= .089, p < .1) and stronger line-length elasticities (δ8 = .157, p < .05).

[Insert Table 8 about here]

Robustness Checks

In the empirical analyses, we made a number of modeling decisions. To assess the sensitivity of our findings to these choices, we ran a series of robustness checks. We first describe and motivate the various sensitivity analyses (for which we provide full estimation details in the Web Appendices), and then summarize the findings.

Operationalization of EM versus DM. The focal analysis used a dichotomous distinction between EMs and DMs. As a first robustness check, we use (one at a time) three alternative, continuous, measures for a country’s development level: GDP per Capita, Global

Competitiveness Index and Human Development Index, as defined in Table 2. Because these continuous measures increase with a country’s degree of development, and hence tend to be lower for EMs compared to DMs, we have reverse-coded them in the analysis, so that a consistent result is obtained when the associated parameter estimates are in the same direction compared to the EM indicator variable.

Market share as a covariate in the second-stage regression. A possible covariate for marketing elasticity is market share. We excluded it as a driver of elasticity, because the elasticity is calculated using market share (Eq. 4), causing a built-in correlation. To assess whether including market share in the model would alter the conclusions for the focal drivers of marketing elasticities, we run all models augmented with market share.

International brand indicator as a covariate in the second-stage regression. We have allowed for the fact that international brands (i.e., brands active in more than one country) may be associated with different marketing-mix elasticities, which react differently to the EM versus

DM dichotomy and/or face a differential impact of brand equity. We re-estimated Eq. 5 by including an additional indicator variable, equaling 1 for international brands that we observe in more than one country, and 0 otherwise.

Alternative operationalization of the long-term marketing effect. As both the elasticities as well as brand equity are derived from market shares, there may be another built-in correlation.

To rule out that this is driving the results, we estimated models where the dependent variable is

βlcbk βlcbk not the elasticity, (1 − MS̅̅̅̅lcb), but the long-term response parameter . This dependent 1−γlc 1−γlc variable is not formed using market share, and therefore does not suffer from a potential built-in correlation.

Alternative approach to calculate carry-over effects. The model allows carry-over effects to differ by category-country, but not by brand. Relaxing this assumption by letting the carry- over parameters also vary by brand caused many carry-over parameters to be negative, interfering with the validity of this approach. Therefore, as a robustness check, we assess the impact of differential, brand- and marketing-mix instrument-specific, long-term effects by removing the homogenous lagged market share term from the focal model, and including instead directly brand-specific lagged marketing-mix variables to the attraction equation (2).

Alternative calculation of elasticities. To calculate long-term elasticities, we use the analytical equation (4). Köhler et al. (2017) suggest that for models that include logarithmically transformed variables, it may be better to use dynamic simulations instead. Therefore, we also ran such simulations, which we explain in Web Appendix B.

Summary of robustness analyses. We summarize the key insights from these analyses in

Table 9, and report all parameter estimates in Tables A7-A9 in the Web Appendix. Focusing on the impact of the EM indicator, the role of brand equity, and the interaction between both for

each of the three marketing instruments, we find that in 93% of the cases (67 of the 72 cases: 8 robustness checks x 3 elasticities x 3 focal impacts), the same substantive conclusion as in our focal analyses was obtained. For all effects related to the distribution elasticity and line-length elasticity, we even obtain a full consistency with our focal results. This is also the case for the role of brand equity and its interaction with the EM indicator in the price-elasticity analyses.

Only for the main effect of the EM indicator on a brand’s price elasticity do we observe some variation. Importantly, we find that also in this case the results remain fully robust in terms of the sign of the effect, while the significance level occasionally varies between marginally significant

(p = .09 in our focal analysis) to marginally insignificant (p = .11 and p = .12), with also the effects reported as non-significant in Table 9 still having a p-value of .16.

[Insert Table 9 about here]

A similar robustness was obtained for the control variables, where in 89% (107/120) of the cases, the same effect was obtained as in the focal analysis.

Model Extensions: Advertising and non-constant elasticities

Advertising. Due to data limitations, the models do not include advertising. This could lead to an omitted-variable bias in the estimates of the other marketing elasticities. To study the seriousness of this concern, we bought advertising spend data from Kantar (for several tens of thousands of US dollars)10 for one EM (China) and one DM (Hong Kong). Web Appendix Table

A10 compares the results of the market share models that include advertising spend, with our focal model.

Two key conclusions emerge. First, the average advertising elasticities are tiny (.005, p < .01), in line with much recent work (e.g., Allenby and Hanssens 2004; Sethuraman et al.

10 This high cost precluded us from also obtaining similar data on the other countries. 30

2011; van Heerde et al. 2013). Second, the estimates for the elasticities of price, distribution and line length are not statistically different whether or not advertising is included in the model.

Non-constant response parameters. The response parameters were assumed to be constant over time. However, it is not inconceivable that marketing-mix effectiveness varies over time, especially in emerging countries that go through a phase of rapid economic change. To test whether our assumption of constant response parameters is defendable, we re-estimate our focal model on a split data set (early, versus late in our observation period), and compare the estimated elasticities across emerging and developed markets (see Web Appendix Table A11). We find that elasticities are remarkably stable. In emerging markets, the response elasticities are not different across both time windows for all marketing-mix instruments (p > .10). In developed markets, only price elasticities tend to become somewhat weaker over time (i.e., less negative).

DISCUSSION

The globalization of the marketplace is arguably one of the most important challenges that companies continue to face today. Given the rapid growth of EMs that gives billions of consumers additional spending power, DM companies are increasingly venturing into those markets. At the same time, an increasing number of EM brands are going global as well, and

“break out” of their traditional market boundaries to compete in more developed markets. While most companies have considerable experience competing in one level of economic development, they typically are much less familiar with markets of a different development level.

Given that it is easy to observe considerable differences in the average consumption level of most product categories between richer and poorer nations, while simultaneously noting substantial institutional, cultural and/or socio-political differences, managers often start with the presumption that there will also be large and parallel differences in market response. Similarities

in marketing elasticities between EMs and DMs would then come as a surprise. A similar presumption can be found among proponents of contingency theory, who “expect or at least hope to find differences rather commonalities”, and among empirical researchers, who are likely to face “a publication bias in favor of differences and against the null hypothesis of sameness”

(Farley and Lehmann 1994, p. 114, italics added).

However, the discussion on whether response elasticities differ systematically and predictably between DMs and EMs has thus far remained largely anecdotal or conceptual. Our study is one of the first large-scale, comprehensive analyses that addresses the validity of this presumption empirically, capitalizing on a data set that is unique in its scope. Not only does it cover 14 countries, it also stretches across 14 diverse durable goods categories and covers more than 1,600 brands, many of them for more than ten years. Moreover, it does so for three key marketing instruments: price, distribution and line length.

We now summarize the core findings. For price and distribution, there is a systematic difference in market-response parameter between EMs and DMs. EMs are more responsive to price than DMs, while DMs are more responsive to distribution. The difference in price sensitivity reflects tighter budget constraints in EMs. The finding for distribution brings to the fore that infrastructural hurdles still prevail in EMs, in line with the observation of Reinartz et al.

(2011) that distribution remains one of the biggest challenges of doing business in emerging markets. No such direct differences are found for line-length elasticities.

