Turn left or right: Are political preferences in the Netherlands changed by media developments? ARIMA models and impact assessment analysis

1. Turn left or right:
Are political preferences in the Netherlands changed
by media developments?
ARIMA models and impact assessment analysis
Assignment 2
Mark Boukes (markboukes@Hotmail.com)
5616298
1st semester 2010/2011
Dynamic Data Analysis
Lecturer: Dr. R. Vliegenthart
November 23, 2010
Communication Science (Research MSc)
Faculty of Social and Behavioural Sciences
University of Amsterdam

2. Table of contents
INTRODUCTION.............................................................................................................................................1
METHOD........................................................................................................................................................2
RESULTS........................................................................................................................................................3
ARIMA MODEL.......................................................................................................................................................3
IMPACT ASSESSMENT ANALYSIS........................................................................................................................................5
CONCLUSION.................................................................................................................................................6
REFERENCES..................................................................................................................................................7
APPENDIX 1: DO FILE......................................................................................................................................I

3. Introduction
The public broadcasting system in the Netherlands is part of a recent political discussion:
should the state invest the same amount of money in it or can the budget for Nederland 1, 2
and 3 be reduced to limit the budget deficit of the state? Advocates of public broadcasting
point to the (democratic) benefits of this system: it provides citizens with news from different
stands, involves people with politics and culture, and gives a voice to minority groups in
society without having much commercial pressure. According to former chairman of the
BBC, Gavyn Davies (1999), should public broadcasters “… inform, educate and entertain in a
way which the private sector, left unregulated, would not do.” (p. 10) However, opponents of
public broadcasting think that financing television channels by the state is false competition to
players on the market that gather revenues by advertising only. Those opponents claim also
that public broadcasting is a waste of tax money, because they believe that commercial parties
can fulfil the tasks of public broadcasting as long as there is no false competition. General
manager of RTL 4 (commercial broadcaster in the Netherlands) Bert Habets thinks that a first
step in decreasing the budget of public broadcasting is cooperation with commercial channels
on areas as sports and entertainment.1 Shortly, the position of the Dutch public broadcasting
system is under pressure and therefore I investigated its impact.
The purpose of this study is to research how the introduction of commercial television
in the Netherlands affected political attitudes of citizens. Many studies before investigated the
effects of media use on political attitudes, like political interest, political knowledge, vote
intention, government approval etc. Curran, Iyengar, Brink Lund and Salovaara-Moring
(2009) found that, compared to people in countries with a market model, people had more
knowledge about both international and domestic news topics like the Kyoto Protocol and
news about the French president in countries with a public broadcasting system. Aarts and
Semetko (2003) found a similar effect in the Netherlands. Watching more public broadcasting
instead of commercial broadcasters had positive effects on voter turnout, but also on political
cognition and efficacy. DellaVigna and Kaplan (2007) also found evidence for the importance
of introducing new channels to citizens. They showed that voters in towns in the United
States, in which Fox News was introduced in 2000, were more likely to vote Republican in
the first coming election. I aimed with my study to see if the introduction of commercial
channels to the Dutch range of television channels had a similar effect. The research question
was therefore: Did the introduction of commercial broadcasting to Dutch television affect the
average political preference on a left-right dimension?
1
http://www.rtl.nl/%28/actueel/rtlboulevard/nieuws/articleview/%29/components/actueel/rtlboulevard/2010/11_
november/entertainment/rtl_baas_handreiking_po.xml
1

