In modern society, advertising has evolved into a critical industry with substantial economic significance. Over recent years, global advertising spending has been on an upward trajectory: reaching $504 billion in 2016, $590 billion in 2018, and $732 billion in 2021, with a minor slowdown in 2020 due to the COVID-19 pandemic. In many countries, advertising represents between 0.5% and 1% of GDP. This increasing trend is predominantly driven by the rise in advertising spending in Asian economies, such as China and India. In 2021, the United States led global advertising spending, contributing $284 billion, followed by China and Japan. According to Zenith (2021), global advertising expenditure as a percentage of world GDP was approximately 0.77% in 2021, which remains remarkably consistent with the 0.7% - 0.9% range observed during our sample period (1998-2006) in Western Europe. Also, a longitudinal study by [10] covering 1998-2014 demonstrates a consistent multiplier effect in Europe, where every Euro of advertising spend generates roughly 7 Euros of GDP value through stimulated consumption. These findings suggest that while platforms have shifted (e.g., to digital), the underlying economic elasticity remains robust.

Firms invest in advertising with the belief that disseminating product information can promote sales by influencing consumer preferences and persuading customers to purchase the advertised product ([6]). This holds particularly true from a microeconomic perspective for specific brands, but at the aggregate level, the relationship becomes more uncertain. Advertising can increase sales through various channels, reflecting diverse perspectives on its effects. It may persuade consumers to purchase a product by altering consumer preferences, effectively shifting the demand curve rightward. Additionally, advertising plays an informative role, reducing search and transaction costs for consumers. Lastly, a business-stealing effect may emerge if advertising primarily shifts sales from one brand to another.

The narrower the framework of analysis, the more apparent the perceived effect of market expansion. If analyzing a single product, the eventual market share effect is captured by expanding consumer demand. However, our results indicate that, at the industry level, the market expansion effect diminishes, with increased consumption of advertised products occurring mainly at the expense of other products within the same category or sector. In contrast, from a macroeconomic perspective, when one product gains market share at the expense of others, all replacement effects cancel out, leaving only a potential role for global demand expansion, which is more challenging to measure. Various studies have observed a positive relationship between advertising and consumption from both macro- and microeconomic perspectives. For example, statistics from the United States (1950-1960) show that advertising increased as the economy expanded and decreased during periods of contraction ([33]). [19] analyzed U.S. data from 1900 to 2007 and found that, at the aggregate level, as GDP rises, advertising expenditure increases at a faster rate than GDP growth. Similar results were obtained for Germany ([23]) and China ([7]).

Despite these findings, the debate over whether advertisements influence consumption or vice versa remains unresolved. Both causality narratives seem plausible: consumers may buy more of a brand because increased advertising draws attention and enhances willingness to purchase, holding price constant; alternatively, consumption may drive advertising, as firms are more inclined to advertise when demand for their products increases. Firm-level data show that companies often base their advertising budgets on product sales ([22]; [29]). As a result, empirical analyses examining the effect of advertising on consumption may yield mixed results, where both advertising and consumption jointly influence consumption ([31]). Misleading conclusions on the effects of advertising could lead to inappropriate advertising strategies and misguided public policies regarding advertising.

Economics and management disciplines approach this issue with different perspectives and methodologies. In management and microeconomics, advertising costs are viewed as selling expenses that can be redistributed across firms within an industry without influencing total market size. This perspective primarily focuses on consumer behavior, arguing that advertising increases consumption by changing preferences. However, evidence suggests that advertising may impact total demand in a dynamic manner ([26]). In contrast, macroeconomists typically use time-series data on consumption and advertising spending in multiple countries to examine whether advertising spending positively influences consumption. [36] showed that macroeconomic structure significantly impacts the effect of advertising, emphasizing the importance of considering the broader economic context when assessing the effect of advertising.

