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Ford CEO Jim Farley appeared in a CNBC interview on September 15, 2023 on the UAW strike and its impact on makers and workers. It’s a master class in management mis-direction and shoddy interviewing on the part of the "press." Here's a link to the interview at 2:55.
THE QUESTION: Quickly put in some perspective... The offer that they have, that they’re demanding (there’s that word...), relative to where you are right now, how much damage would that do the bottom line if you were to say, sure, we’ll give you forty percent?
THE ANSWER: If we signed up for the UAW’s request, instead of making money and distributing $75,000 in profit sharing the last ten years (is that $75,000 PER YEAR or $7,500 each year?), we would have lost $15 billion dollars and gone bankrupt by now. The average pay would be nearly $300,000 dollars fully fringed for a 4-day work week. (Per employee?) Per union worker. Our average tenured teacher makes $66,000 a year. Our military, our firemen make mid fifty thousand. This is four, five, SIX times what they make. There’s no way we can be sustainable as a company...
Okay, STOP.
Do you see what he did?
The QUESTION was, from your CURRENT financial position looking FORWARD, what impact will a forty percent wage increase have on the company?
The ANSWER went back to the point of the original concessions after the 2008 meltdown and exrapolated TODAY’s wage plan atop Ford’s position over a DECADE ago. “THEN-Ford” absolutely couldn’t handle wage hikes of that degree, WHICH IS WHY THE UNION DIDN’T GET THEM. That’s why SENIOR workers have eaten a lower wage hike regime for 10+ years. That’s why ENTRY workers have been completely screwed by Ford and their senior union brethren by hiring into a wage structure that tops out at FIFTY PERCENT of the senior scale, putting them behind for their earning lifetime.
“NOW-Ford” is in a better financial position and GOING FORWARD, should be able to afford to correct the pay gap injected into wage scales years ago and correct for the 36 months of drastically higher inflation that shrunk those already-atrophied wages even more over the COVID meltdown and recovery.
Note the additional management jujitsu Farley subliminally snuck into the conversation about auto worker wages versus other historically underpaid workers – teachers, military, firemen… Given those vital functions are being so completely screwed on wages, it would be unfair to pay auto workers more… That’s the subliminal point he’s making. That’s the subliminal point nearly all executives make. There are so many classes of workers exploited for low wages, it would be “unfair” to correct them one group at a time cuz one group would get ahead of another. That’s not an argument for keeping the group currently negotiating down, that’s an argument for raising them all up. He even had the nerve to cite pay multiples between these other professions and auto workers. Do you really want to argue the pay inequity multiple angle, when CEOs are making 200x to 300x times average workers?
And his figure of $75,000 in profit sharing over the last ten years? That’s cumulative. Here are the numbers for the past few years:
For a worker with 2080 hours of compensation per year, these profit sharing checks at Ford are worth about $4.11/hour in a good year. (As an alternate data point, GM’s profit share paid in 2023 was $12,750.)
Farley slipped another significant bit of mathematical slight of hand into the interview as well.
While attempting to stoke resentment of auto workers via comparisons to wages of teachers, firemen and military types, Farley raised the prospect of auto workers earning up to $300,000 per year. Whaaaaaa?
Any discussion dropping in numbers like that is inherently dishonest. Let’s do some math...
The top union wage rate at Ford is $33/hour. $33/hour over a 40 hour week and 52 weeks a year only equates to $68,640. Ford is stating top tier workers could earn up to $98,000 under ITS proposal. That obviously includes the negotiated profit sharing payment and the imputed value of healthcare benefits. CEO Farley is throwing out references about auto workers making up to $300,000. This is a bogus, rigged discussion in a hall of fun-house mirrors. Apples and oranges are being discussed.
When the rest of us have conversations about careers and SALARIES, we all mentally benchmark any salary figure against a typical 40 hour week. If I make $120,000 per year and you make $89,000, we both come away with a relatively accurate first impression that I am being compensated at a higher rate than you. If one of the jobs includes huge management bonuses or commissions, those factors get folded in as well. If one job is hourly and one is "management" and management might be expected to work far beyond 40 hours in exchange for those bonuses, that also gets referenced in the discussion.
When a automotive executive is negotiating a new contract atop an existing contract that caps out at $33/hour and references HOURLY workers making $300,000 per year, he’s not talking about a worker working 40 hours a week. He’s talking about a worker making $68,640 in regular wages and the balance ($231,360) from profit sharing and overtime. We know profit sharing typically comes in around $7500 per Ford’s own numbers. Profit sharing is solely based on company profits, not hours worked. That means that employee is earning $223,860 from overtime. That’s $223,860 / 33 / 2 = 3391 extra hours. That’s a total of 5471 hours or 105 hours/week… EVERY WEEK OF THE YEAR.
That’s a worst-case order of magnitude estimate but it illustrates the level of exploitation involved in making the dollar amounts executives throw out in these conversations. That’s a huge impact on the quality of life of the worker, both at the workplace and in their homelife, which at that point is nearly non-existant. That’s not an apples and apples comparison to make.
And that’s another sign of exploitation on the part of automakers. Working overtime a few weeks throughout the year is one thing. Working overtime nearly EVERY week because paying time and a half or double-time for an extra twenty hours a week is cheaper than hiring more workers has been a core part of the business model. Less training expense, lower healthcare costs.... All of those savings go straight to the company's bottom line. But having someone work even 50-60 hours nearly EVERY week to make up for the fact they are starting below $17.00/hour or have worked for 20 years and are only making $33/hour but need more to keep up is not equitable.
The "journalist" Phil Lebeau conducting this "interview" is really a glorified stenographer / cheerleader. This type of interview doesn’t help CNBC’s supposed audience of investors. It’s just an outsourced public relations event disguised as a newscast with as much integrity as PR Newswire.
Doppelganger – Naomi Klein – 348 pages (399 with notes / index)
Modern networks and media platforms expose the average person to probably HUNDREDS of faces per day which accumulate over time, inevitably resulting in occasional experiences of surprise / curiosity or perhaps awkwardness / disgust after encountering the image or entire online “brand” of someone who looks nearly identical to us or shares the same name. Internet culture resurrected an old word – doppelganger – for that “other” person and the combination of feelings produced after encountering the “other”, whether in person or just as content on a site.
Doppelganger by Naomi Klein is a thought provoking book about the larger concept of doppelgangers in our individual lives, our culture and the very operation of our societies. The book "starts local" from an experience that began affecting the author as far back as the 2000s, but grew rather stark over the past five years. It became apparent to Klein that the combination of social media platforms, search optimizations and suggestions, an overlap of topics of interest and a simple coincidence – Naomi Klein’s internet-synthesized doppelganger is author Naomi Wolf – led to Internet users consistently confusing the two authors and incorrectly directing occasional praise but mostly vitriol at "Naomi #2" instead of "Naomi #1" or vice versa. Adding to the sense of mystery was confusion and surprise by many of how the views of Naomi Wolf had shifted – seemingly in 180 degree fashion – from her 1990s perspective to that of a typical alt-right conspiracy promoter.
These “crossed wires” became more frequent as COVID shutdown were applied globally, giving Klein a seemingly endless amount of time to “observe” her doppelganger to answer her own questions about why her doppelganger underwent such a logical / political 180. Amidst this "research," Klein began making connections between the forces likely contributing to this single doppelganger experience and larger events involving the handling of COVID, anti-vax conspiracies and their ties to autism care and some longer standing problems. The book starts with Klein’s "local" example then expands to implications of "online branding" of individuals on social media and branding of actual professionals / performers online. From there, the concept is used to shed light on the feeling of otherworldness being generated by political movements that seem completely disconnected from reality and ultimately on global issues of exploitation / repression / genocide.
Is Klein’s application of doppelganger dynamics a convenient logical crutch to tie together a book on random current events or does her analysis hold weight? The book provides coherent examples of the behaviors prompting her theory and analysis of the underlying events to help separate ACTUAL causes from the conspiratorial causes suggested by actors operating in a logical doppelganger fashion. Because some of the examples involve her own professional life and role as a mother, the book doesn’t read with as much of an analytical tone as The Shock Doctrine but Klein not only provides numerous examples, she shows how the examples interrelate to each other. The entire analysis is filled with "lightining bolt" prose – sentences or short paragraphs so well crafted I felt compelled to flag them as I read. After completing the book, I had 55 Post-Its across the 348 pages of content flagging key points.
