Media attention in Belgium:

How much influence do citizens and politicians have on news coverage?

Pooled time-series analysis

Assignment 9

Mark Boukes (markboukes@Hotmail.com)5616298

1st semester 2010/2011Dynamic Data Analysis

Lecturer: Dr. R. VliegenthartFebruary 3, 2010

Communication Science (Research MSc) Faculty of Social and Behavioural Sciences

University of Amsterdam

Table of contents

INTRODUCTION AND THEORY.............................................................................................................1

METHOD......................................................................................................................................................1

RESULTS......................................................................................................................................................2

ORDINARY LEAST SQUARES REGRESSION...................................................................................................2

LINEAR REGRESSION MODELS WITH PANEL-CORRECTED STANDARD ERRORS.............................................3

FIXED EFFECT MODELS................................................................................................................................5

CONCLUSION..............................................................................................................................................7

REFERENCE................................................................................................................................................7

Appendix A: Do File

Introduction and theoryIn this study, I aim to investigate whether journalists’ attention is as much influenced by

political happenings and previous media coverage, as it is by the actions of ordinary people

undertaken to attract the attention of journalists as well as politicians. For this reason, I will

study the relationship between media attention, parliamentary attention, new legislation,

meetings of the Cabinet and demonstrations by citizens. As it is the duty of journalists to both

inform citizens about what is happening in society and thus in politics but also to function as a

discussion platform for different opinions in society, my hypothesis is:

Political happenings and demonstrations of citizens have a similar influence on news coverage.

This hypothesis will be investigated by means of a secondary data analysis. The same data

will be used as Vliegenthart and Walgrave (2010) used, what makes it possible to conduct a

pooled time-series analysis, which is considered as a robust analysis method. The advantage

of this method is that it captures over-time and between-issue variations, and therefore will

produce more efficient estimates, while it does not exceed assumptions wrongfully.

MethodTo investigate the hypothesis, the dataset of Vliegenthart and Walgrave (2010)1 was used,

which contained information for the attention of different actors (media, politics, citizens) to

25 issues. The data was collected on a monthly basis from the beginning of 1993 until the end

of 2000. In total 96 weeks were covered and the attention was divided into 25 issues;

therefore there it was possible to analyse 2400 month-issue combinations. That the data was

collected in such a way that attention was organized in 25 different issues, had the advantage

that it was possible to correct for potential differences in the (error) processes of the different

issues in the estimation techniques that were necessary to investigate the hypothesis. Data was

strongly balanced, as observations were available for every issue for every months and every

actor.

The attention for the issues was coded in such a way that it was the relative attention

for a certain issue. This means that for every month every actor had a total attention of 1. This

attention will be divided over the 25 issues. For example, an actor can have 0.5 attention for 2

issues in a month, but then cannot have any attention for other issues that month. When one

issue attracts a lot of attention in one month the other issues will thus get less attention. The

analyses need to take care of this contemporaneous correlation.

1 For more information about how they collected their information, see their article.

1

Different statistical techniques will be used, to test the robustness of the results and the

necessity of pooling the data. First, an ordinary least squares regression will be conducted,

however, this assumes that the error processes for all issues have the same variance and that

all those error processes are independent of each other, even those of the same issues at

different points in time. This absence of panel heteroscedasticity and contemporaneous

correlation seems very unlikely, but for statistical interest the model will be conducted.

When analysing time-series cross-section data it is necessary to structure the error

processes in ways to take care of panel heteroscedasticity and contemporaneous correlation

and also unit-level heterogeneity. Therefore, linear regression models with panel-corrected

standard errors are used which perform remarkably well under conditions of panel

heteroscedasticity and contemporaneous correlation (Beck & Katz, 1995). However, panel-

corrected standard errors still have the disadvantage that errors of processes that show unit-

level heterogeneity, are not taken care of. Hence, the less parsimonious fixed effect model

will be conducted to check if and how the results change compared to the model with panel-

corrected standard errors, by allowing different intercepts for the various issues (Wilson &

Bulter, 2007).

ResultsHere the results of the various analyses will be presented, starting with the most simple

ordinary least squares via regression that has panel corrected standard errors to fixed effect

models. Before doing the analyses, all variables were tested for the presence of panel unit root

using an augmented Dickey-Fuller test. For all these tests the null hypothesis was rejected (χ2

values were found between 1008.27 and 1733.77), so it was not necessary to integrate the

data.

Ordinary Least Squares regression

Ordinary least squares regression has many assumptions that need to be satisfied before its

results can trusted. One of those is that errors and variances are uncorrelated over the

observations. In time-series cross-section (panel) data this is logically not the case, but to

become aware of the consequences of performing such an analysis on such data, the results of

an ordinary least squares regression will also be presented in this paper. Three regression

analyses will be run that differ in their dynamic specification; the static model with only

variables that were observed in the same month, this model supplemented by distributed lag

independent variables and finally a model that contains the independent variables, the lagged

independent variables and the lagged dependent variable. The results of the analyses can be

found in Table 1.

