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Media attention in Belgium: How much influence do citizens and politicians have on news coverage? - Pooled time-series analysis

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Media attention in Belgium: How much influence do citizens and politicians have on news coverage?

Pooled time-series analysis

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  • 1. 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 2010/2011 Dynamic Data Analysis Lecturer: Dr. R. Vliegenthart February 3, 2010 Communication Science (Research MSc) Faculty of Social and Behavioural Sciences University of Amsterdam
  • 2. Table of contentsINTRODUCTION AND THEORY........................................................................................................................1METHOD........................................................................................................................................................1RESULTS........................................................................................................................................................2 ORDINARY LEAST SQUARES REGRESSION.............................................................................................................................2 LINEAR REGRESSION MODELS WITH PANEL-CORRECTED STANDARD ERRORS........................................................................................3 FIXED EFFECT MODELS..................................................................................................................................................5CONCLUSION.................................................................................................................................................7REFERENCE....................................................................................................................................................7APPENDIX A: DO FILE.....................................................................................................................................7
  • 3. Introduction and theoryIn this study, I aim to investigate whether journalists’ attention is as much influenced bypolitical happenings and previous media coverage, as it is by the actions of ordinary peopleundertaken to attract the attention of journalists as well as politicians. For this reason, I willstudy 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 bothinform citizens about what is happening in society and thus in politics but also to function as adiscussion 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 datawill be used as Vliegenthart and Walgrave (2010) used, what makes it possible to conduct apooled time-series analysis, which is considered as a robust analysis method. The advantageof this method is that it captures over-time and between-issue variations, and therefore willproduce 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) to25 issues. The data was collected on a monthly basis from the beginning of 1993 until the endof 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 wascollected in such a way that attention was organized in 25 different issues, had the advantagethat it was possible to correct for potential differences in the (error) processes of the differentissues in the estimation techniques that were necessary to investigate the hypothesis. Data wasstrongly balanced, as observations were available for every issue for every months and everyactor. The attention for the issues was coded in such a way that it was the relative attentionfor a certain issue. This means that for every month every actor had a total attention of 1. Thisattention will be divided over the 25 issues. For example, an actor can have 0.5 attention for 2issues in a month, but then cannot have any attention for other issues that month. When oneissue attracts a lot of attention in one month the other issues will thus get less attention. Theanalyses need to take care of this contemporaneous correlation.1 For more information about how they collected their information, see their article. 1
  • 4. Different statistical techniques will be used, to test the robustness of the results and thenecessity 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 thatall those error processes are independent of each other, even those of the same issues atdifferent points in time. This absence of panel heteroscedasticity and contemporaneouscorrelation 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 errorprocesses in ways to take care of panel heteroscedasticity and contemporaneous correlationand also unit-level heterogeneity. Therefore, linear regression models with panel-correctedstandard errors are used which perform remarkably well under conditions of panelheteroscedasticity 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 modelwill 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 simpleordinary least squares via regression that has panel corrected standard errors to fixed effectmodels. Before doing the analyses, all variables were tested for the presence of panel unit rootusing an augmented Dickey-Fuller test. For all these tests the null hypothesis was rejected (χ2values were found between 1008.27 and 1733.77), so it was not necessary to integrate thedata.Ordinary Least Squares regressionOrdinary least squares regression has many assumptions that need to be satisfied before itsresults can trusted. One of those is that errors and variances are uncorrelated over theobservations. In time-series cross-section (panel) data this is logically not the case, but tobecome aware of the consequences of performing such an analysis on such data, the results ofan ordinary least squares regression will also be presented in this paper. Three regressionanalyses will be run that differ in their dynamic specification; the static model with onlyvariables that were observed in the same month, this model supplemented by distributed lagindependent variables and finally a model that contains the independent variables, the laggedindependent variables and the lagged dependent variable. The results of the analyses can befound in Table 1. 2
  • 5. 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 2375 R2 0.361 0.420 0.776Note. Unstandardized coefficients. Standard errors in parentheses;* p < 0.05 , ** p < 0.01As explained above, those estimates are not really trustworthy; therefore, I would only like topay attention to the differences between the models. First, the effects of new legislation getinsignificant after adding the lags of the independent variables. Those stay insignificant whenalso the autoregressive term (or lagged dependent variable) is added to the model; then alsothe 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 thecoefficients, as those are not as reliable as the results of estimation methods that take bettercare of the regression assumptions. Nevertheless, it is clear that every next model in Table 1did explain more variance, as R2 increased substantially. Therefore and because it gives amore complete overview, the variables in the final model, ARDL(1,1), will be used in the nextanalyses.Linear regression models with panel-corrected standard errorsVariance-comparison tests show that, as expected, variance is different for the differentissues: 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 firstdoes not take a special autoregressive process in the variance into account, but just a laggeddependent variable and therefore gives the same results as the last OLS model; one accountsfor first-order autocorrelation AR(1) and assumes that the coefficient of the AR(1) process is 3