Two Naomis
Naomi Klein’s problem – and the book’s origins – started from a simple mixup. There is Naomi Klein, a Canadian citizen famous in some circles for a series of books including No Logo and The Shock Doctrine which focus on flaws within capitalist systems which accentuate economic inequities and un-democratic political outcomes for the uber-wealthy. There is also Naomi Wolf, an American writer famous for her 1990s book The Beauty Myth which made the case that 1990s women were being indoctrinated with a myth of attainable perfect beauty that would allow success in the business world while actually NOT being achievable and PREVENTING women from achieving actual success by wasting their time chasing unobtainable perfection. Early in her career, Wolf was typically viewed as a left-oriented feminist thinker. However, over the past 8-10 years, Wolf’s writings and online appearances have shifted markedly to the alt-right, to the point where she is a frequent guest and even co-host on Steve Bannon’s podcast.
Klein first began noticing the occasional "crossed wire" in the late 2000s but became more intent on understanding the process during the period of Occupy Wall Street protests when she overheard two other women in a bathroom talking about what an IDIOT Noami Klein was about topic X. Having never written or even thought much about topic X, Klein interrupted the two and said, "um, I think you mean Naomi Wolf, not Klein.
Klein's first key insight into the mechanics of the problem she was facing came after numerous situations where show runners and producers would reach out via her social media account to begin making travel arrangements for upcoming appearances... Appearances for Naomi Wolf. Klein would typically send back a response of mock exasperation and politely remind them they wanted Wolf, not Klein. At one point during the pandemic, Klein was tagged on someone's post stating how "Klein" had been losing her mind for years and now has completely lost it, making comments comparing requirements to present proof of vaccinations with Jews being forced to wear yellow stars in Nazi Germany. Klein curtly replied to the poster "You sure about that?" The poster realized his mistake, took down his post and tweeted her back... "Oh Jesus, it's Wolfe (sic)... damn twitter autocomplete. Sorry about that." BINGO. Having systems with millions of users being confused with "help" derived from search results from other confused users MAGNIFIES the confusion exponentially.
Everyone Has a Brand / Everyone Has a Doppelganger
As with The Shock Doctrine that came before, Klein anchors the entire narrative of the book around a term that strikes the reader as au courant but has a history – in this case – stretching back decades and centuries with psychological and religious connotations. In modern circles, most associate doppelganger with the eerie déjà vu experience one gets after seeing someone who appears to be a body double of someone else, or just another “John Smith” who might have a very similar life to a “John Smith” you know, creating a weird sense of the possibility of a parallel universe.
Klein traces ideas related to doppelgangers and the impacts of a dual self back to early 1900s psychological theories, a painting dating back to 1851 (When They Met Themselves by Dante Gabriel Rossita) of a medieval couple walking through a forest and encountering their doppelganger selves (and freaking out) and much older religious concepts of a soul. It might be an accident that doppelganger’s usage spiked with the rise of internet technologies and the fixation on digital identity but, as Klein argues throughout the book, its use is VERY apropos to the issues arising from these technologies.
From this historical perspective, Klein makes the point that the idea of dual selves is processed by our consciousness as a threat. Multiple theories abound as to why... Fear of how the “other” might become more popular than the “real”. Fear the “other” could become more successful than the “real”, posing an existential risk to “real’s” livelihood and life. Fear that, by declaring a "soul" exists that is subject to eternity, attempting to assign all good attributes to that “soul” will result in an “evil twin” housing all of the real person's bad attributes, resulting in harm to others. Reframing these older historical implications of multiple selves in more modern and less religious overtones, creating dual selves can be viewed as an intrinsically unhealthy approach for dealing with reality. It encourages a partitioning of the person into good and bad hoping to preserve the good but in doing so, takes away internal attention on "bad" attributes, giving them a wider arena in which to act unchecked.
Klein’s first examples of these doppelganger impacts on modern life start at the individual layer, with experiences nearly everyone has had writing a college entrance application essay or creating an online resume for LinkedIn or similar sites.
Klein’s first book No Logo analyzed how modern capitalist emphasis on branding for corporations and products was overtaking individuals. While teaching a course on branding at a university, Klein solicited feedback from students on how they saw this affecting them as a generation warned by their parents to watch everything they do online because ANYTHING can come up on a college or job application years later. Feedback from many students cited their college application as the first point they recognized a schism being FORCED upon them between their true self and their persona via inane questions like Some students have a background, identity, interest or talent that is so meaningful they believe their application would be incomplete without it. If this sounds like you, then please share your story.
There were many nods when one student described the process as “packaging up your trauma into a consumable commodity.” It’s not that the traumas they wrote about were fake, it’s that the process required them to label difficult experiences in specifically marketable ways, and to turn them into something fixed, salable, and potentially profitable (since universities are themselves branded as the requisite first step to any lucrative career). A partitioning was occurring between these young people and this thing they were supposed to become to succeed.
Klein later points out that this first initial self-created schism is then subjected to the full force of the internet with likes, followers, search engines and AI to begin “rewarding” content which serves the need of other parties, creating incentives for individuals to accentuate whatever “pops” those algorithms. Since those search engines and algorithms are exclusively using data about the “digital you” rather than the full “real you”, those technologies INHERENTLY widen that schism, with profound impacts for individuals in not only the expected career realm but in the personal realm. Even for someone succeeding at creating a strong / valuable online “brand,” there is one key pitfall, as Klein ponders regarding her own brand:
Good brands are immune to fundamental transformation. Conceding to having become one at age thirty would have meant foreclosing on what I saw as my prerogative to change, evolve, and hopefully improve. It would have locked me into performing this particular version of me, indefinitely.
Elsewhere, Klein quoted author Zadie Smith and elaborated in this way:
”When a human being becomes a set of data on a website like Facebook, he or she is reduced. Everything shrinks. Individual character. Friendships. Language. Sensibility. In a way, it’s a transcendent experience: we lose our bodies, our messy feelings, our desires, our fears.” But we aren’t transcending to something higher, just less ourselves. And a flattened, reduced version of ourselves is easier to confuse with a flattened, reduced version of someone else.
This updated analysis on individual branding concludes with a key insight. By encouraging people to fixate on their “personal brand,” society is explicitly telling citizens that the individual is the ONLY force that can create change for the individual. This is conditioning people to completely ignore group actions – exactly at a point where existing systems have perfected ways to IGNORE or completely neuter individual calls for change and justice and GROUP action is the only viable approach for achieving required changes – economic, legal, civil or ecological.
Partitioning, Performing, Projecting
The words partitioning, performing and projecting appear frequently throughout the book to emphasize the deeper psychological / psychiatric concerns that stem from a world encouraging billions of people to create and cultivate online personas. People partition their own selves when they start choosing what to include or exclude in their online persona. Once “published”, the very concept of a “brand” requires consistency with those characteristics, whether they are still or ever were accurate. That emphasis on performance becomes more stressful over time, especially if the “brand” continues diverging from the “self” as one continually optimizes the digital version away from reality. The third term of the trio – projecting – refers to a process theorized by some in psychiatry in which the individual who believes in a soul that can “own” their good attributes worthy of eternal life also creates another parallel identity which can take on responsibility for all of their negative qualities. The concept is that someone willing to believe in a soul will be inclined to create another identity to avoid having to actually address their own flaws – just project them onto their evil twin. In this thinking, a person already willing to bifurcate their concept of self into “bodily me” and “soul me” who has also created “evil twin me” for additional false psychological compartmentalization is venturing into seriously warped territory when “online me” becomes equally critical to their short-term survival.
It’s one level of stress and risk for a software developer or account to “manage their brand” by updating a profile on LinkedIn, contributing to an industry blog, etc. For entertainers, writers or similar high profile public figures, the ongoing curation of their online brand poses existential economic and personal risks. One intemperate response on a Webex appearance or one sloppily written Tweet can, at a minimum, trigger an avalanche of condemnation or, in the new vernacular, complete cancellation.