4. To answer this question, I used data from 1977 until 2000, which were analyzed with a
ARIMA model and an impact assessment analysis. As no one conducted such a research before,
I hope to find new information that gives more insight in the importance of public
broadcasting in the Netherlands and enriches the debate about the existence and financing of
public broadcasting.
Method
To investigate which factors have an effect on the left-right preferences of Dutch citizens, I used
a dataset that contains information about this for a long time period. The NIPO Weeksurveys
1962-2000 was found on https://easy.dans.knaw.nl/dms and contained for the period
1977-2000, 1.086.336 individual answers for the following question, Here you see seven boxes
between the words left and right. Could you indicate on this scale how left, right or in between
your political opinion lies? The observations were transformed in such a way that the mean
answer for every week was reported, because the answers were reported individually and
aggregate level data is needed to answer the research question,. This resulted in 1226 weekly
items containing the value for the average left-right preference of Dutch citizens.
I included also data for various happenings that I have in mind to effect political
preferences of voters, to investigate whether they indeed had an effect. Various dummy
variables were created, to study the impact of the introduction of commercial television on the
left-right preferences of citizens. One for the introduction of RTL4 to the Dutch television
viewers on October 2 1989 (dummy variables were 0 before this date and 1 after this date), and
the other for the moment (starting in 1992) it was allowed for commercial television channels to
broadcast in the Netherlands. Also dummy variables were created for the introduction of the
other Dutch commercial channels that attract(ed) many viewers: Veronica, RTL5 and SBS6. All
those effects were expected to have a permanent effect, which came about rather abrupt. In
addition, a variable was created adding the values of the entrance of the different channels
together; for instance, after RTL4 was introduced this variable was one, after Veronica was
introduced this value was two, etc. The effect of this variable can thus be said to be gradual and
permanent. The following events were also taken into account by making dummy variables for
the periods that represent them, to control for other developments in Dutch and the global
society. Moments that were taken into account are the hijack of a train by Moluccans from May
23 1977 untill June 11 1977; the second oil crisis in 1979/1980, the weeks before, during and
after European elections (in 1979, 1984, 1989, 1994, 1999); the fall of the Iron Curtain starting
in April 1989 in Poland and lasting until the end of that year when also the Berlin Wall fell;
Dutch politician Bolkestein who held a speech about the integration of ethnic minorities in
Dutch society on September 6 1991 and finally the happenings in Srebrenica in July 1995. To
2

5. analyse the effects of those events, first an adequate ARIMA model is developed, which is
followed by an impact assessment analysis of the different events.
Results
I specify in this results section how the ARIMA model was created and thereafter what the
results were of the impact assessment analysis to include events in the analysis.
ARIMA model
The timeseries dataset is first being analysed according to the ARIMA-framework described by
Vliegenthart (n.d.). The first step was to establish whether the data was stationary. Therefore, I
made a graph was made (see Figure 1) to inspect the data in a convenient manner and three
augmented Dickey Fuller (ADF) tests were used to check whether the series was a random walk
without drift, a random walk with drift or a random walk with drift and trend (see Table 1). One
of the results of this ADF indicates that the null hypotheses of non-stationarity should not be
rejected and therefore it was necessary to integrate the data and check again for stationarity.
This time only significant results were found; the integrated series was stationary (see Figure 2).
Figure 1. The mean score on a political left-right dimension over time.
Table 1. The various augmented Dickey-Fuller tests.
Augmented Dickey-Fuller test
Random walk without drift -0.374*
Random walk with drift -12.779
Random walk with drift and trend -21.805
After integrating
Random walk without drift -57.741
Random walk with drift -57.718
Random walk with drift and trend -57.696
Note. All tests without a * indicate the presence of an unit root.
3

6. Figure 2. The differenced value for the mean score on a political left-right dimension over time.
The next step was predicting the data as good as possible by accounting for its past, either with
autoregressive (AR) terms, moving average (MA) terms or both. This was done by inspecting
the autocorrelation (ACF) and partial autocorrelation functions (PACF) (see the graphs for both in
Figure 2). Those functions suggest a moving average at lag 1; there was one peak in the ACF
and a decaying order in the PACF. Therefore, a ARIMA(0,1,1) model was used which indeed
has a significant effect for this moving average term. This model was tested for autocorrelation
and heteroscedasticity with the Ljung–Box Q test statistic for both the residuals as well as the
squared residuals. The (significant) results for both tests indicate that there was
autocorrelation present and thus not only white noise. Therefore the model was extended with
a moving average term at lag 2 for tho reasons. First, because the ACF and PACF showed a
peak at the second lag, however, the graphs did not give a clear indication for adding an
autoregressive (AR) or moving average (MA) term. Second, adding a MA term increased the
fit more than adding a AR term according to the values of the Akaike Information Criterion
(AIC) and Schwarz Bayesian Criterion (BIC), therefore I choose to use a resulting ARIMA
(0,1,2) model.
The values of this resulting model can be found in Table 2. The results of the Ljung–
Box Q test statistic for this model was insignificant for the residuals, therefore we can assume
the residuals to be white noise. However significant results were found for the squared
residuals with the Ljung–Box Q test statistic, which indicates the presence of
heteroscedasticity. However for this moment no attention is paid to this, but hopefully it will be
solved with ARCH and GARCH models later in this Dynamic Data Analysis course.
4

