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Cross-Tabs: April 2024 Times/Siena Poll of Registered Voters Nationwide

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Cross-Tabs: April 2024 Times/Siena Poll of Registered Voters Nationwide

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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Tech reversal pushes US megacaps into correction territory

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Tech reversal pushes US megacaps into correction territory

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Four of the so-called Magnificent Seven technology stocks that have powered the US market rally for the past nine months ended the week in correction territory, having fallen by more than 10 per cent from recent peaks. 

Another two — Microsoft and Amazon — are close to the double-digit falls that define a correction. Investors are looking ahead to further tech earnings updates next week amid worries about punchy valuations and the risks that returns from vast artificial intelligence-related spending may not live up to early hopes.

Nvidia and Tesla are each down 17 per cent from their recent peaks while Meta and Google parent Alphabet have fallen 14 per cent and 12 per cent. Apple is the best performer in the group, having lost just 7 per cent while Microsoft and Amazon have slid about 9 per cent each.

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On Wednesday Alphabet sparked a wider market sell-off when, despite it reporting solid quarterly operating numbers, its shares fell more than 5 per cent on concerns about AI-related investments. Its $13bn quarterly capital expenditure was almost double the levels of a year ago.

“For a long time investors were really sold on the premise that AI investment in and of itself — spending money — is good,” said Max Gokhman, a senior vice-president at Franklin Templeton Investment Solutions. “What we’re seeing now is . . . investors saying, ‘Hold up a sec, what are the productivity gains here, when do you expect to see them?’”

Alphabet’s fall helped drag the tech-heavy Nasdaq Composite to its worst one-day decline in 18 months on Wednesday, down 3.6 per cent. The index ended the week down 2.1 per cent.

Microsoft, Meta, Apple and Amazon earnings next week may set up a fresh test of investor faith in the AI narrative that has been a crucial driver of market gains.

“Expectations are high and valuations for the Mag Seven aren’t cheap. We’re also closer to the point when we see some decelerations in earnings from them as a group — from the beneficiaries of AI in general,” said Josh Nelson, head of US equity at T Rowe Price. 

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Investors this week also showed they were prepared to punish companies that missed expectations, with Tesla losing 12 per cent on Wednesday after slowing sales and its own AI spending shrank profits more than expected. And Ford shares tumbled 18 per cent on Thursday when its profits fell short, hurt by unexpectedly high warranty costs.

On average, companies that missed expectations had seen their shares drop 3.3 per cent in the days surrounding their earnings, according to data from FactSet, more than the five-year average of 2.3 per cent.

Companies that beat expectations saw on average no gains in their share price, FactSet reported.

“The trend of misses getting punished more than beats get rewarded is getting a little bit more significant,” said Liz Ann Sonders, chief investment strategist at Charles Schwab. “There is uncertainty and skittishness with regard to just how fast the market, driven by those names ran, without the commensurate improvement in their forward earnings prospects.”

Sonders also pointed to the fact that the earnings season under way had coincided with a “rotation” among investors taking profits in the biggest tech names in favour of backing smaller companies that were more likely to see big benefits if the Federal Reserve begins to cut interest rates in September.

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This week, the Russell 2000 index of small-cap stocks added 3.5 per cent while the blue-chip S&P 500 fell 0.8 per cent.

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Boar's Head recalls 200,000 pounds of deli meat linked to a Listeria outbreak

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Boar's Head recalls 200,000 pounds of deli meat linked to a Listeria outbreak

An electron microscope image of a Listeria monocytogenes bacterium, which has been linked to an outbreak spread through deli meat. Boar’s Head recalled meat on Friday, after two deaths and 33 hospitalizations linked to Listeria.

Elizabeth White/AP/Centers for Disease Control and Prevention


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Elizabeth White/AP/Centers for Disease Control and Prevention

Boar’s Head is recalling more than 200,000 pounds of deli meat that could be contaminated with listeria, the Food Safety and Inspection Service announced Friday.