That high-risk / high-reward dynamic and the algorithm-driven fixation on views and likes has created a unique – and uniquely distorted – environment online, populated with “influencers.” Initial search technologies might have resulted in actual random experts in a field popping up in search results and yielding a higher profile for those players. However, as newer “social media” technology was added to existing search engine technology, “content creators” quickly reverse engineered the algorithms involved and realized they could boost their appearance rates in results by commenting on whatever is trending in the larger internet. Don’t know a thing about vaccines but have an opinion? Blog it, tweet it, vlog it. Since clicks reinforce search results, toss your opinion out early enough as a topic starts trending and you can increase your traffic 20 percent. Actually knowing anything about vaccines is not a requirement.
One fascinating anecdote in the book involves a question many might ask. What happens when your actual self and your online self diverge and the world likes “online you” far more than “real you?” In 2022, South Korea elected a new President, Yoon Suk Yeol. During the campaign, his tech savvy staff created an online ad campaign featuring an obviously AI generated simulation of the candidate and named it AI Yoon. The problem was that the AI incarnation had more charisma (and better script writing?) than the real Yoon, who actually won the election. Many citizens still watch the AI version because he’s more relatable to them. So who actually won the election? Yoon and his team that wrote his human speeches and campaign platforms? Or the coders that created and scripted the AI Yoon content? Will that distinction be so clear in another ten or twenty years?
A Simple Formula
After outlining the new stresses of curating a unauthentic, synthesized doppelganger and the extreme high-risk / high-reward stakes of leveraging it for a career, Klein then tackles a question related to a phenomenon seen more frequently in the past decade. What triggered the surprising intellectual 180 turned by her doppelganger, Naomi Wolf? (Parenthetically, I can think of many more examples… Glenn Greenwald? Matt Taibbi? Lara Logan? Sharyl Atkisson? Seymour Hersch? Is there something new and unique that explains this phenomena?
About halfway through the analysis regarding Wolf’s U-turn, Klein summarizes one possible process behind these transformations in formula form:
I could offer a kind of equation for leftists and liberals crossing over to the authoritarian right that goes something like: Narcissism (Grandiosity) + Social media addiction + Midlife crisis + Public shaming = Right-wing meltdown
In the case of her immediate Naomi (Wolf) doppelganger…
Wolf’s addiction to social media via likes and influence seemed to begin with comments in 2011 regarding Occupy Wall Street protests after the financial collapse of 2008. Despite the organization leading OWS explicitly stating it had no specific list of policy changes that lawmakers could enact that would end the protest, Wolf claimed to know otherwise and actually appeared in public seeming to negotiate her supposed list of OWS demands. By the time OWS protests ended across the country, Wolf claimed the shutdown of protests was ordered by Obama, as part of an growing authoritarian threat. Things just got weirder from there. Aid the US sent to Africa in 2014 wasn’t aimed at combatting the spread of Ebola but returning it to the US to trigger a pandemic to justify more government control. Actual beheadings of American and British captives by ISIS were staged with actors. The NO vote on the 2014 Scottish independence referendum being fraudulent when the margin was 10%. Bizarre.
The mid-life crisis portion of the formula for Wolf stemmed from a book Wolf wrote in 2019 regarding the history of sexual repression in Britain as part of a Ph. D she worked to earn from Oxford University. She appeared on a BBC program for an interview about the book, an interview which led to a HUGE mistake being identified in her book (and Ph. D). Wolf cited the words “death recorded” appearing in public records in Britain related to sodomy charges as proof that Victorian Britain was executing men for gay sex into the 1800s. Her interviewer from the BBC had done his homework. That language at that time didn’t mean they were executed, it meant they were charge and RELEASED. When she made her point, he rebutted her assertion. In real time. Wolf literally found out on live television that the core of her thesis behind her new Ph. D and her new book was EXACTLY wrong. Oxford yanked her Ph. D and her publisher withdrew the book. That chain of events clearly qualifies as one of the larger career failures and public shaming in modern media history.
With Wolf as an example, Klein theorizes that the assimilation of social media into traditional journalism and research into economics and politics has created an ecosystem which
emphasizes “likes” and influencer “clout” rather than actual insight and truth
requires participants to create and maintain a parallel online self to operate within that ecosystem
delivers incentives for participants that further the divergence between their real self and “online self” with outsized rewards, often leading to fringe, unproven ideas and conspiracy theories as content
can impose outsized penalties for failure or perceived transgressions
While trying to understand where Wolf might be going in her commentary, Klein concluded that to some extent, there was no theme to the issues Wolf would engage, except whatever was trending on the internet on a given day. In this system, when a participant self-implodes, there is little chance of resurrecting a career in their original sphere of influence. However, since they understand how the system is manipulated by views, likes, impressions, etc., they can adopt the strategy with a new audience – often by playing the “I’ve seen the light” angle with the new crowd -- and stay in the game.
You Can Ignore Them, But They’re Not Ignoring You
With Naomi Wolf as an example of people crossing intellectual boundaries no one would have predicted decades ago, the book introduces two other key concepts regarding patterns in our existing public discourse. As Wolf continued her shift to the alt-right and Klein continued getting misdirected feedback to the wrong Naomi, Klein started paying more attention to Wolf’s appearances. Many of those involved Steve Bannon so Klein – partly from intellectual curiosity, partly morbid fascination and partly having little else to do during COVID lockdowns – began listening to Bannon’s podcast on a regular basis. From that brain dulling experience, Klein extracted some crucial lessons that those who think they operate in the saner spectrum (left or right) need to understand and incorporate.
First, conspiracists not only make outrageous claims about various issues or put forth specific proposals for addressing them, they frequently coopt existing terminology or use terminology overlapping the opposition in their communication. In a media world driven by search algorithms, this leverages search engine stupidity to essentially “steal” hits from the opposition but subsequently – purposely -- taints the language of the debate to the point where more reasonable thinking individuals not only avoid the LANGUAGE involved, they avoid the ISSUE. In a world with issues A, B, C, X, Y and Z with two parties on either side connected to all six with policy ideas, “tainting” the language of X, Y and Z can result in “normal” people simply AVOIDING those issues entirely. Alt-right players -- and Steve Bannon in particular -- understand this phenomenon very clearly and leverage it explicitly in their strategy. For X, Y or Z to be an issue at all, there is CLEARLY something occurring generating concern in SOME portion of the population. Allowing X, Y and Z to go completely unaddressed because the crazies talk about them can likely cede any voters interested in X, Y or Z to the opposition. This pattern was seen in the 2016 election.
Of course, using this search engine hacking technique to “invade” an issue space and taint it so your opponent avoids it doesn’t require actual SOLUTIONS to be proposed for X, Y and Z. It only requires delivering content that keeps those attracted engaged and enraged enough to avoid mainstream players. Voters interested in X, Y or Z feel like the issues are abandoned (they are), leaving them vulnerable to simplistic solutions from the alt-right. Simplistic solutions which inevitably involve some “other” person, demographic or nationality that can serve as a distraction, long enough to capture a vote before those on the alt-right get elected then continue policies benefiting the “tenth of a percenters.” In the mean time, this strategy injects enormous amounts of conflict, fear and panic into the public sphere, sucking up all available oxygen for legitimate debate and putting citizens in a fight or flight mode rather than a calm mode that can facilitate listening, learning and actual problem solving.
The book uses the term diagonalism for this phenomenon of seeing unexpected actors operating in “issue spaces” not normally expected according to traditional ideas of left / right (pick your favorite dichotomy…) boundaries. The key point regarding all of this per Klein is that the forces that have learned these dynamics are leveraging them with great political effectiveness. Those who think they are operating in a sane world with “real facts” and shunning the crazies must understand they are still acting inside a room with a one-way mirror. We may not be paying any attention to “them” or even if we’re looking at the mirror, we aren’t seeing what they are doing. But “they” are watching the entire political space and finding areas not addressed with effective policies and priorities and co-opting that idea space – either to actually do something or, more likely, simply to use as a point to sow confusion and division and “flood the zone with shit” to keep attention diverted from the things they want that they know the majority would never knowingly support in an election. Instead of the shock doctrine, it’s the shit doctrine.
