ELECTORAL REGULATION RESEARCHNETWORK/DEMOCRATIC AUDIT OF AUSTRALIA JOINT
WORKING PAPER SERIESMEASUREMENT TOOLS FOR COMPARATIVE POLITICAL FINANCE: EXCESSIVE
REDUCTIONISM OR VALUABLE SIMPLICITY?1
William C.R. Horncastle2(Department of Political Science and International Studies, University ofBirmingham)

WORKING PAPER NO. 54 (MAY 2019)1

This working paper is part of a wider thesis chapter which aims to produce an effective measurement tool tocomparatively analyse political finance systems worldwide. The information in this paper provides the basis andjustifications around producing an updated measurement tool, informing subsequent methodological decision
making. Any comments or observations would be welcomed.
2
Email: Wxh723@student.bham.ac.uk

1

Abstract
This working paper examines how we might better develop measurement tools for comparative
political finance. This paper evaluates the current measurement tools that are available tocomparatively analyse political finance regulations and then outlines future directions.

****************************

The act of contesting political elections is a cornerstone of democracy, making them aninherently important part of the political process. As the electorate within a nation can often bemeasured in the millions, projecting political messages can be an expensive task, with patterns
indicating that costs are rising in some areas. The 2018 US midterm elections, for example,
were the most expensive on record, costing more than $5.7billion (USD)(Center for
Responsive Politics, 2019). With the inflated cost of elections over time, all nations now utilise‘at least some regulations concerning political finance’ (Ohman, 2014b: p.2). Research inrecent years has attempted to produce a method for comparatively measuring political financeregulations around the world, with the use of composite index scores being preferred overproxy measures. The methodologies in use range from additive index models, which assume
that an overall score can be built on each regulation having equal impact, to factor analysis
models, which empirically calculate the weight of each regulation in the overall system. In
most cases, the index scores are derived from data provided by International IDEA’s ‘Political
Finance Database’ (International IDEA, 2018).
Whilst the idea of a single score to measure a political finance system provides a simple andeasy to implement measure, is this approach entirely justified? International IDEA’s Political
Finance Database, for example, structures information on 74 political finance regulations
around four strands. A possibility exists, therefore, that index scores reducing a system ofpolitical finance regulation to a single score may be oversimplifying a regulatory system that,in reality, contains several dimensions to be measured. This paper will evaluate the current
measurement tools that are available to comparatively analyse political finance regulations,beginning by introducing International IDEA’s Political Finance Database.

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Political Finance Database (International Institute for Democracy and Electoral
Assistance)
International IDEA’s Political Finance Database (PFD) is a large scale and open source dataset
which provides ‘the leading global resource of comparative political finance data for thoseinterested in money in politics’ (International IDEA, 2018). The PFD contains data for 180
nations where political parties are legally permitted to exist and where elections have takenplace in the past 30 years. First established in 2003, the PFD has since been revised in 2012,2016, and 2018. In its current format, the number of data points have been increased from 43
to 74. Comprised of a mixture of binary and qualitative data, these 74 questions are grouped
thematically into four sections; Bans and Limits on Private Income, Public Funding,Regulations on Spending, and Reporting, Oversight and Sanctions (International IDEA, 2018).
Answers to all questions are given based on official legislative or policy reports that can befound in each nation, ensuring that data is valid and free of bias.
Although the PFD provides a rich resource for the study of political finance, there is anadditional point to consider when using the PFD in research. Whilst the PFD provides acomparative assessment of the de jure political finance regulations in place around the world,
its scope does not extend to assessing the de facto implementation of regulations. A possibility
exists where a nation may be heavily regulated in terms of official legislation and policy, butparties and candidates ignore regulations in practice (Global Integrity et al., 2014b). This
provides limitations to the use of the PFD, with the regulatory approaches that are employedpotentially not showing an accurate image of reality. Whilst this is the case, the 2018 iterationof the PFD has added variables to combat this issue, with a small number of questions
ascertaining whether rules are followed in practice (International IDEA, 2018). In adding new
variables such as these, the updated PFD has gone some way to addressing the issue of
measuring the de facto implementation of political finance regulations. This is commendable,
however measuring the full extent to which political finance regulations are followed may be
an impossible task, as deviant actors within the political finance system are likely to behave ina secretive manner. Despite this, the PFD has provided a platform on which to build, becoming
the basis of several datasets designed to measure political finance, such as the ‘Money, Politics,
and Transparency’ campaign finance indicators (Global Integrity et al., 2014b) and the‘Political Finance Regulation Index’ (van Es, 2016b).
The following sections of this paper will focus on evaluating the current variables that areavailable to measure political finance regulations. For the purpose of this paper, these measures3

