Vincent is among a growing group of middle-class Americans — most recently defined in 2022 by the Pew Research Center as households earning between $48,500 and $145,500 — who don’t feel they can’t afford to live a traditional middle-class life, replete with a home and a comfortable retirement…
Collins suspects that most middle-class Americans feel anxious about their financial situation due to financial shock fatigue — the exhaustion of navigating one big economic shock after another — as well as a lack of financial planning…
Financial anxiety has hit an all-time high, according to a survey from Northwestern Mutual, and a survey from Primerica found that half of middle-class households say their financial situation is “not so good” or outright “poor.”…
Buying a home may be the greatest example of a tenet of middle-class life feeling out of reach for many, and that struggle is very real rather than merely negatively perceived.
The suggestion is that people feel less certain of their social class status because of financial uncertainty at the moment and in recent years. They may have resources, particularly a certain income level, but they do not feel secure.
What might this mean for defining the middle class? Perhaps this should lead to changing what it means. If people do not feel that certain markers provide a middle class status, then change the markers. These variables might need to change as economic conditions change.
It would also be interesting to see what social class those feeling financial anxiety say they are in. Traditionally, being in the middle class was a sign of making it and being successful. Would someone who might be classified as middle class by income and other markers say they are working class? Is there a big shift away from identifying as middle class?
The biggest point of contention between the two camps revolves around “unreported income,” more commonly known as tax evasion. Tax returns are the best data source available for studying income distributions, but they’re incomplete—most obviously because people don’t report all of the income that they’re supposed to. This information gap requires inequality researchers to make some educated guesses about how unreported income is distributed, which is to say, about who is evading the most taxes. Piketty, Saez, and Zucman assume that it’s the people who already report a lot of income: Think of the well-paid corporate executive who also stashes millions of dollars in an offshore account. Auten and Splinter, by contrast, assume that those who evade the most taxes are people who report little or no income: Think plumbers or housekeepers who get paid in cash. They believe, in other words, that members of the 99 percent are a lot richer than they look…
To take the true measure of inequality, economists need a way to account for all the income and expenses that don’t show up on people’s tax returns. The method that Piketty, Saez, and Zucman pioneered, and that Auten and Splinter follow, was to take the gross domestic product—a measure of all of the spending in the national economy every year—and figure out who exactly is receiving how much of it. (Technically, they use something called gross national income, which is a close cousin of GDP.) The benefit of this approach is that nothing gets left out. The drawback is that, well, nothing gets left out. GDP measures the total production of an entire economy, so it includes all sorts of expenditures that don’t seem like income at all.
Much of the difference between the authors’ estimates of inequality hinges on how they treat government spending on things that benefit the public at large, such as education, infrastructure, and national defense. Because this spending is part of gross national income, it must be allocated to someone in order for the math to work out. Piketty, Saez, and Zucman take the view that this stuff really shouldn’t be considered income, so they allocate it in a way that doesn’t change the overall distribution. Auten and Splinter, however, argue that at least some of this money should count as income. Citing research indicating that education spending tends to disproportionately benefit lower- and middle-income kids, they decide to allocate the money in a way that increases the bottom 99 percent’s share of income—by a lot. Austin Clemens, a senior fellow at the Washington Center for Equitable Growth, calculates that in Auten and Splinter’s data set, a full 20 percent of income for those in the bottom half of the distribution “comes in the form of tanks, roads, and chalkboards.”…
The deeper you get into how GDP is actually calculated and allocated, the more you feel as though you’ve fallen through a wormhole into an alternate dimension. Let’s say you own a house. Government statisticians imagine that you are renting out that house to yourself, calculate how much money you would reasonably be charging, and then count that as a form of income that you are, in essence, paying yourself. This “imputed rent” accounts for about 9 percent of all GDP, or more than $2 trillion. Or suppose you have a checking account at a major bank. Statisticians will calculate the difference between what the bank pays you in interest on that account (usually close to nothing) and what you could have earned by investing that same money in safe government bonds. That difference is then considered the “full value” of the benefits you are receiving from the bank—above and beyond what it actually charges you for its services—and is therefore considered additional income for you, the depositor. All of these choices have some theoretical justification, but they have very little to do with how normal people think about their financial situation.