The literature on the relationship between consumption and advertising dates back to the 1960s ([4]). Much of the early literature lacked formal models or hypothesis testing ([33]; [29]). Microeconomic theories, such as [3] model, portray advertising as a means to adjust the price elasticity of a product, thereby encouraging consumers to purchase more, even at higher prices. [13] used microdata from various TV brands to analyze how advertising influences consumer demand for specific brands. However, empirical studies using microdata typically focus on a single product or brand, limiting the representativeness of findings. Additionally, much of the literature has explored advertising’s effects on prices ([27]) and market value ([16]), as well as its potential relationship with economic development ([35]). [10] reviewed the diverse economic effects of advertising. [34], using household level data, showed a jointly determined relationship between consumption and advertising.

A representative body of macroeconomic literature includes studies by [2], who tested the causality between advertising and consumption by applying the methodology of [17]. Related works include [21]. Research on advertising also discusses the substitutability of different advertising media and its impact on industry growth and broader economic development ([14]). However, most studies have used data from a single country or industry, presenting a notable limitation. The lack of sector-level data complicates the task of disentangling the effects of advertising and competition among industries. For instance, vehicles and public transportation may serve as substitutes to some extent, meaning that increasing vehicle sales could reduce public transportation demand, ceteris paribus. In such cases, relying on country-level data without considering industry-level interactions prevents us from fully understanding whether changes in overall consumption are driven by changes in advertising inputs or by the competitive interactions between industries.

To address the mutual influences of advertising and consumption, this paper uses a rich dataset spanning five European countries. Our data encompass 27 industry sectors that represent the entire economy, allowing us to capture both macroeconomic effects on consumption and the competitive dynamics within specific industries. To the best of our knowledge, this is the first study to explore the relationship between advertising and consumption while maintaining an industry-level focus on competitive dynamics.

Our dataset differs from existing literature in two key ways. First, we utilize time-series data across multiple countries and sectors, incorporating interactions among industries and variations across countries. Second, our dataset includes macroeconomic variables for each country, such as GDP, purchasing power, and lending rates, allowing us to control for macro-level factors that may influence consumption and advertising. These features substantially enhance the reliability of our results compared to previous studies.

Our empirical results show that consumption drives advertising, but not vice versa. Past consumption has a significant effect on current advertising expenditures, even after accounting for macroeconomic conditions and country-fixed effects. This finding aligns with common management practices where advertising budgets are set as a percentage of previous years’ sales. Conversely, past advertising expenditures have no significant effect on consumption at the sector level. This suggests that advertising primarily shifts consumption within categories, redistributing consumer spending between brands and potentially across related product categories. Our results are robust across different sectors and countries, further supporting the notion that consumption plays a more substantial role in driving advertising. We also find no significant evidence that the integration of EU countries or differences in industrial structures among these countries substantially affect advertising spending.

Given the complex interactions between advertising and consumption, potential endogeneity remains a concern. Although our data does not allow for the use of instrumental variables or natural experiments, we mitigate endogeneity through a panel data approach that accounts for time, industry, and country dimensions. Additionally, we employ the Arellano-Bond panel estimation to further address endogeneity concerns.

The paper is structured as follows: Section 2 describes the dataset, presents summary statistics, and discusses their advantages. Section 3 outlines the model and methodology employed in the analysis. Section 4 presents the empirical results. Section 5 discusses the implications of our findings for firms and policymakers. Finally, Section 6 concludes the study.

To address our research questions, we collected comprehensive data on advertising and consumption at the industry level. Consumption data were sourced from Eurostat, covering 27 sectors in five European countries—France, Germany, Italy, Spain, and the UK—over the period from 1998 to 2006. Advertising expenditure data were obtained from Nielsen Media Research, which includes expenditures across mainstream media channels: television, radio, newspapers, magazines, outdoor advertising, cinema, and the internet. The use of time-series data across sectors and countries provides key advantages over previous studies, particularly by allowing us to capture inter-sector relationships, such as complementary or substitute effects in consumption. This granular approach enables a more accurate representation of the dynamics between advertising and consumption at the sectoral level, which may differ significantly across industries.

Macroeconomic variables for each year and country were also collected from Eurostat. These variables include the price index, deposit rate, household consumption, GDP, government expenditures, the inflation rate, and household consumption levels. Given that both consumer behavior and firm advertising decisions are heavily influenced by macroeconomic conditions, incorporating these variables allows us to reduce omitted variable bias and improve the robustness of our estimations.

