Although polling accuracy increases throughout the election, polls are always at least a little wrong on election day. In this article, we attempt to understand how characteristics of particular elections may make them harder (or easier) to predict. In particular, we focus on estimating the impact of voter turnout, electoral change, and vote buying on polling error. We find support for two of the three hypotheses. There is little evidence that voter turnout affects polling error. However, polling errors tend to be higher where there have been large changes in parties’ vote share from the previous election. We also find that higher prevalence of vote buying may be associated with larger polling errors.

FWISD8 Early Voting & Ballot by Mail Data

Precincts, Voters, and Election Participation

By Jason Brown [source]

About this dataset

This dataset contains comprehensive information about early voting and ballot by mail for Fort Worth ISD District 8. It includes key data points such as the full name, address lines 1-4, city, state, zip/zip+4 code of the voter; precinct and sub-precinct; ballot style, ballot party and voter party; election code and phone area/prefix/number; early voting site (if applicable) plus firstname and lastname of the voter. In addition to this voting information, the dataset also includes a field for the date that each voter cast their vote or submitted their mail in ballot. All of this data can be used to identify trends in voting behavior within a precinct or across Fort Worth ISD District 8 as a whole. With it, researchers may gain valuable insights into what motivates voters to go out and cast their ballots as well as other key information that can be used to increase democratic experiences in future elections

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How to use the dataset

This dataset provides information on the voting and ballot by mail process in Fort Worth ISD District 8. This data can be used to answer questions about who participated and how they voted, as well as which areas are most active when it comes to voting.

To get started, here are some tips for using the dataset: - Explore the data columns - The columns provided in this dataset include full name, address information (address line 1 through 4), city, state, zip code, precinct number/subdivision numbers for early voting sites and precinct locations , ballot style/party affiliations of voters , election code, phone numbers of registered voters (area code & prefix) and vote-by-mail site . As you explore different use cases of this data set you may find other interesting connections or patterns between one column or another that can help answer questions or provide insights into voting practices in the area. - Look at voter turnout - Using this dataset you can analyze voter turnout over a given period of time to identify trends in voter engagement both within one district but also across various districts across Texas. Pay attention to both early voting sites & traditional polling locations when making comparisons as they tend towards different kinds of participation among residents - people who prefer early voting might not prefer traditional polling locations vice versa so it's important to look at both types together..
- Understand Voter Motivation - Examine what most drives voter involvement in elections? Examining factors such as location (rural vs urban), age demographics etc., can tell us about what motivates voters either positively or negatively regarding engagement in elections held within their district . Comparing these numbers with actual votes casted provides rich insight into motivation behind why people may not have voted .

By understanding the existing patterns between these datasets using sophisticated analytics methods ,we could make highly accurate predictions about which areas will have higher levels of turnout at an upcoming election . With this knowledge we could implement policies that help increase interest/participation even further enabling a more open/fair democratic process for everyone involved!

Research Ideas

• Understanding Voter Behavior: Using this dataset, research organizations and political campaigns can gain insight into how certain demographics are voting and who they are voting for.
• Targeted Campaign Ads: With this dataset, marketing teams can create demographic-specific ads aimed at getting people to turn out to vote or targeting voters with a specific persuasion.
• Polling Place Location: Analyzing the data in regards to polling places could help cities identify where it is most beneficial to open and close polling locations, as well as the length of opening hours needed at each location depending on voter turnout or trend along party lines in a particular area

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: ev_vtrex_isdfw8.csv | Column name | Description | |:--------------------|:---------------------------------------------------------| | full_name | Full name of the voter. (String) | | addr_line1 ...

Reuters Polls gather insights from experts, presenting the perspectives of leading financial market forecasters at specific moments. These forecasters consist of economists, strategists from both the sell-side and buy-side, independent analysts, and some scholars. The polling archives encompass detailed predictions and consensus estimates for over 900 economic indicators, currency exchange rates, central bank policies on interest rates, money market rates, and bond yields.

Description:

This dataset contains comprehensive voting data for the 2024 US elections, focusing on general ballot measures. This information includes voting results from various sources and tracking public opinion about political parties and candidates across states and demographic groups. Each item in the dataset represents a specific poll. Along with detailed information about the dates of the polls. Survey organization, sample size, margin of error, Percentage of respondents supporting each political party or candidates

Key Features:

Poll Date:The date when the poll was conducted.

