rather than on the _Doing Business_ score, which is an absolute measure. With this objective in mind, we develop the following specifications for the panel regression model:

# **Model 3** : media�� � α�β�Δranking���� �∑β�X���� �ε��

Model 3 is similar to Models 1 and 2 discussed above. In Model 3, however, the outcome variable is number _Doing Business_ related media articles in a given country in a given _Doing Business_ publication year and “Δ ranking” represents year on year changes between published rankings. For the delta ranking variable we apply both one and two-year lags, to allow enough time for reform implementation processes to take effect. Model 3 also controls for free press, levels of Doing Business scores and income per capita, expressed as Xjt.

To limit the omitted variable bias, we introduce fixed effects regressions to allow us exploit within country variations over time. We deem the fixed effects model to be an appropriate specification as there is substantial year-on-year country-specific variation of both reform implementation and media coverage. As Model 4 equations below indicate, we apply fixed effects to both country and time variables. In Model 4 below α� and δ� represent country and time fixed effects respectively, while _Xjt_ represents the standard set of controls. We expect results of the fixed effect models to be similar to those of OLS regressions.

**Model 4** : Reforms�� � α� � δ� �β media���� � γ<sup>�</sup> X�� � ε��

The panel regression results support our original hypothesis that local media coverage of _Doing Business_ does tend to influence regulators’ reform agendas, especially its magnitude. As evidenced by the regression output, there is a strong and positive relationship between the number of implemented reforms on _Doing Business_ indicators and local media coverage of _Doing Business,_ applying both one and two-year lags on press releases (see Annex B, Table B1, Model 1.1a, which refers to Model 1 above). Therefore, one could argue that in countries where media is most vocal about _Doing Business,_ regulators tend to pay closer attention to their economies’ performance on the _Doing Business_ indicators, placing business regulatory reforms high on political agendas.

The results are more robust when using Factiva data as an explanatory variable as opposed to the Google data. This is most likely because Google variable includes international press in addition to local one, which dilutes the domestic focus. However, we do get significant results with Google media in specifications 1 and 3 of the model but the coefficient becomes weak and insignificant when we add a control for level of _Doing Business_ score. In the case of Factiva’s data, the relationship is significant at the 1% level in all three specifications, controlling for free press, level of _Doing Business_ score and income per capita. Direction of coefficients on the income controls also indicate that poorer countries tend to reform more than the richer ones. This message is clearly aligned with the findings of annual _Doing Business_ reports since most developed economies already follow international best practices across _Doing Business_ topics and have less need for exponential business regulatory changes.

A curious, albeit not entirely surprising, finding is that freedom of press control variable shows positive and highly significant coefficients across all the models. This finding suggests that countries with less free press reform more and that there is higher _Doing Business_ media coverage in such economies. One explanation could be that most countries with state20