About the Laboratory

The Laboratory for Electoral Systems and Technologies (LiST) is a functional unit of the Department of Political Science at the Faculty of Philosophy, Sofia University St. Kliment Ohridski. It was established by a decision of the Faculty Council of May 29, 2017.

The laboratory studies electoral systems, voting technologies, and electoral security — their impact on the electoral process and on voting outcomes.

LiST co-organizes the annual National Conference on Electoral Systems together with the Bulgarian Electoral Systems Association (BESA). Accepted conference papers are published in Political Studies, the journal of the Bulgarian Political Science Association (BPSA).

Members

Head

Assoc. Prof. Stoycho P. Stoychev, D.Sc.

Sofia University St. Kliment Ohridski, Department of Political Science

Researchers

Assoc. Prof. Daniela Pastarmadzhieva, PhD South-West University "Neofit Rilski"
Assoc. Prof. Stoyanka Balova-Tsvetkova, PhD Central Electoral Commission of Bulgaria
Senior Assistant Professor Vanya Kashukeeva-Nusheva, PhD Sofia University, Department of Political Science
Dimitar Dimitrov Central Electoral Commission of Bulgaria, 1994–2001 and 2019–2026
Donika Stefanova, PhD candidate Sun Yat-sen University, Shenzhen (中山大学，深圳区)
Daria-Lora Dacheva, PhD candidate Sofia University, Department of Political Science
Malinka Valkova, PhD candidate Sofia University, Department of Political Science
Plamen Tsvetkov, PhD candidate Sofia University, Department of Political Science

Associated Researchers

Yoana Atanasova Sofia University, Department of Political Science
Tsvetelin Benkov Sofia University, Department of Political Science

Publications

Research

Electoral Clientelism (2014–2024)

How widespread vote buying and coercion are, how they vary across election types and parties, and why machine voting and video surveillance fail to curb them.

The share of controlled and bought votes ranges from 8.8% (presidential, November 2021) to 29.1% (parliamentary, October 2014); the trend is U-shaped — a decline around the 2020–2021 protests and a rebound above 20% (parliamentary, April 2023: 27.9%; local, October 2023: 28.9%) despite the introduction of voting machines and video surveillance.
Cash offers dominate over threats (2014: 81.5% cash versus 9.4% threats), and the average price per vote has risen with inflation — from about BGN 39 (2014) to BGN 80–87 (2022–2023).
Field-wave estimates put directly bought and controlled votes at 180,000–240,000; including households and paid turnout across roughly 12,000 precincts, the figure rises to over 1 million (nearly one-third of votes cast), worth on the order of BGN 100 million.

Six nationally representative field surveys (N from 936 to 1,699), 2014–2024 · model "Share = Offered × Accepted × Delivered × Turnout" · working manuscript

Campaign Finance in the EU (2012–2024)

How the form of government (semi-presidential or parliamentary), the electoral system, and the financing model shape the relationship between campaign spending and electoral outcomes.

Each additional percentage point of spending yields a different return across countries: +1.04% in France (R²=0.43), +5.0% in Romania (R²=0.64), and, most strikingly, +27.9% in Austria (R²=0.54); the tightest fit is in Croatia (R²=0.86).
Public financing strengthens the spending–outcome relationship the most (coefficient 5.76), while predominantly private financing is statistically insignificant; the three institutional factors together explain only 17.5% of the variance in outcomes.
A comparison of all 27 EU member states (cluster analysis, four groups; ANOVA F=11.06, p<0.001) shows that the most tightly regulated systems have lower turnout and weaker party institutionalization — a "moderate" level of regulation appears optimal.

Original spending and outcome data (13 countries) and a comparative analysis of all 27 EU member states · correlation, regression, and cluster analysis · working manuscripts

Election Administration in Bulgaria

How election administration — legal framework, technology, precinct committees, oversight — affects turnout and trust, seen through the eyes of the administrators themselves.

Twelve in-depth interviews with election administrators (2024) reveal growing complexity: the Electoral Code has been amended more than 20 times since 2014, and preferential voting is a leading source of counting errors.
Machine voting produces "clean" protocols when it is the sole method (July 2021), but under a mixed system the machine functions de facto merely as a printer and slows down the count; reliance on a single vendor constitutes a systemic risk.
Precinct election commissions are the weakest link (an aging, low-paid membership, with no sanctions for systemic errors); trust in the fairness of elections stands at 10% (2024), and turnout has fallen from 49.1% (April 2021) to 33.4% (June 2024).

12 semi-structured interviews (2024) and official CEC turnout aggregates · thematic analysis · working manuscript

European Parliament Elections: The Second-Order Elections Model (1999–2024)

Whether the "second-order" character of EP elections holds up empirically across the entire EU, or whether the 2024 data point to an erosion of this logic.

