How Low Business Tax Rates Attract Multinational Headquarters: Municipality-Level Evidence from Germany



Appendix

Marginal effects in count data models

The marginal effect of any explanatory variable xij on the conditional mean
of the dependent variable
yi is given by

dE(yixi)        0 om n NB

—— = eχp(xiβ)βj = Yj

for the negative binomial model and by

dE (yixi) =    p(xiβ)    β = ZINB

∂xij      (1 + exp(xiβ))2 Y   γi

for the zero-inflated negative binomial model, in the case where the vector
of explanatory variables
x0i is identical in the inflation model and the main
equation. The marginal effects depend on the explanatory variables and need
to be evaluated at some value. We evaluate them at the sample mean.

The standard errors of the marginal effects were computed according to
the asymptotic variance formulas

Asy.Var[γ]NB = (exp(χ03))2[Ik + exp(X03)∕3x0] V [Ik + exp(x0 ∕3)x∕30]

and

. ,                      ...            . . , г» .              .               . . ^                            .               . . ^

Asy.Var[γziNB = [Л(1 - л)] [Ik + (1 - 2Л)вх ]V[Ik + (1 - 2Л)х/3 ]

where x is the K × 1 vector of sample means, / is the K × 1 vector of parameter
estimates,
Ik is an identity matrix of dimension K , V is the K × K estimated
asymptotic covariance matrix of
and Λ = exp(x0)(1 + exp(x03)) is a
scalar.

26



More intriguing information

1. Uncertain Productivity Growth and the Choice between FDI and Export
2. Tourism in Rural Areas and Regional Development Planning
3. The name is absent
4. Impacts of Tourism and Fiscal Expenditure on Remote Islands in Japan: A Panel Data Analysis
5. Large-N and Large-T Properties of Panel Data Estimators and the Hausman Test
6. WP 1 - The first part-time economy in the world. Does it work?
7. Multi-Agent System Interaction in Integrated SCM
8. The name is absent
9. The name is absent
10. Industrial Cores and Peripheries in Brazil