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Cross-Tabs: April 2024 Times/Siena Poll of the Likely Electorate

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Cross-Tabs: April 2024 Times/Siena Poll of the Likely Electorate

How This Poll Was Conducted

Here are the key things to know about this Times/Siena poll:

• We spoke with 1,059 registered voters from April 7 to 11, 2024.

• Our polls are conducted by telephone, using live interviewers, in both English and Spanish. More than 95 percent of respondents were contacted on a cellphone for this poll.

• Voters are selected for the survey from a list of registered voters. The list contains information on the demographic characteristics of every registered voter, allowing us to make sure we reach the right number of voters of each party, race and region. For this poll, we placed nearly 127,000 calls to more than 93,000 voters.

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• To further ensure that the results reflect the entire voting population, not just those willing to take a poll, we give more weight to respondents from demographic groups underrepresented among survey respondents, like people without a college degree. You can see more information about the characteristics of our respondents and the weighted sample at the bottom of the page, under “Composition of the Sample.”

• The poll’s margin of sampling error among registered voters is plus or minus 3.3 percentage points. In theory, this means that the results should reflect the views of the overall population most of the time, though many other challenges create additional sources of error. When computing the difference between two values — such as a candidate’s lead in a race — the margin of error is twice as large.

If you want to read more about how and why we conduct our polls, you can see answers to frequently asked questions and submit your own questions here.

Full Methodology

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The New York Times/Siena College poll of 1,059 registered voters nationwide, including 875 who completed the full survey, was conducted in English and Spanish on cellular and landline telephones from April 7 to 11, 2024. The margin of sampling error is plus or minus 3.3 percentage points for registered voters and plus or minus 3.5 percentage points for the likely electorate. Among those who completed the full survey, the margin of sampling error is plus or minus 3.7 percentage points for registered voters and plus or minus 3.9 percentage points for the likely electorate.

Sample

The survey is a response rate-adjusted stratified sample of registered voters on the L2 voter file. The sample was selected by The New York Times in multiple steps to account for differential telephone coverage, nonresponse and significant variation in the productivity of telephone numbers by state.

First, records were selected by state. To adjust for noncoverage bias, the L2 voter file was stratified by statehouse district, party, race, gender, marital status, household size, turnout history, age and home ownership. The proportion of registrants with a telephone number and the mean expected response rate were calculated for each stratum. The mean expected response rate was based on a model of unit nonresponse in prior Times/Siena surveys. The initial selection weight was equal to the reciprocal of a stratum’s mean telephone coverage and modeled response rate. For respondents with multiple telephone numbers on the L2 file, the number with the highest modeled response rate was selected.

Second, state records were selected for the national sample. The number of records selected by state was based on a model of unit nonresponse in prior Times/Siena national surveys as a function of state, telephone number quality and other demographic and political characteristics. The state’s share of records was equal to the reciprocal of the mean response rate of the state’s records, divided by the national sum of the weights.

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Fielding

The sample was stratified according to political party, race and region and fielded by the Siena College Research Institute, with additional field work by ReconMR, the Public Opinion Research Laboratory at the University of North Florida, the Institute of Policy and Opinion Research at Roanoke College, and the Center for Public Opinion and Policy Research at Winthrop University in South Carolina. Interviewers asked for the person named on the voter file and ended the interview if the intended respondent was not available. Overall, 95 percent of respondents were reached on a cellular telephone.

The instrument was translated into Spanish by ReconMR. Bilingual interviewers began the interview in English and were instructed to follow the lead of the respondent in determining whether to conduct the survey in English or Spanish. Monolingual Spanish-speaking respondents who were initially contacted by English-speaking interviewers were recontacted by Spanish-speaking interviewers. Overall, 12 percent of interviews among self-reported Hispanics were conducted in Spanish, including 13 percent of weighted interviews.

An interview was determined to be complete for the purposes of inclusion in the ballot test question if the respondent did not drop out of the survey by the end of the two self-reported variables used in weighting — age and education — and answered at least one of the age, education, race or presidential election ballot test questions.

Weighting — registered voters

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The survey was weighted by The Times using the R survey package in multiple steps.

First, the sample was adjusted for unequal probability of selection by stratum.

Second, the sample was weighted to match voter file-based parameters for the characteristics of registered voters.

The following targets were used:

• Party (party registration if available, or else classification based on a model of vote choice in prior Times/Siena polls) by whether the respondent’s race is modeled as white or nonwhite (L2 model)

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• Age (Self-reported age, or voter file age if the respondent refuses) by gender (L2)

• Race or ethnicity (L2 model)

• Education (four categories of self-reported education level, weighted to match NYT-based targets derived from Times/Siena polls, census data and the L2 voter file)

• White/non-white race by college or non-college educational attainment (L2 model of race weighted to match NYT-based targets for self-reported education)

• Marital status (L2 model)

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• Home ownership (L2 model)

• National region (NYT classifications by state)

• Turnout history (NYT classifications based on L2 data)

• Method of voting in the 2020 elections (NYT classifications based on L2 data)

• Metropolitan status (2013 NCHS Urban-Rural Classification Scheme for Counties)

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Finally, the sample of respondents who completed all questions in the survey was weighted identically, as well as to the result for the general election horse race question (including leaners) on the full sample.