Across 153 observations (country-years; the 27 EU member states plus the United Kingdom), average turnout in EP elections is 47.3% versus 68.9% in the preceding national elections — a gap of −21.6 percentage points.
Compulsory voting narrows the gap to nearly zero (−7.1 p.p. versus −24.0 without it); the three-group typology is statistically distinct (ANOVA F(2,150)=338.1, p<0.001).
A time-series forecast (ARIMA) through 2034: "second-orderness" is slowly weakening at the aggregate level but remains deep in a subset of countries — the model holds up, but with clear limits.

Comparative panel, 153 observations, 1999–2024 · regression, typology, ANOVA, and ARIMA forecasting · late-stage working draft

Machine Voting and Voter Turnout

How the introduction of machine voting affects turnout and attitudes toward the electoral process.

In a nationally representative survey (N=1,699, April 2024), 16.9% report that they or someone close to them had been offered money or threatened over their vote in the past three years.
Trust in machine voting is split — 16.7% express full trust versus 22.9% full distrust; the leading concerns are the risk of hacking or manipulation (41.1%) and technical malfunctions (40.8%).
No statistically significant relationship is found between party affiliation, education, or age and attitudes toward the machines — skepticism is distributed evenly across groups.

Nationally representative survey (N=1,699, April 2024), ±2.38% · descriptive and inferential statistical analysis · submitted for peer review

Data

BGMP1.3

Data on 51 personal characteristics of all Bulgarian MPs between 1990 and 2015 (9 parliamentary terms), gathered primarily from Central Electoral Commission documents and the National Assembly archives.

Download: BGMP1.3.xlsx (0.5 MB, open access)

Public API

The Electoral Support Index is also available as a static JSON endpoint — free to use, attribution required.

Documentation and endpoint →

Video Lectures

Contact

Laboratory for Electoral Systems and Technologies
125 Tsarigradsko Shose Blvd., Bldg. 4, Fl. 3, Room 316
1113 Sofia, Bulgaria

Phone: +359 2 870 11 93

Email: info@listec.ai

Legal Information

Site Operator

This site is maintained by the Laboratory for Electoral Systems and Technologies — a functional unit of the Department of Political Science, Faculty of Philosophy, Sofia University St. Kliment Ohridski, 15 Tsar Osvoboditel Blvd., 1504 Sofia, Bulgaria. Contact: info@listec.ai. Sofia University Data Protection Officer: dpo@uni-sofia.bg.

Terms of Use

Unless otherwise indicated, copyright and other rights in the original materials on this site belong to their respective authors and/or Sofia University St. Kliment Ohridski under applicable law. Use for educational and scientific purposes with proper attribution is permitted. Permission for use beyond this scope must be obtained from the respective rights holder via the contact above.

Privacy Policy

This site has no forms, registrations or profiling, and uses no cross-site tracking tools (for our own site measurement, see the next section). When you visit, the hosting provider may technically record server logs (IP address, date and time, requested resource, browser) for security and diagnostics, for a limited period under its own rules. Correspondence to the address above is processed to review and answer your inquiry, for administrative record-keeping, and to protect legitimate rights and interests, for the period necessary. You have the rights of access, rectification, erasure, restriction, and objection, and the right to lodge a complaint with the Bulgarian Commission for Personal Data Protection (cpdp.bg); questions — Sofia University DPO: dpo@uni-sofia.bg.

Site measurement

The laboratory keeps its own usage statistics on its own server, without Google Analytics and without passing data to third parties. We measure page views, interface language, device type, scroll depth, the referring site (domain only) and actions in LiSTEC — opening a panel, asking a question and choosing a citation style. For questions put to LiSTEC we keep the language, the number of sources found, the response time and the question text after automatic removal of personal data. Anonymised questions and reference-error reports are also used to improve the library and the service: prioritising fixes and filling gaps in coverage. No cookies are set and nothing is written to the visitor's device. The visitor's address is not retained: it is used only to derive a city — no finer location — and a one-day irreversible pseudonym for counting unique visitors, whose key changes at midnight and is never stored. The legal basis is legitimate interest under Article 6(1)(f) of the General Data Protection Regulation, namely measuring our own service. Browsers that send a Do Not Track or Global Privacy Control signal are not measured.

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Licenses and Attributions

The Xolonium typeface is distributed under the SIL Open Font License 1.1 (the full license text ships with the font files on this site). Portrait images are used under free licenses — authors, sources, and licenses are listed on the attribution page. The BGMP 1.3 dataset is available for download in the Data section; factual data from official sources are not subject to copyright, while rights in the selection and analyses belong to the authors/Sofia University.