Download the Belgian Newspaper Industry

Prices and Network Externalities in Two-Sided Markets:
the Belgian Newspaper Industry
Patrick Van Cayseele∗and Stijn Vanormelingen†
March 14, 2007
Abstract
This paper discusses the newspaper industry in Belgium from a two-sided market perspective. The reader and advertizing market for printed media are closely interlinked with each
other by bilateral network externalities. This requires a specific structural model to estimate
demand parameters for both markets since a profit maximizing publisher takes into account
those externalities. We estimate network effects and demand elasticities for Belgian newspaper
publishers to assess market power and the degree of competition in the newspaper market,
which experienced a large consolidation wave over the last decade.
Keywords: two-sided markets, newspapers, demand estimation
JEL Codes: L11, L82, C23
∗ LICOS,
† LICOS,
Katholieke Universiteit Leuven and Universiteit van Amsterdam.
Katholieke Universiteit Leuven
1
1
Introduction
Recently, there is a surge of interest in so called two-sided markets. These markets are defined
as markets where a platform that connects two distinct types of economic agents. The value of
the good to one type of economic agents depends on the number of agents of the other group
that consume the good (Rochet and Tirole 2003, Armstrong 2005). In these markets not only
price level but also price structure matters. The choice of this price structure is key to the success
of a platform (Rochet and Tirole 2003). Notable examples of two-sided markets are credit card
markets, video game consoles, dating agencies, shopping malls. . . The newspaper market is another
application since advertisers and consumers are connected with each other by cross-group network
externalities. Publishers typically do not sell only newspapers to consumers, but also publicity
space to advertisers. The more readers for a newspaper, the higher the value of advertising space
to the advertisers. On the other side, it could be that the higher the amount of advertisements,
the higher the value of newspapers to consumers. This is true if newspaper readers also want to
be informed about promotions and special offers by firms.
Similar to the rest of the world, the Belgian newspaper market witnessed a large consolidation
process over the last decades (De Bens 2001). The number of newspaper publishers decreased
considerably and large cross-media companies emerged. To address the impact of these mergers on
newspaper publishers’ market power, one needs to take into account the two-sided nature of the
market. Looking for example to the readers’ side alone will lead to wrong conclusions. Therefore
we will estimate demand parameters for both sides of the market to make statements about market
power and the degree of competition in the Belgian newspaper market. Furthermore, the magnitude
of the bilateral network effects is estimated.
[to be completed]
The rest of the paper is organized as follows. Section 2 gives a concise overview of the Belgian
newspaper market. Section 3 describes the data while section 4 discusses the empirical model.
Section 5 discusses the results and section 6 concludes.
2
The Belgian Newspaper Market
This section gives an overview of the Belgian newspaper market since the middle of the nineties.
Table 1 shows daily newspaper titles in Belgium. Newspapers can be divided into different groups,
for example Dutch language versus French language and "quality" newspapers versus "popular"
newspapers. The market for printed dailies is heavily concentrated. There are only five media
companies active in the market, publishing fifteen daily newspapers. The two largest publishers
2
together have a market share of around 60% in the readers market and 50% in the advertising
market.1
One part of our dataset consists of sales figures of all Belgian dailies with a time span ranging
from 1994 to 2005. Figures 1 to 5 show the evolution of the average daily sales per Belgian
newspaper2 from January 1994 to June 2005. One can see considerable variation not only between
newspapers, but also over time. Sales figures vary quite a lot between different months of the year.
Despite the fact that some newspapers manage to increase their average readership, the overall
trend is one of a decrease in total sales as shown in figure 6. This fall in average sales is especially
pronounced in the French speaking part of Belgium.
Figures 7 and 8 show the evolution of the price of one single newspaper copy. The figures also
show the consumer price index, normalized to the average price of a single copy in the beginning
of the sample period. Before the mid nineties, the cover price was fixed at the national level.
Even after price liberalization, prices seem to be more or less exogeneously determined and price
increases occur mostly simultaneously accross all newspapers. The exceptions are the two business
newspapers De Tijd and L’Echo, which have a higher price than the other newspapers. It is clear
that before 2000, price increases in cover prices more or less match inflation (as measured by the
CPI). Afterwards, nominal newspaper prices rise faster than the CPI. The former figures plot the
price of one single newspaper copy. Part of the newspaper sales come from annual (or bi-annual)
subscriptions. The correlation between subscription rates and single copy prices is around 0.95.