Weighting — likely electorate

The survey was weighted by The Times using the R survey package in multiple steps.

First, the samples were adjusted for unequal probability of selection by stratum.

Second, the first-stage weight was adjusted to account for the probability that a registrant would vote in the 2024 election, based on a model of turnout in the 2020 election.

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Third, the sample was weighted to match targets for the composition of the likely electorate. The targets for the composition of the likely electorate were derived by aggregating the individual-level turnout estimates described in the previous step for registrants on the L2 voter file. The categories used in weighting were the same as those previously mentioned for registered voters.

Fourth, the initial likely electorate weight was adjusted to incorporate self-reported intention to vote intention. The final probability that a registrant would vote in the 2024 election was four-fifths based on their ex ante modeled turnout score and one-fifth based on their self-reported intentions, based on prior Times/Siena polls, including a penalty to account for the tendency of survey respondents to turn out at higher rates than nonrespondents. The final likely electorate weight was equal to the modeled electorate rake weight, multiplied by the final turnout probability and divided by the ex ante modeled turnout probability.

Finally, the sample of respondents who completed all questions in the survey was weighted identically, as well as to the result for the general election horse race question (including leaners) on the full sample.

The margin of error accounts for the survey’s design effect, a measure of the loss of statistical power due to survey design and weighting. The design effect for the full sample is 1.19 for registered voters and 1.39 for the likely electorate. The design effect for the sample of completed interviews is 1.23 for registered voters and 1.4 for the likely electorate.

Historically, The Times/Siena Poll’s error at the 95th percentile has been plus or minus 5.1 percentage points in surveys taken over the final three weeks before an election. Real-world error includes sources of error beyond sampling error, such as nonresponse bias, coverage error, late shifts among undecided voters and error in estimating the composition of the electorate.

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Video: Severe Storms and Tornadoes Cause Destruction in Several States

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Video: Severe Storms and Tornadoes Cause Destruction in Several States

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Severe Storms and Tornadoes Cause Destruction in Several States

Severe weather hit several parts of the United States over the weekend, killing more than 20 people and leaving hundreds of thousands without power.

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Persuading Europeans to work more hours misses the point

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Persuading Europeans to work more hours misses the point

Europeans are spending less time at work, and governments would like them to get back to the grindstone. That is the thrust of measures German, Dutch and British ministers have been examining to persuade part-timers to take on more hours, and full-timers to embrace overtime.

But the evidence suggests it will be an uphill battle — and that authorities worrying about a shrinking workforce would do better to help people who might otherwise not want a job at all to work a little.

Rising prosperity is the main reason the working week has shortened over time, as higher productivity and wages have allowed people to afford more leisure. In Germany, for example, it has roughly halved between 1870 and 2000. Across the OECD, people are working about 50 fewer hours each year on average than in 2010, at 1,752.

Average hours have fallen more in recent years because the mix of people in employment has changed, with more young people studying, more mothers working, older people phasing their retirement and flexible service sector jobs replacing roles in the long-hours manufacturing industry.

The latest post-pandemic drop in European working hours is more of a puzzle. The European Central Bank estimated that at the end of 2023, Eurozone employees were on average working five hours less per quarter than before 2020 — equivalent to the loss of 2mn full-time workers.

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There has been a similar shift in the UK, where average weekly hours are 20 minutes shorter than in 2019 at the end of 2022. The Office for National Statistics says this was driven by lower full-time hours among prime-age men and was equivalent to having 310,000 fewer people in employment.

The trend appears to be a European one — there has been no such recent change seen in the US, which simply laid people off during the pandemic rather than putting them on furlough.

One explanation is that employers have been “hoarding” labour — keeping staff on in slack periods while cutting hours, because they are worried they will not be able to hire easily when demand picks up. The ECB thinks this has been a factor, along with a rise in sick leave and rapid growth in public sector jobs.

But Megan Greene, a BoE policymaker, said earlier this month that while there was some evidence of labour hoarding, it was also “plausible that . . . workers may just want a better work-life balance”.

Researchers at the IMF who examined the puzzle reached a similar conclusion. They said the post-Covid drop in working hours was in fact an extension of the long-term trend seen over the past 20 years, which reflected workers’ preferences — with young people and fathers of young children driving the decline. The biggest change was in countries where incomes were catching up with richer neighbours.

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Some economists, however, believe the experience of lockdowns has made people more willing to trade pay for a less pressured lifestyle, and more able to walk away from jobs with antisocial hours.

“A lot of people started to pay more attention to their health,” said one Frankfurt-based economist, noting that Germany, with one of the sharpest drops in working hours, suffered from high rates of depression and other mental health conditions, along with the UK.

Spain has traditionally been at the other extreme. It has some of the longest working hours in Europe — combined with a long lunch break that means many employees cannot clock off till late in the evening, with family life, leisure and sleep patterns all suffering as a result.  