Doppelgangers as States
Considerable space in the book is devoted to applying the doppelganger concept to analyzing current events in Israel and the entire seventy plus year history of the Israeli-Palestinian conflict. The analysis here starts with a key insight. Capitalist societies in general and western victors of World War II in particular decided to frame the Holocaust as a UNIQUE horror with no precedent that we must NEVER FORGET and NEVER REPEAT. That framing of “uniqueness” reflects a self-serving fiction that Hitler and Nazism arose from nowhere, the western allies stepped up to defeat him and, after having done so, were justified in compensating the victims by providing them a home. Western countries prefer this “unique” and “unprecedented” view of the Holocaust because it excuses them from contemplating the longer history of persecution and genocide, not only of Jews but of those involved in their own histories.
From that initial historical schism between “history” as remembered and “truth,” Klein’s analysis states that Israel was formed for the benefit of the victims of a industrialized Holocaust stemming from a government that treated Jews as disposable “others” for the advancement of the German state. In creating the new Israeli state, the new territory was taken from a DIFFERENT set of “others” (Palestinians) who had suffered under similar arbitrary partitions from other European colonial forces.
In reality, Hitler and Nazism did NOT arise from nothing, they were merely the most recent incarnation of a centuries old pattern of pograms, massacres and lower-tech genocides, not only against Jews in Europe but Native Americans in America and Canada. Before Hitler talked about lebensraum (“living space”) justifying the expansion of Germany, Americans and Canadians had “Manifest Destiny” as a justification for displacing natives based on white Christian superiority. Hitler actually cited American laws regarding segregation as influences on establishment of the Nuremberg Laws.
In reality, under this framing, Israel has been forced to operate as a split-brained doppelganger since its inception with a compounding set of existential challenges.
Formed as compensation / justice for victims of a Holocaust.
Territory provided by taking it from another people treated as “others” who already experienced the same treatment under prior colonial powers.
At a time when the western capitalist victors themselves were supposedly recognizing policies based on colonialism and occupation needed to be eliminated because they didn’t work and caused more problems.
While handing the keys to a new state that was immediately put in a position of being in a space to which it never had sole prior political claim.
With little chance of survival of that new nation unless it adopts that colonial strategy 100%.
And no party to this new arrangement is allowed to actually label this mode of operation as colonialist or cite its inherent unfairness.
How is the nation of Israel doing at operating in this perpetual split-brain doppelganger mode? Arguably, not well. The country is currently being torn apart by alt-right Orthodox conservatives working to cripple an independent Israeli Supreme Court to further cement militant policies pushing more settlements, continuing restrictions against Palestinians in those areas and impose restrictions on Israeli media. Oh, they also want to enact new legislation that would exempt Orthodox Jews from conscription in the military that would enforce these militant policies it wants pursued.
Naomi Klein identified two key goals for writing Doppelganger. 1) Formulating a coherent explanation for the otherworldliness quality many people currently report about world events… The feeling that not only friends and family seem to be making bizarre “hyperspace jumps” on the traditional X/Y grid of political / social / economic thought we all think we understand but that journalists and media players are doing the same thing, making us question the information from the entire system. 2) Replacing the sense of chaos and fear from that cognitive dissonance with a sense of calm based on understanding the process by which it operates and an anecdote for combatting it. A sense of calm is required for citizens to resume TALKING with one another and LISTENING to begin formulating solutions as a society. The alternative is to remain in individual cages of fear and ignorance to be preyed upon by the interests actually operating our country and economy for their selfish purposes.
I have yet to read any other analysis over the past ten years that provides a more straightforward explanation for the distorted information ecosystem we see operating today than that in Doppelganger. Applying that same concept to individuals as they attempt to navigate through a career managing an online “brand” and individuals just managing a social profile that makes them feel worse by magnifying the difference between their perfect-life online persona and their actual life was interesting. Applying the concept to the psychology of an entire nation seemingly set up for existential conflict from Day One without the vocabulary to describe its situation was icing on the cake.
Doppelganger serves an admirable purpose by describing a variety of interrelated processes in modern technology that are harming public discourse and being leveraged by groups benefiting from a flawed status quo. Describing the process and providing meaning vocabulary for using the concepts in discussions is a pre-requisite the calm Klein describes that aids meaning conversation about problems and solutions. However, millions of people need to absorb this material – and SOON -- for that calm to take hold.
Mark Meadows' attempt at removing his prosecution from Georgia state courts to federal court has highlighted another example of Republican hypocrisy regarding interpretation of the law. The appeals court hearing his motion to remove issued a question to both Fani Willis and Meadows' attorney aking each side to state their opinion on a crucial legal question regarding the statute Meadows is citing in his request for removal... Does that statute permit former federal officers to remove state actions to federal court or does it permit only current federal officers to remove?
If the law is is not restricted to CURRENT federal officials, that would help Meadows' argument for removal. If the law is interpreted as ONLY applying to current officials, it renders Meadows' motion moot and he remains in state court.
28 U.S. Code § 1442 - Federal officers or agencies sued or prosecuted
(a)A civil action or criminal prosecution that is commenced in a State court and that is against or directed to any of the following may be removed by them to the district court of the United States for the district and division embracing the place wherein it is pending:
(1)The United States or any agency thereof or any officer (or any person acting under that officer) of the United States or of any agency thereof, in an official or individual capacity, for or relating to any act under color of such office or on account of any right, title or authority claimed under any Act of Congress for the apprehension or punishment of criminals or the collection of the revenue.
(other particulars not applying to Meadows omitted for brevity...)
(b)A personal action commenced in any State court by an alien against any citizen of a State who is, or at the time the alleged action accrued was, a civil officer of the United States and is a nonresident of such State, wherein jurisdiction is obtained by the State court by personal service of process, may be removed by the defendant to the district court of the United States for the district and division in which the defendant was served with process.
Note that the language of (a) EXPLICITLY references civil action or criminal prosecution. Note that category (1) of applicable individuals uses the CURRENT TENSE verb acting.
Those distinctions were highlighted by Fani Willis and team in their written response to the appeals court. In a nutshell, they directed the court to compare the language in (a) versus (b) and note that article (b) EXPLICITLY references or at the time when the alleged action accrued was. Their briefing then states:
This Court should recognize that the discrepancy in the language used by Congress in drafting and enacting these two subsections of the same statute
was intentional, and Section 1442(a)(1), properly interpreted, cannot authorize removal of a state criminal prosecution against a former federal officer.
Where "Congress includes particular language in one section of a statute but omits it in another section of the same Act, it is generally presumed that Congress acts intentionally and purposely in the disparate inclusion or exclusion." Russello v. United States, 464 U.S. 16, 23 (1983). And "[t]he interpretive canon that Congress acts intentionally when it omits language included elsewhere applies with particular force" when the disparate statutory provisions are "in close proximity" to one another. Dep't of Homeland Sec. v. MacLean, 574 U.S. 383, 392 (2015) (emphasis added).
Obviously, Meadows submitted a different interpretation, stating words to the effect that, clearly, the law requires contextual interpretation for each circumstance and his circumstance warrants removal. He's essentially arguing judges making this decision should have wide lattitude in which to decide this issue, despite the actual language of the law making no explicit grant of this right to FORMER officials regarding civil/criminal cases filed by state actors while explicitly DOING SO four paragraphs later in the same law regarding actions initiated by external aliens. That's not an accident, that's the INTENT of Congress, which requires no free-lance re-interpretation of a judge on a case by case basis.
It's amazing how quickly conservatives jettison thirty years of psuedo-intellectual religion regarding originalist theories of legal precedent when the noose is around their neck and they are desperately trying to escape justice.
This is a rather long commentary so it's important not to bury the lede... The point of the analysis provided below is this:
Skyrocketing insurance costs are not solely a reflection of opportunistic insurance companies applying the screws to consumers. Typical corporate greed may not even be twenty percent of the root problem. Instead, skyrocketing insurance rates reflect a fundamental shift in the profile of risks facing property owners and insurers alike -- shifts in risk which classic insurance business models cannot distribute across time and customers to allow financial protection to be provided profitably. Skyrocketing rates in many areas may still not accurately reflect the true risks posed to customers and insurers alike. These drastic changes have significant implications for INVESTORS in insurance and construction-related businesses. They have even greater implications for INDIVIDUALS in their personal lives, whether someone choosing to start a career in a particular location, someone considering moving mid-career to a new region or someone considering retiring to a new area facing increases in weather extremes. Making such decisions without contemplating these risks could result in life-altering financial disaster -- an AVOIDABLE life-altering financial disaster.