The other part of our dataset includes the monthly spending on advertising of all advertisers
in each newspaper from January 2001 to June 2005. We observe for each advertiser the value
of advertising spent in each single newspaper. This allows us to get an idea of the degree of
multihoming as defined by Rochet & Tirole (2003). In principle, an advertiser multihomes if he
uses more than one newspaper to communicate its advertising campaign to the consumers. Due
to data limitations we assume that an advertiser multihomes when he buys in one month pblicity
space in more than one newspaper. This is equivalent with assuming that a firm launches at most
one advertising campaign per month. Table 2 shows the amount of multihoming following this
definition. Around 53.5% of the firms singlehome, i.e. buy advertising space in no more than
1 newspaper in one single month. This means that almost half of the advertisers multihome.
Almost 20% advertise in two newspapers. Around 5% of the advertisers place their ads in more
than 10 newspapers. This makes that the majority of advertisements in one newspaper has also
appeared in an other newspaper. Table 3 shows that for example for "Het Volk" less than 1% of
its advertisements are unique ads. In contrast, more than 40% of ads in the business newspaper
"De Tijd", are unique.
We show a comparison between advertising and newspaper sales revenues in figure 9. It can
1 For
the Dutch speaking part of Belgium alone, this fraction is even higher, namely almost 80% in both reader
and advertising market.
2 "Sud Presse" covers a number of different titles which differ only in their regional news. Before 1999, sales for
these titles were reported separately. Afterwards, sales are summed up.
3
be seen that in recent years, nominal revenue from advertisements increased substantially while
nominal sales revenue3 remains fairly constant. As a result advertising revenue is higher than sales
revenue at the end of the sample period4 . Figure 10 shows the 2004 share of advertising revenue
in total revenue for all newspapers5 separately. The ad revenue share ranges from 48% to 75%. In
general, the "quality" newspapers have a higher share. advertising prices are plotted in figure 11
and 12. The ad price showed here is the price of a one page black and white advertisement. Smaller
ads are a fraction of this price, whereby the fraction does not differ largely between newspapers.
Part of the differences between advertising prices can be explained by differences in readership.
However, when we look at advertising revenue per reader6 , this still differs between newspapers.
(Figure 13).
3
Data Description
This section provides a description of the data used in the paper. Our dataset includes all daily
national newspapers in Belgium. Sales figures of Belgian newspapers are provided by the association of Belgian newspaper publishers (BVDU). The dataset includes monthly figures of average
daily newspaper sales, from both subscriptions and sales at local newspaper shops. The time span
of sales figures ranges from January 1994 to June 2005. Also the free distribution of newspapers is
included in the data, which acounts for about 2% of total distribution. We received cover prices and
subscription rates from BVDU. Subscription rates are provided for annual, bi-annual and quarterly
subscriptions. The subscription rate per edition is computed by dividing the annual subscription
rate by the total number of editions published in a given year. Characteristics of newspaper readers
are provided by CIM, an agency that gathers and publishes data about all different media outlets.
The characteristics include educational attainment, age, sex, socio-economic status, professional
status and number of children. These data are gathered on a yearly basis, however for the moment
we only have data for the year 20047 at our disposal.
Data on media advertising come from Aegis Media. This database shows on a monthly basis
the companies that have bought advertising space in each newspaper separately. The agency that
gathers this data, computes ad spending per company, by using the appropriate list price for the
advetizement size. We aggregated advertising spending up to total monthly publicity spending per
newspaper. Data is observed from January 2001 to June 2005. We obtained advertising prices
from Scripta and Full Page, which are the main companies that commercialize and sell publicity
3 Revenue
from subscriptions and daily distribution through newspaper shops
need to be careful in interpreting advertising revenues. These are computed using listed prices and do not
take into account possible rebates. This issue will be further discussed in the data description section and in the
econometric analysis.
5 Metro is a free newspaper that is distributed in railway stations, schools, etc. . . Its share of advertising revenue
in total revenue equals obviously 1 and is thus not included in the graph.