will be pooled into groups which highlight their methodological similarities. These groups are;Simple Additive Approaches, Complex Additive Approaches, Survey Oriented Approaches, andCombined Methods Approaches.3
A Review of Existing Approaches to Political Finance Measurement
Simple Additive Approaches
The first group of measures is the ‘Simple Additive Approach’. These approaches assume equal
weighting of political finance regulations and their simple nature is exhibited by focusing on asingle data source (Cordis and Milyo, 2013), or a single point in time (Whiteley, 2011). Simple
Additive measures provide an easy to interpret scale for the measurement of levels of politicalfinance regulation, often being used as control variables in regression models. They arecommonly produced by taking the total number of regulations that are in place, using this figure
as the score for the related nation/state. Examples of this approach include the ‘Party
Regulation Index’ (PRI), produced by Whiteley (2011), and the ‘Campaign Finance Regulation
Index’ (CFRI), produced by Cordis and Milyo (2013).
The PRI was produced to account for differing regulatory frameworks when testing the
hypothesis that an increased focus on campaign contributions may be a contributing factor in
declining party membership (Whiteley, 2011). In producing the PRI, data linked to 14 types ofpolitical finance regulation was extracted from the PFD, producing a score for each nation thatlies between 0 (no regulation) and 14 (fully regulated). In a similar manner, Cordis and Milyo(2013) produced the CFRI to study the links between campaign finance regulations andcorruption, measuring the levels of regulation over time. The CFRI differs from the PRI, as it
measures regulation across US states. As such, data for the CFRI was taken from a previous
study by Milyo (2012). Values range from 0 (no contribution limits) to 4 (contributions limits
and public funding of gubernatorial and state legislative elections), with the CFRI having muchnarrower scope than the PRI. This raises concerns about the CFRI’s explanatory power as avariable, with a narrow scope potentially sacrificing some validity for increased simplicity.
The main benefit of using a Simple Additive measure is that there is a simplicity in producing
and interpreting results. This makes these measures an attractive option to make results
accessible to non-specialist audiences. However, whilst the benefit of simplicity is visible, thissimplicity can also be viewed as a limitation. Treating each type of regulation as independent
is not recommended, due to the key relationships that exist between regulations (van Es,
3

See Appendix 1 for an overview.

42016b). Further research suggests that the impacts of individual political finance regulations
are unlikely to be observed in isolation. Ohman (2014a) discusses this issue in the context ofpublic funding, suggesting that, if public subsidies are introduced to enhance pluralism andreduce the power of large donors, their impacts are only likely to be felt if they are implemented
alongside donation limits. In this manner, it should not be assumed that each regulation has anindependent and identical impact in the restrictiveness of a nation’s political finance system.
Thus, when approaching the study of legal frameworks in this way, validity may be lost at theexpense of increased simplicity.
Complex Additive Approaches
In addition to ‘Simple Additive Approaches,’ some measures implement a ‘Complex Additive
Approach’. Although these measures assume equal weighting of regulation, their
methodologies are more complex than simple approaches as they gather data from a range ofdiffering sources and produce a measure for more than one time period. Examples of thisapproach are the ‘Campaign Finance Regulatory Stringency Index’ (CFRSI), produced by
Witko (2005) and updated by Kulesza et al. (2015), and the ‘Political Finance Regulation
Index’ (ERCAS-PFRI),4 produced by Lopez et al. (2017).
The updated version of the CFRSI was produced to measure the ‘stringency’ of campaign
finance regulations across American States, with the aim to assess how regulations had evolved
between 1996 and 2016. Using a mixture of official FEC data, documentary analysis throughthe LexisNexis database, and telephone interviews with state regulators, the CFRSI produced
an additive index for the three subcategories of Public Financing/Spending Limits,
Contribution Limits, and Disclosure. These subcategory measures were then aggregated to
produce a composite measure for the overall stringency of campaign finance regulations acrossstates and over time (Kulesza et al., 2015). The composite indicator for overall campaign
finance stringency ranges from 0, where no regulations exist, to 22, where all regulations underreview are present.