2

Table 1. The effects of the (lagged) (in)dependent variables on media attention for an issue

OLS DL(1) ARDL(1,1)

Media attention t-1 0.782 (0.013)**

Demonstrations t 0.088 (0.005)** 0.071 (0.006)** 0.040 (0.004)**Demonstrations t-1 0.032 (0.006)** -0.020 (0.004)**

Parliamentary attention t 0.255 (0.013)** 0.188 (0.013)** 0.072 (0.009)**Parliamentary attention t-1 0.147 (0.014)** 0.006 (0.009)

New legislation t 0.017 (0.007)* -0.001 (0.007) -0.003 (0.005)New legislation t-1 0.009 (0.007) 0.004 (0.004)

Meeting of the Cabinet t 0.149 (0.015)** 0.082 (0.015)** 0.023 (0.009)*Meeting of the Cabinet t-1 0.073 (0.015)** 0.005 (0.009)

Constant 0.022 (0.001)** 0.019 (0.001)** 0.004 (0.001)**Observations (N) 2400 2375 2375R2 0.361 0.420 0.776

Note. Unstandardized coefficients. Standard errors in parentheses; * p < 0.05 , ** p < 0.01

As explained above, those estimates are not really trustworthy; therefore, I would only like to

pay attention to the differences between the models. First, the effects of new legislation get

insignificant after adding the lags of the independent variables. Those stay insignificant when

also the autoregressive term (or lagged dependent variable) is added to the model; then also

the lagged effects of parliamentary attention and meetings of the Cabinet get insignificant.

Perhaps more remarkable is that the effect of the lagged independent variable

‘demonstrations’ turns negative. However, I do not pay much attention to interpretation of the

coefficients, as those are not as reliable as the results of estimation methods that take better

care of the regression assumptions. Nevertheless, it is clear that every next model in Table 1

did explain more variance, as R2 increased substantially. Therefore and because it gives a

more complete overview, the variables in the final model, ARDL(1,1), will be used in the next

analyses.

Linear regression models with panel-corrected standard errors

Variance-comparison tests show that, as expected, variance is different for the different

issues: the hypothesis of equality of variances is soundly rejected by all three variance-

comparison tests. This indicates the presence of group-level heteroscedasticity. Consequently,

linear regression models are conducted with panel-corrected standard errors. Those Prais-

Winsten regression analyses can be conducted by different autocorrelation structures: the first

does not take a special autoregressive process in the variance into account, but just a lagged

dependent variable and therefore gives the same results as the last OLS model; one accounts

3

for first-order autocorrelation AR(1) and assumes that the coefficient of the AR(1) process is

the same to all issues; another specifies that, within panels, there is first-order autocorrelation

and that the coefficient of the AR(1) process is specific to each of the issues. As it seems

plausible that different autoregressive structures can exist for different issues, some might

fade away quicker, because they satisfy the criteria of journalistic institutions less than other

issues (see for example the news criteria specified by Harcup and O'Neill, 2001); therefore,

the last estimation technique seems more helpful. The AR(1) processes seem indeed panel

specific as the rhos (autocorrelation parameters) for the last model vary considerably.

Logically, the proportion of explained variance is also higher for the model that allows the

coefficient of the AR(1) to be specific to each panel. The results of the models can be found in

Table 2.

Table 2. The effects of the (lagged) (in)dependent variables on media attention for an issueIndependent

autocorrelation structure

General AR(1) Panel specific AR(1)

Media attention t-1 0.782 0.020Demonstrations t 0.040 0.005 0.043 (0.005)** 0.044 (0.005)**Demonstrations t-1 -0.020 0.005

0.006 (0.005) 0.007 (0.005)

Parliamentary attention t 0.072 0.011 0.087 (0.012)** 0.090 (0.012)**Parliamentary attention t-1 0.006 0.011

0.059 (0.011)** 0.063 (0.011)**

New legislation t -0.003 0.005 0.001 (0.005) 0.004 (0.005)New legislation t-1 0.004 0.005

0.003 (0.005) 0.006 (0.005)

Meeting of the Cabinet t 0.023 0.012 0.039 (0.012)** 0.045 (0.012)**Meeting of the Cabinet t-1 0.005 0.012

0.030 (0.012)* 0.039 (0.012)**

Constant 0.004 0.001 0.031 (0.001)** 0.032 (0.001)**Observations (N) 2375 2375 2375R2 0.776 0.123 0.228