Advertising data are reported net of discounts for Italy and the UK. However, for France, Spain, and Germany, advertising data are available only at gross (list) prices. To standardize the data across countries, we estimated average discounts by media type to convert the gross figures into net values. For France, we used IREP net figures; for Spain, we obtained average discount rates by media and year from Publiespana; and for Germany, we applied discount rates derived from ZAW and European Advertising and Media Forecast figures.

The use of net data is critical in advertising markets, where discounts are commonly employed at various stages to manage seasonality in demand and to engage in first-degree price discrimination. Contracts typically prevent arbitrage, meaning that each purchase is made on behalf of a specific client, which further underscores the importance of using net figures for accurate comparison and analysis.

Consumption data from Eurostat are classified according to the Classification of Individual Consumption by Purpose (COICOP), which categorizes consumption into 47 groups at the 3-digit level. Nielsen, however, uses a proprietary sector classification that is more detailed than COICOP and is widely accepted within the advertising industry. To align the consumption and advertising data, we reconciled the two taxonomies, ensuring that the sectors in both datasets matched as closely as possible.

Several sectors were excluded from our analysis due to the absence of advertising data or because the consumption figures were not relevant to the study. These include sectors such as secondary education, housing rentals, postal services, prostitution, primary education, narcotics, and tobacco. As a result, these sectors were omitted from both the consumption and advertising datasets.

Additionally, we excluded smaller advertising categories aimed at the professional sector that lacked corresponding consumption figures. These categories include professional services, machinery, information technologies, and institutional advertising, which together represent approximately 8% of total advertising spending. Despite this exclusion, our analysis still encompasses a broad and representative range of sectors.

In certain key advertising sectors, such as banking and retail, where consumption data were unavailable from Eurostat, we estimated turnover using alternative data sources. For telecommunications, we relied on data from the European IT Observatory. For banking, turnover was approximated using the banking intermediation margin, which includes net interest income and service revenues. For retail, we used Eurostat’s value-added data, which captures the difference between sales and the cost of sales. For the insurance sector, turnover was approximated as the sum of premiums paid by clients.

In these instances, we acknowledge a lack of homogeneity between the Eurostat consumption figures and the alternative turnover data used. Some of the figures may include transactions not directly related to final consumption. Nonetheless, we focused on turnover data that reflect the actual industry investment in advertising, which provides a more accurate representation of sectoral advertising dynamics. Ultimately, our dataset includes 27 sectors with homogeneous advertising and turnover data, covering approximately 94%.

We test whether advertisement causes consumption or consumption causes advertisement. Both variables are time-series, so we apply the concept of Granger causality. The idea is that if consumption predicts advertising rather than the other way around, it is evidence that consumption causes advertising, and vice versa.

Since there is endogeneity in our panel data, we need to apply the method by [1] to test for Granger causality. To use this methodology, the key requirement is that the two time series, i.e. consumption and advertising, are both stationary. Let stochastic process {X_t, Y_t:t= 1,2,...} be consumption and advertising, respectively, and for every collection of time indices 1 ≤t₁ ≤t₂ ≤ ...≤t_m, let f( x t 1 , x t 2 ... x t m ) and g ( y t 1 , y t 2 ... y t m ) by the joint distribution of { x t 1 , x t 2 ... x t m } and { y t 1 , y t 2 ... y t m }, respectively.

We use 4 different tests to test the stationarity of the two time series: the Levin, Lin and Chu t^∗-test, Im-Pesaran-Shin test, ADF-Fisher Chi-square test and PP-Fisher Chi-square test. At the beginning of next section, we will present the results. If both advertisement and consumption are stationary time series, we test their causal relationships. Our dataset is panel data as we introduced in last section, and accordingly, our regression model to test whether advertisement causes consumption is (Table 4):

Table 4. Summary Statistics of macroeconomic variables (average across countries).

Mathematically, for regression model (1), we test the following hypothesis:

Hypothesis 1:δ_l = 0, for all l= 1, 2, ..., m.

