Transparency Initiative (EITI)

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from the analysis are quite sobering, given that in most cases
the model has a poor fit. While there is no general rule
of thumb what a ‘good model fit’ is, a minimum of 0.4 (i.e.
40%) explained variance is assumed as a minimum here.
For the variables for which the model meets this requirement,
the cells are highlighted in green. Only for these indicators
does it makes sense to interpret the effect strengths,
indicated by the effect coefficient (in units of the variable)
and its significance. With regard to the level of significance
a maximum of 0.2 for the two-sided test may be acceptable,
the lagged dependent variables are influenced by the nonlagged
independent variables and not the other way around
(highlighted cells in the fourth column). However, the (oneyear)
time lags does not solve the causality issue.
Going by these assumptions, the analysis shows a significant
effect for a country’s EITI candidate status on the Foreign
Direct Investment (FDI) inflows, on the time to prepare
and pay taxes and on CPIA fiscal policy rating if a
candidate of the EITI (→ cf. Table 1), while its membership
appears to further affect FDI flows and the time to prepare
and pay taxes, and additionally the CPIA equity of public resource
use rating (→ cf. Table 2). However, the rather low significance
levels suggest to assess the relationships rather
carefully.
According to these results, the macro-economic data serves
solely to provide evidence on the influence of EITI candidature
and membership only for two indicators respectively:
while EITI candidature and membership both seem to
cause the private sector to spend more time on preparing
and paying their taxes, the CPIA fiscal policy rating seems
to improve when a country becomes an EITI candidate as
does the CEPIA equity of public resource use when it finally
becomes a member.
EITI on the assessments. Since the ratings are based on a
6 step-scale (with 6 being the best value), the changes correspond
with a decrease of 1.6% for candidature and 1.5% for
membership respectively. Thereby, the fact that the effect is
negative, meaning that candidateship and membership of
a country comes along with a lower rating, could indicate
that the involvement of a country increases its awareness
concerning these issues.
At least, it is interesting to note that the results for the impact
of the EITI candidate status of a country on its net FDI
inflows are in line with the findings from Schmaljohann
with regard to its size and significance.
In conclusion,
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Effects are suggested for the ‘net FDI inflow’ and the
‘time to prepare and pay taxes’. These two are the main
findings from macro-data analyses.
These findings are very indicative. Considering the effort
put into the data processing and analysis, the data
only provides little evidence on the broad bandwidth of
potential impacts of the Initiative. In that respect, findings
support the indeterminate results of previous statistical
analysis of large-N datasets as summarized in
Brockmyer 2015: 27-28.
This fact is mostly due to the complex intervention logic
of the Initiative that aims to establish effects on a systemic
policy level, which is influenced by a multitude
of factors.
With regard to the effect sizes (highlighted in yellow) the
change of the time required for preparing and paying taxes
can be considered as relevant. Given a sample average of
about 377 hours per year, the changes equal an increase of
about 5.9% for candidature and 5.1% for membership. Considering
the fact that the required time tends to decrease
over the observation period, the increase is even more
noteworthy. It is also plausible as it can be assumed that
making a proper tax declaration takes more time than bribing
a tax authority official.
In contrast the changes in the country policy and institutional
(CPIA) ratings suggest a rather small influence of the