ρ (autocorrelation parameter) 0.695Sample of six:

between 0.484 and 0.788Note. Unstandardized coefficients. Panel-corrected standard errors in parentheses.** p < 0.01, * p < 0.05

The first thing to note is that the estimates of the models with the AR(1) structure and the

model with the panel specific AR(1) hardly differ. Only some marginal differences between

the coefficients exist. When we compare these results to the ones found by the ARDL(1,1)

model with the ordinary least squares regression model, four differences appear. First, the

negative effect of the lagged value of demonstrations becomes insignificant. Above, it was

already explained that this negative effect would be a strange finding, so it seems a good sign

that this effect disappears. Furthermore, the coefficients of the lagged values of both

4

parliamentary attention and meetings of Cabinet become significant. This suggests a stronger

impact of politics on journalists than the findings found by the ordinary least squares

regression technique. Next to this, the proportion of explained variance falls from 0.776 to

0.228.

Fixed effect models

A regression with panel-corrected standard errors can excellently deal with group-level

heteroscedasticity and contemporaneous correlation in a regression analysis. However, it does

not take good enough care of unit-level heterogeneity: systematic differences between values

of different panels (here issues). Indeed it was found that unit-level heterogeneity is present in

the model (F(24, 2341) = 21.30, p < 0.001). Therefore, it was necessary to also conduct fixed

effect model analyses and compare its results with the results found by the linear regression

models with panel-corrected standard errors. The results of three fixed effect models can be

found in Table 3.

Table 3. The effects of the (lagged) (in)dependent variables on media attention for an issueStatic fixed effect

modelFixed effect model

Fixed effect model with AR(1) disturbance

Media attention t-1 0.470 (0.018)**

Demonstrations t 0.049 (0.004)** 0.039 (0.003)** 0.038 (0.003)**Demonstrations t-1 -0.011 (0.003)** 0.002 (0.003)

Parliamentary attention t 0.074 (0.009)** 0.054 (0.008)** 0.055 (0.008)**Parliamentary attention t-1 0.001 (0.008) 0.027 (0.008)**

New legislation t -0.000 (0.005) -0.003 (0.004) -0.004 (0.004)New legislation t-1 0.002 (0.004) -0.003 (0.004)

Meeting of the Cabinet t 0.018 (0.011) 0.009 (0.009) 0.009 (0.009)Meeting of the Cabinet t-1 -0.008 (0.009) -0.002 (0.009)

Constant 0.035 (0.001)** 0.018 (0.001)** 0.036 (0.001)**Observations (N) 2400 2375 2350Overall R2 0.311 0.770 0.347ψ (variance between issues) 0.033 0.017 0.034θ (variance over time) 0.021 0.019 0.019ρ (intraclass correlation) 0.709 0.464 0.643Note: Cells contain unstandardized coefficients with standard errors in parentheses. ** p < 0.01ψ = unexplained variation at country-level, θ = unexplained variation at individual level, ρ = ψ / total variance

All three models take into account that observations are nested within issues. The first is a

standard static fixed effect model that just takes independent variables into account that are

measured at the same time as the dependent variable; the second is a normal fixed effect

model that is similar to the ARDL(1,1) model; the last model includes a AR(1) disturbance

5

process in which only the previous variance is taken into account, whereas the AR(1)-process

as in the second model continuous to affect media attention in every next step with a

decreasing strength. The estimated coefficients of the three models do not differ a lot, with

two exceptions. In the normal fixed effects model, the negative effect of lagged

‘demonstrations’ becomes significant again, just as in the ordinary least squares regression

ARDL(1,1) model, while it stays insignificant in the fixed effect model with AR(1)

disturbance. The other difference is that the fixed effect model with AR(1) disturbance finds a

significant effect of the lagged value of parliamentary attention, whereas this was not found

by the normal fixed effect model. When the results are compared to the coefficients found

with the regression with panel-corrected standard errors and panel specific AR(1) process, the

biggest difference is the disappeared significance of the effects of meeting of the Cabinet, but

the same is the presence of a significant lagged effect of parliamentary attention.

All together the fixed effect model with AR(1) disturbance seems to give rather robust

results as it matches largely with both the regression models with panel-corrected standard

errors and with the normal fixed effect model. The fixed effect models controlled for unit-

level heterogeneity, but it has not the strength to control for group-level heteroscedasticity

(Modified Wald test: χ2 = 3.5*105, p < 0.001) and contemporaneous correlation (Breusch-

Pagan LM test of independence: χ2 = 498.617, p < 0.001), which both were present.

Therefore, it was valuable to compare the results of the fixed effect models with the findings

of the regression with panel corrected standard errors as both have some weaknesses and there

is not an estimation technique that takes all three offences of the OLS assumptions (group-

level heteroscedasticity, contemporaneous correlation and unit-level heterogeneity) into

account.