However, the picture becomes more nuanced when taking the moderating role of brand equity into account. Brand equity has a consistent negative effect on all three marketing elasticities. This is in support of the idea that high-equity brands have strong intrinsic appeal, which leads to stronger responses to price discounts but lower marginal effects for distribution and line length.

[Insert Figure 2 about here]

Managerial Implications

To understand what these dependencies mean in managerial terms, Figure 2 shows how the estimated elasticities differ between EMs and DMs for high versus low levels of brand equity

(based on a median split). For price elasticity, Figure 2A shows how much of a difference brand equity makes for the effectiveness of price discounts. Weak brands have very low elasticities, both in DMs (-.04) and EMs (-.11), muting the response to price discounts. However, strong brands enjoy a clearly stronger response to price discounts, and even more so in EMs where price sensitivity is nearly 30% larger than in DMs: -1.29 vs. -1.02. The implication is that price discounts should be used cautiously by weak brands as they are hardly effective. In contrast, price discounts are very effective for strong brands, especially in EMs. Of course, price discounting carries the risk of sensitizing consumers to price (e.g., Ataman et al. 2010), so even when price discounts are effective, they should be used judiciously.

For distribution elasticity (Figure 2B), whether a strong brand operates in an EM or a DM does not matter as in both cases the market share response to 1% distribution increase is .67%.

This effectiveness is higher for weak brands, consistent with the notion that marginal distribution changes will affect demand for strong brands less because of their superior intrinsic pull. For weak brands, distribution matters more, and especially in DMs where the elasticity is 24% higher than in EMs: .98 versus .80. The implication is that efforts to enhance distribution are more beneficial for weak brands (as they need to compensate through availability for their lack of intrinsic pull), and then especially in DMs (as the infrastructure in these markets allows brands to capitalize on additional distribution).

For line-length elasticity (Figure 2C), the direct effect of brand equity dominates, with stronger marginal returns to line length for weak brands. Thus, weak brands can compensate for

their low intrinsic pull by offering a more elaborate product line. The implication is that especially weak brands should invest in product differentiation. Strong brands, in contrast, have lower line-length elasticities, and they hardly differ between DM and EM. Thus, strong brands do not have to offer as elaborate product lines as weak brands.

These findings can inform global integration versus localization of marketing strategies.

Whether firms can standardize their marketing programs or rather adapt their strategies to EMs is a topic of great importance to managers. If the effects of a marketing instrument is the same across DMs and EMs, this insight can be used in the development of a globally integrated strategy. If, on the other hand, the effectiveness of a marketing instrument differs between DMs and EMs, managers are recommended to adopt a local strategy for that instrument, i.e., a strategy that is developed and executed locally (Farley and Lehmann 1994, Steenkamp and Geyskens

2014). Figure 2 informs managers of weak brands that there is room for a globally integrated strategy for price and line length. The market-response parameters do not vary significantly between EMs and DMs. On the other hand, they are advised to adopt a localized strategy for distribution. Managers of strong brands should consider global integration on distribution and line length, while adopting a local strategy for pricing.

For firms that manage a portfolio of brands, there is no one-size-fits-all for brand management on a global scale, unless all its brands are either weak or strong. In many cases, a firm has both weak and strong brands. For example, the Chinese appliance and electronics manufacturer Hisense Co., Ltd. manages a diverse portfolio, consisting of its eponymous,

Hisense brand, and several others including Ronshen, Sharp, and Toshiba. For such companies, line length can be a component of a globally integrated marketing strategy regardless of brand equity. However, our results suggest that for the other two marketing mix instruments, using a uniform strategy (integration versus localization) across all brands is not recommended. If the

manager is uncertain whether their brand is strong or weak, they can do (or commission) a straightforward analysis, described in this paper. We summarize these recommendations in Table

10.

[Insert Table 10 about here]

Limitations and Future Research

Our research has limitations that offer avenues for further research. First, a strength of this study was that it covers three key marketing-mix instruments across a broad cross-section of countries and product categories. Future research should consider other marketing instruments, including advertising, ideally across different media, or feature/display activity, not only for durables but also for non-durable product categories and services, and do so across an even broader set of developed and emerging countries, including countries from all continents.

Second, we had access to aggregate market-share data. A comparable study using individual-level data would be able to explore within-country heterogeneity across different consumer segments. While we controlled for the income inequality in each country, this would allow researchers to study within-country differences in marketing-mix response.

Third, it would be useful to look at more disaggregate marketing-mix instruments. For example, when looking at the role of assortment length, it would be insightful to look at the incremental impact of new SKUs, either through new levels of existing attributes or the introduction of entirely new attributes; when looking at the impact of price discounts, to distinguish between the differential impact of promotional depth versus promotional frequency; and when considering distribution, future research could study the potentially differential effects of different types of retail outlets, or on- versus off-line. A particularly interesting follow-up question would be to combine multiple marketing instruments, and to compare, for example, online and offline price elasticities between EMs and DMs.

Fourth, we focused on the key moderating role of brand equity, where we operationalized brand equity as sales-based brand equity. Future research should study the effect of the other major operationalization of brand equity, viz., consumer-based brand equity (Datta et al. 2017).

Moreover, future research could look at other moderators, such as brand relevance in the category (Fischer, Völckner, and Sattler 2010) and consumer involvement (Steenkamp, Van

Heerde, and Geyskens 2010).

Thus, much remains to be studied before we have the kind of empirical generalizations on marketing-mix effectiveness for EMs comparable to our present state of knowledge on DMs

(Hanssens 2015), and whether, and under which conditions, market-response parameters differ – or do not differ – between EMs and DMs. Given the importance of EMs for the future of our companies, we hope that our paper provides an impetus to other econometric marketing researchers to make EM research the focus of some of their own work.

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Table 1: Expected Effects on Marketing-Mix Elasticities Predictors Price elasticity Distribution elasticity Line-length elasticity EM indicator (versus DM)1 +/- +/- - Brand equity - - - EM indicator * Brand equity - + + 1 EM = Emerging market, DM = Developed market.

Table 2: Development Indicators for Countries in Sample1 GDP Global Competitiveness Human Development Country per capita Index Index Developed economies Australia 45,587 5.11 .93 Japan 39,731 5.37 .88 Singapore 37,293 5.48 .91 Hong Kong 29,826 5.30 .90 New Zealand 27,259 4.92 .90 South Korea 17,074 4.93 .88 Taiwan 16,392 5.21 .88 Emerging economies Malaysia 6,897 4.88 .77 Thailand 3,940 4.51 .72 China 3,678 4.84 .70 Indonesia 2,329 4.43 .66 Philippines 1,746 3.96 .67 Vietnam 1,060 4.27 .66 India 1,031 4.33 .58 1 Countries ranked by GDP per capita (in current USD). The Global Competitiveness Index (GCI) captures microeconomic and macroeconomic foundations of national competitiveness. Both measures are extracted from the World Economic Forum’s Global Competitiveness Report (2010/2011, available at https://www.weforum.org/reports/global- competitiveness-report-2010-2011). The Human Development Index (HDI) captures a country’s living standard, healthcare quality and knowledge base. Measure obtained from the UN Development Programme (2010, http://hdr.undp.org/en/content/human-development-index-hdi). The HDI of Taiwan is obtained from Taiwan’s Directorate General of Budget, Accounting and Statistics (2010, http://eng.stat.gov.tw).

