online voting system research paper

Online Voting system

5 Pages Posted: 28 May 2020

Aakash Suryavanshi

Inderprastha Engineering College - Department of Computer Science

Date Written: April 22, 2020

Our paper deals with online voting system that facilitates user(voter), candidate and administrator (who will be in charge and will verify all the user and information) to participate in online voting. our online voting system is highly secured, and it has a simple and interactive user interface. The proposed online portal is secured and have unique security feature such as unique id generation that adds another layer of security (except login id and password) and gives admin the ability to verify the user information and to decide whether he is eligible to vote or not. It also creates and manages voting and an election detail as all the users must login by user name and password and click on candidates to register vote. Our system is also equipped with a chat bot that works as a support or guide to the voters, this helps the users in the voting process.

Keywords: HTML, CSS, Java Script, PHP, MYSQL, phpMyAdmin, XAMPP

JEL Classification: L80

Suggested Citation: Suggested Citation

Aakash Suryavanshi (Contact Author)

Inderprastha engineering college - department of computer science ( email ), do you have a job opening that you would like to promote on ssrn, paper statistics, related ejournals, information systems ejournal.

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Blockchain-based e-voting systems: a technology review.

1. Introduction

2. voting system types and requirements, 2.1. voting systems.

• Paper-based voting: In this method, voters typically mark their choices on the ballot paper by hand next to the candidate or option they wish to vote for, and then the ballots are counted manually [ 2 ]. It can be further categorized into remote and on-site voting. Remote paper-based voting refers to the process of casting a vote by mail or other means of delivery, whereas on-site paper-based voting refers to the process of casting a vote in person at a polling station [ 3 ].
• Mechanical lever machines: They were first used in the 1890s and are operated by the voter indicating their choice by pressing a lever next to the preferred candidate. Once the voter is finished, the voter pulls the large lever again, which causes the counters associated with their choice to be incremented by one and the machine prepared for the next voter [ 4 ].
• Punch-card: Developed in the 1960s, utilized modified Hollerith cards where voters used a stylus to punch out chads corresponding to their candidate choices. After voting, the punched card was deposited in a ballot box. These cards were later counted using a card reader [ 2 ].
• Direct Recording Electronic (DRE): An electronic system that presents ballots and records voter selections directly into computer memory. Voters interact with DREs using push-buttons, touchscreens, or dials. Some DREs feature Voter Verified Paper Audit Trail (VVPAT) printers, allowing voters to confirm their choices on a paper record, which can be used for post-election audits or recounts [ 5 ].
• Optical scanning systems: Specialized computer hardware and software are used to read and interpret votes. Voters mark their choices on machine-readable ballots by filling in symbols next to their preferred candidates. Once marked, these ballots can either be scanned directly at the polling place or collected and scanned at a central location [ 6 ].
• Ballot-Marking Devices (BMDs): Presents ballots electronically, lets voters make selections, and then produces a human-readable paper ballot without storing the vote electronically. Introduced after the Help America Vote Act of 2002 to aid voters with disabilities, BMDs can either mark pre-existing ballots or print summaries, sometimes with barcodes or QR codes. From 2016 onwards, some areas expanded BMD usage to all voters, becoming more common in 2020 [ 7 ].
• I-voting: Internet voting denotes a subset of e-voting methodologies wherein ballots are transmitted and registered via the Internet [ 8 , 9 ]. Terms such as “remote e-voting”, “mobile voting”, and “online voting” are often used in the literature to describe these systems. All of the terms outlined above are, however, grouped under the broader conceptual framework of i-voting systems, which is itself an instance of an e-voting paradigm. Furthermore, Blockchain-based e-voting systems are a type of i-voting that relies on the internet by using a peer-to-peer computer network that employs blockchain technology to cast and count votes in an election [ 10 , 11 , 12 ].

2.2. Voting Systems Requirements

2.2.1. non-security requirements.

• Functional Requirements - User-Centric Voting Design: The concept that a voting system should be easy for all people to use. This means that it should have a user-friendly interface and show choices without giving any candidate an advantage. - Flexibility: It refers to the ability of the system to adapt to a variety of formats, languages, and voting ballots, making it compatible with different platforms and technologies. To provide a flexible and adaptable electronic voting experience, this phrase emphasizes adapting to changes, complying with deadlines, and permitting numerous ballot question types, including open-ended questions.
• Non-Functional Requirements - Equality: It assigns priority to equitable and consistent voter access, ensuring that regardless of the process of voting, all voters have equal voting rights and opportunities and receive the same information and opportunities. - Accessibility: This term highlights the importance of providing individuals with functional limitations or disabilities with the necessary access to vote, ensuring voters have undiscriminating access to the voting infrastructure, and enabling entities to have logical and/or physical access to the voting system. - Openness: For an e-voting system, the functioning of the system (hardware and software) should be transparent to citizens, and the people should be able to understand and verify how the voting system works. - Auditability: It refers to the necessity of being able to verify that all votes in the final election tally are precisely accounted for, along with having reliable and authentic election records with a (possibly) physical but always permanent audit trail that ensures voter secrecy. - Cost-effectiveness: It addresses the need for essentially affordable and reusable systems with implementation and maintenance costs that are acceptable and competitive with traditional voting methods. - Interoperability: In order to ensure smooth integration and compatibility with different components and technologies, it makes sure that voting system data are imported, exported, or reported in an interoperable format using widely accepted, openly available interfaces and communications protocols.

2.2.2. Security Requirements

• Functional Requirements - Authentication and eligibility: * Voter authenticity: requires voter identification based on the voter registration database and ensures that only eligible voters cast their votes. * Uniqueness: the voter can only submit a vote once, and the final result of that vote can never be altered. * Eligibility: guarantees that only legitimate voters are able to vote and that their identities are confirmed precisely. - Anonymity and secrecy: * Anonymity: the voter’s identity remains unlinked to their vote, and personal information or identity should remain concealed. * Secrecy: ensuring that no one involved in the voting process can link a specific ballot to a particular voter, preserving voter anonymity; in addition, the content of their vote remains confidential. - Uncoercible ballot assurance: * Uncoercibility: the fundamental principle of an e-voting system is to prevent any external influence, coercion, or vote-selling, ensuring that voters cannot prove or reveal their voting decisions, thereby safeguarding the integrity of the voting process and obstructing attempts at manipulating or pressuring voters for electoral gain. * Non-valid voting capability: voters should be able to cast ballots that they know are invalid if they so desire without compromising the integrity of the election in any way.
• Non-functional Requirements - Integrity and reliability: * Data protection: guarantee that each vote is reliably recorded and remains tamper-proof, while also applying rigorous data protection measures to prevent unauthorized access to or manipulation of voting data. * System integrity: ensure resistance against security failures or vulnerabilities, the voting system needs to maintain its functionality by preventing reconfiguration during operation and using multiple levels of controls. * Reliability: ensure the system functions robustly without losing any votes, even in the presence of multiple failures, including those related to voting machines and network communication, and prevent malicious code or bugs, thus providing voters with the utmost confidence in its secure and efficient operation under anticipated physical conditions. - Detection and monitoring: * Testing: The principle that electoral authorities, political parties, and social organizations should have the ability to put the voting systems to the test to ensure they meet the established criteria. This testing process should be thorough and conducted by experts to evaluate and verify that the systems meet the required security standards. * Monitoring: record important activities through event logging mechanisms in a format suitable for automated processing while also generating, storing, and reporting error messages in real time as they occur during the voting process. - Fairness: the importance of maintaining a fair voting environment by avoiding biased or misleading information, ensuring that the voting system does not provide evidence about any voter’s intention before the end of the voting phase, and remaining neutral so that the system does not influence the eligible voter’s intention during the voting process. - Verifiability and accuracy: allowing voters and election officials, parties, and independent observers to verify that the votes are accurately recorded and counted, ensuring the system can securely record votes, enabling them to use control mechanisms accurately with direct control of ballot changes and selections, providing voters the ability to verify their intentions in the vote without alterations, and offering sound and independently verifiable evidence that each authentic vote is accurately reflected in the election results. - Availability: the system’s ability to remain consistently available to all eligible voters, protect against denial of service attacks, establish redundant communication paths, ensure continuous availability during the election, have alternative support and election sites ready in case of failures, maintain a minimum Mean Time Between Failures (MTBF), have updated backups readily available for disaster recovery, and protect sensitive information.

3. Background, Related Work, and Objectives

3.1. blockchain technology, 3.2. blockchain applications across domains.

• Blockchain in healthcare: In healthcare, blockchain is employed for secure data sharing, patient privacy, and interoperability among different healthcare systems [ 22 ]. Its application in healthcare shares some aspects of e-voting, such as the emphasis on data security and privacy. However, whereas blockchain in healthcare deals with continuous data flow and personal health records, in e-voting, it addresses the singular event of casting and recording votes.
• Blockchain in financial services: In financial services, blockchain technology revolutionizes transactions and trust mechanisms. Similar to e-voting, where blockchain brings transparency and verifiability to the voting process, in financial services, it introduces a new concept of trust and efficiency in transactions [ 23 ]. The key difference lies in blockchain’s role in handling continuous financial transactions as opposed to the discrete event of voting.
• Blockchain in supply chain management: blockchain technology in supply chain management focuses on improving transparency, reducing fraud, and enhancing efficiency [ 24 ], whereas both supply chain management and e-voting systems benefit from blockchain’s immutability and transparency, supply chain management uniquely utilizes blockchain for continuous tracking of goods and transactions, in contrast to the periodic nature of elections.
• Blockchain in cloud computing: In cloud computing, blockchain enhances security, data provenance, and creates new service models like Blockchain-as-a-Service (BaaS). The integration of blockchain in cloud computing shares similarities with e-voting in terms of improving security and reliability. However, the use cases in cloud computing are more varied and continuous, focusing on service enhancement and data integrity across diverse cloud-based applications [ 25 ].
• Blockchain in education: Blockchain technology in education mainly focuses on enhancing data security, credential verification, traceability, and record management. Through its immutable feature, blockchain technology not only ensures the integrity of educational records and certificates, consequently creating trust in academic credentials, additionally, it effectively secures and tracks the progress of academic patents, copyrights, and research innovations, significantly enhancing the management and protection of property within the educational domain [ 26 , 27 , 28 ]. Compared to its application in e-voting, where blockchain ensures vote integrity and transparency, in education, it serves to preserve academic achievements and automate administrative processes.
• Blockchain in IoT: Blockchain technology in IoT includes enhancing security, scalability, and trustworthiness in diverse applications like smart cities. The decentralized nature of blockchain in IoT addresses issues similar to those in e-voting, like ensuring security and scalability [ 29 ]. However, IoT applications deal with a broader range of data types and greater scalability challenges than electronic voting systems.

3.3. Related Work

3.4. implementations of blockchain-based e-voting systems.