Most consumers understand that insurance rates have risen substantially and there are obvious events taking place that are triggering some of those increases. However, mere intuition may not adequately convey to everyone with an interest HOW volatile insure markets are likely to remain. More importantly, not everyone will immediately understand the PERSONAL financial risks they are assuming when all of the big players adjust their hands at the table. Conveying that level of clarity requires providing a clear explanation of how insurance works as a financial product and a visualization of how current events overwhelm the traditional mechanics of insurance as a product.
All of this has been attempted here by devising a mathematical model reflecting current insurance processes then feeding current market inputs (home prices, premium payments, claim statistics) to that model to validate it produces realistic results. Once validated, new inputs were provided reflecting higher disaster event rates to see how rapidly the outputs change. Those market dynamics are then discussed, then related to more individual concerns.
Insurance in a Nutshell
Insurance is a financial service sold on a mass market basis using boilerplate contracts. The service involves the insurer agreeing to pay large sums of money to the customer for specific, carefully proscribed events IN EXCHANGE for the customer making a series of much smaller payments to the insurer. The net result of the contract is an arrangement in which the customer pays for POTENTIAL future losses on an installment plan and the insurer pays for covered damages in full at the time they occur in exchange for a profit. Both parties consider it a win if the customer doesn't face an outsized, uncovered loss and if the insurer accurately predicts the probability of a customer loss and sets premium levels accordingly.
Consumers want to buy the product because it appears to prevent them from being hit with a giant cash expense to fix/replace something they already own. Even if the customer is financially and economically literate enough to understand their own premiums essentially pay for their eventual losses, they are not likely to understand the statistics related to the frequency of loss events to do the math on their own and simply save for the potential loss. In many cases, a customer may understand the idea of "self-insurance" but realizes the loss could come well before they've saved enough to cover it themselves.
Insurers can sell coverage profitably because they can collect statistics across THOUSANDS of customers to accurately predict loss rates and costs, set appropriate premium levels, then optimize claims processing for efficiency. Since premiums are collected across ALL customers but a very small fraction of customers actually file claims in any given period, that asymmetry usually results in premiums exceeding payouts and profits for the insurer, provided their statistics remain valid.
In a typical worse-case world, insurers themselves can minimize their financial risk by purchasing their own insurance, called re-insurance. Re-insurance works similarly to insurance as a product, only with a different financial entity as the "insurer" (the "re") and the original insurer as the customer. The concept of a deductible also appears in re-insurance, requiring the insurance company customer to eat the first $XXX million of losses in a given year or over an period of years before re-insurance coverage kicks in. This provides an incentive for the original insurer to remain diligent about its sales tactics, pricing and claim processing / validation to eliminate avoidable losses rather than immediately transferring all losses to the re-insurer.
The insurance model works well for LARGE, randomly occurring costs. The model is NOT an efficient solution for handling SMALL, perfectly predictable ROUTINE costs. A deductible amount is usually applied to each claim to avoid using an insurance model to pay for small claims whose paperwork costs exceed the cost of the claim being processed. As an example, auto insurance policies don't include the cost of routine oil changes because no insurance company can process a claim for $49.95 and the need for an oil change is so predictable that if the customer cannot plan for routine $49.95 oil changes, they cannot afford the vehicle. Any insurance company that DID cover oil changes would likely add $300 dollars to the yearly policy --- $150 for oil changes and $150 for back-office paperwork fees, resulting in no net benefit to the customer. Deductibles also serve to apply some pain to the customer for each claim event to avoid the "moral hazard" of the customer feeling like they don't have to avoid taking undue risks with the asset since they won't pay anything out of pocket for damages.
The Consumer's Cash Flow Problem
Insurance benefits the customer by transferring the risk of incurring a single LARGE expense to another party in exchange for paying a smaller series of premiums which fit into the customer's cash flow. If a customer purchases a home for $248,700 with a 30 year fixed mortgage at 7% and 20% down payment ($49,740), they will borrow $198,960 and carry a monthly mortgage payment of $1446. Given they had to borrow money to afford the house the first time, they likely cannot afford to REBUILD the house if it is totally destroyed during that same period, so they purchase insurance. For insurance to be worth the cost to the consumer, the consumer must believe insurance premiums are cheaper than the loss eventually incurred over the term. If there is a 100% chance a hurricane will completely destroy the home over 41 years, the expected loss over that period is $248,700. Ignoring inflation momentarily, that means the sum of premiums cannot exceed $248,700 so the customer would need to pay a yearly premium of $248,700 / 41 or $6066 per year.
If the customer KNEW the loss would be incurred in year 41 and they had the discipline to save money, they would of course be better off just saving $6066 per year and setting it aside for year 41. Of course, most consumers DON'T have that discipline so they opt for insurance. They also opt for insurance because the loss COULD occur in year 2 when they would have only saved $12,132 on their own.
KEY TAKEAWAY -- Purchasing insurance doesn't eliminate financial risk, it transforms it by spreading it out over a long period of time for the consumer and allows the short term cost to be covered by a larger financial entity with sufficient cash flow for the large outlay.
The Insurer's Cash Flow Problem
Absent any inflation and its effect on present value, the timing of the loss makes no difference to the profitability to the insurer. As long as premiums collected exceed the expected loss over the term (which requires the insurer to continue offering coverage and requires the consumer continue paying premiums), the insurer makes money.
When inflation and the time value of money ARE factored in, the TIMING of the loss within the term has a DRASTIC impact on the net profitability of selling a policy. When the loss is incurred many years into a long term, the discounting impact on those future claim outlays back to present dollars reduces the net cost of the claim. In the mean time, the insurer has been able to take dollars paid in premiums to invest and earn a return on to use for later claims. If a loss is incurred EARLY in the term, it drastically increases losses for the insurer. At a 4% discount rate, shifting a total loss of $248,700 from year 41 back to year 0 changes the net income from this example policy from +$74,327 to negative $122,571.
KEY TAKEAWAY -- Shifting the timing of claims from the future to the present increases net cost to the insurer.
The impacts of timing for consumers and insurers are illustrated in the video here:
The Insurer's Loss Correlation Problem
So if an earlier claim on a policy poses a risk of a loss to the insurer, how does the insurer mitigate that risk? By selling similar coverage to similar customers. This works provided that loss events do not have a high degree of correlation in time and value BETWEEN multiple policy holders. For example, if the risk of a total loss due to an electrical malfunction and fire is 100% over 41 years, an insurer selling $248,700 of coverage for $6066/year will only collect $126,128.97 in present value for premiums over that period. If the house burns down in year zero, the insurer will lose ($248,700 - $126,128.97) or $122,571 dollars if that is their only customer. However, if they sell 100 identical policies, the odds of electrical fires are not correlated BETWEEN homes so the other 99 policies might still generate as much as $74,327 each in income, balancing out the loss on policy #1. At a 4% discount rate, the insurer's "break even" time horizon is about 17.5 years. Measured from the beginning of a policy relationship, the insurer can make money even for total losses if the loss is incurred more than 17 years in the future.
KEY TAKEAWAY -- Insurers can reduce timing risk by selling to multiple customers for loss types (plumbing, casualty/theft, roofing, minor fire, etc.) which have no statistical correlation to each other.
But therein lies the key problem facing insurers. Regardless of whether one believes climate change is the root cause, actual events reflect:
an increase in the number / severity of hurricanes
an increase in the number / severity of flood events
an increase in the number / severity of wildfire events
These type of events violate the statistical assumptions behind the primary mechanics of risk spreading within insurance because the loss timing is SIMULTANEOUS across hundreds of policies and loss value tends to be near 100% (actually probably close to 150% including home contents).
The Insurer's Time Horizon / Re-Insurance
Insurers make higher profits as losses are shifted into the future. Insurers spread the risk of losses incurred closer to the present by selling coverage to multiple customers and betting losses will NOT be tightly correlated in time. So how do insurers cover the risk of an unexpected spike of claims in the near term? Two ways --- either by raising premiums to build a "cushion" that can smooth out profits over say a 3-5 year span OR by purchasing re-insurance from another firm. In fact, most firms use both of these strategies.