To get a consistent estimate of the parameters in time series models with panel data, one of the most important issues involves addressing endogeneity. Examples of such studies include [1], [18], [12] and [24]. In this paper, we follow the two-step GMM estimator proposed by [1] and implemented in [18].

As [1] pointed out, such methodologies require data to be stationary, and the resulting time series is tested for the existence of unit roots. The results of such tests are tested in the next section. When the tests reject the hypothesis that there are unit roots, we can set up the panel data model, for which the restriction of identical coefficients of lagged X_ijt and Y_ijt variables is imposed. Thus, we estimate a time series VAR model adapted to the panel context, which is similar to [18] model but also unlike it and other literature using data with structures similar to ours, and we control for the country-level macroeconomic time-varying variables.

X_ijt and Y_ijt are respectively consumption and advertising spending on sector iin country jin year t. Five countries are included in our dataset: France, Germany, Spain, Italy and UK. In each of these five countries, there are 28 sectors.² Therefore, 140 observations are observed in each of the T= 9 years (1998-2006).

We include Z_jt as a vector of country-level macroeconomic time-varying variables. Z_jt includes the price index, GDP (both constant and current prices), the unemployment rate, and household consumption (current price). All of these variables can impact both consumption and advertising, and to control for purchasing power and inflation, we include GDP for both constant and current prices.

We also add country fixed effects as µ_j, which is a vector of country dummy indicators, using the UK as the base country. Disturbance u_ijt is assumed to be independently distributed across countries with a zero mean.

We wish to test whether influence runs in both directions. Therefore, in addition to testing whether advertisement drives consumption, we also test whether consumption drives advertisement, and we estimate Model (2).

Hypothesis 2:β_l = 0, for all l= 1, 2, ..., m.

When estimating Equations (1) and (2), we pay special attention to the coefficients associated with vector Y_ijt−l in Equation (1) and vector X_ijt−l in Equation (2). The most important problem is that Equations (1) and (2) are subject to endogeneity because if the country-specific dummy variable impacts consumption in Equation (1) or if advertising in Equation (2) in one period may also have an impact in previous periods, then the dummy variables will be correlated with the unobservables in previous periods. To solve the endogeneity problem, [1] proposed using the lagged dependent variable from at least two periods earlier and the lags of variables on the right-hand side as instrument variables in a General Method of Moments (GMM) estimator. We estimate Equations (1) and (2) using three different methods: pooled OLS, [1] one-step GMM and [1] two-step GMM. Based on the estimation results, we test whether the coefficients associated with Y_ijt−l in Equation (1) and with vector X_ijt−l in Equation (2) are jointly zero by presenting the Wald tests.

One point we must emphasize is that in Model (2), we include one more variable than Equation (1): ∑ l = 1 m λ t − l X ¯ i t − 1 . We expect more advertisement to be spent on larger industries than on smaller industries. Therefore, we include the lagged average consumption of an industry to capture the effect of an industry’s size on advertising spending on it. When the size of an industry changes, it is important to distinguish whether the change in advertising in the industry is driven by the change in consumption or in industry size.

Our findings contribute to the ongoing debate on the reciprocal relationship between advertising and consumption. Consistent with prior research, our results provide compelling evidence that consumption predominantly drives advertising, rather than the reverse. The strength of our findings is reinforced by the unique advantages of our dataset, which allows for a comprehensive analysis across multiple industries and countries. This suggests that advertising primarily reacts to existing consumption trends rather than serving as a direct catalyst for increased consumption at the industry level. [15], using data from Iceland, found that advertising predicts investment, yet the driving force of advertising comes more from consumption.

These insights have significant implications for both firms and policymakers. From a managerial perspective, the absence of a market expansion effect at the industry level indicates that the observed benefits of advertising largely stem from a business-stealing effect rather than an overall increase in total consumption. [11], using consume-level data, displayed business-stealing, indicating that reducing advertising only made consumers switch brands rather than reduce the total consumption. This finding suggests that firms should exercise caution in allocating advertising budgets, particularly in competitive markets where excessive advertising may result in diminishing returns. In oligopolistic industries, aggressive advertising strategies may simply neutralize each other, leading to substantial expenditures without tangible gains. Prior research ([20]) has highlighted the risk of overinvestment in advertising, especially when firms fail to differentiate their strategies effectively. Furthermore, collective advertising campaigns aimed at increasing overall industry demand should be carefully evaluated, as they may not yield the intended expansionary effects. [37], for instance, demonstrated that firms in the automobile sector may either under- or over-invest in advertising, depending on its ability to shape consumer preferences.