Polling Organization: The name of the organization that conducted the poll.

Sample Size: The number of respondents in the poll.

Margin of Error: The statistical margin of error for the poll results.

Party/Candidate Support: Percentage of respondents who support each political party or candidate.

State/Demographics: Geographic and demographic breakdowns of the polling data.

Use Cases:

Analyzing trends in public opinion leading up to the 2024 U.S. elections. Comparing support for different political parties and candidates over time. Studying the impact of key events on voter preferences. Informing political strategies and campaign planning.

Context

It's that time again, a US presidential election will be here soon enough. While state and national polls will receive all the attention, we will hear about a candidates "Likeability". However, these polls are not often analyzed with the same rigor. It's time for that to end.

Content

The data provided includes polling information for polls published measuring a political candidates "Favorables" or "Likeability". Polling data for Hillary Clinton, Bernie Sanders, Joe Biden and Donald Trump are included. The data are in nice tabular format and ready for analysis.

Acknowledgements

Thank you to realclearpolitics.com for openly publishing the data on their website in easy to acquire tabular form.

Inspiration

"Likeability" is often discussed and measured in politics,but is it ever analyzed? Does it receive the appropriate scrutiny or attention? How does "Likeability" relate to electoral success? What factors impact a political candidate's favorables? This data set has enough strength to stand on its own, but also serves as a good input to a broader analysis. Does a candidate's favorables have "seasonality"? Do their ratings go up or down as election day nears? Does distance make the heart grow fonder? Can we forecast a candidate's favorables? What is more important, a candidate's absolute number or their margin relative to their opponent for electoral success? How do these polls correlate with state and national polling?

The dataset comes from The Center for Public Integrity. You can read more about the data and how it was collected in their September 2020 article "National data release sheds light on past polling place changes".

Note: Some states do not have data in this dataset. Several states (Colorado, Hawaii, Oregon, Washington and Utah) vote primarily by mail and have little or no data in this colletion, and others were not available for other reasons.

For states with data for multiple elections, how have polling location counts per county changed over time?

variable class description election_date date date of the election as YYYY-MM-DD state character 2-letter abbreviation of the state county_name character county name, if available jurisdiction character jurisdiction, if available jurisdiction_type character type of jurisdiction, if available; one of "county", "borough", "town", "municipality", "city", "parish", or "county_municipality" precinct_id character unique ID of the precinct, if available precinct_name character name of the precinct, if available polling_place_id character unique ID of the polling place, if available location_type character type of polling location, if available; one of "early_vote", "early_vote_site", "election_day", "polling_location", "polling_place", or "vote_center" name character name of the polling place, if available address character address of the polling place, if available notes character optional notes about the polling place source character source of the polling place data; one of "ORR", "VIP", "website", or "scraper" source_date date date that the source was compiled source_notes character optional notes about the source

This poll, fielded July 20-23, 2000, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. Respondents were asked whether they intended to vote in the November 7, 2000, presidential election and for whom they would vote if the election were held that day, given a choice between Vice President Al Gore (Democratic Party), Texas governor George W. Bush (Republican Party), conservative commentator Pat Buchanan (Reform Party), and consumer advocate Ralph Nader (Green Party). Respondents were asked to assess the importance of the following issues in their electoral decision-making and to specify which candidate they most trusted to do a better job addressing them: holding taxes down, protecting the Social Security system, improving education, improving the health care system, handling the economy, handling gun control, handling foreign affairs, encouraging high moral standards and values, handling the death penalty issue, protecting people's privacy on the Internet, handling the federal budget surplus, managing the federal budget, handling crime, protecting the environment, addressing women's issues, and appointing justices to the Supreme Court. Views were sought on whether presidential debates should be held, which candidates should be invited to participate, and whether respondents were satisfied with the presidential candidates. In addition, respondents were asked which candidate understood the problems of the American people, was a strong leader, would bring needed change to Washington, had the knowledge of world affairs it takes to serve effectively as president, could keep the economy strong, would say or do anything to get elected, had new ideas, said what he really thought, was honest and trustworthy, had an appealing personality, and had the right kind of experience to be president. Those queried were asked whether a difference existed between Gore and Bush on the issues about which the respondent cared and their personal qualities. Opinions were elicited on whether the top priority for the federal budget surplus should be cutting federal taxes, reducing the national debt, strengthening Social Security, or increasing spending on domestic programs. Additional questions covered abortion and the impact of Bush's naming a running mate who supported legalized abortion, Bush's handling of the death penalty while governor of Texas, voter intentions regarding the 2000 Congressional elections, whether a smaller government with fewer services is preferred to a larger government with many services, whether the country should continue to move in the direction that Clinton established, and whether it mattered who was elected president. Background information on respondents includes age, gender, political party, political orientation, voter registration and participation history, education, religion, labor union membership, Hispanic origin, household income, and neighborhood characteristics.