7. Figure 3. ACF and PCF for the differenced mean score on a political left-right dimension.
Table 2. ARIMA model for mean score on a political left-right dimension over time.
ARIMA (0,1,2)
Constant -.000 (.000)
MA(1) -.767 (.026)*
MA(2) -.091 ( .026)*
Ljung-Box Q(20) residuals 20.61
Ljung-Box Q(20) residuals² 103.34*
AIC -3583.93
BIC -3563.49
Note. Unstandardized coefficients. Standard errors in parentheses; * p<.001
Impact assessment analysis
I created various dummy variables were created to test if different events had an effect on the
mean score for the political preference on a left-right dimension over time. The duration of
those events could be temporary or permanent and the onset could be gradual or abrupt
(McCleary & Hay, 1980). How the duration and onset were implemented was based on
simple theory, common sense. The various events were incorporated in the ARIMA-model
that was established before. Because the timeseries used as a dependent variable was
differenced, also differenced values of the independent (event) variables were used in the
analysis.
Three models were the most interesting to study the impact of television developments
on political preferences; the one that makes clear what happened after RTL4’s introduction,
the one for the moment commercial broadcasters had permission to broadcast in the
Netherlands, and the one that can be used for the gradual introduction of more commercial
5

8. broadcasters in the Netherlands. However, as Table 3 shows, none of those developments had
an effect on the mean score for the political left-right dimension.
Table 3. Influence on media developments on the mean score on a political left-right dimension over time.
Permission All TVchannels
ARIMA (0,1,2) RTL4-model
model intro-model
Constant -.000 (.000) -.000 (.000) -.000 (.000) -.000 (.000)
MA(1) -.767 (.026)* -.767 (.026)* -.766 (.026)* -.767 (.026)*
MA(2) -.091 (.026)* -.089 (.026)* -.092 (.026)* -.090 (.026)*
Introduction RTL4 .015 (.048)
Permission for commercial broadcasting .020 (.026)
Introduction different TV channels 0.007 (0.017)
Ljung-Box Q(20) residuals 20.61 20.59 20.98 20.55
Ljung-Box Q(20) residuals² 103.34* 103.54* 103.71* 103.79*
AIC -3583.93 -3582.18 -3582.36 -3582.22
BIC -3563.49 -3556.64 -3556.81 -3556.68
Note. Unstandardized coefficients. Standard errors in parentheses; * p<.001
To check whether this dependent timeseries variable is not influenced by any factor, the
analysis were repeated for various historical events that were specified in the methods section
that might have affected the political preferences of people. Also for those events the
differenced series were used in the analysis. The results showed that only the second oil crisis
in 1979/1980 had an effect on political preferences. As this effect was positive, it can be said
that this crisis caused an increase in the preference for right-wing politics. Perhaps more
remarkable is that all other variables had no effect on the average political preference in the
Netherlands, both when tested alone in the ARIMA(0,1,2)-model or when tested together as
shown in Table 4.
Conclusion
My main aim in this paper was to study the impact of the introduction of commercial
broadcasting to Dutch viewers. It seems that this had no effect on the political preferences of
Dutch people; the mean score on a left-right scale was not affected by the various events. To
do this analysis first a proper ARIMA-model was build by integrating it one time and taking
the first and the second lag into account with moving average terms. The resulting model did
reflect white noise in the residuals, however the squared residuals did still correlate, which
indicates heteroscedasticity. Thereafter, events like the introduction of RTL4, the moment of
permission for commercial broadcasting and the introduction of different commercial
channels were included in the model. All three however seemed to have no effect. Therefore,
other (historical) events were also taken into account to see if this dataseries was not affected
6