These are common issues working with all sorts of variables that matter in life: trying to collect good data, operationalization, missing data, judgment calls, and then difficulty in interpreting the results. In this case, it affects public perceptions of income inequality and big questions about the state of society.
Is this just an arcane academic debate? Since academics tend to want their work to matter for society and policy, this particular discussion matters a lot. Every day, economic news is reported. People have their own experiences. Humans like to compare their own experiences to those of others now and in the past. People search for certainty and patterns. The question of inequality is a recurrent one for numerous reasons and having good data and interpretations of that data matters for perceptions and actions.
The way that academics tend to deal with this is to continue to measure and interpret. Others will see this debate and find new ways to conceptualize the variable and collect data. New studies will come out. Scholars of this area will read, discuss, and write about this issue. There will be disagreement. Conditions in the world will change. And hopefully academics will get better at measuring and interpreting the concept of income.
Lear had already established himself as a top comedy writer and captured a 1968 Oscar nomination for his screenplay for “Divorce American Style” when he concocted the idea for a new sitcom, based on a popular British show, about a conservative, outspokenly bigoted working-class man and his fractious Queens family. “All in the Family” became an immediate hit, seemingly with viewers of all political persuasions.
Lear’s shows were the first to address the serious political, cultural and social flashpoints of the day – racism, abortion, homosexuality, the Vietnam war — by working pointed new wrinkles into the standard domestic comedy formula. No subject was taboo: Two 1977 episodes of “All in the Family” revolved around the attempted rape of lead character Archie Bunker’s wife Edith.
Their fresh outrageousness turned them into huge ratings successes: For a time, “Family” and “Sanford,” based around a Los Angeles Black family, ranked No. 1 and No. 2 in the country. “All in the Family” itself accounted for no less than six spin-offs. “Family” was also honored with four Emmys in 1971-73 and a 1977 Peabody Award for Lear, “for giving us comedy with a social conscience.” (He received a second Peabody in 2016 for his career achievements.)
Some of Lear’s other creations played with TV conventions. “One Day at a Time” (1975-84) featured a single mother of two young girls as its protagonist, a new concept for a sitcom. Similarly, “Diff’rent Strokes” (1978-86) followed the growing pains of two Black kids adopted by a wealthy white businessman.
Other series developed by Lear were meta before the term ever existed. “Mary Hartman, Mary Hartman” (1976-77) spoofed the contorted drama of daytime soaps; while the show couldn’t land a network slot, it became a beloved off-the-wall entry in syndication. “Hartman” had its own oddball spinoff, “Fernwood 2 Night,” a parody talk show set in a small Ohio town; the show was later retooled as “America 2-Night,” with its setting relocated to Los Angeles…
One of Hollywood’s most outspoken liberals and progressive philanthropists, Lear founded the advocacy group People for the American Way in 1981 to counteract the activities of the conservative Moral Majority.
The emphasis here is on both television and politics. Lear created different kinds of shows that proved popular as they promoted particular ideas. He also was politically active for progressive causes.
How might we know that these TV shows created cultural change? Just a few ways this could be established:
-How influential were these shows to later shows and cultural products? How did television shows look before and after Lear’s work?
-Ratings: how many people watched?
-Critical acclaim: what did critics think? What did his peers within the industry think? How do these shows stand up over time?
But, the question I might want to ask is whether we know how the people who watched these shows – millions of Americans – were or were not changed by these minutes and hours spent in front of the television. Americans take in a lot of television and media over their lifetime. This certainly has an influence in the aggregate. Do we have data and/or evidence that can link these shows to changed attitudes and actions? My sense is that is easier to see broad changes over time but harder to show more directly that specific media products led to particular outcomes at the individual (and sometimes also at the social) level.