No insurance company literally sells insurance for exactly the present value of expected future losses -- doing so would yield exactly zero in profit. The premium amount collected always includes a margin which not only covers the firm's desired yearly profit margin it wants to provide its investors and management but also provides a stash of cash to cover claims for a particularly bad year. But retaining an extra billion dollars in cash year to year when average claims total $500 million and premium payments total $600 million is not desirable either. To avoid stranding so much cash as a cushion, insurers buy their own insurance to guard against outsized claims in any given year. This lowers the amount of cash they have to keep on hand, allowing them to invest it to earn additional profits. Firms selling re-insurance, generally referred to as "Res", spread this form of risk by selling re-insurance to multiple insurers across a much wider swath of geography. Thus, a Re selling re-insurance to Mutual of Florida and to Mutual of Michigan can lower the net risk of either firm having a cash flow problem due to a bad claim year under the assumption that the odds of BOTH firms having a bad claim year are extremely low since Michigan doesn't face hurricanes and Florida doesn't face risks from blizzards and lake effect snow events.
KEY TAKEAWAY -- Re-insurance can spread risk BETWEEN insurers with different policy portfolios whose risks and geography are NOT highly correlated.
Risk Shedding Caveats
There are several important caveats to the "risk-shedding" aspect of the insurance process. Events involving high simultaneity ACROSS customers magnify cash outlays of the insurer. This is an inevitable problem with natural disasters such as floods, tornados, hurricanes, wildfires and earthquakes. These events have an "impact radius" or "impact path" that triggers losses among many co-located customers. An insurer cannot spread this risk by selling more policies in that area. Doing so would increase claims at that same point in time.
A second caveat to risk shedding is that geographically clustered losses can be further magnified by geographic skewing of coverage. That's an analytical way of stating that disasters and home buyers seem attracted to the same amenities -- beaches, mountain views, scenic rivers, etc. -- and such locations tend to magnify damages from weather events near them. Insurers with policies in such locations can experience significantly higher losses, first due to the proximity of customers hit by an event and the upward skewed loss amounts as a storm wipes out multi-million dollar homes rather than mere "average" $390,000 homes.
A key wildcard at work with insurance markets currently is that claims resulting from natural disasters are resulting in HIGHER costs than previously estimated by insurers. Again, this is not addressing the NUMBER of claims, this is addressing the resulting COST of each claim exceeding prior estimates. How can this happen? If an appraiser comes by a home, counts up the rooms, counts up the square footage, identifies the number of bathrooms, the finished square footage in the basement and the $20,000 Viking commercial range in the kitchen and says the house would cost $682,000 to rebuild, that number MIGHT hold if that single house is lost in a fire. In that circumstance, there is only one extra contractor in line at 84 Lumber and Appliances-R-Us to purchase replacement materials and only one additional crew needed for reconstruction. If instead that same $682,000 house is lost amid a wildfire or hurricane with 150 other similar homes, that house could cost $1,200,000 to rebuild after materials and labor costs skyrocket due to a spike in demand.
Who is at risk in this scenario? Predominately the homeowner. If coverage only provides for actual cash value, a home purchased for $600,000 with a dwelling that cost $500,000 to construct that is fifteen years old and in need of a new roof might only pay off $390,000. A mortgage lender may only require cash value coverage because the mortgage holder doesn't care if the owner has a place to live, they only care about having the mortgage paid off and a cash value policy will typically make them whole. If replacement cost coverage is purchased, it still relies upon the insurance company or homeowner accurately estimating the rebuilding costs -- often by looking at similar recent claims. Unless the customer requests a larger replacement value and pays a higher premium, that stated limit will apply if the house is lost entirely.
Finally, insurance companies and re-insurance companies must carefully analyze the actual risk diversification they achieve via re-insurance. A Re selling coverage to Mutual of Miami and Mutual of Tampa has not materially reduced any risk since both insurers likely serve customers with nearly identical exposure to hurricanes, storm surge, rising sea levels and related storm damage. A Re might not be materially improving its odds by selling re-insurance to firms serving the Carolinas or Virginia since many hurricanes hitting Florida follow a path up the East coast. A Re cannot necessarily improve its odds by selling re-insurnace to a firm in Texas or California either because they may face similar high risk of weather related disasters.
Designing a Model for Sensitivity Analysis
Besides the financial impacts of time and inflation on risk in insurance described earlier, the model programmed and interpreted here makes one additional crucial assumption. Insurance companies report their financial results reflecting two distinct sources of income. Underwriting Income shows the balance between current year premiums paid by policyholders and current year claims paid out, including related administrative claims processing expenses. Investment Income reflects profits made from investments made with the profits from underwriting each year. If an insurer is netting $15 million in underwriting profits each year over ten years, typically at least some of those $15 million dollar sums are being invested to return additional profits. Those additional profits can be used in two ways -- they can be paid back to stockholders as dividends to boost returns or they can be retained to provide a cushion for years where claims exceed premiums and underwriting losses are incurred.
In theory, underwriting income is the canary in the coal mine for an insurance company's future financial health. A firm failing to correct yearly underwriting losses will soon burn through any cushion created by investment income. As a result, this analysis will only focus on underwriting income simulated directly from premium payments and claim amounts.
To simplify performing the underlying calculations and summarizing the results intuitively, the model was coded using Python so the Manim (Mathematical Animation) library could be used to illustrate the results. For those curious about the mechanics, the organization and logic of the modeling are described below.
Insurance Model Components
The logic in the model was designed to reflect these attributes of an insurance market and policy:
Value
the financial value of the asset being protected by policies
Event Probability
the likelihood of a damaging event occurring during an insured term
Loss Factor
the percentage of value of the protected asset destroyed by a damaging event
Premium
the amount paid by the customer for coverage
Deductible
the portion of loss (either a fixed amount or percentage of claim) born by the customer before insurance coverage kicks in
Customers
the total number of policies sold with a given combination of the above risk parameters
Path Diameter
reflection of how far damage extends from an event's origin point
Path Length
reflection of how far an event moves from origin to termination
Modeling Location Density and Value
Calculations done using averages will only be accurate if events themselves are purely "average" and encounter "average" customers with "average" value properties. A key assumption being tested by this modeling is that climate-related events are NOT impacting "average" homes and instead may impact homes skewed to specific locations with upward skewed values. Rather than create a simple, evenly spaced "grid" of identical average customers, the model should be able to skew customer locations across the wider territory and skew assigned values for coverage for each customer.
The model accomplished this by using random numbers to generate an initial (x,y) coordinate, using another random number to select against a statistical curve to weight whether to keep that (x,y) coordinate or try a new value. Once a final (x,y) was generated, that (x,y) coordinate was mapped to a 0-1 unit scale in the x/y directions to use those unit values to map through different gradients to use in scaling a coverage value from a starting average value and variance amount. The resulting logic supported any of the following distributions of customers by location and coverage value.
The first illustration reflects policies evenly distributed on a simple grid with the identical coverage value.
The second illustration reflects the same number of policies randomly distributed with random values.
The third illustration reflects the same number of policies randomly distributed with a skew to higher values in the northeast corner.
The fourth illustration reflects the same number of policies randomly distributed with a skew to higher values in both the northeast and southeast corners.
Modeling Loss Profiles
In the earlier examples, the cash flows of insurance were illustrated with just the premium stream paid by the customer and one single 100% loss event paid by the insurer. In reality, most customers never suffer a 100% loss but do incur minor claims over the period they stay with any given insurer. The Insurance Institute publishes statistics that identify the overall yearly claim rate and the types of claims and their associated loss. Descriptions and definitions vary from source to source but the model boiled down those statistics into the following categories of risk:
For each entry, frequency is its rate of occurrance within the total claim rate. The lossfactor value is the modeled claim cost as a percentage of the total coverage value. Path diameter reflects the width of the impact circle of the event. For events limited to a single home, pathdiameter is set to 0.0057 miles or 30 feet. The pathlength parameter reflects the distance in miles impacted by the event. The entry for tornado with pathdiameter = 1.0 and pathlength = 3.5 reflects a tornado traveling 3.5 miles on the ground with a 1 mile wide impact along that route.
For most event types, the deductible is a fixed amount set to $1000. For hurricanes, the deductible instead is calculated as 2% of the loss value, which is typical for markets at risk of hurricane storms.