Content

FiveThirtyEight's Pollster Ratings

This directory contains the data behind FiveThirtyEight's pollster ratings.

See also:

• FiveThirtyEight's Pollster Ratings
• The Polls Are All Right
• The State Of The Polls, 2016
• How FiveThirtyEight Calculates Pollster Ratings

Past data:

• 2014
• 2016

pollster-stats-full.xlsx contains a spreadsheet with all of the summary data and calculations involved in determining the pollster ratings as well as descriptions for each column.

pollster-ratings.csv has ratings and calculations for each pollster. A copy of this data and descriptions for each column can also be found in pollster-stats-full.xlsx.

raw-polls.csv contains all of the polls analyzed to give each pollster a grade. Descriptions for each column are in the table below.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight [organization page](https://www.kaggle.com/five...

Content

Poll Quiz - Guns

This folder contains the data behind the quiz Do You Know Where America Stands On Guns?

guns-polls.csv contains the list of polls about guns that we used in our quiz. All polls have been taken after February 14, 2018, the date of the school shooting in Parkland, Florida.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

Online Polling Software Market Outlook

The global online polling software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. This growth can be attributed to the increasing demand for real-time data collection and analysis, along with the rising adoption of digital tools for better engagement and feedback management across various sectors. The expansion of digital literacy and internet penetration worldwide, coupled with the need for cost-effective and efficient data collection methods, are also driving the market growth.

One of the primary growth factors for the online polling software market is the escalation in the utilization of digital platforms for data collection and feedback. Organizations across various sectors, including education, government, and corporate, are increasingly relying on online polling software to gather valuable insights from their target audience. This shift towards digital solutions is driven by the need for real-time data, which helps organizations make informed decisions quickly. Additionally, the ease of use and accessibility of these online tools have made them highly popular among users of all technical skill levels, further propelling the market growth.

Another significant growth factor is the rising emphasis on remote work and virtual engagements. The COVID-19 pandemic has accelerated the adoption of remote working models, leading to a higher demand for online collaboration and communication tools. Online polling software has become an essential component of virtual meetings, webinars, and remote learning environments. These tools provide an interactive platform for participants to engage effectively, ensuring that their voices are heard and contributions are valued. As organizations continue to embrace remote work and virtual engagements, the demand for online polling software is expected to grow substantially.

The continuous advancements in technology and the integration of artificial intelligence (AI) and machine learning (ML) capabilities into online polling software are also driving the market growth. These technologies enable more sophisticated data analysis and predictive insights, allowing organizations to better understand their audience and tailor their strategies accordingly. The incorporation of AI and ML not only enhances the efficiency and accuracy of polling results but also provides deeper analytical capabilities, making online polling software an invaluable tool for businesses and institutions aiming to stay ahead in a competitive landscape.

In the realm of data collection, Questionnaire Software has emerged as an indispensable tool for organizations seeking to gather detailed and structured feedback from their audiences. This software allows for the creation of customized questionnaires that can be distributed across various digital platforms, enabling organizations to capture specific insights tailored to their unique needs. The versatility of Questionnaire Software makes it a valuable asset in sectors such as education, healthcare, and market research, where understanding nuanced opinions and preferences is crucial. By leveraging advanced features such as logic branching and real-time analytics, organizations can enhance their data collection processes, ensuring that the information gathered is both comprehensive and actionable. As the demand for more sophisticated data collection methods grows, Questionnaire Software is poised to play a pivotal role in shaping the future of feedback management.