These are research methodology questions that could involve lots of cultural products. The headline above might be supportable but it could require putting together multiple pieces of evidence and not having all the data we could have.
By the Nielsen company’s count, 7.8 million people watched Amazon Prime’s coverage of last Thursday’s NFL game between New Orleans and Arizona. But Amazon says no, there were actually 8.9 million people watching…
Neither company is saying the other is wrong, but neither is backing down, either. The result is confusion, most notably for advertisers.
Nielsen, as it has for years, follows the viewing habits in a panel of homes across the country and, from that limited sample, derives an estimate of how many people watch a particular program. That number is currency in the media industry, meaning it is used to determine advertising rates.
Amazon, in the first year of an 11-year contract to stream Thursday night games, says it has an actual count of every one of its subscribers who streams it — not an estimate. The games are also televised in the local markets of the participating teams, about 9% of its total viewership each week, and Amazon uses Nielsen’s estimate for that portion of the total…
But with Netflix about to introduce advertising, that can all change very rapidly. And if other companies develop technology that can measure viewing more precisely, the precedent has now been set for publicly disputing Nielsen’s numbers.
There could be multiple methodological issues at play here. One involves who has a more accurate count. If Amazon can directly count all viewers, that could be the more accurate number. However, not all television providers have that ability. A second concern is how different providers might count viewership. Does Amazon reveal everything about its methods? Nielsen is an independent organization that theoretically has less self-interest in its work.
All of this has implications for advertisers, as noted above, but it also gets at understandings of how many people today view or consume particular cultural products. Much has been said about the fragmentation of culture industries with people having the ability to find all sorts of works. Accurate numbers help us make sense of the media landscape and uncover patterns. Would competing numbers or methods lead to very different narratives about our collective consumption and experiences?
A new list ranks the wealthiest cities in the world by the number of the wealthiest residents. Do the wealthy in suburbs count? For New York City, the top city on the list, they appear not to:
The Big Apple is home to 345,600 millionaires, including 737 centi-millionaires (with wealth of USD 100 million or more) and 59 dollar billionaires. New York is the financial center of the USA and the wealthiest city in the world by several measures. It is also home to the world’s two largest stock exchanges by market cap (the Dow Jones and NASDAQ). Perhaps most notably, total private wealth held by the city’s residents exceeds USD 3 trillion — higher than the total private wealth held in most major G20 countries…
It should be noted that there are several affluent commuter towns located just outside New York City that also contain a large amount of top-tier wealth. Notables include: Greenwich, Great Neck, Sands Point and Old Westbury. If these towns were included in our New York City figures, then billionaire numbers in the combined city would exceed 120.
The San Francisco listing, #3, includes a broader set of communities:
The San Francisco Bay area — encompassing the city of San Francisco and Silicon Valley — is home to 276,400 millionaires, including 623 centi-millionaires and 62 billionaires. Home to a large number of tech billionaires, Silicon Valley includes affluent towns such as Atherton and Los Altos Hills. This area has been steadily moving up the list of millionaire hubs over the past decade and we expect it to reach the top spot by 2040.
Los Angeles, #6, also includes suburbs:
This area is home to 192,400 resident millionaires, with 393 centi-millionaires and 34 billionaires. Our figures for this area include wealth held in the city of Los Angeles, as well as nearby Malibu, Beverly Hills, Laguna Beach, Newport Beach, and Santa Monica. Key industries include entertainment, IT, retail, and transport.
And the methodology suggests there are six cities on the list where the city is defined more broadly.
There could be a variety of reasons for looking at wealthy residents just in cities or also including metropolitan regions. Depending on setting these different boundaries, how much might it change the rankings?