The coefficients of this model suggest that both demonstrations and parliamentary

attention have a direct and positive influence on media attention. However, because it is not

possible to see whether those causes precede the effect, it is not totally clear in which

direction the effect runs. Therefore, it is more interesting to see which lagged independent

variables significantly affect media attention. Parliamentary attention is the only lagged

variable that has a significant effect, whereas the other political factors and the citizen factor

do not have a significant lagged effect. It thus seems that politicians have more influence on

media coverage than citizens. A random effect model is not reported in this paper for the

reason that as specified above, it is not expected that unobserved variables are distributed

independently from the observed variables and a Hausman specification test confirmed this

(χ2 = 27.12, p < 0.001).

6

ConclusionOverseeing the strengths and weaknesses of the different estimation techniques and the large

similarity between the findings of both the fixed effect model and the model with panel

corrected standard errors, it becomes clear that the power politicians and citizens have on

journalists is not equal. Though there was found a direct relationship between demonstrations

and media coverage, a lagged effect could not be proved, which would show that the cause

preceded the outcome. For political influence, parliamentary attention, such a lagged effect

was found to be significant. Therefore, the research question needs to be answered negatively;

political happenings and demonstrations of citizens do not have a similar influence on news

coverage, politicians have more influence than citizens.

ReferenceBeck, N., & Katz, J. (1995) What to do (and not to do) with time series cross-section data in

political science. American Political Science Review, 89(3), 634-647.

Harcup, T., & O'Neill, D. (2001). What Is news? Galtung and Ruge revisited. Journalism

Studies, 2(2), 261-280.

Vliegenthart, R., & Walgrave, S. (2010). When the media matter for politics: Partisan

moderators of the mass media’s agenda-setting influence on parliament in Belgium.

Party Politics, 1–22.

Wilson, S.E., & Butler, D.M. (2007). A lot more to do: The sensitivity of time-series cross-

section analyses to simple alternative specifications. Political Analysis, 15(2), 101-

123.

7

Appendix A: Do File

*Assignment9clear

clear

set memory 250m, permanentlyset more off, permanently

use D:\DDA\panel_belgianas

tsset cissue nr

xtfisher mediatot_rxtfisher legislation_rxtfisher parliament_rxtfisher min_rxtfisher demonstrations_r

*OLS//normal OLS regress mediatot_r demonstrations_r parliament_r legislation_r min_rfitstat

//OLS with distributed lags of the independent variablesregress mediatot_r demonstrations_r l.demonstrations_r parliament_r l.parliament_r legislation_r l.legislation_r min_r l.min_rfitstat

//OLS with distributed lags of the independent variables and lagged dependent variable = ARDL(1,1)regress mediatot_r l.mediatot_r demonstrations_r l.demonstrations_r parliament_r l.parliament_r legislation_r l.legislation_r min_r l.min_rfitstatpredict double eps, residuarobvar eps, by(cissue)

xtpcse mediatot_r l.mediatot_r demonstrations_r l.demonstrations_r parliament_r l.parliament_r legislation_r l.legislation_r min_r l.min_r, correlation(independent)

xtpcse mediatot_r demonstrations_r l.demonstrations_r parliament_r l.parliament_r legislation_r l.legislation_r min_r l.min_r, correlation(ar1)

xtpcse mediatot_r demonstrations_r l.demonstrations_r parliament_r l.parliament_r legislation_r l.legislation_r min_r l.min_r, correlation(psar1)xttest2

predict ygen res=mediatot_r-yxtserial rescorr res l.res l2.res

*fixed effects: staticxtreg mediatot_r demonstrations_r parliament_r legislation_r min_r, fe

i

*fixed effects with a ARxtreg mediatot_r l.mediatot_r demonstrations_r l.demonstrations_r parliament_r l.parliament_r legislation_r l.legislation_r min_r l.min_r, fe//contemporenaous correlation: present, yes! negative correlations, if one party goes up in the polls another will go down.xttest2

*null Hypothesis: no significant differences between groups. Reject, group level heteroscedasticity/unit level heterogeneity!xttest3

* including ar(1) error structure in fe model **Or: previous error term influences the next error term, comparable to an AR termxtregar mediatot_r demonstrations_r l.demonstrations_r parliament_r l.parliament_r legislation_r l.legislation_r min_r l.min_r, fepredict mediafe, eestimates stor fixed_effects

xtregar mediatot_r demonstrations_r l.demonstrations_r parliament_r l.parliament_r legislation_r l.legislation_r min_r l.min_r, repredict mediare, eestimates stor random_effects

xtserial mediarehausman fixed_effects random_effects

findit xtserial

ii