The recall includes all Liverwurst products, as well as a variety of other meats listed in the FSIS announcement. The CDC has identified 34 cases of Listeria from deli meat across 13 states, including two people who died as of Thursday. The statement also said there had been 33 hospitalizations.

The CDC warns that the number of infections is likely higher, since some people may not be tested. It can also take three to four weeks for a sick individual to be linked to an outbreak.

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Listeria is a foodborne bacterial illness, which affects about 1,600 people in the U.S. each year, including 260 deaths. While it can lead to serious complications for at-risk individuals, most recover with antibiotics. Its symptoms typically include fever, muscle aches and drowsiness,

The CDC says people who are pregnant, aged 65 or older, or have weakened immune systems are most at risk. It suggests that at-risk individuals heat any sliced deli meat to an internal temperature of 165°F.

The investigation from the CDC and FSIS is ongoing. This is not the first listeria outbreak of the summer, as more than 60 ice cream products were previously recalled during an outbreak in June.

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US charges short seller Andrew Left with fraud

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US charges short seller Andrew Left with fraud

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A federal grand jury in Los Angeles has charged prominent short seller Andrew Left with more than a dozen counts of fraud, alleging that he made profits of at least $16mn from “a long-running market manipulation scheme”, according to a statement from the Department of Justice.

The DoJ added: “Left knowingly exploited his ability to move stock prices by targeting stocks popular with retail investors and posting recommendations on social media to manipulate the market and make fast, easy money.”

The grand jury indictment charged him with 17 counts of securities fraud, one count of engaging in a securities fraud scheme and one count of making false statements to federal investigators.

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The indictment alleged that Left, who has a high profile on social media, publicly claimed that companies’ share prices were too high or low, often with a recommended target price and “an explicit or implicit representation about Citron’s trading position”. This, the DoJ said, “created the false pretence that Left’s economic incentives aligned with his public recommendation”.

Left prepared to quickly close positions after publishing his comments, taking profits on price moves he had caused, according to the indictment.

It also accused Left of presenting himself as independent and concealing Citron’s links with a hedge fund by fabricating invoices and wiring payments through a third party.

If convicted, Left could face decades in prison. Each securities fraud count carries a maximum penalty of 20 years in prison, while the securities fraud scheme and false statements counts each carry a maximum prison term of 25 years and five years, respectively.

The US Securities and Exchange Commission has also filed a separate civil fraud case against Left and his firm Citron Research, claiming the founder made $20mn from a “multi-year scheme to defraud followers.” Left declined to comment on the DoJ and SEC charges.

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“Andrew Left took advantage of his readers. He built their trust and induced them to trade on false pretences so that he could quickly reverse direction and profit from the price moves following his reports,” said Kate Zoladz, regional director of the SEC’s Los Angeles office. “We uncovered these alleged bait-and-switch tactics, which netted Left and his firm $20mn in ill-gotten profits, and we intend to hold Left and his firm accountable for their actions.”   

The practice of betting that a company’s share price will go down has long been controversial — opponents say it gives traders incentives to spread misinformation, while supporters argue that it improves price discovery and holds management accountable. Last year the SEC adopted new rules that require investors to disclose short positions more quickly and fully.

Left has been most vocal recently in his scepticism over GameStop, the ailing video games retailer. In May it raised $3bn selling new shares following a surge in its price driven by the reappearance of Roaring Kitty — whose real name is Keith Gill — who was instrumental in the 2021 meme stock mania that had sent its value rocketing.

Left told followers in mid-June that Citron had closed its short position on the stock not because he had changed his views but because of GameStop’s newly-strengthened balance sheet.

In 2016, Left received a five-year “cold shoulder” ban from regulators in Hong Kong — a landmark ruling for the city — temporarily barring him from its markets after he was found culpable of misconduct related to a research report he published on Chinese property developer China Evergrande.

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Additional reporting by Stefania Palma in Washington and Brooke Masters in New York

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