Table 3: Category Overview1

Category Number of countries Number of Category Top 5 brands type with data brands CRT TVs Electronics 13 68 Sampo, LG, Samsung, Transonic, Videocon Compact cameras Electronics 13 22 Canon, Sony, Samsung, Nikon, Olympus DVD players and Electronics 14 136 Arirang, Local, AJ, recorders California, Polytron Desktop computers Electronics 11 27 Samsung, NEC, Fujitsu, HP, LG LCD TVs Electronics 14 48 Samsung, LG, Sony, Skyworth, Chimei Laptop computers Electronics 12 21 NEC, Acer, LG, HP, Asus Microwaves Appliances 13 50 Galanz, Sampo, Americanhome, Panasonic, Sharp Mobile phones Electronics 14 56 Nokia, Sharp, Samsung, Panasonic, Fujitsu Plasma TVs Electronics 14 47 Samsung, LG, Sony, Skyworth, Panasonic Refrigerators Appliances 13 54 Zipel, Dios, Westinghelu, Panasonic, Condura SLR cameras Electronics 13 6 Canon, Nikon, Sony, Pentax, Olympus Smart phones Electronics 14 21 Samsung, Sharp, Apple, Nokia, Lenovo Tablets Electronics 13 9 Samsung, Apple, Teclast, Onda, Sharp Washing machines Appliances 13 68 Tromm, Simpson, LG, Hauzen, Littleswan 1 Categories shown in alphabetical order. Top 5 brands are determined on the basis of their average volume share across countries, and are listed in decreasing order of their market share.

Table 4: Variable Operationalization

Panel A. Variables in first-stage market-share attraction model

Variable Operationalization

Market sharelcb,t Unit sales for brand b in country l and category c in period t, divided by total unit sales in period t in a market (category/country combination).

Pricelcb,t Average price of brand b in country l and category c in period t, computed as a sales-weighted1 average across its SKUs sold in period t, expressed in local currency and adjusted by a country’s consumer price index2.

Distributionlcb,t Average distribution of brand b in country l and category c in period t, computed as a sales-weighted1 average across its SKUs’ store-weighted distribution in period t (0 = no market coverage, 100 = full market coverage).

Line lengthlcb,t Number of unique SKUs sold by brand b in country l and category c in period t.

Panel B. Variables in second-stage regression3

Variable Operationalization

Brand equitylcb Sales-based brand equity of brand b in country l and category c, derived from the market-share attraction model, standardized by country l and category c. 4 Category concentrationlc Herfindahl index in country l and category c. 5 Category growthlc Average geometric growth rate in country l and category c.

Appliance (versus electronics)c Dummy variable, equaling 1 if category c consists of appliances (washing machines, microwaves and refrigerators), 0 for all other categories. 6 Income inequalityl Gini index in country l. Domestic (versus foreign Dummy variable, equaling 1 if brand b’s headquarter is located in the focal brand)lb country l, 0 if not. 1 Weights equal to a SKU’s unit sales in the most recent quarter (months t-2, t-1, t). 2 Consumer price indices obtained from Thomson Reuters Datastream. 3 All continuous variables are first logged and then mean-centered before model estimation (except brand equity, which is only mean-centered as this variable can take on negative values). 4 Herfindahl index is defined as the sum of squared market shares. 5 Geometric growth rate is the n-th’s square root of the ratio of the total number of unit sales in a market in the last year over the brand’s total number of unit sales in the first year (both years having 12 months of data), where n is equal to the number of years in the observation window. 6 Measure of the inequality of income distribution, obtained from multiple sources (World Bank, OECD and CIA World Factbook) for the year 2010. When 2010 data was unavailable, we used the closest available estimate.

Table 5: Long-Term Elasticities by Marketing-Mix Instrument1 Marketing-mix Number of Mean Median 90%-interval of estimated instrument estimates elasticity2 elasticity elasticities Price 1,687 -.624*** -.616 [-3.374, 2.078] Distribution 1,687 .786*** .787 [-.256, 2.263] Line length 1,685 .690*** .700 [-.332, 2.533] 1 Significance levels: * p < .10, ** p < .05, *** p < .01 (two-sided). The number of observations differs slightly for line length, as some brands in some markets (category/country combinations) do not have sufficient variation on that variable. 2 Elasticities are weighted by inverse standard errors. Significance tested using meta-analytic p-value (Method of added Z’s).

Table 6: Differences in Long-Term Elasticities between Developed and Emerging Markets1 Test for All countries Developed markets Emerging markets differences Marketing-mix Mean Mean Mean t-value from N N N instrument elasticity2 elasticity2 elasticity2 WLS3 *** *** *** ** Price 1,687 -.624 821 -.559 866 -.689 -2.347 *** *** *** ** Distribution 1,687 .786 821 .826 866 .756 -2.408 *** *** *** Line length 1,685 .690 821 .689 864 .691 .061 (n.s.) 1 Significance levels: * p < .10, ** p < .05, *** p < .01 (two-sided). n.s. indicates that the effect was not significant (p > .10). The number of observations differs slightly for line length, as some brands in some markets (category/country combinations) do not have sufficient variation on that variable. 2 Elasticities are weighted by inverse standard errors. Significance tested using meta-analytic p-value (Method of added Z’s). 3 Tests on differences carried out by regressing elasticities (weighted by inverse standard errors) on an intercept and an emerging-market indicator. The reported t-value is obtained from the estimate of the emerging-market indicator.

Table 7: Long-Term Elasticities (Means by Country)1

Country Price Distribution Line length Australia -.610*** 1.005*** .707*** China -.928*** .744*** .728*** Hong Kong -.551*** .819*** .803*** India -.612*** .462*** .334*** Indonesia -.532*** .762*** .815*** Japan -.244*** .978*** .419*** Malaysia -.501*** .858*** .730*** New Zealand -.701*** .998*** .788*** Philippines -.537*** .717*** .728*** Singapore -.665*** .804*** .766*** South Korea -.397*** .586*** .536*** Taiwan -.682*** .769*** .708*** Thailand -.740*** .962*** .630*** Vietnam -.881*** .734*** .800*** 1 Significance levels: * p < .10, ** p < .05, *** p < .01 (two-sided). Elasticities are weighted by inverse standard errors. Significance tested using meta-analytic p-value (Method of added Z’s). Countries shown in alphabetical order.

Table 8: Regression of Long-Term Elasticities on Predictors1 Price Distribution Line-length

elasticity elasticity elasticity EM indicator (versus DM) -.132* -.154** -.049

Brand equity -.935*** -.152*** -.135***

* EM indicator -.125*** .101*** .006

Log of category concentration .113* -.055 -.036

Log of category growth -.273** .132 -.017

Appliances (versus electronics) -.077 -.138 -.265***

Log of income inequality -.224 .095 .543***

Domestic (versus foreign) brand -.189*** .089* .157**

Constant -.493*** .925*** .909***

R-squared .541 .143 .222 Observations 1,687 1,687 1,685 1 Significance levels: * p < .10, ** p < .05, *** p < .01 (two-sided). Elasticities are weighted by inverse standard errors. The number of observations differs slightly for line length, as some brands in some markets (category/country combinations) do not have sufficient variation on that variable.