Regionally, North America is expected to dominate the online polling software market over the forecast period, owing to the high adoption rate of advanced technologies and the presence of major market players in the region. The Asia Pacific region is also anticipated to witness significant growth, driven by the increasing digitalization efforts and growing internet user base in countries such as China and India. Europe, Latin America, and the Middle East & Africa are also likely to contribute to the market growth, supported by the rising adoption of online tools in various sectors and the need for efficient data collection and analysis methods.

Deployment Mode Analysis

The deployment mode segment of the on

This study reports the results of a multiyear program to predict direct executive elections in a variety of countries from globally pooled data.We developed prediction models by means of an election data set covering 86 countries and more than 500 elections, and a separate data set with extensive polling data from 146 election rounds.We also participated in two live forecasting experiments. Our models correctly predicted 80 to 90% of elections in out-of-sample tests. The results suggest that global elections can be successfully modeled and that they are likely to become more predictable as more information becomes available in future elections. The results provide strong evidence for the impact of political institutions and incumbent advantage. They also provide evidence to support contentions about the importance of international linkage and aid. Direct evidence for economic indicators as predictors of election outcomes is relatively weak. The results suggest that, with some adjustments, global polling is a robust predictor of election outcomes, even in developing states. Implications of these findings after the latest U.S. presidential election are discussed.

Live Polling Software Market Outlook

According to our latest research, the global live polling software market size in 2024 is valued at USD 1.42 billion. The market is experiencing robust expansion, with a recorded CAGR of 12.3% from 2025 to 2033. By the end of the forecast period, the market size is projected to reach USD 4.04 billion in 2033. The primary driver fueling this growth is the increasing adoption of real-time engagement and feedback tools across diverse industries, as organizations seek to enhance interactivity, gather actionable insights, and foster audience participation in both physical and virtual environments.

One of the most significant growth factors for the live polling software market is the rapid digital transformation witnessed across multiple sectors. As businesses, educational institutions, and event organizers increasingly shift toward hybrid and remote models, the demand for interactive solutions that facilitate instant communication and feedback has soared. Live polling software enables seamless audience engagement, data-driven decision-making, and real-time sentiment analysis, making it indispensable in today’s highly connected world. The proliferation of smartphones, tablets, and high-speed internet has further democratized access to these tools, allowing organizations of all sizes to leverage live polling for training sessions, conferences, webinars, and classroom instruction. This widespread adoption is expected to continue, especially as user expectations for interactive and personalized experiences grow.

Another crucial factor propelling market growth is the integration of advanced technologies such as artificial intelligence, machine learning, and data analytics into live polling platforms. These technologies empower organizations to extract deeper insights from poll responses, automate result analysis, and personalize user experiences. AI-driven sentiment analysis, for example, can provide presenters and organizers with a nuanced understanding of audience moods and preferences, enabling more effective communication strategies. Additionally, the rise of cloud-based solutions has significantly reduced the barriers to entry, offering scalable, cost-effective, and easily deployable platforms that cater to organizations with varying needs and budgets. As vendors continue to innovate and differentiate their offerings, the market is expected to witness further diversification and specialization, catering to niche use cases and verticals.

Regulatory compliance and data privacy concerns have also contributed to the market’s evolution. Organizations are increasingly prioritizing secure and compliant polling solutions, especially when handling sensitive information or operating in regulated sectors such as healthcare, government, and education. The development of robust security features, end-to-end encryption, and GDPR-compliant data handling practices has become a key differentiator for leading vendors. As awareness of data privacy grows among end-users, the demand for trustworthy and transparent solutions is likely to intensify, shaping the competitive landscape and driving further innovation in security and compliance features.

From a regional perspective, North America currently dominates the live polling software market, owing to its advanced digital infrastructure, high internet penetration, and a culture of interactive communication in both corporate and educational settings. However, the Asia Pacific region is emerging as a high-growth market, fueled by rapid urbanization, increasing adoption of digital technologies, and government initiatives to modernize education and public administration. Europe, with its focus on data privacy and regulatory compliance, is also witnessing steady growth. Latin America and the Middle East & Africa, although smaller in terms of market share, are expected to register notable CAGRs as digital transformation initiatives gain momentum and local enterprises seek innovative engagement solutions.

Component Analysis

The live poll

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau... Visit https://dataone.org/datasets/sha256%3A05707c1dc04a814129f751937a6ea56b08413546b18b351a85bc96da16a7f8b5 for complete metadata about this dataset.

