현대판 노옹화구(老翁化狗)들은 언제쯤 개[狗]가 되어 가출할까?
한국 정치판에 우글거리는 꼴불견들
문무대왕
신라시대의 설화(說話) 가운데 ‘노옹화구(老翁化狗)’가 있다. 김유신(金庾信)을 찾아온 한 노인이 변신술을 부리다가 끝내는 개가 되어 가출 해버렸다는 내용의 설화다.
재주를 부리고 능력을 과시하여 권력을 잡거나 권력에 빌붙어 부귀영화를 누리려는 무리들은 현대사회에도 우글거린다. 특히 한국 정치판이 그렇다. 인사청문회장에서 자신만이 한방 터뜨릴 뛰어난 능력을 가진 것처럼 잔재주를 부리는 얄미운 국회의원이 그 같은 얄팍한 부류다. 입으로는 “정치하지 않는다”며 떠들다가도 권력 주변에 무슨 일이 생기면 잽싸게 나타나 궤변을 늘어놓는 추한 잡새와 날라리, X파리들도 있다.
특히 나잇살이나 든 자가 정치 대가나 되는 것처럼 으스대는 모양이야 말로 노추(老醜) 중의 노추다. 나서야 할 곳, 나서지 말아야 할 곳을 분간도 못하고 X개처럼 꼬리를 흔들어 대는 그 꼴불견은 ‘현대판 노옹화구’가 아닐 수 없다. 언제 그 X개들이 잔재주와 꼼수를 부리다 개가 되어 가출해 돌아오지 않을지 두고 볼 일이다. (발췌)
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재앙이 몰려온다
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여권과 검찰, 조국 구하기 나섰나? 뜬금없이 공직자윤리법 수사라니 [문갑식의 진짜 TV, 생방송]
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정경두 "종전선언 후 한미 동맹 유지…장관 자리 걸겠다"
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What's falling faster than #China's economy? Nothing.
중국의 경제보다 빠르게 추락하는 것은? 없다!
Stephen Joske
China’s targeted stimulus is not working. “The PMI indicated a drop in new orders, which also reflected a lack of domestic demand.”
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Head of US Missile Defense Agency says he does not see what will prevent #China and #Russia from proliferating hypersonic missiles to #Iran and #NorthKorea. My answer: sanctioning Beijing and Moscow into the ground, something we should be doing anyway, for a multitude of reasons.
중국과 러시아가 극초음속 미사일을 이란과 북한으로 확산
시키는데, 누가 이를 저지할지 모르겠다고 한다.
중국과 러시아를 끝까지 제재해야 한다. 제재의 이유는 너
무나 많다.
David Maxwell
Iran and North Korea: Soon To Build Hypersonic Missiles?
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上海嘉定区允许自动驾驶汽车载客测试
上汽、宝马和滴滴出行获得首批智能网联汽车示范应用牌照，使上海成为首个允许自动驾驶汽车载客测试的中国城市。
상해에서 무인차 시험 운행을 허가하다.
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Keith Zhai
Blackstone's Schwarzman said China knows it must change its trade and business practices but it’s reluctant to do so because of the spoils it’s reaped by protecting its economy.
중국도 현재의 경제 관행을 바꿔야한다는 것을 알지만, 지
금 손에 콩고물이 너무 많이 들어와 바꿀 수가 없다.
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Niall Ferguson
Read and despair:
아래 글을 읽고 절망하라!
Woke History Is Making Big Inroads in America's High Schools
By John Murawski, RealClearInvestigations
September 17, 2019
Like growing numbers of public high school students across the country, many California kids are receiving classroom instruction in how race, class, gender, sexuality and citizenship status are tools of oppression, power and privilege. They are taught about colonialism, state violence, racism, intergenerational trauma, heteropatriarchy and the common thread that links them: “whiteness.” Students are then graded on how well they apply these concepts in writing assignments, performances and community organizing projects.
미국의 학생들이 좌파들의 성, 인종, 젠더 담론을 교육받고 있다.
그들은 본능적으로 "백인임"을 혐오하도록 세뇌되고 있다.
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“stepparenthood is the strongest risk factor for child abuse ever identified...a stepparent is 40 to 100 times more likely to kill a young child, even when confounding factors— poverty, mother’s age, the traits of people who tend to remarry, are taken into account.”
계부(모)는 아동 학대의 최대의 위험 요소이다.
거의 모든 동화에 계모가 악녀로 등장하는 이유가 있었군.
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BY CHRIS BULLIVANT
Environmentalism is often anti-capitalism in disguise – but it doesn’t have to be
Much of modern-day environmentalism is informed by a deep suspicion of capitalism and the human population growth it has supported. Groups like Extinction Rebellion are concerned that our future will resemble a post-apocalyptic landscape where mankind is often reduced to hunter-gatherer protagonists raiding supermarkets for tinned goods.
