Job growth slowed modestly from 2016 to 2017. Many Americans, a recent survey finds, don’t realize that.
About 2.1 million jobs were created last year, down slightly from the 2.2 million that were added the year before, according to government estimates. But in a December HuffPost/YouGov poll, 41 percent of Americans said that more jobs were created in 2017 than in 2016, while just 24 percent thought 2016 was a better year. Another 35 percent weren’t sure.
Donald Trump, who as a candidate slammed unemployment statistics as “phony,” has trumpeted the growth in jobs during his first year as president.
Americans may be less inclined to give him credit, as an experiment within the HuffPost/YouGov survey shows.
Only half of those polled were asked the question comparing the years 2016 and 2017. The other half were asked about the difference between “Obama’s last year in office” and “Trump’s first year in office.”
In that second group, just 34 percent thought Trump had presided over more job creation in 2017, while 39 percent thought Barack Obama’s 2016 record won out. (Again, the first group came out 41 to 24 percent thinking 2017 was the better year.)
A deeper look into the data suggests at least part of the reason for the difference: While Trump voters were equally upbeat about job creation last year regardless of whether the president’s name was mentioned, everyone else in the group who saw Trump’s name turned decidedly more bearish.
The results on the job creation question tie into a larger trend that has persisted throughout Trump’s first year in office: The public’s opinions of the president have, unusually, failed to keep pace with their feelings about the economy.
Other questions in the survey found Trump’s name to have less of an effect. The differences were notably smaller between those Americans asked to compare their personal finances and the overall national economy now to “the beginning of the year” and those asked to compare the current situation to “when President Trump took office.” Partisan divisions still showed up, however, with Trump voters in both groups far more enthusiastic than Clinton voters.
Americans were close to evenly split on which president deserves more responsibility for the current state of the economy, with 36 percent naming Obama, 37 percent naming Trump and the rest unsure. Clinton voters credited Obama, rather than Trump, by a 49-point margin, while Trump voters were 65 points likelier to name Trump than Obama.
Use the widget below to further explore the results of the HuffPost/YouGov survey, going to the menu at the top to select survey questions and the buttons at the bottom to filter the data by subgroups:
MORE OF THE LATEST POLLING NEWS:
TRUMP’S APPROVAL RATING IS LOWER THAN THE STATE OF THE ECONOMY WOULD SUGGEST – Nate Cohn: “Setting aside the question of how much credit first-year presidents deserve for a strong economy — they have less influence than you might think — President Trump’s ratings should be much better. A 4.1 percent unemployment rate, the lowest in 17 years, is more typically associated with a 60-plus-percent approval rating for a first-term president. … If the economy were to overcome Mr. Trump’s unpopularity and send his approval ratings up, you would think we would have started to see signs of it. It is certainly possible that the economy — or other good news — will still lift his ratings. But it seems just as likely that Mr. Trump will continue to feel the burden of his time in office.” [NYT]
LAST YEAR’S SPECIAL ELECTIONS WERE VERY GOOD FOR DEMOCRATS – Daniel Donner: “Democrats are doing well right now, but just how well? What’s our baseline? It’s better than the past few years for sure, but is this how well Democrats were doing back in 2006, or 2008, the last times we saw big Democratic wave elections? Now we can answer that question, courtesy of our new Special Elections Index seen above. And the answer is big: Democrats haven’t performed this well in special elections since the late 1980s. … With the Special Elections Index … we compare special election results to past election results for the same position in the same district, which means we can look back in time as far as we can find results for the special elections themselves, giving us an even more robust — and predictive — data set.” [Daily Kos]
GALLUP WILL STOP REPORTING DAILY PRESIDENTIAL APPROVAL RATINGS – Frank Newport: “Beginning in 2018, Gallup will start updating presidential job approval on a weekly basis, rather than on a daily basis. Gallup remains committed to tracking presidential approval using probability-based, telephone interviewing, but is reducing the sample size from 3,500 to 1,500 U.S. adults per week. As a result, Gallup will aggregate and report presidential job approval each week, rather than daily, beginning on Jan. 8 at 1 p.m. ET. We will continue to report subgroup differences for presidential job approval using the large sample sizes collected each month.” [Gallup]
What’s behind the decision, and what it means for polling consumers – Steven Shepard: “The reason for the change? Gallup had been tacking a presidential approval question onto polls it was conducting for the Gallup-Sharecare Well-Being Index since 2008 — a continuous, privately sponsored health survey. But that poll is now transitioning from phone to mail surveys, which don’t enable reporting of daily results. … Because of the smaller overall sample sizes, Gallup will no longer report detailed results by subgroup — gender, age, race, geographic region, educational attainment, income, marital status, church attendance, political ideology and party identification — on a weekly basis. … The change is the latest retrenchment for Gallup, which has been the dominant name in public polling for decades. The company did not conduct any horse-race polling for the 2016 election, despite earning a reputation dating back to the 1930s for predicting presidential winners.” [Politico]
WHAT THE POLLING AVERAGES SAY AS OF MONDAY AFTERNOON:
Trump job approval among all Americans: 41% approve, 54% disapprove (but that holiday bounce may already be on its way down)
Trump job approval among Democrats: 10% approve, 87% disapprove
Trump job approval among Republicans: 84% approve, 14% disapprove
Trump job approval among independents: 37% approve, 55% disapprove
Generic House: 42% Democratic candidate, 35% Republican candidate
Obamacare favorability: 51% favor, 40% oppose
‘OUTLIERS’ – Links to the best of news at the intersection of polling, politics and political data
-A new Monmouth poll finds little confidence that the American system of government is basically sound. [Monmouth]
-Sam Levine looks into the Justice Department’s efforts to add a citizenship question on the Census. [HuffPost]
-Kathryn Casteel, Julia Wolfe and Mai Nguyen have the numbers on what we know about the victims of sexual assault. 
-Benedict Carey reports on a new study measuring the scope and impact of fake news. [NYT]
-Drew Altman argues that polling overstates the role health care will play in the 2018 midterms. [Axios]
-Here are Trump’s most- and least-liked tweets of 2017. [HuffPost]
-A lot of people would like to kill off Twitter (and Tinder). [Recode]
-This may be your new favorite map of the 2016 election. [xkcd]
The HuffPost/YouGov poll consisted of 1,000 completed interviews conducted Dec. 11-12 among U.S. adults, using a sample selected from YouGov’s opt-in online panel to match the demographics and other characteristics of the adult U.S. population.
HuffPost has teamed up with YouGov to conduct daily opinion polls. You can learn more about this project and take part in YouGov’s nationally representative opinion polling. More details on the polls’ methodology are available here.
Most surveys report a margin of error that represents some, but not all, potential survey errors. YouGov’s reports include a model-based margin of error, which rests on a specific set of statistical assumptions about the selected sample rather than the standard methodology for random probability sampling. If these assumptions are wrong, the model-based margin of error may also be inaccurate. Click here for a more detailed explanation of the model-based margin of error.