6 Advertising revenue per reader is computed as total ad revenue in one month divided by the number of editions
appearing in that particular month times the number of newspapers sold. As a result advertising revenue per reader
can be compared with the cover price of one single copy.
7 In fact, this includes data from surveys conducted between May 2003 and may 2004.
4 One
4
space in Belgian newspapers. Because some years were missing, we complemented them with data
from Mediabook, a yearly publication about the Belgian media market. Unfortunately, prices we
observe are list prices while it is likely that rebates are granted for larger advertisers and during
the summer period. However, as long as these do not differ too much between newspapers or over
time, this is not a serious problem for our empirical analysis8 . Also data about newspaper formats
come from Mediabook. There are three different newspaper formats in our sample, namely (1)
broadsheet, (2) Belgian, and (3) tabloid. A number of newspapers shifted to a smaller publishing
size over the sample period.
4
Empirical Model
This section presents the econometric model. It takes into account both sides of the markets,
namely readers and advertisers and the interaction between them. The model is mainly based on
that of Rysman (2004). A newspaper publisher receives revenue from both the readers and advertisers. In its pricing strategy, he takes into account the fact that advertisers value the readership
of the newspaper.
4.1
advertising demand
Given the numbers on multihoming shown in the previous section ,a discrete choice model would
not be appropriate. Therefore we rely on a representative advertiser model. Suppose there are N
advertisers. The representative advertiser choses aj , the amount of advertising in newspaper j,
j = 1 . . . J. We assume advertisers act as price takers. Profit from advertising is a function of the
amount of advertising, profit per informed consumer and the number of newspaper readers9 :
Π = f (a1 , R1 , P1A , π
e1 , ..., aN , RJ , PJA , π
eJ )
whith Rj the number of readers, PjA the advertising price and π
ej the profit per consumer that
remembers the advertisement. Under the assumptions that readers singlehome and that there is
constant profit per reader who notices the advertisement, the profit function of the advertiser is
separable in aj . If these assumptions are satisfied, there is no reason why the choice to advertise
in one newspaper should be influenced by the choice to advertise in another newspaper.
From the demand side, if consumers singlehome they can only be reached by advertising in the
specific newspaper they read. As a result, the advertiser’s decision about the amount of advertising
in newspaper j is affected only by the amount of readers , their characteristics and the price of
an ad in that particular newspaper. The assumption of constant profits per consumer, says that
serving many consumers because of an advertisement in newspaper j does not affect the benefit
8 Note
that the list prices are correct to infer advertising quantity from advertising revenue, since the same prices
are used to compute adertizing revenue.
9 Time subscripts are omitted for expositional reasons.
5
of serving extra consumers through an advertisement in newspaper i (Rysman 2004). The profit
function of an advertiser can therefore be written as:
Π = [e
π 1 G(a1 , R1 ) − P1 a1 ) + ... + (e
π J G(aJ , RJ ) − PJ aJ ]
G(aj , Rj ) measures the number of readers that notice and remember the advertisement. We
β
assume that G(., .) takes the Cobb-Douglas functional form, namely G(aj , Rj ) = aα
j Rj . We expect
α to lie between 0 and 1, so there are decreasing returns to larger advertisements. β is expected to
be positive, capturing potential network effects. The advertiser chooses aj to maximize advertising
profits:
aj =
Ã
Pj
αe
π j Rjβ
1
! α−1
Hence the total amount of advertising demand for newspaper j is given by N · aj :
Aj =
Ã
Pj
απj Rjβ
1
! α−1
where π j = π
ej N (1−α) . Assume that ln(π j ) can be written as a linear function of some observable
and unobservable characteristics of the readership of newspaper j. The above can be written as:
1
β
(1)
ln(Pj ) +
ln(Rj ) + Xj γ + ηj
α−1
1−α
Equation 1 allows to estimate demand parameters using aggregate data on advertising prices
and quantities. We stress that the advertising price is likely to be endogeneous, so we need proper
instruments to get consistent estimates. This issue is further addressed in the next section.
ln(Aj ) =
4.2
Readers’ demand
To model reader demand for newspapers, we use of a nested logit model. The utility consumer i
derives from newspaper j depends on both product and consumer characteristics. As such, utility
can be written as10 :
uij = δ j + ν ij
where δ j represents the mean utility of consuming newspaper j which is common to all consumers and ν ij is the deviation from this mean and is specific to each individual consumer. Consumers choose the newspaper which gives them the highest utility and buy one unit of it (discrete
choice). Mean utility can be expressed as a function of the newspapers’ observable characteristics
XjN , its cover price PjN and a "taste" parameter ξ j , which is unobservable:
1 0 Again,
time subscripts are omitted.