What is unclear is whether modern environmental campaigners think this prediction is something to be avoided or achieved.
Most environmentalist approaches demand a backpedalling on technology and running down population numbers. The logic seems to be that if we don’t accomplish post-apocalyptic nirvana ourselves, it will be forced on us through catastrophe.
환경보호론은 종종 위장된 반자본주의이다.
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I've always said the decision to build Hadrian's wall was probably a bureaucratic imperial decision that turned out to be a mistake, but once it started nobody could stop it. Because it is so ancient, people are reluctant to accept that it probably did not "work".
하드리아누스의 성벽이 일단 시작된 이후에는 누구도 막을 수 없었다. 그 성벽은 제 기능을 하지 못했지만, 고대의 성이라 사람들은 그런 사실을 받아들이려 하지 않았다.
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‘I Basically Just Made It Up’: Confessions of a Social Constructionist
written by Christopher Dummitt
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정부는 소득과, 행복 또는 복지를 측정할 수 없다.
국민소득이나 복지를 집합적으로 다루게 되면, 소득, 부, 생산, 소비 등의 달러화 통계에 의존하게 된다. 그렇게 되면 조사해서 숫자화 할 수 있는 것만이 가치를 지니는 행위로 인정받게 된다.
하지만 인간의 행동에는 달러화로 환산하기 어려운 일들이 너무나 많다.
예를 들어 한 부모가 직장을 떠나 육아를 한다면, 고용율과 전체 임금은 그만큼 하락한다. 하지만 통계는 육아로 인한 정신적 혜택에 대해서는 아무 말도 하지 않는다.
현대의 회계 기법을 이용해 노예 해방 전후의 경제에 대한 노예의 기여를 측정하면, 노예 해방이 생산성에 상당한 감소를 가져왔다고 할 수 있다.
하지만 개인당 소득은 하락했지만, 사람들은 더 잘 살았다. 그들은 일을 덜 했고, 집에서 필요한 물건을 만들었다.
인간 행동과 인간적인 시장 경제의 핵심은 개인들의 가치 평가이다.
조사된 소득이 올라가면 정책이 성공했다고 믿는다면, 우리는 잘못된 측정 방식에 의거에 성공을 측정하고 있는 것이다.
Why the Government Can't Measure Income, Happiness, or Well-Being
Ryan McMaken
One of the central problems of aggregate measures of national income and well-being is the fact so much of it relies on incomes, wealth, production, and consumption measured in terms of dollars.
This means only activities that can be tracked, counted, and ranked based on dollar values and production totals will be among those activities reported as having value. These activities include the payment and receipt of wages, and the purchasing of consumer goods and services with money.
For the purposes of economic statistics, these are fairly easy to measure. We can measure how many people have jobs, what wages are being paid, and how much those workers are producing. We can measure how many houses are purchased, how much people are paying in rent, and how many cars are sold. Most of this is measured in unit totals and dollar amounts.
But there is an enormous amount of human effort that goes to activities that cannot be measured in this way.
Child Rearing
For example, if a parent leaves his or her wage work to stay home and educate a child, that will be measured as a drop in total employment and total wages earned in the economy. Meanwhile, the statistics tell us nothing about how much benefit — i.e., psychic income — the parent gained by spending time with the child, or engaging in other activities we call "parenting."
Moreover, a parent who stays home may also impact the economy by reducing dollars spent in the marketplace on child care, meal preparation, and education services. Instead of buying meals at restaurants and professionally-provided childcare services, this parent instead provides those services at home, without any money changing hands. Thus, the benefits remain "off the books" and uncounted in terms of national statistics.
What the statisticians will see is a drop in revenues for restaurants and childcare services. And we may be told that's a bad thing.
Even worse — from the statistician's point of view — the parent may elect to homeschool the child, which means a significant portion of the cost of education will remain unrecorded in the marketplace, and government schools will have less enrollment to report, justifying education spending.
Naturally, if a large number of parents elected to do the same, this could lead to a significant impact to household income statistics, as many households become single-income households, or as parents elected to work fewer hours so as to spend more time with children.
If this were to a happen enough, it is entirely possible that we would hear about a "crisis" in household income as households in the child-raising age range were seeing a decline in incomes and spending. We might even start hearing from "experts" who go on television talk shows to tell us that public policies must be devised to encourage parents to pursue more wage work and purchase more goods and services.
What can't be seen in this sort of analysis, of course, is the fact that the decline in family wages and consumption may only be reflecting choices being freely made by parents to forego wages and consumption so as to increase the parents' perceived benefits of child rearing.
Retirement
We may encounter similar problems when it comes to workers making the decision to retire.
After all, when a worker retires — or cuts back his or her hours — wage income naturally goes down. Given uncertainty about the future, the retiree may then also elect to spend less on goods and services such as cars, vacations, and housing.