• Luxoft: Luxoft Holding Inc., a global IT service provider of technology solutions, is developing an e-voting infrastructure that will enable the world’s first consultative vote on blockchain in Zug, Switzerland. Hyperledger Fabric was used to create an authorized blockchain that included a network, applications, and algorithms. In order to allow voters to cast their ballots, Zug’s digital ID registration app based on Ethereum was authorized through uPort. Luxoft announces its intention to open source this technology and creates a Government Alliance Blockchain to encourage blockchain use in public institutions [ 39 ].
• Votem: A company specializing in election management, its main product is the CastIron platform. This platform is built on blockchain technology and offers several distinctive features, including a distributed database, immutability, permission-based access, and an audit trail. Votem has successfully handled over 13 million voters, serving both government elections and various associations in the United States and around the world. Notably, their track record boasts zero instances of fraud, compromise, attacks, or hacking, highlighting the security and reliability of their system [ 40 ].
• Voatz: A blockchain-based mobile voting tool that was launched in 2018 in West Virginia for overseas military voters participating in the 2018 midterm elections in the United States. Voatz includes biometric validation, such as fingerprints or retinal scans, so that voters validate their applicants and themselves on the application. A recent study found Voatz has major security flaws that allow attackers to monitor votes and edit or block ballots in large amounts [ 41 ].
• POLYAS: In the summer of 1996, Finland held the first POLYAS online election, with 30,000 voters participating in three languages. The company uses blockchain technology to offer an electronic voting system to the public and private sectors. Germany’s Federal Office for Information Security granted the first online election certification in 2016. The online voting system satisfies anonymity, accuracy, singularity, verifiability, and auditability. In Europe and the USA, several important companies employ POLYAS to manage their electronic voting systems [ 42 ].
• Polys: An online voting system that increases confidence in the voting process and results. Because it is based on blockchain technology, it is secure and transparent. Both the voting procedure and the results are immutable. Transparent cryptographic techniques are employed on the top of the blockchain to protect voter anonymity. Voters can check at any moment to ensure that their vote is valid and unmodified [ 43 ].
• DecentraVote: A blockchain-based solution for virtual meetings was originally developed by a team at the iteratec location in Vienna. DecentraVote uses a public Ethereum network based on Proof of Authority consensus with permissioned validator nodes. The smart contract constructed a Merkle tree of all voting rights on-chain, and the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) generated a proof for every voting right off-chain. DecentraVote does not address national political elections [ 44 ].

3.5. Research Gap and Objectives

• To conduct a comprehensive comparison of blockchain-based e-voting systems against traditional and e-voting systems, focusing on understanding their relative benefits and challenges.
• To review and analyze the concrete implementation techniques of blockchain in e-voting systems, identifying how they address existing challenges.
• To provide the potential implications of blockchain-based e-voting systems for addressing existing challenges in the blockchain-based e-voting systems.
• To establish an up-to-date roadmap for future research, emphasizing areas that require further investigation in the rapidly evolving landscape of blockchain-based e-voting.

3.6. Contribution of the Review

• Identify and analyze the benefits and challenges of blockchain-based e-voting systems in comparison to traditional voting and other e-voting systems, identifying the impact of blockchain-based e-voting systems on various aspects of the voting process.
• Explore the implementation technologies utilized in blockchain-based e-voting systems.
• Provide summarizing observations and recommendations for future research and development in this field.
• Benefits: What are the benefits of using blockchain technology in e-voting systems over other implementation approaches? The benefits are expressed in terms of requirements met by blockchain-based e-voting systems but not by other voting and e-voting types.
• Challenges: What are the challenges faced in implementing blockchain-based e-voting systems? These are expressed in terms of requirements that are already satisfied by other types of voting and e-voting systems but generally not yet met by blockchain-based e-voting systems.
• Impact: What are the impacts of proposed blockchain-based e-voting systems on different qualities? Impacts are expressed in terms of requirements that have been shown as satisfied (becoming a benefit of these) or not satisfied (becoming a challenge for blockchain-based e-voting systems).
• Technologies: what are the common technologies and implementations used in blockchain-based e-voting systems, including popular blockchain frameworks, consensus algorithms, security and privacy enhancing techniques?
• Future Research: based on the challenges identified and technologies reviewed, what future research and development directions should be explored in blockchain-based e-voting systems to enhance their functionality and quality?

4. Methodology

• Search query: (evoting OR ivoting OR e-voting OR i-voting OR ((electronic OR internet) AND (voting OR vote OR election))) AND (blockchain OR "distributed ledger" OR DLT)
• Papers that are directly related to or contribute to the comprehension of blockchain-based e-voting systems are relevant to the title.
• Papers should be available in English to ensure accessibility and comprehension.
• Papers with an available full-text version, which allows for a comprehensive analysis and extraction of data.
• To avoid repetition and ensure a unique set of papers, it is necessary to remove any duplicate titles.
• Exclude papers that are not written in English, as they can hamper comprehension and analysis.
• Exclude book chapters and focus on research articles and conference papers.
• To ensure the inclusion of valid and reliable research, papers that are officially retracted are excluded.
• Exclude papers if their topic does not align with the blockchain-based e-voting systems.

5. Results—Benefits, Challenges, and Impacts

• We address benefits, challenges, and impacts before looking at implementation technologies and summarizing future research in the following sections.
• For each, we comment on all properties mentioned in relation to the specific blockchain perspective.
• We also list the properties in the order of their frequency for the specific concern across the selected study papers, summarizing total occurrences and normalized numbers for better comparison.

5.1. Results—Benefits of Blockchain-Based E-Voting Systems

• Integrity: holistic assurance of security aligned with the design [ 45 ].
• Immutability: once a vote is recorded, it cannot be altered, ensuring the voting process’s finality [ 46 ].
• Durability: robust against data loss and ensures the permanency of stored data.
• Stability: Resistance to disruptions or manipulations like hacking. Stability is enhanced by strong encryption systems, often inherent in blockchain technology [ 47 ].
• Non-repudiation: a voter cannot dispute the validity of their cast vote [ 48 ].
• Transparency: The blockchain-based e-voting system’s inherent design encourages open voting, recording, management, and counting procedures. It facilitates independent audits [ 49 ] and ensures that all transactions (votes) on the blockchain are visible to all participants and can be independently verified.
• Anonymity: protecting a voter’s identity [ 50 ].
• Confidentiality (secrecy): the voters’ choices are private, and outcomes are not presented ahead of time [ 51 ].
• Untraceability: prevent the tracing of a vote back to its individual voter [ 50 ].
• Pseudoanonymity: voters’ actual identities are masked, but their voting activities are linked to unique identifiers similar to pseudonyms or addresses [ 52 , 53 ].
• Public verifiability: the ability of all to verify the entire election process [ 54 ].
• Individual verifiability: the ability for every voter to verify that their vote was precisely recorded and counted [ 54 ].
• Auditability: ensure the voting process accuracy and truthfulness [ 55 ].
• Availability: blockchains generally ensure that voters are able to cast their votes anytime within the stipulated period without facing any issue.
• Broad turnout: technology allows substantial participation of eligible voters.
• Universal access: the ability of the system to be used effectively by all eligible voters.
• Decentralization: Refers to the distribution of voting system authority, responsibility, and operations across a network compared to a central entity. This property is fundamental to blockchain technology and is essential for enhancing confidence among citizens by minimizing control of a potentially corrupt third party [ 36 ].
• Simplicity: how simple and straightforward the system is to operate.
• Understandability: clarity in system operation ensures that voters cast their votes as intended.
• Cost efficiency: The system’s capacity to carry out voting operations at a cost that is affordable. This can involve a lower-cost setup and maintenance, material distribution, and human expenses.
• Time efficiency: the system’s ability to speed up voting and vote tallying.
• Performance efficiency: the ability to handle massive amounts of data (votes), process, and count votes accurately, securely, and swiftly.
• Eligibility: only eligible voters can participate [ 58 ].
• Fairness: election results are not exposed before the voting process finalizes [ 58 ].
• Accountability: ability to determine whether or not the official vote record is inaccurate is facilitated by the blockchain [ 59 ].
• Uniqueness: each eligible voter merits one and only one vote.
• Accuracy: each vote is precisely accounted for, ensuring there is no modification, omission, or unauthorized inclusion [ 14 ].
• Credibility: how much voters, politicians, and the general public trust and believe in the e-voting system.
• Reliability: the system’s consistency in performance through time ensures accurate, error-free function and availability [ 60 ].
• Adaptability: ability of an e-voting system to alter or adjust in order to accommodate various circumstances or necessities that may emerge [ 61 , 62 ].
• Flexibility: ability to adapt to different frameworks, election types, voting methods, and voter interfaces.
• Resistance to coercion: capacity of an e-voting system to shield voters from potential manipulations or coercions [ 36 , 63 ].

