Table 4

Results of regression for advertising time or space

Number of observations = 214

Adjusted r2 = 0.6116

F statistic = 56.90

P of F statistic < 0.0001

Variable

Coefficient

SE (Robust)*

T (Robust)*

P (Robust)*


ln GDP per capita

-0.592

0.0967 (0.1310)

-6012 (-4.52)

0.000 (0.000)

ln of population in service area

0. 425

0.0461 (0.0482)

9.20 (8.81)

0.000 (0.000)

Dummy if costs are for TV

2.215

0.2559 (0.2705)

8.66 (8.19)

0.000 (0.000)

Dummy if costs are for newspaper

2.055

0.2286 (0.2093)

8.99 (9.82)

0.000 (0.000)

Dummy for Eastern Europe

-8.668

4.1304 (4.1141)

-2.10 (-2.11)

0.037 (0.039)

Dummy for Eastern Europe * ln GDP per capita

1.0670

0.4688 (0.4673)

2.28 (2.28)

0.024 (0.026)

Constant

-5.1640

1.1805 (1.4728)

-4.37 (-3.51)

0.000 (0.001)


*The results as reported after a robust and country clustering are reported in the parenthesis.

Dependent variable: log ratio of price of media and advertising to GDP per capita

Breusch-Pagan test of heteroskedasticity: 1.91 (p = 0.1667 (Chi2)).

VIF test for multicollinearity is not appropriate for this model due to the inclusion of interaction variables.

Johns et al. Cost Effectiveness and Resource Allocation 2006 4:8   doi:10.1186/1478-7547-4-8

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