Moreover, following the theoretical framework of [25], if advertising serves primarily to expand the total market (market-scale effect), we would expect a significant positive correlation between aggregate advertising and total industry growth. However, our results show that while advertising correlates with individual consumption patterns, it does not significantly expand the total market size in mature European sectors. This “zero-sum” outcome strongly implies that advertising functions as a tool for market share redistribution—where one firm’s gain is a competitor’s loss—rather than primary demand creation. We have added this nuance to the manuscript to acknowledge that industry-level data serves as a robust proxy for these firm-level competitive dynamics.

From a policy perspective, governments should recognize the nuanced relationship between advertising and consumption when formulating economic policies or regulatory measures. Given that advertising responds to, rather than drives, consumption, policymakers should carefully assess the effectiveness of advertising incentives. Encouraging firms to invest in advertising as a mechanism for stimulating economic growth may not yield substantial macroeconomic benefits. Instead, policies that promote product innovation and differentiation—which have a stronger influence on industry vitality—may be more effective in fostering sustainable economic growth.

Our results are robust to the exclusion of specific countries or sectors, reinforcing the validity of our findings. If substitute or complementary relationships between sectors played a significant role in shaping the advertising-consumption dynamic, we would expect variations in our results when certain industries were removed from the analysis. However, our robustness checks confirm that consumption remains the primary driver of advertising across different contexts. Moreover, the consistency of our findings across multiple European countries suggests that national economic structures and institutional differences do not significantly alter the fundamental relationship between advertising and consumption.

Additionally, our dataset provides granular insights by capturing advertising expenditures across different media channels. Our analysis finds no evidence that specific media types (e.g., television, radio, print, or digital) exert a significant influence on overall consumption trends. [5] find similar results that exposure to TV has insignificant impact on people’s consumtion behaviors. This suggests that the medium of advertising is less critical than previously assumed in determining consumption behavior.

From a strategic management standpoint, our findings underscore the need for firms to focus on innovation and product differentiation rather than relying solely on advertising to drive growth. While advertising can effectively redistribute market share among firms, it does not create new demand at the industry level. Long-term competitiveness is more likely to be sustained through investments in product development, technological advancements, and superior consumer experiences rather than through excessive reliance on advertising expenditure.

Finally, an interesting aspect of our dataset is that it spans the turn of the millennium, a period marked by heightened advertising activity. While the year 2000 provided firms with significant advertising opportunities, external factors such as the dot-com bubble may have also influenced advertising dynamics. Whether major economic or technological events can amplify advertising’s impact on consumption remains an open question. However, due to data limitations, we are unable to draw definitive conclusions on this matter, leaving room for further research on the role of extraordinary events in shaping advertising effectiveness.

In conclusion, this paper contributes to the debate on whether advertising influences consumption or whether consumption drives advertising using a unique macrolevel dataset from EU countries. Our data allow us to control relationships between the consumption of different sectors and the relationships between spending on different types of media on advertising. Our empirical results demonstrate that consumption positively and significantly drives advertising, but advertising does not significantly drive consumption. Our results imply that advertising is more likely to reflect a reaction to consumers’ behaviour rather than a means to change consumers’ behaviour. Additionally, the effect of advertising is more related to business stealing from competitors rather than market expansion. Therefore, a firm’s marketing strategy should be coordinated with its innovation, which distinguishes its products from other firms, and advertising should be used as a way to help consumers understand the uniqueness of different products. We believe that this is the best way to develop the leading firm in an industry.

¹Data for consumption in Spain in year 2006 is missing.

²These include a “sector” named “total”, which denotes the sum of advertising spending on all sectors. In our data analysis, we do not use the “total” sector.