This poll, fielded February 12-14, 2000, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. Respondents were asked to give their opinions of President Bill Clinton and his handling of the presidency, foreign policy, and the economy. Views were sought on the condition of the national economy, the projected federal budget surplus, and the most important problem for the government to address in the coming year. Several questions asked how much attention respondents were paying to the 2000 presidential campaign, the likelihood that they would vote in the Republican or Democratic primary, which candidate they expected to win the nomination for each party, and for whom they would vote in the presidential primary and general election. Respondents were asked for their opinions of Republican presidential candidates George W. Bush, John McCain, and Alan Keyes, Democratic presidential candidates Al Gore and Bill Bradley, the main reason they held a favorable or unfavorable opinion of each candidate, and the importance of a candidate's personal qualities and position on issues. Opinions were also solicited of First Lady Hillary Clinton, former President George H.W. Bush, the Democratic, Republican, and Reform parties, and how well members of the United States Congress were handling their jobs. Additional topics included abortion, campaign finance reform, and the effect of elections on the federal government. Information was also collected on the importance of religion on respondents' lives, whether they had access to a computer, Internet access, and e-mail, whether they had served in the United States armed forces, and whether they had a child graduating high school in the class of 2000. Demographic variables include sex, race, age, marital status, household income, education level, religious preference, political party affiliation, political philosophy, voter participation history and registration status, the presence of children and teenagers in the household, and type of residential area (e.g., urban or rural).

In November 2025, approximately 19 percent of people in the UK would vote for the governing Labour Party in a potential general election, behind Reform UK on 27 percent, with the Conservatives, and the Green Party in joint-third on 16 percent. Since returning to power, support for the Labour Party has fallen considerably, with the government's sinking approval rating approaching the unpopularity of the previous government. Labour's return to power in 2024 On May 22, 2024, Rishi Sunak announced his decision to hold the 2024 general election on July 4. Sunak's surprise announcement came shortly after some positive economic figures were released in the UK, and he may have hoped this would boost his poor job ratings and perhaps also his government's low approval ratings. This was a long-shot, however, and as predicted in the polls, Labour won the 2024 general election by a landslide, winning 412 out of 650 seats. The sting in the tale for the Labour Party was that despite this large majority, they won a relatively low share of the votes and almost immediately saw their popularity fall in the second half of 2024. Sunak's five pledges in 2023 After a tough 2022, in which Britain suffered through its worst cost of living crisis in a generation, the economy was consistently identified as the main issue facing the country, just ahead of healthcare. To respond to these concerns, Rishi Sunak started 2023 with five pledges; halve inflation, grow the economy, reduce national debt, cut NHS waiting times, and stop small boats. By the end of that year, just one pledge can be said to have been fully realized, with CPI inflation falling from 10.1 percent at the start of 2023 to 4 percent by the end of it. There is some ambiguity regarding the success of some of the other pledges. The economy shrank in the last two quarters of 2023 but started to grow again in early 2024. National debt increased slightly, while small boat arrivals declined compared to 2022, but were still higher than in most other years. The pledge to cut NHS waiting times was not fulfilled either, with the number of people awaiting treatment rising in 2023.

Market researchers investigate clients' target markets' behaviour, values and opinions, providing insights that allow them to tailor their products, services and marketing. Researchers rely on hefty European research and development expenditure to fuel demand for market research. The surge in digitalisation has opened new doors for market research providers while intensifying competition. Artificial intelligence is increasingly important in analysing, identifying and generating research insights from social media posts using a flood of data. Meanwhile, digital surveys have allowed research companies to expand their outreach, save resources and costs and often attain more accurate and comprehensive insights for clients. Over the five years through 2025, industry revenue is expected to contract at a compound annual rate of 1.1% to reach €25.2 billion. The high inflationary environment in recent years has taken a toll on market research budgets. A sharp contraction in business sentiment squeezed corporate profit in 2022, discouraging companies from investing in research and development activities and negatively affecting professional research providers. A greater availability of data and alternative research methods means that researchers are competing more and more with in-house research departments. In 2025, industry revenue is expected to drop by 0.3% as consumers are finding their research needs met by AI tools such as ChatGPT, however, this trend is expected to be short-lived as research companies will strive to prove their value to clients. Over the five years through 2030, industry revenue is forecast to swell at a compound annual rate of 3.7% to reach €30.3 billion. Over the coming years, market research companies will face higher external competition from technology specialists leveraging insights internally, constraining revenue growth. Nonetheless, researchers will benefit from expanding online advertising activity. Those incorporating advanced data analytics systems and digital market research technology will remain competitive and benefit from greater digitalisation. Smart mobile surveys will also become an invaluable tool for consumer research companies.

