Manifesto Coding
Also known as: CMP coding, MARPOR coding, Manifesto content analysis, Party manifesto coding
Manifesto coding is the quantitative content-analysis methodology of the Comparative Manifesto Project (CMP/MARPOR) for measuring parties' policy preferences from their election manifestos. Trained coders break each manifesto into quasi-sentences and assign every unit to one of a fixed set of policy categories. Counting how often each category appears yields salience measures, and combining pro- and anti- categories produces position scores such as the left–right RILE index, giving comparable estimates of party positions across more than fifty democracies since 1945.
Key highlights
- Provides the longest-running, most comprehensive comparable dataset of party positions across democracies since 1945.
- Grounded in parties' own authoritative documents rather than perceptions, giving a direct behavioral measure of emphasis.
- A fixed, theory-driven category scheme enables consistent cross-national and over-time comparison.
- Transparent and reproducible: raw category counts are published, so alternative scales and reanalyses are possible.
Intuition
This section is available to Pro members. Upgrade to Pro
How it works
This section is available to Pro members. Upgrade to Pro
When to use it
Use manifesto coding when you need comparable, long-run measures of party policy positions and issue emphases grounded in parties' own authoritative statements, especially for cross-national and over-time analysis of party competition, government policy, and representation. The CMP/MARPOR dataset is the standard source. It is less appropriate when you need positions on a specific contemporary issue not captured by the fixed scheme, when manifestos are unavailable or unrepresentative of a party's true stance, or when the salience-based logic does not match a confrontational issue dimension; expert surveys or text-scaling may then fit better.
Strengths & limitations
- Provides the longest-running, most comprehensive comparable dataset of party positions across democracies since 1945.
- Grounded in parties' own authoritative documents rather than perceptions, giving a direct behavioral measure of emphasis.
- A fixed, theory-driven category scheme enables consistent cross-national and over-time comparison.
- Transparent and reproducible: raw category counts are published, so alternative scales and reanalyses are possible.
- Human coding is labor-intensive and subject to inter-coder unreliability, particularly across languages and over time.
- The saliency-based RILE index conflates emphasis with direction and has contested measurement properties.
- The fixed category scheme can miss newly salient issues and forces diverse content into predefined boxes.
- Manifestos may not reflect a party's revealed behavior in government, limiting validity as a preference measure.
Common pitfalls
This section is available to Pro members. Upgrade to Pro
Applications
This section is available to Pro members. Upgrade to Pro
Frequently asked
What is a quasi-sentence and why does it matter?
A quasi-sentence is the project's unit of analysis: a statement expressing a single political idea, which may coincide with a grammatical sentence or be a clause within one when a sentence makes several distinct points. It matters because every salience and position measure is a count of quasi-sentences, so inconsistent segmentation across coders or languages introduces measurement error before any category is assigned. Reliable unitization is therefore a prerequisite for valid manifesto data.
What are the main criticisms of the RILE left–right index?
RILE sums right-emphasis categories and subtracts left-emphasis ones, which mixes how much a party talks about an issue (salience) with which side it takes (position), and treats the two directions symmetrically. Critics show this can make scores unstable and sensitive to the fixed category list, and that it behaves more like a salience difference than a true spatial position. Alternatives such as the logit ratio scale of Lowe and colleagues address some of these measurement concerns.
How does manifesto coding compare to automated text scaling?
Manifesto coding is human content analysis using a fixed, theory-driven category scheme, yielding interpretable, comparable category counts but at high cost and with inter-coder reliability concerns. Automated scaling methods like Wordscores and Wordfish estimate positions from word frequencies far more cheaply and are often validated against manifesto data. The approaches are complementary: manifesto codings provide a rich, validated benchmark, while automated methods scale to large corpora and new texts.
Sources
- 1.Budge, I., Klingemann, H.-D., Volkens, A., Bara, J., & Tanenbaum, E. (2001). Mapping Policy Preferences: Estimates for Parties, Electors, and Governments 1945–1998. Oxford: Oxford University Press.ISBN 9780199244003
- 2.Volkens, A., Bara, J., Budge, I., McDonald, M. D., & Klingemann, H.-D. (Eds.) (2013). Mapping Policy Preferences from Texts: Statistical Solutions for Manifesto Analysts. Oxford: Oxford University Press.ISBN 9780199640041
- 3.Lowe, W., Benoit, K., Mikhaylov, S., & Laver, M. (2011). Scaling Policy Preferences from Coded Political Texts. Legislative Studies Quarterly, 36(1), 123–155.
You have read it. What now?
Cite this page
ScholarGate. (2026, June 22). Manifesto Coding. ScholarGate. https://scholargate.app/political-science/manifesto-coding