5.2. Results—Challenges in Blockchain-Based E-Voting Systems

• Privacy: It encompasses efforts to protect the secrecy of everyone who casts a vote, keep sensitive voter information from leaking out, and minimize the risk of tracking individual voters. However, ensuring privacy in e-voting causes challenges due to the conflicting objectives of auditability and transparency with privacy [ 64 , 65 ].
• Security: It is a crucial aspect of blockchain-based e-voting systems, as it encompasses various measures to maintain the voting process’s integrity, and availability. Defensive measures against cyber-attacks, Zero-Day exploits, and smart contract vulnerabilities are challenges for the blockchain security fundamental qualities. In [ 66 ], several types of attacks on blockchain such as hash-based attack, centralization attack, traffic attack, network level attack, injection attack, integrity attack, and private key leakage attack are discussed. It is necessary to mitigate such threats and prevent fraudulent use or disclosure of sensitive voter data without authorization [ 67 , 68 ].
• Scalability: As the number of participants and transactions increases, it becomes crucial to maintain high performance and throughput. The inherent characteristics of blockchain, such as the need for consensus among distributed nodes and the necessity of storing every transaction on the blockchain, present scalability challenges. The decentralized nature of blockchain can lead to slow transaction processing times and increased resource requirements. In order to reach scalability in blockchain-based e-voting systems, it is necessary to address transaction throughput, network bandwidth, and data storage capacity. To ensure that blockchain-based e-voting systems can accommodate an increasing number of participants and transactions while maintaining the security and decentralization nature of blockchain, scalability concerns need to be dealt with [ 36 , 69 ].
• Technical aspects: various implementation challenges for blockchain-based e-voting systems arise, encompassing algorithm restrictions, technical complexity of consensus algorithms, hardware platform compatibility, integration with existing systems, complexity of technology, interoperability (including protocol interoperability), technical limitations, transparency in certain implementations, implementation challenges, complexity of implementation, complex design requirements, automating configuration, and limitations of authentication schemes [ 70 , 71 , 72 , 73 ].
• Efficiency and feasibility: This encompasses various factors, including computation resource efficiency, energy consumption, performance efficiency, cost efficiency, and feasibility. Computation resource efficiency includes minimizing computational overhead associated with the consensus protocol and effectively allocating resources to handle the increasing workload. For minimizing the operational costs of blockchain-based e-voting systems, energy efficiency is crucial. The development of energy-efficient protocols, algorithms, and hardware can help reduce energy consumption [ 31 , 74 , 75 , 76 ].
• Acceptability and immaturity: It refers to the level of trust and confidence stakeholders have in blockchain-based e-voting systems. To address this, it is necessary to achieve security, privacy, transparency, and reliability, thus building an environment that encourages the acceptance of blockchain-based e-voting systems. The immaturity of blockchain technology in e-voting leads to a lack of real-world experiments, extensive testing, stakeholder engagement, and comprehensive evaluation [ 11 , 34 , 38 , 77 , 78 ].
• Usability: it is necessary to achieve a balance between a user-friendly interface and the security and integrity of the voting process [ 38 , 79 ].
• Coercion freeness: it refers to challenges to protect voters from external pressures or coercive influences that could compromise their right to vote freely [ 33 , 64 , 80 ].
• Accuracy and reliability: Ensuring accuracy is paramount to guaranteeing that each vote is recorded and counted correctly, without any errors or omissions. Blockchain technology has the potential to enhance accuracy by creating a transparent and tamper-proof record of all voting transactions. However, to achieve a reliable and credible e-voting system, it is crucial to design a protocol that is fair, prevents double-voting, and avoids reliance on a central authority [ 81 , 82 ]. By developing and implementing robust cryptographic techniques, secure consensus algorithms, and comprehensive auditing mechanisms, blockchain-based e-voting systems can enhance accuracy, reliability, and credibility, ensuring the integrity and fairness of the electoral process [ 83 , 84 ].
• Accessibility: Access to voting opportunities is a fundamental principle. Limited internet access in certain locations presents a significant challenge to accessibility in blockchain-based e-voting systems. Providing a method such as offline voting that is consistent with the overall system is complex [ 85 , 86 , 87 ].
• Regulatory and governance: Implementing blockchain-based e-voting systems requires adherence to legislation as well as adjusting to a constantly evolving legal landscape. Addressing regulatory and legal difficulties entails managing jurisdictional requirements, data privacy legislation, and electoral laws, and ensuring legal standards are challenging. Furthermore, ensuring interoperability and compatibility across different e-voting systems and platforms needs to establish common standards and protocols for blockchain-based e-voting, as it can provide seamless integration and collaboration among various stakeholders. Addressing regulatory and governance challenges, including the establishment of standards, is a significant challenge for blockchain-based e-voting systems [ 88 , 89 , 90 ].
• Decentralization and consensus mechanisms: The distribution of authority, control, and decision-making power throughout the e-voting process, from registration to result calculation, is referred to as decentralization at all stages. Achieving the appropriate level of decentralization is a challenge for ensuring transparency, avoiding central points of failure, and increasing system trustworthiness. Furthermore, for reaching a proper level of decentralization, selecting a suitable consensus mechanism to securely and quickly validate and confirm transactions is a related issue [ 91 ]. Consensus techniques are crucial for assuring network participant agreement and defending against fraudulent operations. Choosing the best consensus mechanism necessitates careful consideration of variables such as scalability, security, energy efficiency, and the specific needs of the e-voting system [ 92 , 93 ].
• Enhanced privacy: Recent advances in cryptographic techniques, such as zero-knowledge proofs and homomorphic encryption, blind signatures, ring signatures, and mix networks, have significantly enhanced the privacy aspect of blockchain-based e-voting systems. These methods enable the verification of votes without revealing the voter’s private information, simultaneously balancing privacy with the necessary transparency and auditability.
• Enhanced security: In response to security challenges, there have been significant developments in both blockchain architecture and cryptographic defenses. In addition, enhanced consensus algorithms, like Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT), have been implemented to counteract various blockchain-specific attacks. Additionally, the integration of advanced security protocols and mechanisms could become standard methods, improving these systems against cyber threats.
• Scalability improvement: To address scalability issues, innovative solutions such as off-chain transactions, sharding, optimized consensus protocols, and layer-2 scaling solutions like Lightning Networks have been introduced. These technologies have proven effective in increasing transaction throughput, allowing for more scalable e-voting systems.
• Technical improvement: to address the technical complexities, approaches for optimizing the chosen consensus algorithm for efficiency, simplifying technical complexities, ensuring hardware platform compatibility, ensuring interoperability with existing systems and protocols, implementing automation for configuration, and constantly seeking feedback for refinement are some of the steps taken or that need further research to evolve the system.
• Energy and cost efficiency: The shift towards more energy-efficient consensus mechanisms, like Delegated Proof of Stake (DPoS), has notably reduced the operational costs and energy consumption of blockchain networks. Further, ongoing research into optimizing blockchain infrastructure and in other layers (on-chain and non-chain) can lead to the economic feasibility of blockchain-based e-voting systems.
• Increasing acceptability: Experimental projects and real-world evaluations can play an important role in building trust and demonstrating the viability of blockchain-based e-voting systems. By developing educational resources and engaging stakeholders, this technology can be accepted and understood by a broader audience.
• User-friendly interfaces: Significant efforts can be made to develop interfaces that are both simple for voters and secure. These interfaces often include guiding instructions and reliable verification mechanisms to ensure a seamless and secure voting experience.
• Provide coercion-resistant: To achieve this aim in a blockchain-based e-voting system, there are several methods in the literature: implementing strong end-to-end encryption, utilizing zero-knowledge proofs, enforcing receipt-freeness, using blind signatures, employing multi-step authentication, securing physical components, maintaining a transparent blockchain, implementing auditing and monitoring, and ensuring user-friendly interfaces. Together, these strategies ensure the integrity of the voting process, prevent coercion, and enable voters to participate freely and without fear of repercussions.
• Accuracy and reliability enhancements: By adopting robust cryptographic techniques and providing a decentralized ledger with transparent, auditable transactions, accuracy and reliability can be enhanced. By using identity verification mechanisms and smart contracts to ensure fairness, double voting can be prevented, whereas decentralized oracles and on-chain storage of critical data can reduce reliance on centralized sources. Consensus mechanisms and regular security testing are key to overall reliability. In all these cases, blockchain-based e-voting systems become more accurate and reliable.
• Improved accessibility: Efforts to expand accessibility include developing offline voting mechanisms and protocols in mobile voting apps and establishing remote voting centers in areas with limited internet access. These centers can be equipped with the necessary technology to ensure that mobile voting applications are accessible to voters. Provide features for people with disabilities, such as screen readers, voice-guided interfaces, etc. Consider having backup plans in place in case of technical failures or disruptions in areas with limited internet access.
• Regulatory compliance and governance: establishing legal frameworks and standards is a key focus, ensuring that these systems comply with the regulatory challenges associated with blockchain-based e-voting.
• Decentralization and consensus mechanism optimization: customized consensus mechanisms that adjust to the unique requirements of e-voting systems can enable achieving a balance between speed, security, and decentralization.

5.3. Results—Impacts of Blockchain-Based E-Voting Systems

5.4. in-depth analysis of results, 6. results—technologies and implementation of blockchain-based e-voting systems.

• Blockchain platforms;
• Consensus algorithms;
• Security and privacy techniques;
• Authentication and identity verification techniques;
• Other techniques (cryptography, development, testing).

6.1. Blockchain Platforms

6.2. consensus algorithms.

• Proof of Work (PoW): Commonly used consensus algorithm, including Bitcoin. It is a technique that requires members, known as miners, to solve computationally demanding puzzles in order to secure the network and validate transactions [ 94 ].
• Proof of Stake (PoS): a consensus process in which block creators (validators) are selected depending on their wealth or stake in the network, and their possessions act as a guarantee, inciting honesty and network security [ 95 ].
• Proof of Authority (PoA): A consensus approach used with authorized entities or individuals as block validators. Unlike other consensus methods, PoA is based on a predetermined set of reliable validators who proved their credibility in the network [ 96 ].
• Byzantine Fault Tolerance (BFT): A technique that obtains agreement among participants even in the presence of malfunctioning or malicious nodes. BFT consensus algorithms are designed for dealing with Byzantine failures, in which nodes behave unexpectedly and inconsistently [ 97 ].
• Practical Byzantine Fault Tolerance (PBFT): A specific algorithm that provides BFT in distributed systems. A leader node is selected to propose a block of transactions, which the other nodes, called replicas, validate and agree on [ 98 ].
• Raft consensus algorithm: Developed for fault-tolerant log management to handle replicated logs. The Raft algorithm elects a leader to replicate logs across all nodes. The leader logs client requests and replicates them to cluster nodes. After a majority of nodes acknowledge log entries, the leader commits them and informs the followers [ 99 , 100 ].
• Delegated Proof of Stake (DPoS): A PoS consensus algorithm variant. DPoS relies on the PoS concept by delegating block creation and validation commitments to a selected number of trusted delegates elected through vote [ 101 ].
• Crash Fault Tolerant (CFT): A type of consensus method established for distributed systems that can endure crash failures, in which nodes in the system stop responding or crash. In it, a simple majority voting method is frequently used, in which nodes vote on the proposed state or decision. The system considers a value or decision to be acceptable if a majority of nodes agree on it [ 102 ].
• Stellar consensus protocol (SCP): It combines the principles of federated agreement and Byzantine agreement to offer the Stellar network with a decentralized and fault-tolerant consensus mechanism. It enables nodes to agree on the state of the blockchain and keep the security and integrity of system transactions [ 103 ].
• Hybrid (Proof of Credibility (PoC) combined with Proof of Stake (PoS): The weight of each vote in the consensus process is determined by the value of the tokens staked by validators through the Proof of Stake (PoS) mechanism. The method brings Proof of Credibility (PoC) to address the issue of coin collapse in the PoS consensus mechanism. This combination of PoS and PoC is a safe hybrid structure that ensures full security when deployed in e-voting systems [ 104 ].

6.3. Security and Privacy Techniques

• Zero-Knowledge Proofs (ZKPs): a cryptographic technique that enables one party to prove to another party the truthfulness of a statement or claim without disclosing any extra information [ 33 , 105 ].
• Homomorphic Encryption (HE): a cryptographic technique that facilitates computations to be executed on encrypted data without the need for decryption [ 106 , 107 , 108 ].
• Blind Signature (BS): a cryptographic method that enables a party to receive a valid signature on a message without disclosing the message’s contents to the signer [ 109 ].
• Ring Signatures: A cryptographic technique that offers anonymity and unlinkability to the signer within a group (ring) of potential signers. In the context of cryptographic protocols, a ring signature allows the signer to generate a signature on a specific message, thus convincing the verifier that the message was signed by an entity within a specific group while at the same time obscuring the true identity of the singer [ 110 ].
• Shamir’s Secret Sharing Scheme (SS): a cryptographic method that enables the division of a secret into multiple shares that are distributed among participants [ 92 ].
• Quantum Key Distribution (QKD): a method of establishing secure cryptographic keys between two parties that makes use of the concepts of quantum physics [ 111 , 112 ].
• Mix Network (MN): This technique is used to protect the privacy of voters and the secrecy of votes. Through serving as a channel between voters and the authority responsible for counting the votes [ 113 , 114 ].
• Time-lock encryption (TLE): in this technique, a time-based delay is added to the encoding of encrypted data [ 114 ].
• Machine Learning (ML): By integrating machine learning and blockchain technology, along with deep learning algorithms, significant enhancements can be achieved in biometric ID authentication. This involves utilizing machine learning methods to analyze facial features and verify the identities of users [ 84 , 115 ].
• Circle Shuffle (CS): this method relies on a circular arrangement of votes, wherein each vote is assigned to a particular place in the circular structure [ 92 ].
• Reputation-Based PayOff algorithm (RoPO): An incentive mechanism that is used in different decentralized systems to motivate players based on their reputation or performance history [ 116 ].
• Proxy Multi-Signature Scheme (PMS): a variant of the common multi-signature method that includes the idea of a proxy or delegate to make signing on behalf of multiple individuals [ 117 ].
• Bit Commitment (BC): a cryptographic technique in which one party (the committer) makes a commitment to another (the verifier) about a value without initially disclosing that value to the verifiers until the committer decides to reveal the committed value at a later time [ 118 ].
• Differential Privacy (DP): It intends to maintain voters’ sensitive data private while still allowing effective aggregate voting data analysis. It provides a structure for protecting voters’ anonymity by adding random noise or perturbations to the data in a controlled manner [ 119 ].
• Provenance-Based solution (PB): this solution involves tracking the origin and transformations of data (provenance) within the blockchain [ 120 ].