Psychologists have long observed that people conform to majority opinion, a phenomenon sometimes referred to as the “bandwagon effect.†In the political domain people learn about prevailing public opinion via ubiquitous polls, which may produce a bandwagon effect. Newer types of informationpublished probabilities derived from prediction market contract prices and aggregated polling summariesmay have similar effects. Consequently, polls can become self-fulfilling prophecies whereby majorities, whether in support of candidates or policies, grow in a cascading manner. Despite increased attention to whether the measurement of public opinion can itself affect public opinion, the existing empirical literature is surprisingly limited on the bandwagon effects of polls. To address this gap, we conducted an experiment on a diverse national sample in which we randomly assigned people to receive information about different levels of support for three public policies. We find that public opinion as expressed through polls impacts individual-level attitudes, although the size of the effect depends on issue characteristics.

Market researchers investigate clients' target markets' behaviour, values and opinions, providing insights that allow them to tailor their products, services and marketing. Researchers rely on hefty European research and development expenditure to fuel demand for market research. The surge in digitalisation has opened new doors for market research providers while intensifying competition. Artificial intelligence is increasingly important in analysing, identifying and generating research insights from social media posts using a flood of data. Meanwhile, digital surveys have allowed research companies to expand their outreach, save resources and costs and often attain more accurate and comprehensive insights for clients. Over the five years through 2025, industry revenue is expected to contract at a compound annual rate of 1.1% to reach €25.2 billion. The high inflationary environment in recent years has taken a toll on market research budgets. A sharp contraction in business sentiment squeezed corporate profit in 2022, discouraging companies from investing in research and development activities and negatively affecting professional research providers. A greater availability of data and alternative research methods means that researchers are competing more and more with in-house research departments. In 2025, industry revenue is expected to drop by 0.3% as consumers are finding their research needs met by AI tools such as ChatGPT, however, this trend is expected to be short-lived as research companies will strive to prove their value to clients. Over the five years through 2030, industry revenue is forecast to swell at a compound annual rate of 3.7% to reach €30.3 billion. Over the coming years, market research companies will face higher external competition from technology specialists leveraging insights internally, constraining revenue growth. Nonetheless, researchers will benefit from expanding online advertising activity. Those incorporating advanced data analytics systems and digital market research technology will remain competitive and benefit from greater digitalisation. Smart mobile surveys will also become an invaluable tool for consumer research companies.

The most recent polling data from February 2025 puts the approval rating of the United States Congress at 29 percent, reflecting a significant increase from January. The approval rating remained low throughout the 118th Congress cycle, which began in January 2025. Congressional approval Congressional approval, particularly over the past few years, has not been high. Americans tend to see Congress as a group of ineffectual politicians who are out of touch with their constituents. The 118th Congress began in 2023 with a rocky start. The Democratic Party maintains control of the Senate, but Republicans took back control of the House of Representatives after the 2022 midterm elections. The House caught media attention from its first days with a contentious fight for the position of Speaker of the House. Representative Kevin McCarthy was eventually sworn in as Speaker after a historic fifteen rounds of voting. Despite the current Congress having a historic share of women and being the most diverse Congress in American history, very little has been done to improve the opinion of Americans regarding its central lawmaking body. Ye of little faith However, Americans tend not to have much confidence in many of the institutions in the United States. Additionally, public confidence in the ability of the Republican and Democratic parties to work together has decreased drastically between 2008 and 2022, with nearly 60 percent of Americans having no confidence the parties can govern in a bipartisan way.

We study voters' response to marginal changes to the fine for electoral abstention in Peru, leveraging variation from a nationwide reform. A smaller fine has a robust, negative effect on voter turnout, partly through irregular changes in voter registration. However, representation is largely unaffected, as most of the lost votes are blank or invalid. We also show that the effect of an exemption from compulsory voting is substantially larger than that of a full fine reduction, suggesting that non-monetary incentives are the main drivers behind the effectiveness of compulsory voting. Note: This is data and code accompanying the article.