Table 9: Overview of Robustness Checks1

Alternative measures for a Estimating second-stage country’s development regression (eq. 5) with extra Alternative ways to calculating degree (reverse-coded)2 control variables long-term elasticities

Indicator for DV is Marketing-mix Focal GDP per Market international 훽 and brand-specific Dynamic 3 4 model capita GCI HDI share brand 1−훾 dynamics simulation

Price elasticity EM indicator (versus DM)   n.s.  (-)5  n.s. n.s. (-)6 Brand equity          Brand equity * EM indicator         

Distribution elasticity EM indicator (versus DM)          Brand equity          Brand equity * EM indicator + + + + + + + + +

Line-length elasticity EM indicator (versus DM) n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Brand equity          Brand equity * EM indicator n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. 1 Table indicates direction (+ or -) of focal effects for long-term price, distribution, and line-length elasticities. n.s. indicates that the effect was not significant (p > .10). 2 Models replace the EM indicator by alternative (continuous) measures for a country’s development degree, specifically its GDP per capita (in current USD), Global Competitiveness Index (GCI), and Human Development Index (HDI); these measures are defined in Table 2, and log-transformed and reverse-coded for model estimation. 3 Estimated in a model without lagged market share, but with lagged marketing-mix instruments to allow for brand- and marketing-mix specific carry-over effects. 4 Instead of analytically deriving elasticities from the parameter estimates, we dynamically simulate elasticities; details about this simulation are in Web Appendix B. 5,6, p = .12, and .11 respectively.

Table 10: Recommendations Regarding Global Brand Strategy Across Developed and Emerging Markets Marketing-mix instrument Price Distribution Line length Weak brand Global integration Local adaptation Global integration

Strong brand Local adaptation Global integration Global integration

Portfolio of brands Adopt brand-specific Adopt brand-specific Global integration across strategy in function of strategy in function of brands equity of the brand equity of the brand

Figure 1: Conceptual Framework

Country Development Level Price Elasticity (Emerging Market Versus Developed Market) Distribution Elasticity

Brand Equity Line-Length Elasticity

Covariates:  Category Concentration  Category Growth  Appliances (Versus Electronics)  Income Inequality  Domestic Brand (versus Foreign)  Random effects for country, category, brand

Figure 2: Marketing-Mix Elasticities for Weak and Strong Brands in Developed and Emerging Markets A. Price elasticity

B. Distribution elasticity

C. Line-length elasticity

Note: Plots show estimated long-term marketing-mix elasticities (weighted by inverse standard errors), based on a country’s degree of development (EM versus DM), and a median split (by category) on brand equity.

WEB APPENDIX A

Table A1: Country-Of-Origin for Brands in Sample1 Country of Number origin of brands List of brands China 49 Alllike, Amoi, Aone, Casarte, Changhong, Coolpad, Fortress, Frestech, Fujimaru, Galanz, Genuine, Gfive, Giec, Gionee, Gplus, Haier, Hisense, Homa, Huawei, Idall, JNC, Jyeproud, Kenuo, Konka, Lemel, Lenovo, Malata, Meiling, Midea, Nintaus, Oppo, Qisheng, Ranso, Ronshen, Rowa, Sast, Shinco, SKG, SKY, Skyworth, Soniq, Soyea, TCL, Teclast, Thtf, Vido, Xingxing, Xoceco, ZTE Japan 42 Aiko, Akai, Avox, Azuma, Canon, Casio, Cube, Dxantenna, Extend, Fisherpaykel, Fujifilm, Fujitec, Fujitsu, General, Hitachi, Imarflex, JVC, Kenwood, Kyocera, Littleswan, Mitsubishi, NEC, Nikon, Olympus, Orion, Panasonic, Pentax, Pioneer, Ricoh, Sanken, Sansui, Sanyo, Sanyowhirlp, Sharp, Sony, Sonyericsson, Teac, TMY, Topcon, Toshiba, Uing, Unitcom USA 37 Apple, Bird, Blackdecker, Bluesky, Boston, California, Camel, Coby, Compaq, Dell, Emachines, Eureka, Focus, Frigidaire, GC, GE, HP, IBM, Imobile, Kelvinator, Kodak, Mito, Motorola, MSI, Philco, Polaroid, Polytron, Sampo, Syntax, Transonic, Vivitar, Vivo, Vizio, Westinghelu, Weston, Whirlpool, Whitewesting Indonesia 25 Advance, Akari, Asatron, Asiafone, Axioo, Base, Crystal, Daiichi, DAT, Denpoo, Digitec, Evercosscross, GMC, Ichiko, Maxtron, Mitochiba, Modena, Mpix, Nexian, Niko, Panda, Sogood, Spectra, Teckyo, Vitron South Korea 24 AJ, Alpha, Arirang, Curitel, Daewoo, Dios, Dongyangmagic, Hauzen, Hyundai, Jooyontech, Klasse, Ktft, LG, Pantech, Paoview, Plasma, Rasonic, Samsung, TG, Tromm, VTB, Wing, Wolfnfox, Zipel Philippines 23 ACE, Americanhome, Astron, Cherrymobile, Condura, Devant, Dowell, Eurotek, Fujidenzo, Fukuda, Ganzklar, Hanabishi, Kolin, Matrix, Minami, Myphone, Neopc, Nextbase, Olevia, Onda, Union, Xenon, Xtreme Taiwan 20 Acer, Afun, Asus, Chimei, Dennys, Dopod, Eupa, Gigabyte, Greenhouse, Heran, Hiplus, HTC, Okwap, Philo, Synco, Taiwanmobile, Tatung, Teco, Tobishi, Vito Australia 19 Abode, Audiosonic, AWA, Beston, Breville, Cascade, Conia, Dicksmith, Digitor, GVA, Homemaker, Kambrook, Laser, Misnapz, Olin, Palsonic, Premier, Rankarena, SVA Thailand 17 Aconatic, Astina, Brica, Distar, Elze, EVE, Family, Gnet, Kenny, KIA, Nano, PAL, Sherman, Soken, Svoa, Vzio, Wellcom Malaysia 12 Advante, CSL, Dmercury, Hitec, Khind, Meck, Orbitz, Pensonic, Pentec, Quayle, Techstar, Weige India 11 Godrej, IFB, IGO, Intex, Karbonn, Kenstar, Lava, Maxx, Micromax, Santosh, Spice Vietnam 10 Acnos, Ascent, CMS, Darling, Elead, Fmobile, Funiki, Goldsun, Mobistar, Tiendat Italy 8 Coral, Elba, Fantasia, Promac, Rainbow, Smeg, Tecnogas, Zanussi Germany 7 Bosch, Com1, Heller, Miele, Siemens, Telefunken, Videocon France 6 Brandt, Jinling, Local, Micam, Thomson, Yoshiielectr Singapore 5 Akira, Bajaj, Cornell, Mobell, Palladine Hong Kong 4 Goodway, Summe, Teledevice, Vdigi New Zealand 4 Benq, Telecom, Visione, ZIP

Table A1: Country-Of-Origin for Brands in Sample1 (continued) Country of Number origin of brands List of brands Sweden 2 Electrolux, Simpson Luxembourg 2 Hotpointaris, Singer Canada 1 Blackberry Panama 1 DSE Bangladesh 1 Hicon Finland 1 Nokia Netherlands 1 Philips Great Britain 1 Vodafone Spain 1 BBK Cambodja 1 Ktouch Egypt 1 Royalstar United Arab 1 Onida Emirates Turkey 1 Leona Pakistan 1 Qmobile 1 Countries are ordered by the number of brands from a given country; brand names shown in alphabetical order.

