Transparency Initiative (EITI)

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from the study, the model was amended with further independent
variables, which are supposed to have an influence
on the observable outcomes were included in the regression
model:
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Received ODA
Inflation rate
Industry value added (in local currency units)
Total labor force
Average interest rate
Since these variables are presumed to affect the outcome
variables under study, controlling for them allows to estimate
the effect of EITI status without being biased by the
covariates included into the model. As the effects of the covariates
on the outcome are also temporally delayed, they
are lagged by one year in order to take this circumstance into
account. Finally, because the value of a given outcome
variable in one year is always affected by its value on the
same outcome variable one year before, the outcome variable
is also lagged by one year and included into the model
as a further independent variable.
The macro data analysis at this point of time in the development
of the Initiative has, to a degree, some limitations.
This is common knowledge among many stakeholders. In
conclusion, the management of the International Secretariat
expresses, that “we must embrace quantitative assessments
but also be mindful of the pitfalls with such methodologies.
At the EITI, our role is to make ourselves, our
data and information available. We can facilitate contacts
and draw attention to good case studies and solid research,
whether it demonstrates impact or not.” (Moberg: 2016). In
that sense, the study contributes to the availability of information,
research methodologies and results:
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Upon request, the calculations can be made available
through the GIZ Sector Program “Extractives for Development
– X4D” for interested EITI stakeholders or researchers.
To sum up, the pooled OLS regression models consist of
four basic components:
I. the EITI status as our main independent variable of
interest,
II. the time-variant control variables (as listed above),
III. the country and year dummies, and
IV. the one-year lag of the outcome variable.
With regard to model evaluation, the analysis focuses on
the within country model fit (i.e. to which extent the independent
variables of the model explain the variance of the
dependent variable in each country), the signs, magnitudes
and levels of significance of the regression coefficients of
the independent variables.
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In total, the model design included 1,569 data points for
98 countries.