6
δ j = Xj β + α ln(PjN ) + ξ j
The nested logit model puts more structure on the consumer specific part of utility. It allows
consumer utility to be correlated accross products belonging to the same group. So, in response to
for example a price increase of newspaper j a consumer is more likely that consumers will substitute
away to newspapers in the same group than to other, more different products. We apply a nested
logit model with two levels (Verboven 1996). Assume that the market can be divided in G groups.
Each group g, g = 1, . . . G can be further divided into Hg subgroups. The individual specific part
of utility can be written as:
ν ij = εig + (1 − σ g )εihg + (1 − σ hg )εij
Where εig captures consumer i’s preference for group g, similarly εihg captures preference
for subgroup hg of group g. They are both the same for each product in in the same group
and subgroup respectively. We assume that σ hg and σ g are common accross (sub)groups, so
σ hg = σ 1 and σ g = σ 2 The σ parameters must satisfy the following condition to be consistent with
random utility maximization: 0 ≤ σ 2 ≤ σ 1 ≤ 1. The higher σ 2 , the more consumer preferences
are correlated accross newspapers belonging to the same group. Similarly, the higher σ 1 , the more
consumer preferences are correlated accross newspapers in the same subgroup. When σ 2 approaches
σ 1 , correlation accross newspapers in the same subgroup is the same as between newspapers in the
same group, but belonging to an other subgroup. As a result, we are back in the one level nested
logit model. When both σ2 and σ 1 are equal to zero, the model is the simple logit. If εij , εig and
εihg have the standard nested logit distribution such that εig , εig + (1 − σ 2 )εihg and ν ij have the
extreme value distribution, the market share of newspaper j, sj , can be written as (Berry 1994,
Verboven 1996):
ln sj − ln s0 = XjN β + α ln(PjN ) + σ 1 ln sj|hg + σ 2 ln shg|g + ξ j
(2)
Where s0 represents the market share of the outside good, sj|hg is market share of newspaper j
in subgroup hg and shg|g is market share of subgroup hg in group g. So market share of newspaper
j can be written as a linear function of mean utlility of the newspaper and the logarithm of its group
and subgroup market shares. which allows us to estimate β, α, σ hg and σ g using linear estimation
techniques.Note that sj|hg and shg|g are endogeneous by definition and need to be instrumented.
4.3
Equilibrium
[to be added]
7
5
5.1
Results
advertising Demand
We estimate equation 1 to get advertising demand parameters. As noted above, advertising price
is likely to be endogeneous, since an increase in advertising demand for unobservable reasons is
expected to have an impact on the advertzing price too. As instrument we use the size of the
newspaper. At the beginning of our sample period, most newspapers were published in Broadsheet
format. Some of them switched to Belgian format, others switched to Tabloid format.11 Changes
to narrower newspaper formats are decided at least one year in advance since they require large
investments in printing facilities. Format changes can be seen as cost shifters. First, pages are
smaller and thus printing costs per advertising page are lower, and second, format changes coincide
with investments in newer and more efficient printing rolls. More compact formats are assumed
to have no other impact on advertising quantity than through advertising prices.12 Another instrument we use to identify the price coefficient is the share of subscriptions in total newspaper
sales. Most newspapers announce their advertising rates for the whole year at the beginning of that
year. Newspapers with higher subscription rates have more loyal readers and are more certain of
the number of newspapers they will sell in the following year. Consequently, we expect a positive
correlation between advertising prices and the share of subscriptions.