Table A2: Overview of Markets (Category/Country Combination) in Sample (With Number of Selected Brands Indicated per Cell)1 Austra- Hong Indo- Ma- New The Phi- Singa- South Tai- Thai- Viet- Category China India Japan lia Kong nesia laysia Zealand lippines pore Korea wan land nam CRT TVs 17 15 6 10 16 N/A 19 7 19 13 4 10 14 12 Compact cameras 13 10 12 N/A 14 10 10 12 10 10 10 14 11 8 DVD players and recorders 21 24 14 12 21 14 18 13 20 14 7 21 22 19 Desktop computers 9 N/A 10 N/A 6 14 8 N/A 7 7 8 10 9 8 LCD TVs 16 14 15 11 9 8 9 14 10 10 3 18 11 8 Laptop computers 11 N/A 13 N/A 11 12 11 9 13 12 11 11 11 10 Microwaves 14 7 9 12 8 N/A 9 13 10 9 5 12 8 9 Mobile phones 8 17 6 11 12 14 7 6 8 6 7 9 9 10 Plasma TVs 16 15 15 11 9 8 9 13 10 11 3 18 10 7 Refrigerators 13 19 15 9 10 10 12 N/A 12 10 6 13 11 11 SLR cameras 5 4 5 N/A 4 5 4 4 4 5 6 6 4 3 Smart phones 9 12 8 7 8 7 8 6 7 8 4 8 8 6 Tablets 3 7 3 N/A 3 4 3 2 3 3 3 5 3 2 Washing machines 13 14 18 11 13 7 15 N/A 17 16 6 12 14 10 1 Number of selected brands per market (category/country combination) indicated per cell; N/A in the case that data is not available, or insufficient to estimate models. Categories shown in alphabetical order.

Table A3: Overview of Physical Search Attributes per Category1 Category Attribute N Mean SD Min Max CRT TVs Screensize: Indicator variable, equaling 1 if screensize <= 24 inch, 0 otherwise 15,506 .737 .240 .000 1.000 (baseline: unknown screensizes) CRT TVs Screensize: Indicator variable, equaling 1 if screensize > 24 inch, 0 otherwise 15,506 .225 .226 .000 1.000 (baseline: unknown screensizes) Compact cameras Megapixels 17,429 9.535 3.984 .300 19.663 Compact cameras Digital zoom: Indicator variable, 17,429 4.534 1.473 .000 17.508 equaling 1 if available, 0 otherwise DVD players and Blueray: Indicator variable, equaling 1 if 27,321 .075 .201 .000 1.000 recorders available, 0 otherwise DVD players and Recording function: Indicator variable, 27,321 .050 .152 .000 1.000 recorders equaling 1 if available, 0 otherwise Desktop RAM (in megabytes) 9,801 2,391.174 1,607.831 137.028 7,991.005 computers Desktop CPU (in megahertz) 9,801 2,665.147 374.339 1,452.535 3,675.187 computers Desktop Hard disk size (in gigabytes) 9,801 444.517 319.809 38.673 4,261.363 computers LCD TVs Without LEDs: Indicator variable, equaling 1 for LCD TVs without LEDs, 0 17,793 .714 .391 .000 1.000 otherwise LCD TVs Screensize: Indicator variable, equaling 1 if screensize < 40 inch, 0 otherwise 17,793 .764 .242 .000 1.000 (baseline: unknown screensizes) LCD TVs Screensize: Indicator variable, equaling 1 if screensize >= 40 inch, 0 otherwise 17,793 .216 .229 .000 1.000 (baseline: unknown screensizes) LCD TVs 3D: Indicator variable, equaling 1 if 17,793 .042 .108 .000 .873 available, 0 otherwise Laptop computers RAM (in megabytes) 13,918 2,338.535 1,473.935 251.327 8,192.000 Laptop computers CPU (in megahertz) 13,918 1,935.700 230.797 911.037 2,864.068 Laptop computers Weight (in kilograms) 13,918 2.167 .338 .911 3.800 Laptop computers Touchscreen: Indicator variable, equaling 13,918 .023 .078 .000 .957 1 if available, 0 otherwise Laptop computers Webcam: Indicator variable, equaling 1 if 13,918 .717 .401 .000 1.000 available, 0 otherwise Microwaves Capacity (in liters) 14,184 10.761 3.740 1.667 41.162 Microwaves Functionality: Indicator variable, equaling 1 for only microwave 14,184 .685 .316 .000 1.000 functionality, 0 otherwise (e.g., if microwave comes with a grill) Microwaves Power (in watts) 14,184 21.717 6.437 1.000 46.856 Microwaves Digital time controller: Indicator variable, equaling 1 if available, 0 14,184 .597 .337 .000 1.000 otherwise Mobile phones Touchscreen: Indicator variable, equaling 13,894 .125 .191 .000 1.000 1 if available, 0 otherwise Mobile phones Wifi: Indicator variable, equaling 1 if 13,894 .061 .143 .000 1.000 available, 0 otherwise Mobile phones Bluetooth: Indicator variable, equaling 1 13,894 .537 .334 .000 1.000 if available, 0 otherwise Plasma TVs Screensize: Indicator variable, equaling 1 if screensize < 40 inch, 0 otherwise 18,063 .599 .295 .000 1.000 (baseline: unknown screensizes) Plasma TVs Screensize: Indicator variable, equaling 1 if screensize >= 40 inch, 0 otherwise 18,063 .319 .263 .000 1.000 (baseline: unknown screensizes)

Table A3: Overview of Physical Search Attributes per Category1 (continued) Category Attribute N Mean SD Min Max Plasma TVs 3D: Indicator variable, equaling 1 if 18,063 .044 .110 .000 .873 available, 0 otherwise Refrigerators Freezer: Indicator variable, equaling 1 if 18,236 .815 .333 .000 1.000 available, 0 otherwise Refrigerators Number of doors 18,236 1.924 .637 1.000 5.319 SLR cameras Megapixels 6,483 12.571 4.569 4.696 27.505 Smart phones Touchscreen: Indicator variable, equaling 9,753 .863 .292 .000 1.000 1 if available, 0 otherwise Smart phones Screensize (in inches) 9,753 3.467 .647 1.906 5.363 Smart phones Wifi: Indicator variable, equaling 1 if 9,753 .799 .342 .000 1.000 available, 0 otherwise Smart phones Memory (in gigabytes) 9,753 6.768 8.966 .010 64.000 Tablets RAM (in megabytes) 2,174 2,162.437 4,108.402 128.000 29,715.649 Tablets Weight (in kilograms) 2,174 .470 .096 .249 .730 Tablets Screensize (in inches) 2,174 8.140 1.069 4.800 10.092 Washing machines Capacity (in liters) 19,862 27.185 7.495 1.000 69.188 Washing machines Functionality: Indicator variable, equaling 1 if washing machine comes 19,862 .042 .111 .000 1.000 with extra functionality; 0 if washing only Washing machines Front loader: Indicator variable, equaling 1 if front loader; 0 otherwise (baseline is 19,862 .182 .294 .000 1.000 others) Washing machines Top loader: Indicator variable, equaling 1 if top loader; 0 otherwise (baseline is 19,862 .497 .361 .000 1.000 others) 1 The unit of analysis is the brand-month level. Indicator variables are either 0 or 1 at the SKU-month-level; when aggregating them to the brand-month level for the analysis, they are averaged and hence measure the share of a brand’s SKUs that carry a particular product attribute. Categories shown in alphabetical order.