4

The index is abbreviated in this way to allow this paper to decipher between the Political Finance Regulation
Index produced by Lopez et al (2017) and the one produced by van Es (2016b), which will be introduced in thesubsequent section. As they both have the same name, this paper includes a reference to the text in which they
are developed. As Lopez et al’s (2017) index is cited from a ‘European Research Centre for Anti-Corruption andState-Building’ working paper, the abbreviation ‘ERCAS’ has been added as a prefix. As van Es’ (2016b) index
comes from a book titled ‘Checkbook Elections? Political Finance in Comparative Perspective’, the prefix ‘CE’ hasbeen added.

5

An additional measure that accounts for the evolution of political finance regulations comesfrom Lopez et al. (2017), who produce the ERCAS-PFRI. Although data for this measure istaken from the PFD, the ERCAS-PFRI differs from other measures as it utilises the legal andlegislative references that are available, in order to determine the year of implementation foreach regulation. In doing so, a panel dataset is produced which spans 19 years (1996-2015),
tracing each nation’s additive score at annual points over this period. By utilising the data inthis way, the ERCAS-PFRI can trace the trends in political finance regulation within and acrossnations, by triangulating cross national and longitudinal trends.
In producing a wide reaching and robust panel dataset, the ERCAS-PFRI data provide a uniqueand pertinent insight into political finance trends worldwide. The CFRSI provides a similar
insight into the evolution of campaign finance regulations across American states, withtriangulation across cases and over time being key strengths of these approaches. However,despite the introduction of a longitudinal element and the aggregation of data from different
sources, Complex Additive Approaches suffer from the same limitation as Simple Additive
Approaches, in assuming equal weighting of all regulations.
Survey Oriented Approaches
The third group, ‘Survey Oriented Approaches’, build a measure based solely upon survey
responses provided by political finance experts. They are mostly built around data gathered
through Likert style responses and are often part of broader research projects. For example, the‘Perceptions of Electoral Integrity Database’ (PEI) is a bi-annual study which assesses thequality of worldwide elections, being developed through questionnaires given to experts ineach nation that is holding elections within the study period. Questionnaires are broken down
into eleven sections, with the Campaign Finance module being of interest in this context. Thissection of the PEI is based upon five questions related to the effectiveness of regulations in thenation under study. Answers are provided on a 5-point Likert scale, with a cumulative scorebeing provided, standardised to a 100-point scale (Norris et al., 2018a).
The PEI offers a useful interpretation as to the strengths and weaknesses of electoral regulations
around the world. When specifically focussing on campaign finance, however, there arelimitations with its use. As the PEI measures eleven aspects of elections, the campaign financesection is only comprised of five data points, lacking substantive variance in scores. An
additional limitation of Survey Oriented Approaches is that they are focussed upon expert
opinions of the qualities of campaign finance regulations. Whilst this data may prove useful in6