9. at all by events. Only the second oil crisis seemed to affect peoples’ political preferences.
Most events however are unlikely to influence the average political preference on a left-right
dimension; the media developments did not achieve it and also most historical events did not
have an effect.
Table 4. Influence on historical events on the mean score on a political left-right dimension over time.
Historical events model
Constant -.000 (.000)
MA(1) -.767 (.026)**
MA(2) -.091 (.026)**
Introduction different TV channels .009 (.016)
Train hijack -.025 (.054)
Second oil crisis .036 (0.015)*
European elections .014 (.017)
Fall of Iron Curtain .022 (.027)
Bolkestein’s speech .020 (.298)
Srebrenica .013 (.036)
Ljung-Box Q(20) residuals 21.43
Ljung-Box Q(20) residuals² 101.81**
AIC -3575.97
BIC -3519.77
Note. Unstandardized coefficients. Standard errors in parentheses; *p<0.05, ** p<.001
References
Aarts, K., & Semetko, H. A. (2003). The divided electorate: Media use and political
involvement. The Journal of Politics, 65(3), 759-784.
Curran, J., Iyengar, S., Brink Lund, A., & Salovaara-Moring, I. (2009). Media system, public
knowledge and democracy. European Journal of Communication, 24(1), 5-26.
Davies, G. (1999). The Future Funding of the BBC. Report of the Independent Review Panel
to Department of Culture, Media and Sport. Retrieved on November 23, 2010, from
http://news.bbc.co.uk/hi/english/static/bbc_funding_review/reviewco.pdf
DellaVigna, S. & Kaplan, E. (2007). The Fox News effect: Media bias and voting. Quarterly
Journal of Economics, 122(3), 1187-1234.
McCleary, R., & Hay, R. (1980). Applied Time Series Analysis for the Social Sciences.
London: Sage.
Vliegenthart, R. (n.d.). Moving up. Applying aggregate level time series analysis in
communication science. Unpublished manuscript.
7

11. Appendix 1: Do file
* declare data to be time series
replace nr = nr + 766 + 52 + 52
tsset nr, weekly
* making differences
gen dleftright=leftright - l.leftright
* graphic
twoway (tsline leftright, lcolor(black))
* 3 tests for stationarity, 1)without constant=random walk without drift,
2)normal ADF: random walk with drift, 3) ADF trend: random walk with drift
and trend
dfuller leftright, noconstant
dfuller leftright
dfuller leftright, trend
twoway (tsline d.leftright, lcolor(black))
dfuller d.leftright, noconstant
dfuller d.leftright
dfuller d.leftright, trend
* arima model
ac leftright
pac leftright
ac d.leftright
pac d.leftright
arima d.leftright, ma(1,2)
estat ic
predict r, res
wntestq r, lags(20)
gen r_s=r*r
wntestq r_s, lags(20)
ac r
pac r
ac r_s
pac r_s
drop r r_s
* create intervention for RTL. eerste uitzending was op 2 oktober 1989 =
week 40
gen RTL=0
replace RTL = 1 if nr>1520
*Veronica
gen veronica=0
replace veronica = 1 if nr>1825
*rtl5 2 oktober 1993
gen rtl5=0
replace rtl5 = 1 if nr>1725
*sbs6 28 augustus 1995
gen sbs6=0
i

12. replace sbs6 = 1 if nr>1823
*Media development
gen media = RTL+ veronica+ rtl5+ sbs6
*in 1992 werden binnenlandse commerciële omroepen toegestaan
gen comm=0
replace comm = 1 if nr>1635
*EU elections 1979,1984, 1989,1994,1999
gen euelect=0
replace euelect = 1 if nr>=991 & nr<=993
replace euelect = 1 if nr>=1245 & nr<=1247
replace euelect = 1 if nr>=1502 & nr<=1504
replace euelect = 1 if nr>=1759 & nr<=1761
replace euelect = 1 if nr>=2016 & nr<=2018
*Fall of iron curtain starting in Poland, then berlin wall, untill and of
1989, minus christmas weeks
gen IronCurtain=0
replace IronCurtain = 1 if nr>=1493 & nr<=1529
*Bolkestein 6 september 1991
gen bolkestein=0
replace bolkestein= 1 if nr==1618
*Molukse acties: Treinkaping bij De Punt 23 mei (21)-11 juni 1977(23)
gen moluk=0
replace moluk= 1 if nr>=904 & nr<=906
*2e oliecrisis: March 26 1979 (13): OPEC makes full 14.5 percent price
increase for 1979 effective on April 1
*December 1980 (48): Collapse of OPEC's pricing structure. Saudis use $32
per barrel marker, others use $36 per barrel benchmark.
gen oil=0
replace oil= 1 if nr>=983 & nr<=1067
*Srebrenica 11 juli 1995 (28)
gen srebrenica=0
replace srebrenica= 1 if nr>=1818 & nr<=1828
arima d.leftright d.srebrenica, ma(1,2)
estat ic
predict r, res
wntestq r, lags(20)
gen r_s = r*r
wntestq r_s, lags(20)
drop r r_s
arima d.leftright d.media d.euelect d.IronCurtain d.bolkestein d.moluk
d.oil d.srebrenica, ma(1,2)
estat ic
predict r, res
wntestq r, lags(20)
gen r_s = r*r
wntestq r_s, lags(20)
drop r r_s
ii