We use Generalized Method of Moments (GMM) to estimate advertising demand in equation
1, applying three different specifications. The first (GMM1) uses a quality dummy and a Dutch
language dummy to capture the main reader characteristics. The quality dummy is equal to 1
when the newspaper is considered as a "quality" newspaper, the Dutch dummy is equal to one
if the newspaper is written in Dutch(cf. Table 1). The second specification (GMM2) uses data
from the CIM survey about Belgian newspaper readers. We include the percentage of readers that
belong to the two highest socio-economic groups (High Soc. Group %), the percentage of readers
that have at least one child younger than 15 years old (With children %), the percentage of male
readers (Male %) and the percentage of readers that consider themself as responsible for the daily
purchases (Purchases Resp. %). The last specification is a GMM estimation with newspaper fixed
effects (GMM-FE). A time trend and a seasonal dummy are also included in all specifications.
Although, there are no real first stage regressions in GMM, we report OLS regressions of the
advertising price on the instruments to assess their appropriateness. Results are shown in Table 4.
The instruments excluded from equation 1, are Belgian, Broadsheet and Subscription Share in the
1 1 Broadsheet measures 540X385 millimeters (8 columns) , Belgian format is slightly smaller namely 490X336 mm
(7 columns). Tabloid is the smallest format with 385X250 mm (5 columns).
1 2 "Gazet van Antwerpen" was published simultaneously on broadsheet and tabloid format in the period before
the definite switch to tabloid. This allowed the measurement of the influence of different formats on the impact of
an advertisement on consumers. The results were that not the absolute size, but rather the relative size to total
newspaper size mattered for consumer responsiveness (MediaMarketing 2004) Consequently we do not expect the
newspaper format to have a direct impact on advertizing quantity. This is confirmed in a simple OLS regression of
equation 1 where also newspaper format is included. The coefficients on newspaper formats were not significant at
the 10% level.
8
previous year. Belgian is a dummy equal to one if the newspaper has the Belgian format, similarly
Broadsheet is a dummy equal to one when the newspaper has a Broadsheet format. Results are in
line with expectations. The bigger the size of the newspaper, the higher the advertising price. The
lagged subscription share has the expected sign in GMM1 and GMM2, but is negatively correlated
with advertising price in the fixed effects estimation. However, the coefficient is never significant.
Also, the included instruments show the expected partial correlations with the advertising price.
The more readers a newspaper has, the higher the price. "Quality" newspapers charge higher
prices. Dutch language newspapers charge lower prices, given the other variables. Newspapers
with more readers from higher socio-economic groups, female readers and readers who are the
purchasing responsibles charge higher advertising prices. The F -statistic of joint significance of
the excluded instruments is higher than 10 in all specifications. The partial R2 statistics are also
satisfactory, namely around 0.6.
Table 5 shows the results of estimating equation 113 . Results from an OLS regression are
reported in column 1. The coefficient on advertising price is negative and highly significant despite
the upward endogeneity bias. Column 3 (GMM1) corrects for this endogeneity problem and the
coefficient on advertising price goes in the right direction, i.e. increases in absolute value. However,
the difference is not that large. The number of readers have a strong and positive impact on the
advertising quantity, pointing to a network effect. The more readers a newspaper has, the more
advertising it can attract14 . Results from the GMM1 specification point to a demand elasticity of
-1.61, impying returns to scale to advertising quantity of 0.38. β is estimated to be close to one.
There is significantly more advertising in quality newspapers and somewhat less in Dutch language
newspapers. In column 4, reader characteristics are included in the regression. It can be seen
that especially the percentage of readers fom the highest socio-economic groups has a significant
and positive impact on advertising quantity. Also the percentage of purchases responsables has
a positive impact, although only significant at the 10% level. advertising quantity drops as the
relative number of male readers increases, but again, this result is only significant at the 10% level.
The results from the fixed effects estimates (column 2 and 5) are more or less similar, although
the demand elasticity is estimated to be somewhat smaller. For all specifications, the Hansen test
does not reject validity of the instruments.
5.2
Readers’ demand
We use equation 2 to retreive information about readers’ demand parameters. First, whether the
newspaper is published in Dutch or not, divides newspapers into groups. Second, each group is
1 3 All estimations are done by taking into account the panel structure of the data. Standard errors are robust
against heteroskedasticity and intra-group correlation in all specifications. In the GMM estimations, the weighting
matrix is constructed such that the coefficient estimates are efficient in the presence of heteroskedasticity and
intra-group group correlation.
1 4 We also tried a specification where the number of readers was assumed to be endogeneous, but this did not
change the results.