6.4. Authentication and Identity Verification Techniques

• Biometric authentication: This method uses an individual’s unique characteristics to validate their authenticity. These qualities can include fingerprints, facial recognition, iris or retina patterns, and even voice.
• OTP (One-Time Password): a password that can only be used for one login session or transaction, often used to give a higher level of protection to sensitive transactions or systems [ 123 , 124 ].
• Aadhaar ID verification: the Unique Identification Authority of India (UIDAI) issues Indian residents a 12-digit Aadhaar number based on the resident’s self-portrait, ten fingerprints, and two iris scans [ 125 , 126 ].
• Multifactor authentication: this is the safety mechanism that requires multiple authentication methods from different categories to validate a user’s identity for a login or other transaction.
• Multi-step authentication: a security procedure that requires a user to provide extra evidence of identification when an additional level of assurance is required.
• PKI-based X.509: PKI-based X.509 is a widely adopted standard that outlines how public key certificates are structured [ 127 , 128 ].
• Unique IDs based on hash values: this method entails creating a unique identifier by applying a hash function to the biometric data, name, and date of birth of the voters [ 129 ].

6.5. Other Concepts

• Cryptography techniques;
• Choice of development environments for smart contracts;
• Utilization of testing and benchmarking tools.

6.6. Analysis of Results

7. discussion and outlook, 7.1. results—suggested roadmap for blockchain-based e-voting systems.

• Scalability and Performance Improvements (Scal&Perf): Future work in this matter concentrates on developing more efficient consensus algorithms and investigating how to integrate blockchain technology into large-scale e-voting systems. The primary goal is to improve transaction processing rates, block generation rates, and block sizes while maintaining privacy, security, and energy efficiency [ 32 , 133 , 134 , 135 ].
• Security and Privacy (Sec&Priv): This requires the development and implementation of advanced cryptographic techniques, such as zero-knowledge proofs, secure multiparty computation, blind signatures, ring signatures, and homomorphic encryption, to safeguard the identities and voting preferences of voters. To ensure a robust, anonymous, and trustworthy e-voting system, research concentrates on enhancing transparency and mitigating various types of attacks, like scalability attacks and transaction malleability [ 136 , 137 , 138 ].
• Implementation, Evaluation, and Testing (Impl&Eval): This involves implementing, evaluating, and testing blockchain-based e-voting systems on a larger scale to measure their performance, scalability, and usability in real-world scenarios. Additionally, efforts will be made to address security evaluations, incorporate privacy-by-design features, explore different blockchain protocols, and conduct user acceptance testing with real voters to validate the system’s effectiveness and feasibility for large-scale elections [ 113 , 133 , 139 , 140 , 141 ].
• Authentication and Identity Verification (Auth&ID): Future work involves creating a comprehensive and secure authentication system for applications in e-voting using biometric measures and blockchain technology. This should focus on enhancing biometric algorithm accuracy and efficiency, investigating decentralized identifiers, incorporating several biometric recognition technologies, and addressing issues related to user eligibility and trust assumptions throughout the voting process. These schemes intend to improve the overall security and convenience of user authentication and verification in blockchain-based e-voting systems [ 125 , 142 , 143 , 144 ].
• Coercion-Resistance (Coerc-Res): Future research should examine techniques that allow voters to make choices without the influence of coercers. This can be achieved by enabling voters to modify their votes multiple times, incorporating randomized tokens, leveraging face expression analysis, and employing facial tracking to enhance coercion detection. Additionally, ensuring receipt-free voting can be accomplished using various techniques, including ring signatures, while safeguarding voter privacy and security. The focus should remain on the proper design and execution of these tools to protect the integrity and privacy of the voting process [ 104 , 145 , 146 , 147 ].
• Accessibility (Access): This involves deploying a voting module on mobile devices that supports offline voting and provides accessibility options for disabled voters. Proper mobility, enhanced design, and increased system availability seek to provide all eligible voters with a user-friendly, accessible, and effective voting experience, with potential solutions proposed for locations where remote voting is not feasible [ 115 , 148 , 149 ].
• Legal and Governance Aspects (Leg&Gov): Future work refers to the establishment of regulations and standards for the deployment of blockchain technology, particularly in the context of electoral integrity. It comprises researching the influence of blockchain-based systems on election processes, developing a privacy-compliant framework, and exploring the sociological and psychological variables influencing online voter behavior in order to make blockchain technology more adaptable and suitable in more countries [ 89 , 150 ].
• Integration and Interoperability (Int&Inter): The creation and testing of blockchain-based e-voting systems that effectively interact with current voting infrastructures while maintaining compatibility with various legacy systems. The aim is to investigate the growth of blockchain-based voting solutions beyond elections, including agent-based methods and smart city services, as well as support adjusting in other industries like healthcare and auctions [ 151 , 152 , 153 ].
• Consensus Algorithms and Smart Contracts (Cons&SC): Future work for e-voting systems aims to develop self-administering blockchain systems that do not require central authorities while improving scalability and privacy using new consensus algorithms and privacy-preserving approaches such as homomorphic encryption and zero-knowledge proofs. The investigation looks at the use of various consensus techniques, such as PBFT, BFT, and PoW, as well as smart contracts, to automate electoral processes, integrate complex voting rules, and increase security in e-voting systems. Furthermore, improving consensus techniques can also contribute to scalability and energy efficiency [ 154 , 155 , 156 , 157 ].
• Usability and User Interface (Usab&UI): future work includes User Interface Enhancement, integrating it with a mobile app [ 156 , 158 ].
• Machine Learning (ML): future work in Machine Learning for e-voting systems consists of detecting fraudulent behavior and fake voters, predicting voting patterns and identifying anomalies for enhanced security and transparency, and investigating the use of deep learning mechanisms to optimize sidechain parameters [ 84 , 159 , 160 ].
• Acceptance (Accept): it involves conducting User Acceptance Testing (UAT) with a diverse group of stakeholders in order to improve system quality, reduce failures, and promise voter satisfaction [ 161 , 162 , 163 ].
• General Concept (Gen): future research includes studying a variety of electoral systems employing blockchain technology.
• Hybrid Systems (HS): future work should address the integration of paper ballots with electronic or blockchain-based voting mechanisms, studying the possibility of combining online and offline voting methods in different scenarios such as quadratic voting [ 125 , 164 ].
• Blockchain and IoT (BC&IoT): The future should involve integrating blockchain and IoT technologies in e-voting systems to improve voting process security, transparency, and verifiability. The focus of the research is on developing IoT-based applications to ensure easy data exchange between devices and the blockchain network, checking user authentication through biometrics and other secure methods, and examining the integration of blockchain to revolutionize different industries [ 38 , 70 , 165 ].

7.2. Final Observations

• Security: This is the most frequently named property in relation to e-voting systems in general and blockchain-based systems in particular. An initial discrepancy emerges in that security appears at rank 1 or 2 in all lists, showing it as a demonstrated benefit as well as an open challenge. A closer investigation, however, shows that some principle blockchain properties such as integrity, immutability, and durability are acknowledged, but specific concerns relating to attacks on keys or smart contracts still exist, and possible remediation techniques such as zero-knowledge proofs, signature schemes, and homomorphic encryption are proposed.
• Privacy: As a property specifically relevant to the voter and their votes, this is separated from security. Here the picture is consistent by being ranked higher on challenges and future research (ranks 1 and 2, compared to 3 and 4 for benefits and impact), thus clearly showing this as a concern to be better addressed.
• Scalability: not even listed in the benefits, with positions 3 and 1 in challenges and future work, it is clearly seen as a serious open problem of blockchain solutions on a par with security and privacy.
• Usability: Although not a core property associated with blockchain platforms, it is mentioned in the context of a wider e-voting system with front end being integrated. As for privacy, it is consistently discussed across the factors. The ranks (between 8 and 10) are slightly lower, probably showing this as important but not being a core concern of blockchains but of a wider e-voting system.
• Coercion-freeness: this is similar to usability consistently ranked, with ranks 10 and 12 for benefits and impacts and 7 and 10 for impact and future also seen as a property still to be demonstrated, though with potential to improve via blockchains as a transparent and secure ledger mechanism.
• Technical concerns: these appear in the challenges and future work at a relatively high rank (between positions 3 and 4), referring to general implementation and evaluation methods, but also more specifically to interoperability and integration with other platforms and concrete blockchain-specific research needed on consensus protocols and smart contracts.
• Transparency and auditability: these are the only ones that are undisputed as demonstrated benefits of blockchain-based e-voting systems, with no concerns or open problems noted.
• Other properties: properties such as verifiability, accessibility, accuracy/reliability, and acceptability are also consistently referred to as properties of relevance, but not as critical ones.

7.3. Insights and Implications from the Observations

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Hajian Berenjestanaki, M.; Barzegar, H.R.; El Ioini, N.; Pahl, C. Blockchain-Based E-Voting Systems: A Technology Review. Electronics 2024 , 13 , 17. https://doi.org/10.3390/electronics13010017

Hajian Berenjestanaki M, Barzegar HR, El Ioini N, Pahl C. Blockchain-Based E-Voting Systems: A Technology Review. Electronics . 2024; 13(1):17. https://doi.org/10.3390/electronics13010017

Hajian Berenjestanaki, Mohammad, Hamid R. Barzegar, Nabil El Ioini, and Claus Pahl. 2024. "Blockchain-Based E-Voting Systems: A Technology Review" Electronics 13, no. 1: 17. https://doi.org/10.3390/electronics13010017

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Modern Online Voting System Thesis

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• Jomo Kenyatta University of Agriculture and Technology

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Ballot Box Representation: Spatial Voting and the Effects of Information in Direct Democracy Elections

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• Cheryl Boudreau   ORCID: orcid.org/0000-0002-4021-1609 1 &
• Scott A. MacKenzie 1  

In states and localities with direct democracy, citizens can advance their policy interests without the aid of elected officials. Research documenting citizens’ lack of political knowledge raises questions about their ability to do so. We conduct three studies during real-world direct democracy elections to determine whether citizens choose alternatives (the ballot proposal or status quo) that are closest to their ideological positions and whether political information improves this outcome. Using original surveys, our first two studies estimate citizens’ ideological positions and show that citizens regularly choose alternatives that are closest to these positions. Using a survey experiment, our third study indicates that political information (party cues, policy information, and spatial maps) further improves such spatial voting. These results demonstrate citizens’ capacity to advance their policy interests in direct democracy elections and identify conditions under which political information strengthens the relationship between citizens’ policy interests and choices about ballot propositions.

