MÉTODOS

I start by testing the model by including the advertising in linear form as well, which allows computing elasticities by including advertising investments in the formula. Table 5 contains the results of linear and logarithmic regressions applying pooled OLS, brand fixed effects and product fixed effects models. In these regressions, I tested the model with the British market only.

Table 5 Outputs of Linear and Logarithmic Regressions with OLS, Brand and Product Fixed Effects in the UK Market

The pooled OLS yields the highest and the most significant coefficients for both logarithmic and linear specifications. In order to hold, the pooled OLS requires the exogeneity of the explanatory variable. The endogeneity of prices has been discussed in Chapter 3 already and that is probably one reason why the price gets positive coefficients in the regressions above.

The other reason is that the market share of the inside goods grows by the time with new introductions and therefore the sales of more expensive products occur more. This is one

OLS, log UK Brand FE, log, UK Product FE, log, UK OLS, lin, UK Brand FE, lin, UK Product FE, lin, UK

LHS LHS LHS LHS LHS LHS

* significant at 10%; ** significant at 5%; *** significant at 1%

pitfall of the specification, but since I can obtain plausible coefficients for the advertising I do not find it too serious a problem. The endogeneity of advertising is not as straightforward as the endogeneity of prices, but some bias probably exists because advertising gets negative coefficients in some regressions. This can be due to measurement error for instance.

I computed product specific elasticities applying formulas (12)-(15). These are not worth reporting because of their implausibility discussed above. The elasticity computed from the linear specification takes into account the advertising investment as well. Therefore the deviation was higher among them than among the elasticities computed form the logarithmic specification. Interestingly, some products of Samsung and Apple obtained the elasticities of over 1 from the linear specification (high advertising investments count more). Same time their elasticities were lower from the logarithmic specification (high market shares count more). This proves the correlation between market shares and advertising investments.

Elasticities computed using the logarithmic specification almost equal to the estimated coefficients. Thus, as mentioned above, the logit model is handy in comparing the differences between the advertising elasticities by countries, channels or advertisers. Including advertising in logarithmic form in the estimated equation allows us both to fulfill the assumption of diminishing marginal utility and returns to scale as well as treat the obtained coefficient β as the advertising elasticity.

I continue by dividing the advertising investments into channels. I apply the logarithmic specification so we can see approximate elasticities directly from the coefficients. I ran regression by using the British data only and by using the data of all countries. Table 6 contains the results of regression.

Table 6 Advertising Divided by Channels

The regression run by advertising channels does not yield very significant results neither on a country level nor on the whole sample level. I assume that one main reason is that the data does not capture the product level advertising accurately enough and thus its division into advertising investments by channels is not accurate enough either. However, the coefficients for cinema and print are significant and reasonable as well. In the whole sample level print and tv got significant and plausible coefficients. The qualitative aspects of advertising are not

UK All Countries

of the main interest in this paper. However, the cinema would be such advertising channel that suits well for persuasive and captive advertising. Print suits well for both persuasive and informative advertising as was mentioned by Kaldor (1950-51) as well. Discussing the results of the regression above should be kept in mind that the advertising is included as monetary investments only. The investments are probably the highest for print and tv in most of the countries. The advertising response by the channel could probably be captured better if advertising was measured in GRP because it captures the exposure better.

Next I divide the advertising investments by advertiser. Hence the division between operator and retailer in the data is somewhat ambiguous I aggregated all non-manufacturer advertising into one variable s_OPRET. Table 7 contains the output of this regression.

Table 7 Advertising Divided by Advertisers

The coefficients of manufacturer’s advertising are significant in both regressions. The coefficients of non-manufacturer advertising are not significant and they are implausible (negative in the UK). This actually proves that Figure 1 in Chapter 4 is misleading and the data roughly underestimates the non-manufacturer advertising investments. One reason for this is that operators’ advertising has been encoded in the data with operator’s brand and product name even if the operator was advertising the tie of a subscription and handset.

Again should be remembered that the data contains only monetary investments. The effectiveness of non-manufacturer advertising investments should be taken into account in non-monetary terms as well. The operator or retailer will advertise and sell in ties only such products which are good for the image of their own brand. Therefore, if a product is advertised by a non-manufacturer, it should be taken as a recommendation for that product.

The assumption is supported by the fact that almost no operators’ advertising was observed for the products of other than leading brands. However, this regression does not tell anything about the manufacturers’ retailers’ or operators’ domination. This could be discussed more thoroughly with the data of the acquisitions of handsets alone and in ties with subscriptions.