  • 6. the same to all issues; another specifies that, within panels, there is first-order autocorrelationand that the coefficient of the AR(1) process is specific to each of the issues. As it seemsplausible that different autoregressive structures can exist for different issues, some mightfade away quicker, because they satisfy the criteria of journalistic institutions less than otherissues (see for example the news criteria specified by Harcup and ONeill, 2001); therefore,the last estimation technique seems more helpful. The AR(1) processes seem indeed panelspecific 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 thecoefficient of the AR(1) to be specific to each panel. The results of the models can be found inTable 2.Table 2. The effects of the (lagged) (in)dependent variables on media attention for an issue Independent autocorrelation General AR(1) Panel specific AR(1) structureMedia 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 Sample of six:ρ (autocorrelation parameter) 0.695 between 0.484 and 0.788Note. Unstandardized coefficients. Panel-corrected standard errors in parentheses.** p < 0.01, * p < 0.05The first thing to note is that the estimates of the models with the AR(1) structure and themodel with the panel specific AR(1) hardly differ. Only some marginal differences betweenthe 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, thenegative effect of the lagged value of demonstrations becomes insignificant. Above, it wasalready explained that this negative effect would be a strange finding, so it seems a good signthat this effect disappears. Furthermore, the coefficients of the lagged values of bothparliamentary attention and meetings of Cabinet become significant. This suggests a strongerimpact of politics on journalists than the findings found by the ordinary least squares 4
  • 7. regression technique. Next to this, the proportion of explained variance falls from 0.776 to0.228.Fixed effect modelsA regression with panel-corrected standard errors can excellently deal with group-levelheteroscedasticity and contemporaneous correlation in a regression analysis. However, it doesnot take good enough care of unit-level heterogeneity: systematic differences between valuesof different panels (here issues). Indeed it was found that unit-level heterogeneity is present inthe model (F(24, 2341) = 21.30, p < 0.001). Therefore, it was necessary to also conduct fixedeffect model analyses and compare its results with the results found by the linear regressionmodels with panel-corrected standard errors. The results of three fixed effect models can befound in Table 3.Table 3. The effects of the (lagged) (in)dependent variables on media attention for an issue Static fixed effect Fixed effect model with Fixed effect model model AR(1) disturbanceMedia 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 varianceAll three models take into account that observations are nested within issues. The first is astandard static fixed effect model that just takes independent variables into account that aremeasured at the same time as the dependent variable; the second is a normal fixed effectmodel that is similar to the ARDL(1,1) model; the last model includes a AR(1) disturbanceprocess in which only the previous variance is taken into account, whereas the AR(1)-processas in the second model continuous to affect media attention in every next step with adecreasing strength. The estimated coefficients of the three models do not differ a lot, with 5
  • 8. two exceptions. In the normal fixed effects model, the negative effect of lagged‘demonstrations’ becomes significant again, just as in the ordinary least squares regressionARDL(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 asignificant effect of the lagged value of parliamentary attention, whereas this was not foundby the normal fixed effect model. When the results are compared to the coefficients foundwith the regression with panel-corrected standard errors and panel specific AR(1) process, thebiggest difference is the disappeared significance of the effects of meeting of the Cabinet, butthe 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 robustresults as it matches largely with both the regression models with panel-corrected standarderrors 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 findingsof the regression with panel corrected standard errors as both have some weaknesses and thereis not an estimation technique that takes all three offences of the OLS assumptions (group-level heteroscedasticity, contemporaneous correlation and unit-level heterogeneity) intoaccount. The coefficients of this model suggest that both demonstrations and parliamentaryattention have a direct and positive influence on media attention. However, because it is notpossible to see whether those causes precede the effect, it is not totally clear in whichdirection the effect runs. Therefore, it is more interesting to see which lagged independentvariables significantly affect media attention. Parliamentary attention is the only laggedvariable that has a significant effect, whereas the other political factors and the citizen factordo not have a significant lagged effect. It thus seems that politicians have more influence onmedia coverage than citizens. A random effect model is not reported in this paper for thereason that as specified above, it is not expected that unobserved variables are distributedindependently from the observed variables and a Hausman specification test confirmed this(χ2 = 27.12, p < 0.001). 6
  • 9. Conclusion Overseeing 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. Reference Beck, 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., & ONeill, 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.Appendix A: Do File*Assignment9clearclearset memory 250m, permanentlyset more off, permanentlyuse D:DDApanel_belgianastsset cissue nrxtfisher mediatot_rxtfisher legislation_rxtfisher parliament_rxtfisher min_rxtfisher demonstrations_r 7
  • 10. *OLS//normal OLSregress 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_rl.parliament_r legislation_r l.legislation_r min_r l.min_rfitstat//OLS with distributed lags of the independent variables and laggeddependent variable = ARDL(1,1)regress mediatot_r l.mediatot_r demonstrations_r l.demonstrations_rparliament_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_rparliament_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_rl.parliament_r legislation_r l.legislation_r min_r l.min_r, correlation(ar1)xtpcse mediatot_r demonstrations_r l.demonstrations_r parliament_rl.parliament_r legislation_r l.legislation_r min_r l.min_r,correlation(psar1)xttest2predict 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*fixed effects with a ARxtreg mediatot_r l.mediatot_r demonstrations_r l.demonstrations_rparliament_r l.parliament_r legislation_r l.legislation_r min_r l.min_r, fe//contemporenaous correlation: present, yes! negative correlations, if oneparty goes up in the polls another will go down.xttest2*null Hypothesis: no significant differences between groups. Reject, grouplevel 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 ARtermxtregar mediatot_r demonstrations_r l.demonstrations_r parliament_rl.parliament_r legislation_r l.legislation_r min_r l.min_r, fepredict mediafe, eestimates stor fixed_effects 8
  • 11. xtregar mediatot_r demonstrations_r l.demonstrations_r parliament_rl.parliament_r legislation_r l.legislation_r min_r l.min_r, repredict mediare, eestimates stor random_effectsxtserial mediarehausman fixed_effects random_effectsfindit xtserial 9