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The extent to which citizens’ preferences are represented in the activities of government is a central question in the study of democratic politics. Regular elections offer citizens repeated opportunities to staff local, state, and federal offices with representatives who share their policy interests. In states with direct democracy, citizens can also advance their policy interests by supporting ballot propositions that reflect these interests and opposing those that do not. Today, citizens in these states decide many important issues. In November 2022, for example, citizens in California, Kentucky, Michigan, Montana, and Vermont turned to the ballot box to determine the fate of abortion policies following the Supreme Court’s Dobbs decision.

The frequent occurrence of such opportunities, however, does not guarantee that election outcomes will reflect citizens’ policy interests. As Downs ( 1957 ) emphasizes, citizens have weak incentives to acquire information that would help them make informed political decisions. Incentives are particularly weak in direct democracy elections, where citizens might see little benefit to learning about ballot propositions if they can simply allow a tolerable status quo to continue. Moreover, many ballot propositions involve complex issues and the costs of becoming informed can be high. Previous research indicates that citizens are often confused about ballot propositions (Bowler & Donovan, 1998 ; Cain & Miller, 2001 ; Cronin, 1989 ; Magleby, 1984 ). These deficiencies raise questions about citizens’ ability to advance their policy interests in direct democracy elections.

We argue that citizens can choose ballot proposition alternatives that reflect their policy interests and that political information helps them do so. Like other scholars (Lupia, 1992 ; Romer & Rosenthal, 1978 ), we describe citizens’ choices in direct democracy elections with a spatial model in which the alternatives (the ballot proposal and status quo) are compared against citizens’ ideal positions. Unlike these scholars, we hypothesize that citizens’ preferences for these alternatives are generated from a low-dimensional policy space that summarizes their positions across many issues. Rather than identify an ideal policy for every issue-specific space, citizens evaluate ballot propositions based on their own ideological position along a single low-dimensional policy space. That is, citizens will compare ballot proposition alternatives and choose the one closest to their own ideological position in this space. This implies that ballot propositions will map into a low-dimensional space and that this space is the same one used to evaluate other policies considered by state government. In addition, citizens’ positions in this space will accurately predict their preferences for many ballot propositions.

We test this hypothesis by conducting three studies during real-world direct democracy elections. In the first two studies, we analyze citizens’ decisions about California ballot propositions under active consideration in November 2016 and 2020. To this end, we administered original surveys before these elections asking respondents to express their opinions about policies that divided state lawmakers, as well as propositions that would appear on the ballot. This enables us to create objective estimates of respondents’ ideological positions (ideal points) on the same scale as the ballot propositions. We assess the extent to which respondents choose alternatives in direct democracy elections that are closest to their ideological position.

In the third study, we conduct survey experiments to assess whether and when political information improves respondents’ ability to pursue their policy interests in direct democracy elections. We randomly assign respondents to receive either (1) the Democratic and Republican parties’ official positions on ballot propositions (party cues), (2) policy information about each proposition’s likely consequences, (3) spatial maps that provide “complete information” about respondents’ own ideological position and the positions of ballot propositions, or (4) no additional information. Using this design, we examine two widely disseminated types of information (party cues and policy information) and compare their effects to a control group and to a real-world analogue of the complete information conditions of spatial voting models (spatial maps).

Our first two studies demonstrate that respondents’ choices about ballot propositions can be accurately predicted from their positions along the same liberal-conservative dimension that divides state lawmakers. A one-dimensional spatial model predicts 80.28 percent of respondents’ choices on 10 ballot propositions, reducing prediction errors by nearly 20 percent relative to the commonly used minority vote benchmark. Footnote 1 Our third study shows that party cues and policy information improve respondents’ ability to choose the ballot proposition alternative closest to their own ideological position, relative to the control group. These effects are similar in size and significance to those of spatial maps. Our analyses also uncover variation in the strength of individual propositions’ connections to the ideological dimension. We consider several explanations for this variation and show that information is helpful when citizens are confused about ballot propositions or have weak attitudes about them. Together, these results shed light on citizens’ capacity to advance their policy interests in direct democracy elections and identify conditions under which political information improves this outcome.

Spatial Voting in Direct Democracy Elections

Citizens in representative democracies are responsible for choosing elected officials and, in direct democracy settings, public policies via elections. However, if citizens cannot identify candidates who share their policy interests and/or make decisions about ballot propositions that reflect these interests, then election outcomes will inaccurately convey citizens’ preferences. A growing body of research indicates that citizens can choose candidates whose ideological positions are closest to their own (i.e., engage in spatial voting) in presidential, congressional, and local elections (Boudreau et al., 2019 ; Jessee, 2012 ; Shor & Rogowski, 2016 ).

Can citizens similarly advance their policy interests in direct democracy elections? Citizens’ lack of substantive knowledge about ballot propositions raises questions about their ability to make informed choices (Bowler & Donovan, 1998 ; Cain & Miller, 2001 ). Nonetheless, citizens arguably need not know many details about the choice at hand if they have access to knowledgeable and trustworthy information providers (Lupia & McCubbins, 1998 ). Lupia ( 1994 ) shows that uninformed citizens who knew an information shortcut made choices about ballot propositions that resembled those of well-informed citizens. Other studies find that citizens’ preferences about ballot propositions are related to their partisanship, self-reported ideology, and other characteristics (Bowler & Donovan, 1998 ; Branton, 2003 ; Gerber & Lupia, 1995 ; Magleby, 1984 ). Finally, recent scholarship indicates that citizens can articulate reasons for their choices (Colombo, 2016 ) and use information about ballot propositions objectively (Boudreau & MacKenzie, 2014 ).

While scholars have extensively studied citizens’ choices about ballot propositions, to our knowledge no existing study directly tests whether citizens choose ballot proposition alternatives that are closest to their ideological positions (i.e., engage in spatial voting) in direct democracy elections. This partly reflects the difficulty of developing comparable measures of citizens’ ideological positions and those of ballot propositions. There are also no experimental studies examining how political information affects spatial voting in direct democracy elections. Observational research on this topic relies on citizens’ observed levels of political knowledge or comparisons of uninformed citizens who know an information shortcut to well-informed citizens (Lupia, 1994 ; Shor & Rogowski, 2016 ). However, absent random assignment, respondents in these studies might differ in ways other than possessing political information that could explain differences in their choices (Arceneaux & Kolodny, 2009 ). Of course, many studies do manipulate political information (Bullock, 2011 ; Nicholson, 2011 ), but they rarely examine spatial voting as the outcome (see Sniderman & Stiglitz, 2012 and Boudreau et al., 2019 for exceptions).

We contribute to research on spatial voting, direct democracy, and political information in several ways. First, we apply a low-dimensional or basic space theory of spatial voting (Enelow & Hinich, 1984 ) to direct democracy elections. Whereas existing theoretical models characterize direct democracy elections as high-dimensional—with policy spaces specific to particular ballot propositions (Lupia, 1992 ; Romer & Rosenthal, 1978 )—we hypothesize that most ballot propositions map into a low-dimensional space. We expect that citizens’ decisions about ballot propositions will be accurately predicted from their positions on the same liberal-conservative dimension that divides state lawmakers.

Second, we develop comparable measures of citizens’ ideological positions and those of ballot propositions. Specifically, we conduct original surveys that ask respondents to express their opinions about the same policy issues that divide state lawmakers, as well as their views about pending ballot propositions. We use this information to estimate ideal points for respondents and cut points for ballot propositions, which allows us to determine which alternative (the ballot proposal or status quo) is closest to respondents’ ideological positions. We assess the extent to which respondents’ choices are consistent with spatial voting theory.

Third, we use experiments to examine how political information affects spatial voting on ballot propositions. We assess whether two types of information (party cues and policy information) that are widely disseminated in direct democracy elections enhance spatial voting relative to respondents who receive no information. We also compare their effects to a second baseline (spatial maps) to assess whether they substitute for the “complete information” needed for perfect spatial voting.

Theory and Hypotheses

Methodological innovations that enable scholars to measure the ideology of candidates, citizens, and others have transformed the study of political representation. These methods draw on Converse ( 1964 , p. 206), who defined a belief system as “a configuration of ideas and attitudes in which the elements are bound together by some form of constraint or functional interdependence.” Ideology captures this notion of constraint and implies that an individual’s positions across many issues can be predicted from her position on a small number of dimensions—the basic space. Enelow and Hinich ( 1984 ) expanded this notion of constraint by explaining how individuals’ positions in this low-dimensional basic space map onto the high-dimensional “action” space encompassing all political issues and government policies (Poole, 2005 , pp. 1–18). As Downs ( 1957 , p. 98) explains, individuals use ideologies to focus on the differences between alternatives, using this “short cut … to save himself the cost of being informed on a wider range of issues.”

Although Converse ( 1964 ) provides a well-articulated theory of constraint, his and other studies question whether most citizens hold stable ideological positions (Kinder & Kalmoe, 2017 ; Tausanovitch & Warshaw, 2018 ; Zaller & Feldman, 1992 ). Nonetheless, a large body of research argues that citizens do hold meaningful ideological positions. Citizens’ ideological positions predict their partisanship (Sniderman & Stiglitz, 2012 ), candidate evaluations (Carmines & Stimson, 1980 ), and vote choices in various settings (Jessee, 2012 ; Boudreau et al., 2019 ; Shor & Rogowski, 2016 ). Citizens’ issue positions are strongly related to their choices even after accounting for partisanship (Ansolabehere et al., 2008 ). Further, studies suggest that increasing elite polarization strengthens ideological constraint among citizens by communicating what issue positions partisans should hold (Barber & Pope, 2019 ). Whether derived from core values, economic circumstances, partisan or other attachments, there is ample evidence that citizens have a sense of what goes with what.

Direct democracy elections are a natural environment for spatial voting models. The universe of ballot propositions is high-dimensional, encompassing many different policies. It makes sense that citizens would rely on the same low-dimensional evaluative or ideological dimension(s) to inform their opinions about ballot propositions. While ballot propositions differ from the simple ideas and attitudes that comprise individuals’ belief systems (because they involve contests between concrete, often multi-faceted proposals for change and the status quo), the mapping from the ideological dimension(s) to alternatives in direct democracy elections is more straightforward than in candidate elections. This is because candidates are evaluated on many criteria (e.g., performance, likeability, race/ethnicity) besides their policy positions.

Previous research applying spatial models to direct democracy elections assumes the existence of issue-specific spaces where citizens have ideal policy positions. Romer and Rosenthal ( 1978 ), for example, cite packages of local school spending and expenditure proposals for new bridges as dimensions over which citizens have single-peaked preferences. Lupia ( 1992 , p. 392) similarly imagines a “finite continuum of possible policy alternatives” over which citizens have symmetric and single-peaked utility functions. A world where citizens have well-defined preferences over each issue-specific space, while theoretically useful, is unrealistic. In the 2018 general election in California, citizens considered 11 ballot propositions; in the November 2016 election in San Francisco, citizens decided 25 local measures. Given mounting evidence that legislators’, candidates’, and citizens’ ideologies are low-dimensional, the policy space that theoretical models of direct democracy elections ought to be concerned with is the evaluative, or ideological one. The mismatch between what existing theoretical models require of citizens and what empirical studies indicate citizens know about ballot propositions helps explain many scholars’ pessimism about direct democracy elections.