a variety of contexts, it does not objectively focus upon the measurable levels of politicalfinance regulation. As a result, the Survey Oriented Approach is not best suited to all types ofpolitical finance research.
Combined Methods Approaches
The final group of ‘Combined Methods Approaches’ utilise a number of methods, relying ona multitude of quantitative methods, or on a mixture of quantitative and qualitative approaches.
Examples of these approaches include the ‘Money, Politics, and Transparency, Campaign
Finance Indicators’ (MPT) (Global Integrity et al., 2014b) and the ‘Political FinanceRegulation Index’ (CE-PFRI) (van Es, 2016b).
As a joint venture between Global Integrity, the Sunlight Foundation, and the Electoral
Integrity Project, the MPT utilises political finance experts to produce ‘Campaign FinanceIndicators’ for 54 nations around the world (Global Integrity et al., 2014b). Each nation hasscores for five categories; Direct and Indirect Public Funding, Contribution and Expenditure
Restrictions, Reporting and Public Disclosure, Third-Party Actors, and Monitoring and
Enforcement. A unique strength of the MPT dataset is that each nation has a score for de jureregulations, de facto implementation, and a composite for each section. These different
measurements allow for an insight into the reality of political finance regulation in thesenations. In addition to the quantitative indicators, each nation has several supporting statements
provided by political finance experts, ensuring that the MPT is a ‘rich source of granular
information’ (Global Integrity et al., 2014a). The array of different categories and scores makes
the MPT a useful resource, and the authors remain sympathetic to the view that a single
aggregated score cannot fully encapsulate the measurement of a political finance system,
stating: ‘quantitative scores fail to capture the scope of an indicator answer or full country
scorecard, as do aggregated scores’ (Global Integrity et al., 2014a).
Whilst the MPT is a useful resource for the measurement of political finance regulations, itcomes with a few limitations. Firstly, in a similar manner to Survey Oriented Approaches, thefocus of the indicators is not exclusively upon revealing the dimensions of political financeregulation, but upon transparency and integrity. In addition, the deep levels of information
sacrifice some breadth from the dataset, with only 54 of the 180 PFD nations being studied.
This may be problematic when undertaking quantitative research, as statistical methods often
become more reliable with a larger sample size, potentially rendering the depth of the MPT to
be obsolete.
7A further Combined Methods Approach is the CE-PFRI, produced by van Es (2016b) as partof Checkbook Elections? Political Finance in Comparative Perspective. This is an edited
volume which aims to describe and evaluate the types of regulations that are in use around theworld, as well as developing an understanding as to why regulations may be used. Within thistext, the CE-PFRI is developed in order to produce an empirically robust measure for assessing
the ‘degree of state interventionism’ in each nation’s political finance system.
The CE-PFRI differs from any of the measures discussed thus far, as it utilises statisticalanalysis as part of its methodology. In doing so, van Es treats political finance regulation in adifferent way, ‘conceptualising de jure political finance regulations as a single continuum
defined by the degree of state intervention’ (2016b: p.209), recognising that:the degree of state interventionism in political finance … is only an indirectly
observable characteristic of a political economic system in the following sense: weobserve actions and behaviours that indicate state interventionism (van Es, 2016b:
p.210)
Like several previous measures, the CE-PFRI is produced based on data from the PFD. Eachnation is first given an average summed score from the binary variables in each of the four
modules of the PFD. These four scores consequently enter a Bayesian Factor Analysis model,
with subsequent factor loadings indicating the weights to be applied when creating an averageof the previous four scores. This produces a single comparative index score which integrates
all binary responses from the PFD, whilst also considering the varied impacts of differing typesof regulation.
In structuring the methodology in this way, the CE-PFRI moves beyond a simple additivemodel, to a more complex model which accounts for nuances in different types of regulation.
This provides a measure which produces different results to a linear model, with the correlation
between the weighted and unweighted calculations being .79 (van Es, 2016a). When taking theUnited Kingdom and United States as examples, Table 1 displays the impact of a weighted
approach:8

Table 1 - Table Showing the Differences between Weighted and Unweighted Approaches in the
CE-PFRI
Weighted Unweighted
United Kingdom

0.77

0.57

United States

0.56

0.81

Source: Adapted from van Es (2016a: p.276)
As shown in Table 1, when using a weighted approach, the United Kingdom displays higher
levels of state interventionism than the United States, however when the variables areaggregated with the inclusions of weighting, this observation is reversed. These observations
highlight the importance of methodology selection in producing a measure for political financeregulations, with differing approaches having the potential to produce significantly different
results.
The CE-PFRI clearly shows a strong methodology and produces an indicator that can measurepolitical finance systems on a single scale. However, is this approach warranted? As the PFD
is broken into four main groups, with a total of 74 variables, is it possible to reduce thisinformation into a single score, whilst still maintaining enough of the original variance? The
decision to group the PFD in four separate groups appears to indicate that these variables aremeasuring at least four different aspects of political finance regulation. Reducing the PFD data
into a single variable clearly provides simplicity, but this may be counterintuitive. This is anarea which certainly warrants further research.
Towards a new measure for political financeAll the measures discussed thus far have their own strengths in measuring different aspects ofpolitical finance,5 from measuring transparency to electoral integrity. However, whilst several
strengths can be found in the differing approaches, none of these measures comes without
limitation. The most common issue with preceding measures has been a propensity to utilise
an additive approach (Whiteley, 2011; Cordis and Milyo, 2013; Lopez et al., 2017; Norris etal., 2018a), with the assumption that each additional piece of regulation has the same effect onregulatory restrictiveness as any previous regulation, regardless of its focus. As discussed
previously, approaching legal frameworks in this way has the potential to downplay the5

See Appendix 1 for overview of approaches and Appendix 2 for strengths and limitations of different
methodological approaches.