9
further divided into subgroups on the base of whether it is a "quality" or "popular" newspaper.
Again, there are some identification issues since sj|hg and shg|g are endogeneous. Total market
size is defined as the population older than 15 years, consequently the outside good is given by
the population above 15 years who do not buy a newspaper. We assume the cover price to be
exogeneous, given the price pattern in figures 7 and 8. The cover price variable was deflated using
the consumer price index, and is included in logs. As instruments we use the average subscription
share of other newspapers belonging to the same group and subgroup. The artificial first stage
regressions are shown in table 6. The Shea partial R2 and F-statistics are considerably lower than
in first stage regressions for advertising demand, pointing to weaker instruments. However, there is
still some significant part of variation in the endogeneous variables explained by the instruments.
Table 7 shows results for OLS, fixed effects and GMM estimation of equation 2. The OLS
estimates for σ 1 and σ 2 are estimated to be equal to one. GMM gives estimates of 0.94 for σ1 and
0.86 for σ 2 . These estimates are consistent with the two-level nested logit model, although the
difference between σ 1 and σ 2 is not significant. The coefficient on cover price has a negative sign
but is not significant at all.
The models with newspaper fixed effects perform rather bad. Fixed effects estimation returns
σ parameters inconsistent with the two-level nested logit model. GMM estimation with newspaper
fixed effects gives insignificant σ parameters, pointing to a standard logit model. An explanation
could be that our instruments do not vary enough over time to get consistent estimates.
We also ran regressions with the amount of advertzing included as explanatory variable to test
for a feedback loop between advertisers and newspaper readers. The coefficient on the number of
readers was not significant in the OLS regressions15 , so we excluded the variable in order to use
the longer time span of newspaper sales and prices.
6
Conclusions
[to be added]
1 5 This
is in line with empirical results of Argentesi and Filistrucchi (2006). See also Gabszewicz et al. (2002).
10
7
References
Argentesi, E. and L. Filistrucchi (2005): "Estimating Market Power in a Two-Sided Market: the
Case of Newspapers", forthcoming in Journal of Applied Econometrics.
Armstrong (2005): "Competition in Two-Sided Markets" forthcoming in Rand Journal of Economics.
Berry S. (1994) "Estimating Discrete-Choice Models of Product Differentiation", Rand Journal
of Economics, vol. 25(2), pp. 242-262.
De Bens E. (2001) "De Pers in België", Lannoo Uitgeverij, 454 p.
Gabszewicz J, D. Laussel and N. Sonnac (2002): "Press advertising and the Political Differentiation of Newspapers", Journal of Public Economic Theory, vol. 4(3), pp. 317-334
Mediabook (2001-2006), Kluwer Publishing, Diegem.
Rochet, J-C. and J. Tirole (2003a): "Platform Competition in Two-Sided Markets", Journal
of the European Economic Association, vol. 1(4), pp. 990-1029.
Rysman, M. (2004b): "Competition Between Networks: A Study of the Market for Yellow
Pages", Review of Economic Studies vol. 71, pp. 483-512.
Verboven F. (1996): "International Price Discrimination in the European Car Market", Rand
Journal of Economics, vol. 27(2), pp. 240-268.
11
8
Tables and figures
Table 1: Belgian Newspapers
16
Dutch
French
German
Quality
De Morgen (DM)
De Standaard (DS)
De Tijd (FET)
Le Soir (LS)
La Libre Belgique (LLB)
L’Echo (LECH)
Popular
Het Laatste Nieuws (HLN)
Het Nieuwsblad (NB)
Het Volk (HV)
Gazet van Antwerpen (GVA)
Belang van Limburg (BVL)
Sud Presse (SUD)
Vers l’Avenir (VA)
La Dernière Heure (DH)
Grenz-Echo (GRE)
Table 2: Number of advertizements per newspaper
Nr. of newspapers Frequency Percentage
1
19662
53.5%
2
7015
19.1%
3 to 5
5781
15.7%
6 to 10
2527
6.9%
More than 10
1796
4.9%
Total
36781
100%
12
Free
Metro NL (METN)
Metro FR (METF)
Table 3: Multi per newspaper
Newspaper
Percentage
Het Volk
99.4%
Het Nieuwsblad
97.1%
Metro FR
94.7%
Grenz-Echo
91.5%
Sud Presse
90.9%
Metro NL
89.1%
Belang van Limburg
87.3%
Gazet van Antwerpen 87.1%
Vers l’Avenir
84.5%
La Dernière Heure
84.4%
Het Laatste Nieuws
80.4%
De Standaard
79.4%
De Morgen
76.8%
Le Soir
72.7%
La Libre Belgique
72.5%
L’Echo
69.8%
De Tijd
57.6%
Table 4: First Stage Regressions Advertizing Demand
Belgian
Broadsheet
Subscr. Share [t-1]
Nr. Readers
Qual.