We hypothesize that rather than evaluate ballot propositions based on issue-specific preferences, citizens’ opinions are generated from their positions in a single low-dimensional basic space. This distinction preserves the assumption that citizens are policy-seeking in direct democracy elections (they prefer alternatives closer to their own ideal policy) but focuses attention on ideology as key to advancing their policy interests in the issue-specific “action” spaces occupied by ballot propositions. Thus, we expect that citizens will compare ballot proposition alternatives (the ballot proposal and status quo) and choose the one closest to their own ideological position. Replacing issue-specific policy spaces with an evaluative space of one or two dimensions is more than a distinction without a difference. It implies, for example, that ballot propositions will map into a low-dimensional space and that this space is the same one used to evaluate other policies carried out by state government. Whereas other theories offer little reason to expect that preferences for local school spending and proposals for new bridges will be linked, we anticipate that measures of citizens’ positions in the evaluative space will accurately predict their preferences for many ballot propositions:

Hypothesis 1:

If citizens generate their opinions about ballot propositions from their positions in a low-dimensional basic space, then we will observe a strong relationship between citizens’ ideological positions and their decisions about ballot propositions.

A potential barrier to spatial voting in direct democracy elections is citizens’ lack of information about how their policy interests relate to their choices about ballot propositions. Previous research identifies different types of political information that might help citizens in this regard. Here, we focus on two widely available information sources, party cues and policy information, and a third, spatial maps, that provides the information needed for perfect spatial voting (the locations of the ballot proposal and status quo relative to their own ideological position).

Party cues are among the most important information sources in direct democracy elections. The Democratic and Republican parties regularly contribute to the campaigns for or against ballot propositions and advertise their positions. Because the parties are perceived as knowledgeable about political matters and have well-known ideological reputations, their endorsements can help citizens determine where their own interests lie (Lupia & McCubbins, 1998 ; Sniderman & Stiglitz, 2012 ). The two parties take opposing positions on most ballot propositions, thereby providing signals about the relative ideological positions of the ballot proposal and status quo. That is, a party’s support for (opposition to) a ballot proposition communicates to citizens that the proposed policy change (status quo) is among the set of policies preferred by party members and consistent with its ideological reputation.

Hypothesis 2:

Citizens who receive party cues are more likely to choose ballot proposition alternatives that are closest to their own ideological position than citizens who do not receive this information.

Policy information, which we conceive of as clarifying the substance and likely consequences of a ballot proposition, is often circulated by nonpartisan experts seeking to educate policymakers and citizens. In states like California, government agencies analyze proposed ballot propositions and report their findings to the public. We contend that such information can help citizens determine the direction of the proposed policy change relative to the status quo and, as such, improve their ability to relate ballot proposal and status quo alternatives to their own position along the ideological dimension. When such policy information comes from a nonpartisan expert, citizens are likely to trust its characterization of a proposition’s substance and likely consequences:

Hypothesis 3:

Citizens who receive policy information from a credible source are more likely to choose ballot proposition alternatives that are closest to their own ideological position than citizens who do not receive this information.

Spatial maps, which are based on legislators’, candidates’, and/or citizens’ responses to a set of roll calls or policy questions, offer a visual summary of these actors’ positions along the ideological dimension(s). They are increasingly used by civic organizations to educate voters about which candidates hold policy views closest to their own (Boudreau et al., 2018 ; Garzia et al., 2017 ). In direct democracy elections, spatial maps similarly convey which alternative (the ballot proposal or status quo) is closest to a citizen’s ideological position. To the extent that citizens can interpret such spatial maps, they can strengthen spatial voting in direct democracy elections:

Hypothesis 4:

Citizens who receive spatial maps depicting their own ideological position and cut points for individual ballot propositions are more likely to choose ballot proposition alternatives that are closest to their own ideological position than citizens who do not receive this information.

Our predictions about low-dimensional spatial voting and political information are neither obvious nor empirically settled. Scholars’ skepticism about citizen competence (Converse, 1964 ), especially in direct democracy elections (Cain & Miller, 2001 ; Cronin, 1989 ; Magleby, 1984 ), weighs against observing strong spatial voting on ballot propositions. Moreover, if citizens cannot connect their policy interests to ballot proposition alternatives, they might also have trouble using political information. Studies of candidate elections find that partisanship and party cues weaken spatial voting, as citizens react by choosing party-endorsed candidates over more ideologically-similar alternatives (Jessee, 2012 ; Boudreau, Elmendorf, and MacKenzie 2015). With respect to policy information, citizens might ignore expert advice because they cannot relate the source’s interests to their own (Calvert, 1985 ). Studies of policy information find that it moves Democrats’ and Republicans’ opinions in the same direction (Boudreau & MacKenzie, 2014 ), indicative, perhaps, of a quality rather than ideological signal. To the extent that political information triggers non-ideological reactions, it will not increase spatial voting in direct democracy elections.

Our focus on ideology as the linchpin for policy-seeking behavior in direct democracy elections also raises questions about how well particular ballot propositions will map onto the liberal-conservative dimension and whether this will condition the effects of information. Although we predict that most ballot propositions will map onto the same space used to evaluate other state government policies, the strength of this relationship likely varies. Some propositions address issues that are obviously related to the liberal-conservative dimension, while others involve esoteric policies with weak connections to this dimension. Given our hypotheses that political information will convey ideological content, we expect it to be especially helpful on propositions with weaker connections to the ideological dimension (where citizens might be confused and/or lack strong attitudes).

Study 1: Spatial Voting in Five 2016 Direct Democracy Elections

Our first study examines whether citizens choose ballot proposition alternatives that are closest to their own ideological position in real-world direct democracy elections. We begin by estimating citizens’ ideological positions and cut points for five 2016 ballot propositions contested in California. First, we scaled roll call votes in the California Assembly between 2013 and 2016. These analyses indicated that a dominant first (liberal-conservative) dimension explains a large share of assemblymembers’ votes. We selected 34 votes that ranked high in their ability to discriminate California legislators along the liberal-conservative dimension.

Next, we measured citizen ideology on the same liberal-conservative dimension that explains voting in the California Assembly. To do so, we recruited 3,040 Californians from the Survey Sampling International (SSI) panel. Footnote 2 We administered our survey online using Qualtrics software from August 5 to August 11, 2016, three months before the 2016 general election. We asked respondents to express their opinions about the 34 policy proposals that successfully distinguish California legislators’ ideological positions. Table A1 in the Online Appendix (OA) summarizes these questions and respondents’ answers. Using these answers, we estimated each respondent’s position along the dominant liberal-conservative ideological dimension.

To determine the cut points of ballot propositions on the same ideological dimension, we also asked respondents to express their opinions about five citizen-initiated propositions under active consideration. Footnote 3 These included 1) a referendum on California’s law that prohibits grocery stores from providing single-use plastic bags, as well as initiatives that would 2) require background checks before individuals can purchase ammunition, 3) increase the cigarette tax by $2 per pack, 4) allow inmates convicted of nonviolent crimes to receive early parole consideration, and 5) require a two-thirds vote in the state legislature to change how fees that hospitals pay to Medi-Cal (California’s health-care program for low-income patients) are used. While time/attention considerations prevented us from examining all 17 propositions on the ballot, these five represent a range of important issues and offer a suitable test of Hypothesis 1 . Table 1 summarizes the propositions, as well as respondents’ opinions.

Respondents’ opinions about the ballot propositions, as well as their answers to the policy questions, enable us to identify a cut point for each proposition. In a one-dimensional spatial model, a ballot proposition’s cut point is the point equidistant between the ballot proposal and status quo. It separates citizens with ideal points closer to the ballot proposal from those with ideal points closer to the status quo. In Fig.  1 , for example, the dashed line shows the cut point of a hypothetical ballot proposition (BP) that seeks to move policy leftward, relative to the status quo (SQ). Respondents with ideological positions to the left of the line are, according to the model, likely to support this proposition. Respondents with ideological positions to the right of the line, like Respondent X, are likely to oppose it. Respondents with ideological positions close to or the same as the ballot proposition’s cut point are equally likely to support or oppose it. Together, our measures of respondents’ ideological positions and the cut point for each ballot proposition enable us to assess whether respondents’ opinions about the ballot propositions accord with their policy interests.

Spatial map with respondent ideal point and ballot proposition cut point

Data Analysis

To estimate respondents’ ideological positions and the ballot proposition cut points, we scaled respondents’ answers to the 34 policy and five ballot proposition questions together using the item-response model developed by Clinton et al. ( 2004 ). Footnote 4 The model assumes that each respondent i ’s utility from a policy proposal j ’s yea and nay outcomes (BP j and SQ j ) declines with its squared distance from the respondent’s ideal point, x i . The statistical model implied by this Euclidean spatial voting model is equivalent to the following two-parameter item-response model used in education testing applications (Jackman, 2001 : 228–229):

where y ij  = 1 if y * ij  > 0 and 0 otherwise. The additional assumption, ε ij  ~  N (0, 1), implies a probit model with respondents’ ideal points, x i , and policy proposal parameters, γ j and α j , as predictors to be estimated. Because the policy proposal parameters, γ j and α j , are functions of the positions of the yea and nay alternatives, BP j and SQ j , the probit model recovers cut points for the policy proposals rather than the exact positions of the alternatives.

While most studies of spatial voting focus on measuring candidate and citizen ideal points, we also examine the policy proposal parameters. The item difficulty parameter, α j , is related to a policy proposal’s general level of support. Holding ideology constant, higher values of α j reduce the probability that a respondent will support the proposal. The item discrimination parameter, γ j , indicates how strongly a proposal distinguishes respondents along the different dimensions of the ideological space (Jackman, 2001 ). In a one-dimensional model, γ j measures the extent to which a respondent’s ideal point, x i , translates into support for policy proposal j . Large and significant γ j indicate that support for the proposal j and ideology are strongly related.

To further investigate the influence of citizens’ ideological positions on their choices about ballot propositions, we also estimate models of support for each proposition using respondents’ ideal points and partisanship as predictors. To ensure that our measure of respondents’ ideology is independent of their opinions about ballot propositions, we re-estimated respondents’ ideal points by scaling only their answers to the 34 policy questions. Our dependent variables in these models indicate whether a respondent “strongly supports,” “somewhat supports,” “somewhat opposes,” or “strongly opposes” a ballot proposition (rescaled to range from 0 [least supportive] to 1 [most supportive]). For ease of presentation, we estimate a separate OLS model for each proposition and plot first differences (changing ideology and partisanship from their 25th [relatively liberal/Democratic] to 75th [relatively conservative/Republican] percentile values). Given that the five 2016 ballot propositions we examine sought to move policy leftward, we expect to observe negative first differences.