9complex relationships that exist between each type of regulation, as well as potentiallygarnering significantly different results (van Es, 2016a).6
An additional point of note is the recent update of the PFD. As the strength of a quantitative
indicator relies heavily on the validity of the data upon which it is based, previous indicators
must be restructured to account for the updated political finance data (International IDEA,
2018). This would ordinarily be a simple task, however the updated PFD has expanded from
43 variables to 74, suggesting that calculations must be tailored to account for these changes.
Thus, if previous measures are to retain their validity as an indicator of political finance going
forward, they must be updated to account for new data.
In addition to this, several robust measures exist that measure subjects such as ‘transparency’(Global Integrity et al., 2014b) and ‘integrity’ (Norris et al., 2018b). The objective of this paper
is not to denigrate previous works, as they undoubtedly offer pertinent insight into their subject
areas, however these measures do not provide an objective indicator of levels of regulation
across nations. As eluded to previously, none of the previous indicators fully meet this target.
Additional research is warranted to ascertain whether complex political finance systems canbe measured in this way and, if so, how measurements should be produced and kept up to date.
Conclusion: Beyond libertarian vs egalitarian
General debates in political finance research are centred on the notion of ‘libertarian’ vs‘egalitarian’ approaches to regulation, suggesting a one-dimensional approach to politicalfinance measurement. Those who support a libertarian view conflate political spending with adeclaration of political speech, supporting a political finance system which is unregulated andallows for unencumbered political spending by private interests. On the contrary, those who
adopt an egalitarian standpoint focus their ideals on equality, supporting a restrictive politicalfinance system that equalises the power of political donations (Sunstein, 1995; Smilov, 2007).
As this is the dominant debate in political finance, it may be assumed that it is possible tocategorise political finance systems on a continuum that ranges from libertarian focussed, toegalitarian focussed systems. Previous efforts at comparative political finance measurement
have often worked with this assumption, however this is a theoretical assumption which has
yet to be empirically analysed. Research that focusses on these issues should be prioritised
before further measurement tools are produced.

6

See Table 1 on page 8 for an example.

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References
Center for Responsive Politics. (2019) Most expensive midterm ever: Cost of 2018 election
surpasses $5.7 billion. Available at: https://www.opensecrets.org/news/2019/02/costof-2018-election-5pnt7bil/.
Cordis A and Milyo J. (2013) Do state campaign finance reforms reduce public corruption?
George Mason University, Mercatus Center, Working Paper.
Global Integrity, Sunlight Foundation and Electoral Integrity Project. (2014a) Methodology.
Available at: https://data.moneypoliticstransparency.org/methodology/.
Global Integrity, Sunlight Foundation and Electoral Integrity Project. (2014b) Money, Politics
and Transparency.
International IDEA. (2018) Political Finance Database.
Kulesza C, Witko C and Waltenburg E. (2015) State Campaign Finance Regulatory Stringency
Index, 1992-2012. V1 ed.: Harvard Dataverse.
Lopez A, Rodriguez M and Valentini M. (2017) How does political finance regulationinfluence control of corruption? Improving governance in Latin America. ERCAS
Working Paper Series.
Milyo J. (2012) Do state campaign finance reforms increase trust and confidence in state
government? Annual Meeting of the Midwest Political Science Association in Chicago.
Norris P, Wynter T and Cameron S. (2018a) Perceptions of Electoral Integrity (PEI) 6.5
Codebook. Perceptions of Electoral Integrity, (PEI-6.5). V1 ed.: Harvard Dataverse.
Norris P, Wynter T and Cameron S. (2018b) Perceptions of Electoral Integrity, (PEI-6.5). V1
ed.: Harvard Dataverse.
Ohman M. (2014a) Getting the political finance system right. In: Falguera E, Jones S and
Ohman M (eds) Funding of political parties and election campaigns: a handbook onpolitical finance. Stockholm: International IDEA, 12-37.
Ohman M. (2014b) Introduction to Political Finance. In: Falguera E, Jones S and Ohman M(eds) Funding of Political Parties and Election Campaigns: A Handbook on Political
Finance. Stockholm: International IDEA, 1-11.
Smilov D. (2007) Introduction: Party Funding, Campaign Finance and Corruption in Eastern
Europe. In: Toplak J and Smilov D (eds) Political Finance and Corruption in Eastern
Europe: The Transition Period. Hampshire: Ashgate Publishing, 1-32.
Sunstein CR. (1995) Democracy and the Problem of Free Speech: Free Press.
van Es AA. (2016a) Technical Appendix. In: Norris P and van Es AA (eds) Checkbook
elections? Political finance in comparative perspective. New York: Oxford University
Press, 271-280.
van Es AA. (2016b) Why Regulate Political Finance? In: Norris P and van Es AA (eds)
Checkbook Elections? Political finance in comparative perspective. New York: Oxford
University Press, 199-226.
Whiteley PF. (2011) Is the party over? The decline of party activism and membership acrossthe democratic world. Party Politics 17: 21-44.
Witko C. (2005) Measuring the Stringency of State Campaign Finance Regulation. State
Politics & Policy Quarterly 5: 295-310.