Dutch
Multi
High Soc. Group %
With Children %
Male %
Purchases Resp. %
Constant
GMM1
Coef.
S.E.
0.245
[0.081]
0.480
[0.080]
0.107
[0.214]
0.695
[0.052]
0.358
[0.059]
-0.120 [0.053]
0.095
[0.130]
0.831
F-stat excl. instr.
Partial R2 excl. instr.
Standard errors in brackets.
[0.673]
GMM2
Coef.
S.E.
0.188
[0.077]
0.423
[0.068]
0.228
[0.124]
0.855
[0.053]
GMM-FE
Coef.
S.E.
0.221
[0.070]
0.369
[0.071]
-0.208 [0.509]
0.395
[0.084]
0.146
1.291
0.412
1.381
2.630
-3.616
0.099
[0.110]
[0.199]
[0.844]
[0.683]
[0.518]
[0.974]
[0.065]
12.29
14.45
22.23
0.61
0.58
0.68
+
, * , **, significant at 10%, 5% and 1% respectively
13
Ad Price
Nr; Readers
Quality
Dutch
Table 5:
OLS
-1.341
[0.188]**
1.234
[0.139]**
0.813
[0.102]**
-0.171
[0.093]+
Advertizing Demand
FE
GMM1
-1.360
-1.606
[0.104]** [0.173]**
1.183
1.418
[0.190]** [0.128]**
0.935
[0.085]**
-0.158
[0.084]+
High Soc. Group %
With Children %
Male %
Purchases Resp. %
Multi
-0.025
[0.324]
-1.048
[0.128]**
-0.039
[0.269]
GMM2
-1.447
[0.272]**
1.325
[0.245]**
GMM-FE
-1.163
[0.272]**
1.053
[0.572]+
2.572
[0.530]**
0.303
[1.359]
-2.672
[1.416]+
2.316
[1.183]+
-0.579
[0.242]*
-1.064
[0.193]**
Nr. Obs
810
810
810
756
810
R2
0.62
0.54
P-value Hansen test
0.98
0.41
0.43
+
Standard errors in brackets. , * , **, significant at 10%, 5% and 1% respectively
Table 6: First stage regressions readers’ demand
GMM
GMM-FE
sj|hg
shg|g
sj|hg
shg|g
Subscr. group
-0.711
5.902
1.141
0.802
[3.260]
[1.212]** [0.946]
[0.406]+
Subscr. subgroup 3.208
-3.392
1.256
-0.860
[1.201]* [0.700]** [0.537]* [0.333]*
Cover price
-1.431
-1.336
-0.341
0.272
[0.821]
[0.570]*
[0.181]+ [0.130]+
Trend
0.010
0.007
-0.009
-0.002
[0.033]
[0.012]
[0.006]
[0.004]
F-stat excl. instr.
Shea Partial R2
Standard errors in brackets.
3.86
15.2
13.32
3.86
0.08
0.22
0.07
0.04
+
, * , **, significant at 10%, 5% and 1% respectively
14
Cover Price
sj|hg
shg|g
Trend
Constant
Table 7:
OLS
0.249
[0.825]
1.033
[0.112]**
1.004
[0.184]**
-0.020
[0.015]
-2.003
[0.510]**
Readers’ demand
FE
GMM
-0.121
-0.209
[0.070]
[1.455]
0.895
0.941
[0.062]** [0.440]*
1.191
0.860
[0.100]** [0.420]*
-0.016
-0.012
[0.003]** [0.028]
-2.218
-2.437
[0.146]** [1.521]
GMM-FE
-0.012
[0.269]
0.185
[0.395]
0.034
[0.725]
-0.013
[0.007]*
Nr. Obs.