Table A4: Summary Statistics and Correlations for Variables in Market-Share Attraction Model1 Summary statistics Correlations 1st 3rd Market Variable Median quantile quantile share Price Distribution Line length Market share ([0,1]) .058 .022 .138 1.000 Price 238.205 99.113 521.418 .095*** 1.000 Distribution ([0,100]) 24.063 9.842 43.357 .434*** .222*** 1.000 Line length 24.000 11.000 46.000 .124*** -.001 -.033*** 1.000 1 Significance levels: * p < .1; ** p < .05; *** p < .01 (two-sided). Summary statistics and correlations for variables prior to the log-operation and mean- centering. Prices have been converted to USD for comparison (local currencies used in the analysis).

Table A5: Correlation Among Long-Term Marketing-Mix Elasticities1 Price elasticity Distribution elasticity Line-length elasticity Price elasticity 1.00 Distribution elasticity .03 1.00 Line-length elasticity .03 .05** 1.00 1 Significance levels: * p < .1; ** p < .05; *** p < .01 (two-sided).

Table A6: Long-Term Elasticities (Means by Category)1 Category Price Distribution Line length CRT TVs -.596*** .921*** .945*** Compact cameras -.400*** .860*** .755*** DVD players and recorders -.699*** .866*** .790*** Desktop computers -.452*** .611*** .831*** LCD TVs -.762*** .740*** .759*** Laptop computers -.664*** .704*** .717*** Microwaves -.971*** .702*** .536*** Mobile phones -.343*** .884*** .742*** Plasma TVs -.842*** .740*** .674*** Refrigerators -.567*** .664*** .427*** SLR cameras -.377*** .711*** .583*** Smart phones -.529*** 1.270*** .833*** Tablets -.424*** .447*** .208*** Washing machines -.579*** .663*** .518*** 1 Significance levels: * p < .10, ** p < .05, *** p < .01 (two-sided). Elasticities are weighted by inverse standard errors. Significance tested using meta-analytic p-value (Method of added Z’s). Categories shown in alphabetical order.

Table A7: Robustness Checks for Long-Term Price Elasticities1 Marketing Indicator Market -mix and Dynamic Focal for DV is Log GDP Log GCI Log HDI share 훽 brand- simula- model internat. controls 1−훾 specific tion brands dynamics (1) (2) (3) (4) (5) (6) (7) (8) (9) * * EM indicator (versus DM) -.132 -.119 -.131 -.129 -.061 -.119 (.077) (.077) (.077) (.092) (.071) (.074) Log GDP (reverse-coded) -.057** (.027) Log GCI (reverse-coded) -.584 (.418) Log HDI (reverse-coded) -.445* (.252) *** *** *** *** *** *** *** *** *** Brand equity -.935 -1.001 -1.003 -.999 -.947 -.935 -1.045 -.776 -.889 (.034) (.024) (.024) (.024) (.033) (.034) (.037) (.028) (.034) *** *** *** ** ** *** Brand equity * EM indicator -.125 -.124 -.125 -.120 -.083 -.136

(.048) (.047) (.048) (.052) (.038) (.048) *** Brand equity * Log GDP -.057

(reverse-coded) (.018) *** Brand equity * Log GCI -.979 (reverse-coded) (.256) Brand equity * Log HDI -.351** (reverse-coded) (.167) Log of category .113* .120* .118* .114* -.036 .111* .019 .066 .123** concentration (.063) (.063) (.063) (.063) (.066) (.063) (.070) (.054) (.060) Log of category growth -.273** -.260** -.289** -.271** -.330*** -.277** -.361** -.275** -.264** (.129) (.129) (.130) (.129) (.126) (.130) (.144) (.117) (.125) Appliances -.077 -.074 -.076 -.076 -.062 -.075 -.083 -.180* -.060 (versus electronics) (.112) (.111) (.111) (.111) (.107) (.112) (.127) (.102) (.110) Log of income inequality -.224 -.274 -.317 -.282 -.217 -.228 -.268 -.413* -.211 (.244) (.228) (.246) (.236) (.242) (.243) (.291) (.226) (.231) Domestic brand -.189*** -.186*** -.186*** -.191*** -.128* -.149 -.187** -.168*** -.165** (versus foreign) (.069) (.069) (.069) (.069) (.069) (.100) (.075) (.057) (.067)

International brand .046

(.081) *** Brand mean market share 1.196 (.177) Constant -.493*** -.562*** -.561*** -.560*** -.548*** -.536*** -.582*** -.387*** -.501*** (.072) (.060) (.062) (.061) (.070) (.105) (.084) (.067) (.070) R-squared .541 .540 .540 .541 .551 .541 .556 .587 .509 Observations 1687 1687 1687 1687 1687 1687 1687 1687 1698 1 Significance levels: * p < .1, ** p < .05, *** p < .01 (two-sided). Regression of long-term elasticities (all models; except 훽/(1 − 훾) in model 4) on explanatory variables and three random effects, for brands, categories, and countries, respectively. Estimated using a linear mixed-effects model. Elasticities are weighted by inverse standard errors. Standard errors in parentheses. Model (1) is the focal model; models (2)-(4) replace the emerging market indicator by alternative (continuous) metrics that measure a country’s degree of development, in specific log GDP (Gross Domestic Product), log GCI (Global Competitiveness Index), and log HDI (Human Development Index), see also Table 2 for the exact definitions. Because these metrics are increasing with a country’s degree of development, they have been reverse-coded in model estimation, so that coefficients from models (2)-(4) can directly be compared with the estimate of the binary EM indicator. Model (5) controls for a brand’s market share, while model (6) controls for international brands (indicator variable, 1 for brands active in more than 1 country, 0 otherwise). Model (7) replaces long-term elasticities by 훽/(1 − 훾). Elasticities in model (8) are calculated from an alternative market-share attraction model, allowing for marketing-mix and brand-specific dynamics by adding lagged marketing mix variable (but removing the common carry-over parameter); elasticities in model (9) are dynamically simulated, see Web Appendix B for details.

Table A8: Robustness Checks for Long-Term Distribution Elasticities1 Marketing Indicator Market -mix and Dynamic Focal for DV is Log GDP Log GCI Log HDI share 훽 brand- simula- model internat. controls 1−훾 specific tion brands dynamics