In measures of wages and spending these activities will show up as a drop in incomes and in household spending.
But what the decision to retire really shows is that some people preferred leisure time more than earning wages in the marketplace.
The picture is even more complicated than this, however. Not everyone retires voluntarily. Some people retire because they "have to." That is, in some cases, workers become disabled due to old age or illness, and either cannot physically work, or cannot find an employer willing to hire them. Those people drop out of the work force although they might have preferred to keep earning wages.
At the same time, some people keep working in the marketplace because they did not save enough in the past to allow for retirement at the present time.
And then, of course, there are people who have a sufficient amount of savings to maintain their current standard of living even without their current wages — but nonetheless remain at their jobs because they prefer it to leisure.
The benefit of retirement varies depending on whether it is voluntary or due to disability. How shall we measure this? Government data-collection methods offer little in terms of a solution. Certainly, survey data might tell us a little about how many retirees do so voluntarily, but even then, we can't measure the benefit of retirement in comparison to the foregone wages.
Moving for a Higher Wage
Sometimes, workers choose to remain in the workforce — but forego higher wages in order to gain other non-monetary benefits.
For example, some workers prefer jobs that allow for greater flexibility in working hours and more time off. These jobs, however, often pay lower wages. The benefit comes in the form of more leisure and greater control over one's schedule.
Non-monetary benefits can also be gained from refusing to re-locate to a new location to pursue a higher paying job. This translates into lower mobility for workers. According to the LA Times, when this happens, "economists worry" that "with declining job movement may come slower gains in overall employment, wages, productivity and, ultimately, economic growth."
Maybe.
It may also be the case that many of these less-mobile workers are trading wage gains for gains in terms of family stability and community life that come from staying put. As an April 2019 study from the Federal Reserve concluded: "individuals face substantial non-pecuniary costs to moving ... and that they also place high value on proximity to family ... and on agreeableness of local social and cultural norms."
In other words, workers gain substantial benefit from not moving, even though moving to a "better" job would bring higher wages as measured in dollars.
Thus, we find, yet again, statistical measures of "well-being" — if measured in terms of wages and spending — tell us very little about how people truly become better off or at least avoid becoming worse off.
An Extreme Case: Slavery
As a final illustration of the problem with conventional measures of well-being, we can look to the case of slavery.
One of the justifications given for slavery by pro-slavery activists was the claim that slave labor was more productive than wage labor. According to Jeffrey Rogers Hummel, some slavery defenders saw the intense workloads and long hours endured by slaves as evidence of the system's superiority. As recounted by one Edmund Ruffin: "Free laborers, if to be hired for the like duties, would require at least double the amount of wages to perform one-third more labor in each day."
The slaves, however, were not convinced of the system's benefits. Hummel notes how, after emancipation, "Women and children abandoned the fields, and even black males cut back," leaving most of the field labor to adult men. Moreover, "The total labor supplied by former slaves fell by approximately one-third."
Were we to use modern government accounting methods to measure the contribution of slaves to the economy before and after emancipation, we would likely find emancipation produced a sizable decline to productivity, arguably making the nation worse off.
In reality, this isn't what happened at all, as Hummel concludes:
Here we encounter a dramatic demonstration of the limitations of economic aggregates for measuring well-being. Income per capita went down because people were better off. They were working less or producing household amenities, both of which represented improvements in the quality of life."
The aggregate data doesn't tell us anything about the benefits and psychic incomes earned from not working the fields for fourteen hours per day. Measuring only worker output, the data would suggest declining productivity and impoverishment. In reality, the opposite was true as more workers' demonstrated preference was to work fewer hours.
How Well-Being Should be Measured
Unfortunately, economics as a discipline has suffered from the fact so many of its practitioners have become preoccupied with those things that can often be measured — such as jobs and wages — while neglecting the more hidden benefits gained from activities like parenting or choosing to not earn a wage.
But when you're a hammer, all the world looks like a nail — and this means economists have turned to spinning theories about maximizing wages and spending when it is likely — especially in an advanced economy — many people gain greater benefit from doing something else.
Competent economists, of course, have not lost sight of this fact. As Murray Rothbard noted in his essay "Toward a Reconstruction of Utility and Welfare Economics,"
economics does not deal with things or material objects. Economics analyzes the logical attributes and consequences of the existence of individual valuations. "Things" enter into the picture, of course, since there can be no valuation without things to be valued. But the essence and the driving force of human action, and therefore of the human market economy, are the valuations of individuals.
Unfortunately for the government number crunchers, many people choose to value things that aren't being added up on spreadsheets. And even if they were, we still wouldn't know how much people value reading a story to a child, or living close to family and friends.