Our results provide evidence of significant spatial voting in direct democracy elections. Table 1 contains the item parameters for the five 2016 ballot propositions. Each of the discrimination parameters, γ j , is significantly different from zero, which indicates that all are substantively related to the liberal-conservative ideological dimension. There are differences in the strength of this relationship, with the ammunition limits and plastic bag ban propositions having values of – 1.345 and – 1.538, respectively, and the Medi-Cal fees proposition having a value of – 0.785. The discrimination parameter, γ j , is also significantly different from zero for each policy question that respondents answered (see Table A1 in the OA). This affirms that the same policy disputes that divide state lawmakers along liberal-conservative lines also divide our respondents.

With both the policy questions and ballot propositions related to the liberal-conservative dimension, we have a solid basis for expecting spatial voting in these elections. The success of the one-dimensional spatial model in predicting respondents’ ballot proposition choices underscores this point. A one-dimensional spatial model correctly classifies 83.19 percent of respondents’ choices on the five ballot propositions. Table 1 reports the proportional reduction in error (PRE) for each proposition and the aggregate proportional reduction in error (APRE) for all five propositions. Footnote 5 A one-dimensional model reduces classification errors by 19.8 percent above the commonly used minority vote benchmark.

Consistent with our first hypothesis, respondents’ ideal points have large effects on their support for the five ballot propositions. Figure  2 plots first differences from our models of support. The right-hand panel, for example, indicates that changing a respondent’s ideal point from its 25th to 75th percentile value reduces support for the plastic bag ban referendum by 0.23, a significant difference. The effects of ideology on the other propositions are also significant. We observe the smallest effects for the Medi-Cal fees proposition, which Table  1 indicates is weakly-related to the liberal-conservative dimension. The effects of ideology are comparable to those of respondents’ partisanship.

Effects of ideology and partisanship on support for 2016 ballot propositions

Note: Circles (triangles) are predicted first differences [with 95 percent critical intervals] of the effects of ideology (partisanship) on support for each ballot proposition generated from the models in Tables A2 and A3 of the OA

Study 2: Spatial Voting in Five 2020 Direct Democracy Elections

Our second study replicates Study 1 by estimating citizens’ ideological positions on the same scale as five new ballot propositions contested in 2020. As in Study 1, we first scaled roll call votes taken by members of the California Assembly, this time between 2017 and 2020. We selected 20 votes that ranked high in their ability to discriminate California legislators along a dominant liberal-conservative dimension. We then recruited 645 Californians from the Lucid panel and asked them to express their opinions about the 20 policy proposals, which we used to determine their position along the liberal-conservative dimension. We administered our survey online using Qualtrics software from October 2 to October 22, 2020, just before the 2020 general election.

We also asked respondents to express their opinions about five 2020 citizen-initiated ballot propositions. These included (1) a referendum on California’s law that would replace money bail with a system for pretrial release based on public safety, as well as initiatives that would (2) tax commercial properties based on current market value rather than purchase price, (3) authorize felony sentences for certain crimes defined as misdemeanors and restrict eligibility for a state parole program, (4) define app-based drivers as “independent contractors” and restrict local regulation of them, and (5) allow local governments to establish rent control on properties over 15 years old. As in 2016, time/attention considerations prevented us from examining all 12 propositions on the ballot, but these five encompass a range of important issues. Table 2 summarizes these propositions and respondents’ opinions.

Data Analysis and Results

We use the same procedures described for Study 1 to estimate respondents’ ideological positions and cut points for the 2020 ballot propositions on the same scale. Our model also estimates item parameters for the 20 policy proposals and five propositions. Table 2 contains the item parameters for the five 2020 ballot propositions. Each of the discrimination parameters is significantly different from zero. As in Study 1, we find variation in the connections between the ballot propositions and the liberal-conservative dimension. Three propositions (split roll tax, rent control, bail reform) are strongly related to this dimension while two others (felony charges, app-based drivers) appear disconnected. All the discrimination parameters for the 20 policy proposals are significantly different from zero (see Table A4 in the OA).

The predictive validity of the spatial model offers further evidence for hypothesis 1. A one-dimensional spatial model correctly classifies 75.33 percent of respondents’ choices on the five 2020 ballot propositions. Table 2 reports the PRE for each proposition and the APRE for all five propositions. The APRE is similar to what we observe in 2016; that is, a one-dimensional model reduces classification errors by 18.3 percent above the minority vote benchmark.

As in Study 1, we estimated models of respondents’ support for each 2020 ballot proposition with ideology and partisanship as predictors. Because respondents expressed opinions about all five propositions, we were also able to pool these responses and use factor analysis to calculate an overall support score. Ansolabehere et al. ( 2008 ) show that combining multiple survey items into a scale factor or simple average reduces measurement error. We re-estimated respondents’ ideal points by scaling only their answers to the 20 policy questions and rescaled their support for the ballot propositions, separately and combined, to range from 0 (least supportive) to 1 (most supportive).

Figure  3 plots first differences for the five 2020 ballot propositions. As in Study 1, ideology is a significant predictor of support for the five propositions (separately and combined), with effects comparable to respondents’ partisanship. We observe the largest effects of ideology on respondents’ support for the three propositions that Table  2 indicates are strongly related to the liberal-conservative dimension. Ideology has modest effects on support for the app-based drivers and felony charges propositions. Nonetheless, the pattern of first differences resembles what we observe in Study 1. The left panel of Fig.  3 plots first differences for both ideology and partisanship on the five-item support score. Changing ideology from its 25th to 75th percentile value reduces support by 0.19, a large and statistically significant difference. Footnote 6

Effects of ideology and partisanship on support for 2020 ballot propositions

Note: Circles (triangles) are predicted first differences [with 95 percent critical intervals] of the effects of ideology (partisanship) on support for each ballot proposition generated from the models in Tables A5 and A6 of the OA

Collectively, these results from two studies conducted four years apart and examining 10 ballot propositions that vary in substance, complexity, and salience, provide strong evidence that citizens can advance their policy interests in direct democracy elections. To be sure, we find variation in the strength of the relationship between citizens’ ideological positions and their opinions about ballot propositions. Although spatial voting theory is agnostic as to why this variation occurs, one explanation we can rule out is that citizens’ decision-making reflects more than one ideological dimension. In the OA, we show that adding dimensions to our spatial model seldom improves our ability to predict citizens’ choices about the 10 ballot propositions.

Study 3: The Effects of Political Information on Spatial Voting

In light of the significant spatial voting we observed in Study 1, we conducted a follow-up survey with an embedded experiment to test our hypotheses about how political information affects this outcome. Specifically, we recruited an additional 998 Californians, none of whom participated in Study 1, from the SSI panel. We administered this survey online using Qualtrics from October 1 to October 8, 2016, one month before the 2016 general election. To place these respondents’ ideological positions on the same scale as the five ballot propositions from Study 1, we asked them 18 of the policy questions that respondents in Study 1 answered. Based on their answers, we were able to estimate their ideal points.

We then randomly assigned respondents to either a control group or one of three treatment groups. All respondents were asked to express their opinions about the five 2016 ballot propositions. In the control group, respondents receive only the brief description of each proposition that respondents in Study 1 received. For example, on the parole credits ballot proposition, control group respondents read the following:

This November, Californians will be asked to vote on a ballot measure that would allow inmates convicted of nonviolent crimes to be given parole consideration upon completion of their primary sentence. Currently, many prisoners receive both a primary sentence for a crime and “enhancements” or extra time if there are multiple victims or if they previously were in prison. This measure would allow prison officials to award credits toward early release to prisoners who demonstrate good behavior, efforts to rehabilitate themselves, or participate in prison education programs.

Respondents were then asked whether they strongly support, somewhat support, somewhat oppose, or strongly oppose the proposition, or whether they don’t know. The exact wording of the ballot proposition questions and treatments is provided in the OA.

In the “party cues” treatment group, respondents also received the Democratic and Republican parties’ official positions on each ballot proposition. On the parole credits proposition, for example, respondents were told, “The Democratic Party supports this measure. The Republican Party opposes it.” In this example, the party cues imply that the proposition would replace the status quo with a more liberal policy.

In the “policy information” treatment group, respondents received information about the likely consequences of passing each ballot proposition. This information clarifies the direction of the proposed policy change, relative to the status quo. The policy information is drawn from materials produced by California’s nonpartisan Legislative Analyst’s Office (which estimates the fiscal and other impacts of ballot propositions). For example, on the parole credits ballot proposition, respondents in this treatment group received the following information:

This initiative would help reduce significant overcrowding problems in state prisons by increasing the number of non-violent inmates eligible for parole consideration. California’s nonpartisan Legislative Analyst’s Office estimates that this initiative could save the state tens of millions of dollars each year in correctional and other costs.

In this example, the policy information indicates that the early parole measure would move policy in a more liberal direction (because it would reduce correctional costs and allow more inmates to be eligible for early release).

Respondents assigned to the “spatial map” treatment group were shown a visual depiction of their own ideological position relative to each ballot proposition’s cut point. That is, respondents learn whether they should support or oppose each measure based on their actual ideological position. To create these spatial maps, we used nine policy questions representing a range of issues that respondents in Study 3 answered to create 512 “citizen profiles,” one for every combination of yes/no answers to these questions (e.g., nine “yes,” nine “no,” “yes” to the first five and “no” to the last four questions, etc.). We obtained an estimated ideal point for each profile by scaling the 512 profiles along with the survey responses of Study 1 respondents. Footnote 7 We then drew spatial maps that depict the estimated ideal point for each profile, as well as the cut point for each ballot proposition. Respondents were shown the spatial map that corresponds to their answers to the nine policy questions. Figure  4 provides an example of a spatial map that a respondent in this group might view before expressing an opinion about the parole credits ballot proposition.

Example of spatial map treatment for parole credits proposition

To measure Study 3 respondents’ ideological positions on the same scale as the ballot proposition cut points, we scaled their responses to the 18 policy questions together with Study 1 respondents’ answers to the 34 policy and five ballot proposition questions. This yielded estimated ideal points for respondents in Study 3 and new cut points for the five ballot propositions. Importantly, Study 3 respondents’ opinions about the ballot propositions did not influence our estimates of their ideal points or the ballot proposition cut points. This ensures that our measure of these respondents’ ideal points and the positions of the five ballot propositions remain independent of the political information manipulated in our experiments. It also reduces the accuracy of respondents’ estimated ideal points (by ignoring their opinions about ballot propositions), thus biasing us against finding effects for information.

To assess whether respondents’ choices about ballot propositions are consistent with spatial voting theory (i.e., preferring the alternative closest to one’s ideal point), we calculated the distance between each respondent’s estimated ideal point and the cut point for each ballot proposition. Recall that the cut point is the position at which a respondent is indifferent between the ballot proposal and status quo. In a one-dimensional spatial model, each ballot proposition’s cut point is given by the ratio (see Clinton & Jackman, 2009 ):

For each ballot proposition, subtracting the cut point from the estimated ideal point, i.e., (x i  – τ j ), provides a measure of how far away the cut point is from respondents’ ideal policy positions. Because γ j  < 0 (the position of the ballot proposal is to the left of the status quo) for all five 2016 ballot propositions, the spatial model predicts that a respondent will support (oppose) the ballot proposition when this distance is negative (positive).