1938
1938
1938
1938
R2
0.86
0.80
Standard errors in brackets. + , * , **, significant at 10%, 5% and 1% respectively
Figure 1: Sales Dutch language quality newspapers
20000
40000
Sales
60000
80000
100000
Sales per Newspaper
1994m1
1995m1
1996m1
1997m1
1998m1
1999m1
2000m1
2001m1
2002m1
2003m1
2004m1
Maand
De Standaard
De Morgen
15
De Tijd
2005m1
Figure 2: Sales Dutch language popular newspapers
80000
100000
Sales
120000
140000
Sales per Newspaper
1994m1
1995m1
1996m1
1997m1
1998m1
1999m1
2000m1
2001m1
2002m1
2003m1
2004m1
2005m1
Maand
Gazet van Antwerpen
Het Volk
Belang van Limburg
Figure 3: Sales Dutch language popular newspapers (ctd.)
200000
220000
Sales
240000 260000
280000
300000
Sales per Newspaper
1994m1
1995m1
1996m1
1997m1
1998m1
1999m1
2000m1
2001m1
2002m1
2003m1
Maand
Het Laatste Nieuws
16
Het Nieuwsblad
2004m1
2005m1
Figure 4: Sales French language quality newspapers
0
50000
Sales
100000
150000
200000
Sales per Newspaper
1994m1
1995m1
1996m1
1997m1
1998m1
1999m1
2000m1
2001m1
2002m1
2003m1
2004m1
2005m1
Maand
La Libre Belgique
L'Echo
Le Soir
Figure 5: Sales French language popular newspapers
60000
80000
Sales
100000 120000
140000
160000
Sales per Newspaper
1994m1
1995m1
1996m1
1997m1
1998m1
1999m1
2000m1
2001m1
2002m1
2003m1
2004m1
2005m1
Maand
Dernière Heure
Sud Presse
17
Vers l'Avenir
400000
600000
Sales
800000
1000000
Figure 6: Total sales Belgian newspapers
1994m1 1995m1 1996m1 1997m1 1998m1 1999m1 2000m1 2001m1 2002m1 2003m1 2004m1 2005m1
Dutch
French
1
.8
.6
Cover Price €
1.2
1.4
Figure 7: Cover price Dutch language newspapers
1994m1
1996m1
1998 m1
2000m1
Date
2002m1
2004m1
DM
DS
FET
GVA
HLN
NB
HV
cp i
18
2006m1
BVL
1
.6
.8
Cover Price €
1.2
1.4
Figure 8: Cover price French language newspapers
1994m1
1996m1
1998m1
2000m1
Date
2002m1
DH
SUD
LECH
LLB
VA
cpi
2004m1
2006m1
LS
Advertizing and sales revenue for all newspapers
40000
30000
20000
10000
Revenue X 1000 €
50000
60000
Figure 9: Advertizing and sales revenue for all newspapers
1995m1 1996m1 1997m11998m1 1999m12000m1 2001m1 2002m12003m1 2004m12005m1
Sales Revenue
19
Ad Revenue
Figure 10: Share of ad revenue in total revenue
0
.1
.2
Share of Ad Revenue
.3
.4
.5
.6
.7
.8
Share of Ad Revenue in Total Revenue
LLB
LS
DS
DM LECH GRE SUD
NB
HV
DH
VA
HLN FET BVL GVA
5000
Ad Price of 1 B/W Page, €
10000
15000
20000
25000
30000
Figure 11: Advertizing prices Dutch language newspapers
2001m1
2002m1
2003m1
Date
2004m1
2005m1
DS
DM
FET
HLN
GVA
BVL
HV
METN
20
NB
0
Ad Price of 1 B/W Page, €
5000
10000
15000
20000
Figure 12: Advertizing prices French language newspapers
2001m1
2002m1
2003m1
Date
2004m1
2005m1
LECH
LS
LLB
DH
SUD
VA
GRE
METF
Figure 13: Advertizing Revenue per Reader (2004)
0
.5
Ad Revenue per Reader
1
1.5
2
2.5
Ad Revenue per Reader
LLB LECH LS
DS
DM GRE SUD
21
NB
FET
DH
HV
HLN
VA
GVA BVL