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Appendix 1 – Comparison Table Showing Available Measures for Political Finance Regulation
MethodologicalApproachName of Measure

Number andtype of cases
36 nations

Methodology

Focus

Non-weighted, additivemodel. Based on 14 data
points from PFD

Levels of regulation

Campaign FinanceRegulation Index

50 US states

Non-weighted, additivemodel. Based on data from
Milyo (2012).

Campaign FinanceRegulatory Stringency
Index

50 US states

ERCAS Political FinanceRegulation Index

180 nations

Perceptions ofElectoral Integrity
Database

285 elections in
164 countries

Money, Politics andTransparency
Database

54 nations

CE Political FinanceRegulation Index

169 nations

Party Regulation Index

Simple Additive
Approaches

Complex
Additive
Approaches

SurveyOriented
Approaches

Multiple
Methods
Approaches

Available
Values1 to 14

Strengths

Limitations

Simple methodology provides an easy tointerpret scale

Additive measure assumes equal weight;
may reduce validity

Comparing levelsof regulation
across states andover time

0 to 4

Simple methodology provides an easy tointerpret scale. Triangulation across states andover time

Additive measure assumes equal weight;
may reduce validity. Focussed on US
states and not cross-national

Uses a mixture of FEC data,
telephone interviews, andLexisNexis searches to
produce an additive index.
Comparative index uses datafrom 1996 and 2016 to assess
how overall stringency ofregulations has changed.

‘Stringency’ of
campaign financeregulations

0 to 22

Simple methodology provides an easy tointerpret scale. Triangulation across states andover time

Additive measure assumes equal weight;
may reduce validity. Focussed on US
states and not cross-national. Only has
data for two time points; not enoughyears to utilise time series element

Utilises data from PFD, as wellas supplementary legal
sources, to produce a panel
dataset for nations over 19
years (1996-2015). Nonweighted, additive model.
Uses five questions on a fivepoint Likert scale. Nonweighted summation of
responses from thesevariables is transposed into a
100-point scale
Mixed methods approach
utilises data from PFD as wellas qualitative statementsfrom experts in each nation

Comparing levelsof regulation
across continents
and over time

0 to 100

Provides data from 1996-2015; could be utilised
in time series. Triangulation across nations andover time

Additive measure assumes equal weight;
may reduce validity

Electoral integrity
around the world

0 to 100

May be useful to use alongside a comparativeindex to assess effectiveness of de jureimplementation of regulations. Provides datafrom 2012 to 2017; could be utilised in timeseriesAdditive measure assumes equal weight;
may reduce validity. Political finance is
not the primary focus; there are only fivevariables that make up this measure

Transparency inthe politicalprocess

0 to 100 (alsobacked up withqualitativedata)

Measures de facto regulations, as well as how
they are implemented in practice. Mixed
methods improve validity through triangulation

Focussed on transparency, notrestrictiveness of the system as a whole.
Only focussed on 54 nations when the
PFD has 180

Uses factor analysis toLevels of state-3 to 3
Strong methodology which incorporates
Does not contain time series data so isdetermine the weights ofinterventionism in
empirical testing on weighting of variables.
only fully accurate at one point in time.
regulation types on the latent
political financetrait of ‘state interventionism’
regulations
Sources: Witko (2005); Whiteley (2011); Cordis and Milyo (2013); Global Integrity et al. (2014a); (2014b); Kulesza et al. (2015); van Es (2016a); (2016b); Lopez et al. (2017); Norris et al. (2018a); (2018b)

12

Appendix 2 – Strengths and Limitations of Approaches to Political Finance Measurement
Strengths

Limitations

Simple Additive Approach

Simple to use, easy to interpret, and easily
replicatedPotentially sacrifices some validity at theexpense of increased simplicity

Complex Additive Approach

Simple to use, increased validity in relation to
Simple Additive Approach

Although validity may be increased,additive approach still has potential
validity issues

Survey Oriented Approach

Utilises the expertise of political finance experts
around the world

May suffer from subjective biases

Combined Methods Approach

Can potentially increase the validity of themeasure through methodological triangulation

Methodologies may be complex andchallenging to interpret or replicate

13