(1) (2) (3) (4) (5) (6) (7) (8) (9) ** ** ** ** ** ** EM indicator (versus DM) -.154 -.154 -.154 -.177 -.137 -.148 (.075) (.068) (.075) (.081) (.064) (.064) *** Log GDP (reverse-coded) -.080 (.023) ** Log GCI (reverse-coded) -.868 (.377) *** Log HDI (reverse-coded) -.692 (.200) *** *** *** *** *** *** *** *** *** Brand equity -.152 -.102 -.098 -.101 -.143 -.151 -.171 -.151 -.149 (.022) (.015) (.015) (.015) (.022) (.022) (.024) (.019) (.022) *** *** *** *** *** *** Brand equity * EM indicator .101 .092 .101 .113 .068 .100 (.030) (.029) (.030) (.033) (.025) (.029) *** Brand equity * Log GDP .047 (reverse-coded) (.011) *** Brand equity * Log GCI .571 (reverse-coded) (.154) *** Brand equity * Log HDI .360 (reverse-coded) (.099) Log of category -.055 -.055 -.053 -.057 .039 -.054 .003 -.044 -.066 concentration (.048) (.047) (.048) (.047) (.047) (.048) (.052) (.043) (.045) Log of category growth .132 .164* .147 .154 .168* .134 .145 .116 .101 (.100) (.099) (.100) (.099) (.097) (.100) (.111) (.091) (.098) Appliances -.138 -.136 -.138 -.136 -.134 -.141 -.145 -.122 -.107 (versus electronics) (.112) (.113) (.112) (.113) (.107) (.112) (.131) (.110) (.109) Log of income inequality .095 .047 .009 .042 .060 .098 .106 .134 .148 (.240) (.193) (.227) (.194) (.219) (.241) (.259) (.206) (.203) Domestic brand .089* .088* .090* .089* .063 .052 .085* .049 .104** (versus foreign) (.048) (.048) (.048) (.048) (.042) (.066) (.047) (.040) (.044) International brand -.050 (.058) *** Brand mean market share -1.283 (.133) Constant .925*** .848*** .848*** .847*** .922*** .966*** 1.008*** .789*** .823*** (.075) (.062) (.065) (.062) (.068) (.089) (.082) (.069) (.067) R-squared .143 .148 .145 .143 .125 .143 .110 .149 .085 Observations 1687 1687 1687 1687 1687 1687 1687 1687 1698 1 Significance levels: * p < .1, ** p < .05, *** p < .01 (two-sided). Regression of long-term elasticities (all models; except 훽/(1 − 훾) in model 4) on explanatory variables and three random effects, for brands, categories, and countries, respectively. Estimated using a linear mixed-effects model. Elasticities are weighted by inverse standard errors. Standard errors in parentheses. Model (1) is the focal model; models (2)-(4) replace the emerging market indicator by alternative (continuous) metrics that measure a country’s degree of development, in specific log GDP (Gross Domestic Product), log GCI (Global Competitiveness Index), and log HDI (Human Development Index), see also Table 2 for the exact definitions. Because these metrics are increasing with a country’s degree of development, they have been reverse-coded in model estimation, so that coefficients from models (2)-(4) can directly be compared with the estimate of the binary EM indicator. Model (5) controls for a brand’s market share, while model (6) controls for international brands (indicator variable, 1 for brands active in more than 1 country, 0 otherwise). Model (7) replaces long-term elasticities by 훽/(1 − 훾). Elasticities in model (8) are calculated from an alternative market-share attraction model, allowing for marketing-mix and brand-specific dynamics by adding lagged marketing mix variable (but removing the common carry-over parameter); elasticities in model (9) are dynamically simulated, see Web Appendix B for details.

Table A9: Robustness Checks for Long-Term Line-Length Elasticities1 Marketing Indicator Market -mix and Dynamic Focal for DV is Log GDP Log GCI Log HDI share 훽 brand- simula- model internat. controls 1−훾 specific tion brands dynamics

(1) (2) (3) (4) (5) (6) (7) (8) (9) EM indicator (versus DM) -.049 -.056 -.051 -.058 -.068 -.038 (.058) (.055) (.057) (.063) (.060) (.052) Log GDP (reverse-coded) -.015 (.021) Log GCI (reverse-coded) -.060 (.309) Log HDI (reverse-coded) -.241 (.179) *** *** *** *** *** *** *** *** *** Brand equity -.135 -.132 -.132 -.133 -.130 -.135 -.152 -.152 -.140 (.026) (.018) (.018) (.018) (.025) (.026) (.028) (.024) (.024) Brand equity * EM indicator .006 .002 .006 .010 .014 .018 (.035) (.034) (.035) (.037) (.031) (.033) Brand equity * Log GDP .004 (reverse-coded) (.013) Brand equity * Log GCI .040 (reverse-coded) (.185) Brand equity * Log HDI .032 (reverse-coded) (.115) Log of category -.036 -.034 -.035 -.034 .156*** -.033 .024 -.034 -.064 concentration (.050) (.050) (.050) (.050) (.045) (.050) (.051) (.044) (.046) Log of category growth -.017 -.021 -.033 -.002 -.033 -.013 -.033 -.048 -.048 (.093) (.093) (.093) (.093) (.066) (.093) (.092) (.079) (.086) Appliances -.265*** -.265*** -.266*** -.263*** -.262*** -.271*** -.291*** -.280*** -.220*** (versus electronics) (.078) (.078) (.077) (.079) (.047) (.078) (.072) (.059) (.069) Log of income inequality .543*** .520*** .512*** .525*** .506*** .549*** .630*** .502*** .542*** (.184) (.182) (.185) (.173) (.178) (.183) (.203) (.193) (.166) Domestic brand .157** .157** .155** .158** .109* .022 .153** .152*** .140** (versus foreign) (.067) (.067) (.067) (.067) (.059) (.089) (.068) (.056) (.061) ** International brand -.192 (.084) *** Brand mean market share -1.919 (.155) Constant .909*** .884*** .885*** .884*** .888*** 1.050*** .968*** .867*** .777*** (.062) (.055) (.056) (.055) (.049) (.088) (.062) (.056) (.054) R-squared .222 .222 .222 .222 .217 .222 .190 .219 .130 Observations 1685 1685 1685 1685 1685 1685 1685 1685 1696 1 Significance levels: * p < .1, ** p < .05, *** p < .01 (two-sided). Regression of long-term elasticities (all models; except 훽/(1 − 훾) in model 4) on explanatory variables and three random effects, for brands, categories, and countries, respectively. Estimated using a linear mixed-effects model. Elasticities are weighted by inverse standard errors. Standard errors in parentheses. Model (1) is the focal model; models (2)-(4) replace the emerging market indicator by alternative (continuous) metrics that measure a country’s degree of development, in specific log GDP (Gross Domestic Product), log GCI (Global Competitiveness Index), and log HDI (Human Development Index), see also Table 2 for the exact definitions. Because these metrics are increasing with a country’s degree of development, they have been reverse-coded in model estimation, so that coefficients from models (2)-(4) can directly be compared with the estimate of the binary EM indicator. Model (5) controls for a brand’s market share, while model (6) controls for international brands (indicator variable, 1 for brands active in more than 1 country, 0 otherwise). Model (7) replaces long-term elasticities by 훽/(1 − 훾). Elasticities in model (8) are calculated from an alternative market-share attraction model, allowing for marketing-mix and brand-specific dynamics by adding lagged marketing mix variable (but removing the common carry-over parameter); elasticities in model (9) are dynamically simulated, see Web Appendix B for details.

Table A10: Differences in Long-Term Elasticities in Models With and Without Advertising Spending1 Advertising not included Advertising included Tests on differences (focal model) Marketing-mix Mean Mean t-value instrument N elasticity2 N elasticity2 from WLS3 Advertising 0 85 .005*** Price 85 -.748*** 85 -.858*** -.540 (n.s.) Distribution 85 .803*** 85 .809*** .050 (n.s.) Line length 85 .719*** 85 .650*** -.452 (n.s.) 1 Significance levels: * p < .1, ** p < .05, *** p < .01 (two-sided). n.s. indicates that the effect was not significant (p > .10). We add to our focal model the advertising expenditures for 85 brands, and re-estimate marketing-mix elasticities in all markets for which advertising data is available to us (in total 17 markets from 10 categories in two countries, China and Hong Kong). We then compare the marketing-mix elasticities from this model to those elasticities estimated from our focal model; in the comparison, we only include those 85 brands for which advertising expenditures are available. 2 Elasticities are weighted by inverse standard errors. Significance tested using meta-analytic p-value (Method of added Z’s). 3 Tests on differences carried out by regressing elasticities (weighted by inverse standard errors) on an intercept and an indicator variable which is 1 if advertising was included in the model, and 0 otherwise. The reported t-value is obtained from the estimate of the indicator variable.