Media stories and/or reports can be counted in multiple ways. Count articles, headlines, the number of words written, social media posts, time spent on it during television broadcasts. Look at where and when stories are reported or not; does it lead the news or come later? Is it buried on a webpage or a newspaper page? How many resources are devoted to the topic could involve looking at how many reporters are on a story or the length of stories and reports.
But, this measurement question is complicated by the issue of knowing when the coverage is enough or not. My sense of most of the Internet arguments about this is that one political side feels for one reason or another that a story is not getting sufficient attention. Would an accurate count or measurement of coverage be convincing? What is an appropriate level of coverage depends on who is asking.
Additionally, the media has its own logics and pressures regarding what stories it covers and how it displays them. Not everything can be the top headline. Resources for covering the news are limited.
This might just be a perfect kind of argument for our politicized and fragmented current age. For those who really care about an issue, no level of media coverage might be enough. For those who are less interested or less aware, they might not care or know what they are missing. Media sources will provide information but not so do necessarily evenly across all news stories. And social media, the Internet, and politics provides space to express concern or outrage about the coverage or lack thereof.
City officials and the Downtown Cleveland Alliance say the U.C. Berkeley study doesn’t provide an accurate accounting. The “downtown area” in the study doesn’t match what Cleveland locals would describe as their downtown, according to maps shared with cleveland.com.
Data that the DCA publishes each month is less grim, but also doesn’t point to a full recovery.
DCA’s recovery report said there were 4.01 million visits to downtown in May, a 71% recovery compared to May 2019. Visits improved to 4.14 million in June, a 77% recovery, according to the DCA.
There is the matter of measuring this well and the matter of interpreting and using the data for particular purposes. If the consensus of researchers is that downtown activity is down in many places, what policy, economic, and social implications would this have?
There are many metrics that we wanted to include but for which we couldn’t find data.
Religion was at the top of that list. The Public Religion Research Institute sent us breakdowns of religious affiliation by county. But some counties contain dozens of places. Cook County, for instance, includes Chicago and is home to a large number of Black Protestants. The county also includes Chicago’s northern suburbs, where very few Black people live. Assigning the same statistics to every place within Cook County would have been misleading.
(We did use county- or metropolitan-level statistics for a handful of metrics — but only when we thought values were unlikely to vary significantly within those areas.)
This explanation makes some sense given the data available. Counties can have significant variation within them, particularly when they are large counties and/or have a lot of different municipalities. The example of Cook County illustrates the possible variation within one county: not only does the county contain Chicago, there are scores of other suburbs with a variety of histories and demographics.
On the other hand, it is a shame to not be able to include any measure of religion. People do not necessarily gather with similar religious adherents in their own community. People regularly travel for religious worship and community. There are Black Protestant congregations in Cook County outside of Chicago even as they may not be evenly distributed across the county. Because this religion data is at the county level, perhaps it could be weighted less in the selection of places to live and still included as a potential factor.
This also speaks to a need for more systematic data on religious affiliation on a smaller scale than counties. This requires a tremendous amount of work and data but it would be a useful research tool.
On May 25, 1991—30 years ago Tuesday—Billboard started using Nielsen SoundScan data to build its album chart, with all of its charts, including singles hub The Hot 100, eventually following suit. Meaning, the magazine started counting album sales with scanners and computers and whatnot, and not just calling up record stores one at a time and asking them for their individual counts, often a manual and semi-accurate and flagrantly corrupt process. This is the record industry’s Moneyball moment, its Eureka moment, its B.C.-to-A.D. moment. A light bulb flipping on. The sun rising. We still call this the SoundScan Era because by comparison the previous era might as well have been the Dark Ages.