But even here, habits are changing. Ignacio de la Torre, chief economist at Madrid-based investment bank Arcano Partners, thinks Spanish bars and restaurants have struggled to fill vacancies since the pandemic because former waiters have begun training for better jobs.

In many countries, unions have made shorter hours a focus of collective bargaining, and some employers are experimenting with offering four-day weeks — or more flexible working patterns — as a way of attracting staff.

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The shift in habits is a challenge for European policymakers. Since productivity growth has been weak, they fear that shorter hours will exacerbate labour shortages, fuel inflationary pressures, hold back growth and make it harder to fund welfare systems.

Unless productivity growth improves, de la Torre argues, the only way to boost economic growth is to bring more people into the workforce, embrace immigration or work longer. It is unrealistic to earn the same while working less: the outcome would be “a lower salary at the end of the month”.

But Anna Ginès i Fabrellas, director of the Labor Studies Institute at the Esade law school, cites evidence that young people are willing to accept this trade-off, valuing free time “when they assess the quality of a job”.

Some policymakers think shorter hours and greater wellbeing should be the goal. Spain’s minister of labour, Yolanda Díaz, caused uproar earlier this year by suggesting restaurants should no longer open into the small hours, and the governing coalition has pledged gradual cuts to the legal maximum working week.

The IMF’s researchers made a more pragmatic argument.

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Governments can and should do more to help people who want longer hours, they said, including supporting retraining, job-hunting and childcare, as well as promoting flexible work and removing perverse incentives in tax and benefit systems.

This will have only a small effect, the IMF estimates. Some policies will simply “reshuffle hours” between mothers and fathers. But in general, most people will want to work slightly less provided their living standards advance. That means there’s a limit to what policymakers can do. 

A more realistic goal, the IMF reckons, is to raise the total number of hours worked across the economy, not least through better parental leave policies that could bring more people into work in the first place. Recent trends in the EU are promising: participation in the workforce has risen since 2020.

This feels like the better approach. If employers offer better part-time and flexible roles, people who might otherwise stay outside the labour force entirely might at least work a little — and be happier for it. That would be more productive for governments than pushing against the tide.

delphine.strauss@ft.com

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After severe weather across the South, East Coast braces for potential flooding, tornadoes

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After severe weather across the South, East Coast braces for potential flooding, tornadoes

A man looks at a damaged car after a tornado hit the day before, Sunday, May 26, 2024, in Valley View, Texas. Powerful storms left a wide trail of destruction Sunday across Texas, Oklahoma and Arkansas after obliterating homes and destroying a truck stop where drivers took shelter during the latest deadly weather to strike the central U.S.

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Julio Cortez/AP

A large swath of the eastern U.S. was bracing for severe weather as the Memorial Day weekend came to a close. Deadly storms over the long weekend also knocked out power to hundreds of thousands across the South and disrupted holiday travel at busy airports in the northeast.

Severe storms were expected to stretch from Alabama to upstate New York on Monday evening, according to the National Weather Service. Forecasters said the storms could lead to intense rainfall in the Northeast and Mid-Atlantic, with flash flooding possible. Hail, heavy winds and tornadoes were also possible from northeast Maryland to the Catskill Mountains of New York, according to the NWS.

The threat of severe weather Monday followed a string of powerful and deadly storms that swept through the South and parts of the Midwest over the holiday weekend. At least 23 people were killed in Texas, Oklahoma, Arkansas, Alabama and Kentucky as a result of severe weather.

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Earlier in the week, a deadly tornado also hit Iowa.

In a news conference Monday, Kentucky Gov. Andy Beshear said four people were killed in four different counties after storms ripped through most of the state Sunday. Later Monday, Beshear confirmed a fifth storm-related death.

The tiny southwestern Kentucky community of Charleston took a direct hit from a tornado, officials said.

Beshear said the twister appeared to have been on the ground for 40 miles.

“It could have been much worse,” Beshear said of this weekend’s storms. “The people of Kentucky are very weather aware with everything we’ve been through.”

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To the east of Charleston, parts of Hopkins County, Kentucky, also saw damage Sunday night. Western Kentucky, including a number of communities in Hopkins County, endured a series of devastating tornadoes in 2021 that killed 81 people.

“There were a lot of people that were just getting their lives put back together and then this,” Hopkins County emergency management director Nick Bailey was quoted by The Associated Press as saying. “Almost the same spot, the same houses and everything.”

The website Poweroutage.us reported hundreds of thousands without power on Monday. More than 120,000 customers in Kentucky were without power as of 5:30 p.m. ET, according to the website. Data showed Arkansas and West Virginia each had more than 40,000 customers without electricity.

The White House said the Federal Emergency Management Agency was on the ground conducting damage assessments with state and local authorities. President Biden has directed federal agencies to provide support as needed.

Holiday travel had also been disrupted as a result of the weekend storms.

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According to the flight-tracking website Flight Aware, more than 400 flights in the U.S. had been canceled as of 5:30 p.m. Monday — and another 5,200-plus flights had been delayed. New York’s LaGuardia Airport and Newark Liberty Airport in New Jersey were most affected by delays and cancellations.

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