Cross-National Differences in Market Response: Line-Length, Price, and Distribution Elasticities in 14 Indo-Pacific Rim Economies

Abstract

Our field’s knowledge of marketing-mix elasticities is largely restricted to developed countries in the North-Atlantic region, even though other parts of the world—especially the Indo-Pacific Rim region—have become economic powerhouses. To better allocate marketing budgets, firms need to have information about marketing-mix elasticities for countries outside the North-Atlantic region. We use data covering over 1,600 brands from 14 product categories collected in 7 developed and 7 emerging Indo-Pacific Rim countries across more than 10 years to estimate marketing elasticities for line length, price, and distribution, and examine which brand, category, and country factors influence these elasticities. Averaged across brands, categories, and countries, line-length elasticity is .459, price elasticity is -.422, and distribution elasticity is .368, but with substantial variation across brands, categories, and countries. Contrary to what has been suggested, we find no systematic differences in marketing responsiveness between emerging and developed economies. Instead, the key country-level factor driving elasticities is societal stratification, with Hofstede’s measure of power inequality (power distance) as its cultural manifestation and income inequality as its economic manifestation. As the effects of virtually all brand, category, and country factors differ across the three marketing-mix instruments, the field needs new theorizing that is contingent on the marketing-mix instrument studied.

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“…The mean distribution elasticity of .3392 is consistent with the value of .40 in Datta, Ailawadi, and Van Heerde (2017) and the value of .368 in Datta et al (2022). Finally, our mean elasticity for line length (.6396) is in the range of values (from .348 to 1.511) reported by Jindal et al (2020) and comparable with the value of .459 reported by Datta et al (2022).…”

“…5 To control for state dependence in market share, we also include lagged market share as a regressor in the model (Gielens 2012). By including Gaussian copulas, we account for potential endogeneity of marketing-mix variables that might arise due to unobservables that are not accounted for in our model (Datta, Ailawadi, and Van Heerde 2017; Datta et al 2022; Papies, Ebbes, and Van Heerde 2017; Park and Gupta 2012): 6 , 7 where q denotes quarters and DUMQTR tq represents quarterly dummies, and thus its coefficient (α ijq ) holds brand- and quarter-specific intercepts. ADSTOCK ijt , PRICE ijt , PROMO ijt , LL ijt , and DIST ijt represent advertising stock, regular price, price promotion depth, product line length, and distribution by brand i in category j during month t, respectively, and ATTR aijt (a = 1 … n j ) represents different product attributes, which are defined separately for each category (see Web Appendix E).…”

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“…For example, the collection of adequate control variables is often difficult. To capture seasonality in purchase patterns across a wide range of geographical markets and calendar years, researchers have used APIs to construct continuous (vs. dichotomous) variables that accurately reflect national holidays (e.g., HolidayAPI; Datta et al 2022). Web data also allow researchers to efficiently operationalize new measures at scale, such as weather conditions based on the location of users’ IP addresses (e.g., Weather Underground; Li et al 2017).…”