To capture this intuition, our dependent variable, Vote_Spatial ij , is coded as 1 when (x i  – τ j ) < 0 and the respondent strongly or somewhat supports the proposition or when (x i  – τ j ) > 0 and the respondent strongly or somewhat opposes the proposition, and zero otherwise. We calculated the percentage of opinions in each treatment group and the control group that are consistent with spatial voting theory. We conducted difference-of-means tests to examine whether more respondents choose the ballot proposition alternative closest to their ideal point when they receive party cues, policy information, or spatial maps, relative to the control group. We also examined how well party cues and policy information approximate the “complete information” baseline that spatial maps provide. We report the results of these analyses having pooled responses to the five proposition questions and then separately for each proposition.

Our results indicate that political information significantly strengthens spatial voting in direct democracy elections. The large effects of political information are apparent in Fig.  5 , which plots for each group the percentage of choices about the five ballot propositions where respondents choose the alternative closest to their ideal point. In the control group, respondents choose the alternative closest to their own ideal point 71.2 percent of the time. Respondents in the party cues treatment group do so 74.7 percent of the time. Footnote 8 The difference between these groups is significant and supports our second hypothesis. The effects of party cues are indistinguishable from those of spatial maps. Indeed, in the spatial map treatment group, respondents choose the alternative closest to their ideal point 74.3 percent of the time. In this way, our results demonstrate that a widely disseminated “information shortcut” can substitute for “complete” spatial information about ballot propositions.

Spatial voting by control and treatment groups

Note: Numbers are percentages of respondents who chose the alternative closest to their own ideal point (see Table A10 of the OA). *Difference with control is significant (p< 0.05, one-tailed)

Consistent with our third hypothesis, we find that policy information also strengthens spatial voting. As Fig.  5 shows, respondents in the policy information treatment group choose the ballot proposition alternative closest to their own ideal point 75.4 percent of the time. This is a significant increase in spatial voting relative to the control group. It is also comparable to the effects of party cues and spatial maps. The sizable effects of policy information confirm that respondents can connect substantive information about ballot proposals to their policy interests and testify to the value of real-world efforts to disseminate expert advice.

As expected, the extent to which political information improves spatial voting varies across the five propositions. Figure  6 plots the difference between each treatment group and the control group. All three types of information increase the percentage of respondents who choose the alternative closest to their ideal point on the plastic bag ban and ammunition limits propositions, which Table  1 suggests have the strongest relationships with the liberal-conservative dimension. For example, spatial maps increase spatial voting on the plastic bag ban referendum by 7.6 percent. Party cues (6.5 percent) and policy information (11.0 percent) have comparable effects on this proposition.

Average treatment effects for 2016 ballot propositions

Note: Symbols are differences [with 90 percent confidence intervals] in percentage of respondents who chose the alternative closest to their own ideal point for all five ballot propositions and each proposition individually, generated from Table A10 of the OA

In contrast, Fig.  6 shows that political information does not significantly increase the percentage of respondents who choose the alternative closest to their ideal point on either the cigarette tax or parole credits propositions, which have weaker connections to the liberal-conservative dimension. However, both party cues and spatial maps increase spatial voting on the Medi-Cal fees proposition. This is somewhat surprising as this proposition appears disconnected from the ideological dimension. In what follows, we explore different explanations for the variation in the ballot propositions’ connections to the liberal-conservative dimension and the effects of political information.

Explaining Variation in Spatial Voting and the Effects of Political Information

We consider two potential explanations for the variation in the 2016 and 2020 ballot propositions’ connections to the liberal-conservative dimension. One possibility is that a proposition’s weak connection stems from confusion about its substance. Alternatively, a proposition might be weakly related to the ideological dimension because its popularity transcends ideological divisions. To assess these explanations, we analyzed the amount of time respondents take to express their opinions, their propensity to respond “don’t know,” and the strength of their support or opposition on each proposition. If respondents are confused about a proposition’s substance, then we should observe longer response times and more “don’t know” responses. If a proposition is overwhelmingly popular, then we should observe shorter response times, fewer “don’t knows,” and stronger attitudes. We should also observe high levels of overall support.

The top two panels of Fig.  7 plot the absolute value of each proposition’s discrimination parameter against response times and attitude strength. As Fig.  7 a shows, two propositions (Medi-Cal fees, felony charges) with the smallest discrimination parameters (i.e., the weakest connections to the liberal-conservative dimension) exhibit the longest response times. These propositions also generated more “don’t know” responses (see the OA). These results are consistent with respondent confusion. A third proposition (app-based drivers) that appears disconnected from the liberal-conservative dimension was overwhelmingly popular. Respondents spent less time making their choice, were more likely to offer “strong” support or opposition, and were less likely to say “don’t know.” As Table  2 conveys, this proposition also registered the highest support among the 2020 ballot propositions we examined.

Discrimination and treatment effects by ballot proposition

Note: Points in panels a and b indicate absolute value of discrimination parameters from Tables 1 and 2 . Points in panel c and d indicate effects of party cues from Tables A10 and A17. See Tables A15 and A16 for average response times and measures of attitude strength for individual ballot propositions

Can political information improve respondents’ ability to relate their policy interests to their choices about ballot propositions that appear disconnected from the liberal-conservative dimension? While we did not conduct a follow-up experimental study in 2020, we utilize data from 278 additional respondents from the Lucid panel who participated in a different study, but who answered the same policy questions as respondents in Study 2 and received party cues before registering their opinions about the 2020 ballot propositions (see the OA for details).

The bottom two panels of Fig.  7 plot our estimates of the effects of party cues on spatial voting against response time and attitude strength for both 2016 and 2020. As Fig.  7 c indicates, party cues tend to have stronger effects on ballot propositions characterized by longer response times. Indeed, the two propositions with the longest response times (Medi-Cal fees and felony charges) exhibit the largest increases in spatial voting. Figure  7 d shows that the effects of party cues also tend to be largest on propositions where respondents’ attitudes are relatively weak. Overall, these analyses suggest that party cues tend to be particularly effective at increasing spatial voting on ballot propositions when respondents are either confused or lack strong attitudes.

Our results provide three new types of evidence of citizens’ ability to advance their policy interests in direct democracy elections. First, our surveys of Californians making choices about 10 ballot propositions across four years revealed significant spatial voting. We compiled detailed measures of ideology by asking citizens over four dozen policy questions that divided state lawmakers along a dominant liberal-conservative dimension. Using a one-dimensional spatial model, we found that citizens’ ideological positions accurately predict 80.28 percent of their choices. Second, our experimental analyses of citizens’ choices on these ballot propositions indicate that party cues and policy information strengthen spatial voting. Indeed, citizens who receive this information behave as if they possess the “complete” information (spatial maps) needed for spatial voting. Third, the few ballot propositions that appear disconnected from the ideological dimension are those that respondents either find confusing or overwhelmingly support. While the small number of such propositions in our study does not permit us to be conclusive, political information appears to help citizens connect their policy interests to their choices on these propositions.

The fact that we conduct our three studies in California, where Democrats predominate in the state legislature and electorate, might be viewed as a limitation. Nonetheless, with the nation’s largest, most diverse population, and the world’s fifth largest economy, direct democracy elections in California address myriad issues and are consequential in their own right. Although legislators and voters trend liberal, low signature thresholds enable groups aligned with both parties to put propositions on the ballot (see Tables A21 and A22 in the OA). Several ballot propositions we examine were sponsored by right-leaning groups or endorsed by the Republican Party. Beyond these propositions, right-leaning groups historically have used direct democracy to lock in low property tax rates, voice opposition to illegal immigration and affirmative action, and adopt sentence enhancements for repeat criminal offenders. California citizens might have distinct preferences, but they resemble citizens in other direct democracy settings in having policy interests motivating their behavior.

For scholars, our findings demonstrate the benefits of focusing on ideology as key to understanding how citizens pursue their policy interests in direct democracy settings. In doing so, our study advances empirical assessments of the quality of citizens’ decisions by facilitating individual-level measures of “improvement” in citizens’ decisions—as opposed to relying on group-level comparisons of informed and uninformed citizens. It also offers a more thorough explanation of how and when political information will be helpful. Finally, it raises questions about how citizens form such evaluative dimensions in the first place and how spatial voting in direct democracy elections compares to candidate elections.

Our findings also have implications for practical efforts to inform citizens about their choices in direct democracy elections. The improved spatial voting we observe in response to policy information highlights the salutary effects of state laws that provide for nonpartisan expert evaluation of pending ballot propositions. Nonetheless, the significant effects of political information we observe also indicate that many citizens lack information about ballot propositions near Election Day. Increasing citizens’ access to the types of information we examine (e.g., party cues), either on the ballot as in many candidate elections or via official ballot pamphlets or websites, can improve citizen competence in these settings. Indeed, the success of the spatial maps we examined suggests a heretofore unstudied tool for helping citizens bring their policy interests to bear in direct democracy elections. Such interactive spatial maps are often provided on the Internet by civic groups and public agencies for citizens seeking information about candidates. Our study demonstrates that such efforts could be extended to direct democracy elections with potentially powerful results. In Europe, voter advice applications (VAAs) help citizens apply their issue preferences to ballot propositions, albeit with different information than spatial maps (Stadelmann-Steffen et al., 2023 ). Future research can examine whether VAAs or other information, such as about donors to campaigns for and against ballot propositions, similarly improve spatial voting.

Finally, our results place recent efforts to restrict citizens’ use of direct democracy (by increasing filing fees, tinkering with signature-gathering rules, or requiring voter supermajorities) in a different light. Proponents of such measures frequently cite concerns over citizen competence as justification for using direct democracy sparingly. Our results suggest efforts to manipulate the process might say more about the intentions of groups seeking particular outcomes, as in Ohio where abortion opponents sought to raise the threshold for passing constitutional amendments in advance of a November 2022 pro-abortion rights measure. For practitioners with sincere concerns about citizens’ understanding of ballot propositions, our methods could be used to identify propositions that are likely to be confusing to voters. This might inform legal challenges to ballot propositions, the drafting of ballot proposition titles and descriptions, or voter education efforts to improve citizen decision-making.

Data Availability

Data from these studies are available on Dataverse at the following URL: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi: https://doi.org/10.7910/DVN/C40CHP .

This benchmark model predicts that all respondents take the majority position on each proposition, thereby making classification errors equal to the number of minority votes. The relevant comparison is how much a spatial model reduces classification errors relative to this benchmark.

Our respondents resemble California’s population in many respects, though they are better educated than the general population. Our samples more closely resemble voters, the population that decides direct democracy elections (see the OA).

To minimize fatigue, respondents answered a random subset of 23 to 26 of the 34 policy questions and two to three of the five ballot proposition questions.

We used the pscl R package to estimate a one-dimensional model with uninformative priors for all model parameters with 200,000 iterations after discarding the first 10,000 and thinning by 100. The first dimension correctly classifies 79.67 percent of responses.

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These studies were funded by the Institute for Social Sciences, the Public Opinion Workshop, and the Committee on Research at the University of California, Davis. We thank participants in the Mathew McCubbins Memorial Conference at the University of California, San Diego for helpful feedback.

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Boudreau, C., MacKenzie, S.A. Ballot Box Representation: Spatial Voting and the Effects of Information in Direct Democracy Elections. Polit Behav (2024). https://doi.org/10.1007/s11109-024-09964-4

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