Moreover, the fact we can know so little about how, why, or when people make certain economic decisions belies the idea that we can formulate economic policy in a way that makes everyone better off. If we believe that policy "works" when measured money incomes go up, we're measuring success based on a flawed and partial measure. It's time to stop pretending otherwise.
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빅 데이터가 기업가들에게 해줄 수 있는 것과 없는 것
Per Bylund: Big Data versus Big Ideas
Economics for Entrepreneurs Podcast
Hunter Hastings / Per Bylund
Predictive analytics can’t predict! That was Dr. Per Bylund’s provocative introduction to our discussion of the uses and drawbacks of big data in the context of the entrepreneurial mission.
Key Takeaways and Actionable Insights
The claims made on behalf of the analytical powers of big data may be exaggerated, and entrepreneurs should learn what they can and can not expect from the application of big data analytics to business. Otherwise there is the chance of both error and wasted spending on the tools of business intelligence. It’s important to distinguish between the different roles of multiple data types.
Pattern recognition is not prediction. Dr Bylund contrasted what Big Data can and can’t do for entrepreneurs. He used an example of analytics predicting the outcomes of future NFL games. Here there are large sets of historical data on players, teams, plays and previous outcomes. There are limited potential outcomes (e.g. one team will win the game — there is no third team that will unexpectedly turn up to change the range of possible outcomes). The predictive analytics got the outcome right about 75% of the time. In a world of more open-ended results (e.g. predicting the outcome of a multi-team tournament), big data could be expected to be right fewer times. There is danger in over-reliance on the law of large numbers and tendencies like reversion to the mean. Pattern recognition from historical data sets (which is what big data does well) is not prediction.
In fact, in the world of economics and entrepreneurship, there is no prediction. Entrepreneurs deal with social phenomena that emerge from individuals’ actions and interactions, across billions and trillions of instances. Entrepreneurial outcomes depend on how people act, and how they act depends on their feelings, how they see the world (subjectivism) and what they feel like doing. We can’t know or predict that. There may be some general rules that apply in many cases (for example, raising prices rapidly and significantly in a competitive market will, all other things being equal, result in a reduced unit volume of sales). But those rules don’t predict the decisions of specific individuals in specific cases.
Mainstream economists and central planners long for a mechanistic world: turn a dial, get a result. But this approach is not valid. In the economy or any market, all variables are dependent on all other variables. Everything affects everything. The consequences of any action — like central bank interest rate tinkering — affect different people in different ways, and whoever is affected first or last will experience different consequences and react in different ways.
The core of the issue is that human behavior is unpredictable. Subjective choices can’t be predicted. Prediction implies precision, and that’s not available.
Yet the entrepreneur must deal with the future. The entrepreneur seeks to produce a good or a service that consumers will consider valuable at some point in the future. Even if they tell you today that they will value your offering in the future, they may change their minds.
Is there any contribution that big data can make, any help that it can offer? We discussed these areas:
It’s hard to know what people might want in the future. But it might be possible to identify what specific people will not want, based on their past behaviors. Data can show you which purchases cluster together, and which don’t. Beef purchasers may also buy red wine. Vegans won’t buy beef. Facebook and other ad targeting tools (which use big data effectively) can help you avoid marketing beef to vegans or pasta to keto diet followers.
Data can sometimes detect dissatisfactions, which are the universal raw material for entrepreneurs. Analysis of sentiments expressed in reviews can guide you in the right direction. Writing a negative review on Yelp or Trip Advisor is both a behavior and an expression of sentiment and data analytics can detect patterns here. But Dr.Bylund advises us that it can only provide a guide – there is no substitute for talking directly to consumers, human to human.
Data can help with segmentation. If you want to better understand a geographical market segment or a demographic segment or a behavioral segment, there are lots of data that can detect the differences between segments, and this can help you with targeting of communications (but not necessarily with the message).
Quantitative data can be combined with qualitative data to sharpen insights. Dr. Smita Bakshi, in our episode #24 described how analysis of student performance data (50% of computer science students don’t complete their first year course) combined with personal discussions with students in class, delivered an empathic understanding of their struggles, from which her team developed a winning interactive learning tool for computer programming languages.
Sometimes an entrepreneur can skip the big data analytics, but never the empathic diagnosis. Entrepreneurship consists of understanding the mind of the consumer and understanding the economics of the marketplace. Where the market is heading and what will be in consumers’ minds in the future are more the realm of judgment than analytics.
Entrepreneurs behave differently than dig data driven large corporates. They think harder about the customer, they study human motivation, they utilize the rich qualitative data that comes from talking to customers, and they concentrate their capital and resources on developing and extrapolating their customer understanding. They uncover subjective value — the value that only exists in the mind of the consumer. Imagination is the key to the future. Entrepreneurs try to succeed in bringing about that imagined future. Big data might help them avoid mistakes, but it’s impossible to rely on the past to produce the future.
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