First SoundScan revelation: Albums opened like movies, so for anything with an established fan base, that first week is usually, by far, the biggest. First beneficiary: Skid Row. And why not? “Is Skid Row at the height of their imperial period?” Molanphy asks of this ’91 moment. “For Skid Row, yes. But Skid Row is not Michael Jackson, Whitney Houston, Bruce Springsteen, or Stevie Wonder. Skid Row is a middle-of-the-road hair-metal band at the peak of their powers, relatively speaking. So it’s not as if they are commanding the field. It’s just the fans all showed up in week no. 1, and it debuts at no. 1. And then we discover, ‘Oh, this is going to happen every week. This is not special anymore.’”
Next SoundScan revelation: Hard rock and heavy metal were way more popular than anybody thought. Same deal with alternative rock, R&B, and most vitally, rap and country. In June 1991, N.W.A’s second album, Efil4zaggin, hit no. 1 after debuting at no. 2 the previous week. That September, Garth Brooks’s third album, the eventually 14-times-platinum Ropin’ the Wind, debuted at no. 1, the week after Metallica’s eventually 16-times-platinum self-titled Black Album debuted there. In early January 1992, Nirvana’s Nevermind, released in September ’91, replaced MJ’s Dangerous in the no. 1 spot, a generational bellwether described at the time by Billboard itself as an “astonishing palace coup.”
Virtually overnight, SoundScan changed the rules on who got to be a mega, mega superstar, and the domino effect—in terms of magazine covers, TV bookings, arena tours, and the other spoils of media attention and music-industry adulation—was tremendous, if sometimes maddeningly slow in coming. Garth, Metallica, N.W.A, Nirvana, and Skid Row were already hugely popular, of course. But SoundScan revealed exactly how popular, which of course made all those imperial artists exponentially more popular.
This is all about measurement – boring measurement! – but it is a fascinating story. Thinking from a cultural production perspective, here are three things that stand out to me:
This was prompted in part by a technology change involving computers, scanners, and inventory systems. The prior system of calling some record sales and getting their sales clearly has problems. But, how to get to all music being sold? This requires some coordination and technology across many settings.
The change in measurement led to changes in how people understood the music industry. What genres are popular? What artists are hot? How often do artists have debut #1 albums as opposed to getting discovered by the public and climbing the charts? Better data changed how people perceived music.
The change in measurement not only changed perceptions; it had cascading effects. The Matthew Effect suggests small initial differences can lead to widening outcomes when actors are treated differently in those early stages. When the new measurement system highlighted different artists, they got more attention.
Summary: some might say that good music is good music but how we obtain data and information about music and then act upon that information influences what we music we promote and listen to.
Through the trade group Video Advertising Bureau, the networks are perplexed by Nielsen statistics that show the percentage of Americans who watched their televisions at least some time during the week declined from 92% in 2019 to 87% so far this year.
Besides being counter-intuitive in the pandemic era, the VAB says that finding runs counter to other evidence, including viewing measurements from set-top cable boxes, the increased amount of streaming options that have become available and a jump in sales for television sets…
The number of families, particularly large families, participating in Nielsen measurements has dropped over the past year in percentages similar to the decrease in viewership, Cunningham said. Nielsen acknowledges that its sample size is smaller — the company is not sending personnel into homes because of COVID-19 — but said statistics are being weighted to account for the change…
More people are spending time on tablets and smartphones, which aren’t measured by Nielsen. The podcast market is soaring. Sports on television was interrupted. Due to production shutdowns, television networks were airing far more reruns, Nielsen said.
This sounds like a coming together of long-term trends and short-term realities. The long-term trends include people engaging with media across a wider range of devices, it takes work to measure all of their viewing and finding people to participate in any data collection, and there are a lot of entertainment choices competing with television. In the short-term, COVID-19 pushed people home but it disrupted their typical patterns.
At the same time, how TV is consumed and how this affects what television means could be quite different moving forward. Watching streaming television on a smartphone while commuting is a very different experience than sitting on the couch after dinner for an hour or two and watching a big-screen TV. Teasing out these differences takes some work but a new and/or younger generation of TV viewers might have quite a disparate relationship with television.
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