Table A11: Differences in Long-Term Elasticities Over Time1 Estimated on Estimated on Tests on differences split data set (early) split data set (late) Marketing-mix Mean Mean t-value instrument N elasticity2 N elasticity2 from WLS3 Emerging markets Price 620 -0.802*** 620 -0.705*** 1.421 (n.s.) Distribution 620 0.655*** 620 0.638*** -0.593 (n.s.) Line length 620 0.517*** 620 0.548*** 0.856 (n.s.) Developed markets Price 591 -0.698*** 591 -0.576*** 2.025** Distribution 591 0.720*** 591 0.692*** -0.702 (n.s.) Line length 591 0.435*** 591 0.428*** -0.170 (n.s.) 1 Significance levels: * p < .1, ** p < .05, *** p < .01 (two-sided). n.s. indicates that the effect was not significant (p > .10). We select brands that are available for at least 8 years, and estimate our focal model on early and late observations (i.e., the first and last 60% of observations). The samples need to overlap to ensure that we have a sufficient number of observations for model estimation (i.e., 8 years * 60% = 4.8 years in each sample). 2 Elasticities are weighted by inverse standard errors. Significance tested using meta-analytic p-value (Method of added Z’s). 3 Tests on differences carried out by regressing elasticities (weighted by inverse standard errors) on an intercept and an indicator variable which is 1 if the elasticity was estimated in the later portion of the data set, and 0 otherwise. The reported t-value is obtained from the estimate of the indicator variable.

WEB APPENDIX B: DYNAMIC SIMULATIONS OF MARKETING-MIX ELASTICITIES

Table B1. Comparison of Elasticities (Simulation-Based Versus Analytical)1 Test for Marketing-mix Number of Mean elasticity Mean elasticity differences instrument markets (simulation-based)2 (analytical)2 Correlation (t-value)3 Price 184 -.671*** -.657*** .994 -.223 (n.s.) Distribution 184 .870*** .838*** .990 1.102 (n.s.) Line length 184 .861*** .836*** .992 .817 (n.s.) 1 Significance levels: * p < .1, ** p < .05, *** p < .01 (two-sided). n.s. indicates that the effect was not significant (p > .10). 2 Mean elasticities are weighted by inverse standard error. Significance tested using meta-analytic p-value (Method of added Z’s). 3 Tests on differences carried out by regressing elasticities (weighted by inverse standard errors) on an intercept and an indicator variable which is 1 if the elasticity was derived from the dynamic simulation, and 0 otherwise. The reported t-value is obtained from the estimate of the indicator variable.

Simulation details:

The goal is to compare the analytically derived, long-term market-mix elasticities, ηlcbk = βlcbk (1 − MS̅̅̅̅lcb), to the corresponding marketing-mix elasticities obtained from a dynamic 1−γlc simulation. Table B1 above shows that these are virtually the same.

Below, we outline the steps in the dynamic simulation:

lcb (1) Create dataset Dbaseline for brand b in country l and category c to calculate baseline market shares for all Mlc brands at each brand’s mean values of the marketing mix and physical search attributes. Copula control terms are set to 0 as their presence was only required to ensure the focal parameters are corrected for endogeneity (see, e.g., Datta, Foubert and Van Heerde 2015). The simulation data set covers 60 subsequent months (5 years), and an initial state, whereby brand b’s market share is set to the observed market

share, MS̅̅̅̅lcb, in periods for which the brand has been present in the data. In the simulation, all brands are assumed to be present in the market throughout.

lcb Create simulation datasets Dk for each marketing-mix instrument k (price, distribution, line length), whereby each marketing-mix instruments Xklcb,t for focal brand b in period t is shocked by 1% in month 25, i.e., at the beginning of year 3, after the system has converged to equilibrium market shares from its initial state. Because the linearized attraction model is in the log-space, adding 1% to the marketing variable in levels is achieved by adding log(1.01) to the observed log of the marketing-mix instrument in that period.

lcb lcb (2) For each of the data sets Dbaseline and Dk , predict market shares for all Mlc brands in all periods P (Fok and Franses 1999): a. Transform data to market share attraction model (in its base-brand representation); as base brand, use the same as in model estimation. The dimensionality of this data set is Mlc -1 (= number of brands, less the baseline brand) x C (= columns, or number of variables) x [P+1] (= number of periods, from 0 to 60).

b. Draw  replications of parameter estimates for explanatory variables from a multivariate normal distribution: N(coefficient means, ), where  is the estimated covariance matrix of the parameter estimates.  in the simulation is set to 1,000.

c. For each replication  in : For each period t (t 1) in P:

- Draw realization of the correlated error term, ε̃τlcbt from N(0, ), where  is the estimated covariance matrix of the residuals. - Set lagged market share to the log of the ratio of a focal brand’s b market share to the market share of the base brand in period t-1. MS - Predict log ( τlcb,t) MSτlcB,t o Multiply data by coefficient draws and add ε̃τlcbt to obtain MS predictions of the log ratio of marketing shares log ( 휏lcb,t). MS휏lcB,t

MS휏lcb,t o Transform log ( ) to MS휏lcb,t: MS휏lcB,t

MS Take the exponent of log ( 휏lcb,t). The logistic MS휏lcB,t MS휏lcb,t ̂ MS휏lcB,t transformation implies that MSτlcbt = MS and 1+∑ 휏lcb,t MS휏lcB,t ̂ 1 MSτlcBt = MS . Then, predicted market shares are 1+∑ 휏lcb,t MS휏lcB,t saved for all brands. These eventually feed into the next period t.

lcb lcb d. Per data set (Dbaseline and Dk ), we obtain a matrix of dimension Mlc (= brands) x [P+1] (= periods, including the initial state) x  (simulation draws).

e. Calculate the cumulative (long-term) incremental impact of a temporary 1% shock of marketing-mix instrument k (price, distribution, and line length) on

market share for brand b (of all Mlc brands) for simulation draw  as ̂ 60 ̂ ̂ baseline ΔMSτlcbk,t = ∑t=25(MSτlcbk,t − MSτlcb,t ). Long-term elasticities can be ̂ ̂ baseline computed as ratio of ΔMSτlcbk to the baseline market share MSτlcb,t=24 from the period before shocking. Compute means and standard errors (.025% and .975% quantiles) from the empirical distribution, multiplied by 100, to obtain estimates

for ηlcbk.

References:

Datta, Hannes, Bram Foubert, and Harald J. van Heerde (2015), “The Challenge of Retaining Customers Acquired with Free Trials,” Journal of Marketing Research, 52 (2), 217-234.

Fok, Dennis, and Philip Hans B.F. Franses (1999). Impulse-Response Analysis of the Market Share Attraction Model (No. EI 9955-/A). Econometric Institute Research Papers.