Modeling Events and Impacts
For a given model run, the defined customers value and claim rate were multiplied to yield the number of events to generate. At that point, a loop was run to randomly select a customer, retrieve that customer's (x,y) coordinate, then a random number was used to select an event type from the above array. The lossfactor of that event type was multipled by the customer's coverage amount then a deductible substracted from that for a final loss amount. When the pathlength of the selected loss profile was zero, logic just looked for any other customes with an (x,y) in the radius of the originally selected customer, then repeated the same loss calculation for each.
If the pathlength of the loss profile assigned was greater than zero, additional logic was used to calculate the (x,y) coordinate of the remote end of the path, then a circle around that (x,y) and a rectangle connecting the two.
The entire set of customers was again searched for any with an (x,y) within those areas, then the same loss calculations performed for each policy.
The pathangle shown in the calculation illustration above was always set to 45 degrees for tornados, representing a typical northeast direction taken by most tornados in the US. For hurricanes, if the storm struck on the eastern extreme of the territory, the path angle was set to 140 degrees, roughly northwest or slightly below. If the storm struck on the west extreme of the territory, the path angle was set to 45 degrees reflecting a northeast direction.
Sanity Checking the Model with Real Statistics
Prior to experimenting with changes to factors in the model, the underlying logic was sanity checked by attempting to mimic a real insurance market for which public statistics were available. Since Florida has been a topic of recurring conversations regarding disasters and insurance, the following information was programmed into the model:
average home price in Florida is $390,000 (home price = cost of dwelling + cost of land)
ignoring the panhandle area, the bulk of Florida is rougly 170 miles wide and 270 miles tall
Florida has at least two concentrations of higher prices in Miami (southeast) and Jacksonville (northeast)
average lot size in Florida is 11,043 square feet and average cost per acre across Florida is $34,900
not easy to accurately derive dwelling cost from home sale price since real estate in expensive areas is obviously way above the state average -- for now, coverage will just be set to the average home sale price
some sites show the average homeowner premium in Florida is $6066
other sites such as NerdWallet reflect typical insurance rates between $1675 (Tallahassee) to $4105 (Miami) for $300,000 dwelling, $30,000 additional structures, $150,000 contents, $60,000 living expenses and $1000 deductible
other sites indicate only 1 in 5 Florida residents have flood insurance and most homeowners insurance policies explicitly EXCLUDE damage from storm surge and flooding so these cheaper rates likely do not include full coverage
other stories are showing flood insurance prices alone will be between $5077 and $7097 for the highest risk counties
this modeling will assume that $6066 is a more accurate average of "full coverage" homeowners insurance on an "average" $390,000 home -- this implies average cost per $ of coverage is $6066/$390,000 = $0.0155 (so a customer with $1,000,000 in coverage would roughly pay $15,500 in premiums)
insurers limit contents coverage to 50-75% of dwelling so a "total loss" could be 1.5 to 1.75 times nominal coverage amount of the dwelling
nationwide yearly claim rate is 6% (insurer with 100,000 policies will field 6000 claims total per year)
Florida has a total of 10,257,426 housing units, 66.5% of which are owned
from that, assume 6.82 million policies
Florida has 437 in-state insurers and 1651 out-of-state insurers - total of 2088
the top 25 insurers in Florida range from 577,263 to 68,723 customers (4 million of the total)
modeling a customer count of 68,000 would be a reasonable proxy for a single insurer
nationwide, profit margins for property and casualty insurance are around 10% of revenue
in FLORIDA, insurers have been averaging claims payout rates of 117.5% (for every $100 in premiums, insurers paid $117.50 in claims)
insurers in Florida lost $1.5 billion (underwriting + investment losses) in 2021 when zero hurricanes struck the state
until 2016, Florida insurers had made between $850 and $950 million in underwriting profits and similar amounts in investment gains
With these inputs captured from industry statistics and news reports, a model reflecting these parameters was run five times, constrained to have zero hurricanes, in order to baseline results for a single year.
Simulation#
Premium Revenue
Claims
Profit (Loss)
1
$425,763,762
$407,285,840
$18,477,922
2
$425,763,762
$427,384,587
($1,620,825)
3
$425,763,762
$415,986,924
$9,776,838
4
$425,763,762
$406,627,639
$19,136,123
5
$425,763,762
$426,320,161
($556,399)
Across the five simulations with zero hurricanes, the insurer averages $9,042,732 in profits or about 2% of revenue. That's worse than reported industry averages across all states but significantly BETTER than actual results in Florida between 2016 and 2019, where losses occurred and claims averaged 117.5% of premiums.
Running the Model
Logic in the model generates multiple outputs for review, starting with a visualization of the market territory reflecting the position of each policy and a color coded representation of its coverage value. The policies are also summarized in histogram format to reflect the skew of values. In this run, values were randomly generated using a base value of $280,000 and a variance of $170,000 used to create higher values skewed to the northeast and southest. While the average coverage amount was $403,950, the highest amount was $3.4 million.
After synthesizing policies, the model runs a loop to generate events, calculate losses and summarize them in graph form. The example below shows the result after a run with a single hurricane that struck in the middle of the west coast:
Simulation#
Premium Revenue
Claims
Profit (Loss)
1
$425,763,762
$550,740,777
($ 124,977,015)
2
$425,763,762
$614,799,911
($ 189,036,149)
3
$425,763,762
$1,130,111,065
($ 704,347,303)
4
$425,763,762
$762,164,763
($ 336,401,001)
5
$425,763,762
$507,798,613
($ 82,034,851)
The average loss across the simulations with one hurricane is $287,359,264 with a payout ratio of 507/426 or 119.0%, which is pretty close to the number reported for Florida insurers in 2021.
Losses for the simulations with two hurricanes obviously get much worse.
Simulation#
Premium Revenue
Claims
Profit (Loss)
1
$425,763,762
$708,897,085
($ 283,133,323)
2
$425,763,762
$811,872,203
($ 386,108,441)
3
$425,763,762
$1,195,630,796
($ 769,867,034)
4
$425,763,762
$666,877,073
($ 241,113,311)
5
$425,763,762
$1,103,722,344
($ 677,958,582)
The average loss for two hurricanes is $471,636,138 across the simulated 68,000 customers with a payout ratio of 1103/426 = 258.9%.
The highest claim amounts from the set of simulations involved hurricanes that crossed the high-value sections in the northeast and southeast corners of the market where home values skewed up into the $800,000 to $1,200,000 ranges. On one hand, the simulation of property values may have too evenly skewed values upward in those areas rather than scattering (say) hundreds of multi-million dollar homes amid tens of thousands of homes still around $390,000. On the other hand, the logic used to simulate hurricane impacts was likely more forgiving than reality. The model logic limited damage to about 1.3 times the coverage amount and cut off damages within a fifteen mile path from landfall. Category 5 hurricanes have pushed storm surge water THIRTY miles inland, causing far more extensive flooding damage.
All fifteen event simulations (five with zero hurricanes, five with one, five with two...) can be watched here:
Investment Implications
Mathematically, to the extent this model of Florida is roughly indicative of market forces in real Florida, a few financial conclusions become very obvious.
Unique Florida Market Dynamics
Given the above model for a single insurer with 68,000 policies, a two-hurricane year would trigger underwriting losses of $471 million dollars and average claims of $897 million just for those 68,000 customers. Since 68,000 is one percent of the roughly 6.8 million total policies, that implies a statewide loss of $89.7 billion for two hurricanes. That's actually within an order of magnitude of possibly being correct. Hurricane Idalia alone has already been estimated to have caused $10 billion in damage as a predominately Category 3 storm. But it seems unlikely that many insurance companies will pay out $897 million for their share of the total customer base. As mentioned earlier, this is probably correct because four out of five Florida homeowners do NOT have flood insurance and any of these homeowners hit with storm surge will NOT be covered. Those losses WILL be incurred, but solely by the homeowners.
The fact that Florida insurers are encountering repeated underwriting losses -- even with exclusions for storm surge and flooding and even in years with NO HURRICANES -- means markets have not adjusted premiums to reflect risks from the good old days prior to climate-driven killer storms. The model showed insurers aren't even consistently breaking even in a year without a hurricane. The failure to raise premiums to appropriate levels could be driven by multiple factors.