Then again the role of operating system should be taken into account as well.

By so far I have concentrated mainly on the British market. I have also taken into account the effect of advertising for one period only. However, there can be advertising even before the product availability in the market and then obviously no sales occur. It is obvious that also this kind of advertising has effect on sales. Also the advertising of previous periods can have impact on purchase decision. Therefore I use the advertising stock variable in the next regression. I report pooled OLS regression output in Table 8

Table 8 Pooled OLS Regression by Country with Advertising Stock Variable (logstock)

Pooled OLS gives positive and significant coefficients for advertising stock variable in every country. The coefficient of the price is positive, but that bias has been covered already at the beginning of the analysis. I ran the regressions with the brand and product fixed effects (i.e included brand and product dummies) as well. Including product dummies makes the coefficients of logstock somewhat smaller in every country and non-significant in India. In other countries, there is no effect on significance. Including brand dummies makes coefficients non-significant in Germany, Italy and in the USA. The value of coefficients changes only a little from the regression with product dummies. Table 9 contains summary of advertising coefficients β computed using periodical advertising investments only (log_s_prod_tot) and advertising stock (logstock) for each researched countries with pooled OLS, brand fixed effects and product fixed effects regressions. The r-squared is the highest for the product fixed effects regressions and lowest for the pooled OLS.

CHINA GERMANY INDIA INDONESIA ITALY RUSSIA SAUDI ARABIA UK USA

LHS LHS LHS LHS LHS LHS LHS LHS LHS

(mean) price 0.00745 0.01153 0.01034 0.00348 0.00799 0.00532 0.00292 0.00743 0.01501

(0.00085)*** (0.00079)*** (0.00090)*** (0.00090)*** (0.00068)*** (0.00075)*** (0.00121)** (0.00097)*** (0.00151)***

logstock 0.10702 0.03122 0.09810 0.02448 0.03049 0.08340 0.07666 0.10831 0.02637

(0.01028)*** (0.00775)*** (0.00815)*** (0.01292)* (0.01131)*** (0.01309)*** (0.01601)*** (0.01638)*** (0.01236)**

product_age 0.96343 1.07243 0.22488 0.52717 0.37149 0.45414 0.34449 0.36863 0.19018

(0.12058)*** (0.09729)*** (0.10887)** (0.13413)*** (0.10004)*** (0.12738)*** (0.17402)** (0.14872)** (0.14231)

product_age2 -0.06232 -0.07145 -0.00921 -0.03522 -0.01476 -0.02519 -0.01068 -0.03257 -0.01503

(0.00991)*** (0.00872)*** (0.00890) (0.01108)*** (0.00834)* (0.01080)** (0.01381) (0.01289)** (0.01252)

product_age3 0.00129 0.00133 0.00013 0.00063 0.00013 0.00043 0.00010 0.00075 0.00043

(0.00023)*** (0.00021)*** (0.00021) (0.00026)** (0.00020) (0.00025)* (0.00032) (0.00031)** (0.00030)

Seasonality dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes

Constant -14.37715 -13.22642 -10.88647 -9.88856 -10.83137 -10.51456 -8.50764 -10.07568 -10.27546

(0.73586)*** (0.60665)*** (0.66860)*** (0.85275)*** (0.61598)*** (0.83778)*** (1.07352)*** (0.90046)*** (1.10130)***

Observations 249 294 250 261 281 265 261 277 135

R-squared 0.58 0.63 0.53 0.15 0.47 0.31 0.27 0.44 0.60

Standard errors in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%

Table 9 Advertising Coefficients (β) by Country for Periodical Advertising (log_s_prod_tot) and Advertising Stock (logstock) from Pooled OLS, Brand Fixed Effects and Product Fixed Effects Regressions

China and Russia obtained significant coefficients from each regression. The highest elasticities are (according to the product fixed effects) in China, Indonesia and Russia.

According to pooled OLS the highest coefficients are in China, India and United Kingdom.

The lowest coefficients according to product fixed effects are in USA, Italy, Saudi Arabia and UK, whereas according to pooled OLS in USA, Italy and Indonesia. Generally, China has obtained pretty high and USA pretty low coefficients in every regression.

As it can be seen from the previous regressions, the data does not capture the investments by the channel or by the advertiser sufficiently. Therefore I am critical of the other aspects of the data as well. I move on to conduct the sensitivity analysis and robustness checks of the model.

log_s_prod_tot logstock

* significant at 10%; ** significant at 5%; *** significant at 1%