Insurers may have relied upon storm surge exclusion language to guard against hurricane claims while ignoring the risk of higher losses from tornados and wind damage often correlated with hurricanes after they make landfall. Insurers may have over-optimized claim handling processes that attempted to streamline back-and-forth communications between contractors and adjusters as repair work is done in ways that drove vast amounts of claim fraud. Contractors would gain approval from a homeowner to deal directly with the insurer to replace a roof, then jack up work and materials quotes hoping the insurer would blindly pay the higher amounts without sending out an adjuster to validate. This problem is not unique to Florida but there is CLEARLY something unique about its implementation in Florida -- the state generates 9% of all insurance claims in the US yet accounts for 79% of all insurance fraud litigation in the country. The fraud here is likely split 50/50 in both directions -- insurers falsely denying legitimate claims and contractors submitting inflated claims for work not needed or even performed.
National Market Dynamics
A key goal in crafting the model was to highlight how simultaneous / correlated loss events pose unique threats to insurance profitability. Correlated "mass loss" events are difficult to mitigate because the statistics behind event probabilities are typically assumed to be in "black swan" range (high unlikely and hard to imagine) and the magnitude of losses can quickly erase any financial cushion built up over time. Insurers cannot mitigate this risk by selling more policies into the same pool -- doing so MAGNIFIES the exposure. Insurers cannot blindly "diversify" by selling policies in other markets unless they are confident market B is not equally exposed to similar correlated losses as market A.
These problems are not limited to insurers in Florida. Insurers in any market with exposure to hurricanes, floods, wildfires, earthquakes or tornados face an identicial problem. These "mass loss" event risks DO appear to be rising, but no one can predict by how much. Of course, that's not the real problem for these insurers. Even if they could derive the new risk numbers, crunch their internal models and come up with adjusted premiums to charge customers, a more fundamental problem exists… Many customers cannot afford the higher rates. This is a reflection of two fundamental problems in the US economy -- an income inequality problem and a shortage of affordable housing.
Imagine a homeowner with the following household budget:
Component
Value
Household Income
$87,000
Federal Taxes
$14,700
State Taxes
$4,350
Payroll Taxes
$6,786
Monthly Takehome
$5,097
Home Price
$310,000
Down Payment (20%)
$62,000
Home Loan
$248,000
Mortgage (7%)
$1,650
Monthly Home Insurance
$400
Monthly Car Payment
$682
Monthly Car Insurance
$125
Monthly Gas ($3.87/gallon)
$161
Monthly Utilities
$150
Monthly Food
$1200
Monthly Health Insurance
$700
Monthly Net Savings
$28
$87,000 is the median household income in the US for 2022. For this household to afford another $3000 in homeowners insurance, they would need to save $250 on some other monthly expense. If they could find a $272,000 home and only have a $1447/month mortgage payment, that would do it. But the entire population cannot all move to a cheaper home, the supply isn't there.
Of course, what these considerations REALLY mean is that many Americans are living in a location they cannot actually afford to live in, when the true risks that apply are accurately accounted for.
Individual Implications
The real lessons from the model are important to people at an individual level, and not just those currently living in an area at risk of these types of events. These considerations are worth reviewing for
anyone contemplating retiring and moving to a new location or staying put in a risky location
anyone exiting school and contemplating starting a career in a new location
anyone contemplating a mid-career job change or employer change requiring a relocation
Insurance companies cannot ignore the costs of these risks and will not voluntarily absorb them. Since residential insurance is typicaly purchased on a yearly term, an insurance company's worst case risk horizon is one year -- the minute they know underwriting losses cannot be made up within one or two years, rates will rise or they will stop renewing policies and writing new policies. Either way, numerous risks face individual homeowners in this sitation.
First, no bank is going to lend $248,000 dollars for a home which cannot be insured against a risk which is VERY likely to destroy it within the loan term unless the borrower has other SIGNIFICANT collateral to offer up to secure the loan. And frankly, if they did, they might not be borrowing the money in the first place. Since the number of people with the liquidity to buy a home in cash or offer a bank other near-cash collateral instead of the home itself is small, a contraction in the availability of insurance will lower home prices across the ENTIRE market. Even if an individual homeowner is wealthy enough to afford a house without insuring it, they will not enoy the typical upward price support produced by wider loan availability. This will act as a growing DEPRECIATION force on an asset that is the single biggest component of many people's net worth.
Second, once a home in such a market is purchased, any reduction in demand and lending supply means an existing homeowner trying to SELL is going to wait longer for their money. It's one thing for a house to decline $30,000 to $60,000 thousand in value as insurance premiums skyrocket and loan availability shrinks. It's another for a homeowner who is already going to forgoe $30-60k to also have to wait far longer to sell the home to get their money out. Given that many households have little net monthly savings, this poses an instant LIQUIDITY problem for most homeowners who cannot afford two mortage payments or a mortgage and a rent payment for even a month.
Third, when a catastrophic loss occurs requiring a complete rebuild, it seems likely that actual coverage amounts will fall short of actual costs. Barebones policies often only cover depreciated value so components like roofing and appliances that are viewed as wearing out are only covered on a prorated basis based on the age of the home. More expensive policies cover "actual replacement costs" but "actual costs" are still capped by the coverage amount purchased. A house purchased 25 years ago for $200,000 that would now sell for $510,000 might require $460,000 in replacement costs for the dwelling. If the house is lost to a fire and needs to be rebuilt, it might be possible to rebuild the home for $460,000. If instead the home is wiped out with 150 other homes in the same ZIP code due to a flood or hurricane, it is unlikely $460,000 will cover the costs. Instead of one contractor competing for raw materials and trade labor for the rebuild, demand for those resources has jumped by a factor of 150, just in that local market. In a post-covid world of constant supply chain shortages, imagine 10,000 homes all needing a new air conditioner or furnace at the same time. Getting the work completed will either involve higher cost or a longer wait (triggering other costs) or both.
Does a contraction in insurance markets away from at-risk regions mean NO ONE should live there? That's not only a personal financial and quality-of-life choice but an important public policy topic as well. If insurers withdraw coverage from a beach or mountain retreat, that doesn't immediately suggest no one can or should live there. It means those choosing to do so must understand the physical and financial risks before purchasing and they should be capable of absorbing those risks without a public financial backstop.
Someone worth $20 million wanting to live in a million dollar home in a hurricane zone is more than welcome to build and own a home in that environment. Just don't expect FEMA disaster relief to rebuild. If you want insurance coverage, be prepared to pay whatever the insurer demands or just self-insure and eat the loss and rebuild out of your own pocket.
In contrast, someone struggling to afford a major car repair, save up for their next car or save for college does not have the income and liquidity to tolerate that type of risk. They shouldn't be enticed into doing so by government programs that artificially hide the true cost of living in such a location or subsidize that cost by spreading it across a much larger pool of consumers who have the sense to avoid that risk. If it was merely unfair to the rest of the public but in fact covered the risk, that would be one thing. The point here is that even with incorrectly set insurance rates, when a loss IS incurred, that policyholder still faces a material risk of being completely financially wiped out, whether from denied claims, under-insurance or an inability to handle duplicated expenses during a long rebuild.
Perhaps the best way to phrase the dilemma facing individual customers is this:
When pondering a decision on where to buy a home,
assume every player you deal with has closed many more of these deals than you
assume every player you deal with thoroughly understands THEIR risk in the transaction
assume every player has structured their portion of the deal to protect THEIR financial interest and to carry that risk for as short a period of time as possible
since you are the least knowledgeable player at the table, assume everyone else has figured out a way to transfer as much of their risk to you as possible
In general, the real estate agent will make money. The builder will make money. The bank will make money. The insurance company will make money. Most of the risk facing other parties has been transferred to you. The bank required a down payment, giving them extra pricing room to reposess and quickly sell the house without taking a loss if you default. The bank required insurance to ensure they could be paid off if the house was destroyed. The insurance company is only exposing itself to risk one year at a time and will cut its losses at the drop of a hat. The builder has all of his money at closing. The only person facing all of the risks for the long term is you.
Taking a risk of buying into a market or remaining in a market with the the type of financial risk illustrated with this what-if modeling and real-life headlines merits serious thought. Your long-term financial livelihood likely hinges on it.
WTH
External Sources
General insurance statistics on claim rates, loss types, etc.:
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