As of this writing, the stock market continues the slide it began last week with the largest drop since the Great Recession. Positive economic news abounds, so what gives?
Don't let the President's comments mislead you: the stock market is not the economy. To the contrary, I've argued that at times a high stock market results from a weak overall economy. The Obama Administration illustrated very well this dichotomy: The Dow was just under 8000 points when Obama took office, and when Trump took office, it was nearly 19,900. Over an 8 year period, that's a return of over 12% per year for 8 years in a row. Yet, the real economy-- the American GDP-- grew at a much lower 3.8% nominal value. And that's NOMINAL, so that reflects the typical 1.5%-2% inflation as well. Thus, real economic growth was likely only about 2% per year (on average).
Other contradictions between the real economy and the stock market are readily observed. For example, rising interest rates usually point to improving economic conditions as the economy makes its own money and creates inflationary pressure. Rising home prices, rising fuel prices, etc are evidence of rising demand, increasing spending, and a generally more optimistic view of the economy.
Yet we know that Wall St responds very negatively to a rise in interest rates. Indeed, it's likely that this negative response explains much of the recent tumble in the markets; they are afraid that the Fed will raise interest rates.
Why do they care so much about rising interest rates, even when it means the overall economy is improving?
Recall with me that all loans are denominated in nominal dollars. Inflation reduces the value of those dollars over time. So lenders charge an interest rate partially to protect against that loss of value from inflation. But what happens if the actual interest rates go far above what was used to secure a debt instrument? The lender can actually lose buying power. If I borrow money at 2% interest and inflation goes up to 4%, the lender is actually losing money through inflation.
The vast majority of Wall St profits have to do with finance, and Wall St banks are on the lending side of the transaction. So what harms them is a windfall to the borrower and vice versa.
Meanwhile, main street businesses in your locality benefit from increasing consumer confidence and the spending boom that is driving higher interest rates.
The upshot here: not only is our economy much more than just Wall St., there are times when a declining stock market can be evidence of real economic growth, just as there are times when a high stock market is driven by a stagnant economy that has nowhere else in which to place investment capital.
I won't bother to try and inventory the list of the things Senator Sanders (I-VT) doesn't understand; that list is too lengthy for a blog posting. But I wanted to highlight a fatal flaw in Bernie Sander's Tax-The-Rich philosophy.
What Bernie (and apparently most of his supporters) doesn't understand is that applying a super high tax rate to the top bracket doesn't actually raise more money to the government. The following chart shows the correlation of the top marginal rate for each year since WW2 and the amount of revenue the federal government took in as a percentage of GDP.
Notice that when the top marginal tax rate is in the low 30% range, the actual revenue to the government varies from below 15% of GDP to is high as 20.5% of GDP. And when you crank up the top marginal tax rate to 90%, the range of variation is about the same, with as little as the low 15% range to as high as about 20.5%.
The regression line comparing revenue to top tax rate is nearly flat. In other words, the revenue to the government seems almost entirely unresponsive to the actual top marginal tax rate.
Taxing the rich simply doesn't bring in more money to the government. Government revenue is almost entirely independent of the top marginal tax rate.
Since the pie doesn't get any bigger, does taxing the rich change how the pie is sliced? We'll examine that next.
Few things bother me more than the absurd torturing of logic and reason required to label something "sustainable." I apologize to those educated readers who are aware of the 2nd law of Thermodynamics and already know why nothing is sustainable. But there's more to it than just an abstraction of perpetually increasing entropy. There are practical questions that the environmental movement chooses to ignore and cannot answer reasonably.
What exactly does sustainability mean? If solar output declines due to cloud cover (as often happens), then what percentage of that plant's output is sustainable? If a plant can produce 10MW in bright sun but only 800kW in thick clouds, is only 800kW sustainable? Clearly, 10MW is not sustainable through a passing cloud or at night.
If an engineer defined "sustainability" is would be something like: "an energy source is sustainable if it can be expected to last more than 20 years and will over its lifetime produce greater than 100% of the energy it takes to produce, dispose and/or recycle." Of course the 20 year mark is arbitrary. But that's the point: every definition of "sustainable" is arbitrary.
Why are fossil fuels not sustainable? Humans have been harvesting coal for hundreds of years, oil and natural gas in meaningful amounts for well over a century. All the empirical evidence suggests that the fossil fuels can be sustained indefinitely. If a over a century of sustained use of an energy source doesn't demonstrate sustainability, then what is the standard of proof? Two hundred years of use?
Why are energy sources that receive massive amounts of government subsidy considered sustainable, but not those that receiver proportionally much less or none at all? Doesn't the presence necessity of subsidy inversely reflect the true sustainability of a source? If not, why not?
Is carbon emission the only applicable standard of sustainability? If not, what other measures are germane?
These are some of the questions that easily befuddle many environmentalists. But with enough thought, the smarter ones can be brought around to a realization: sustainability is not a scientific term. It is a political and economic term. It is political because of the power and money the issue attracts.
But more than anything else, it is an economic term. Something can be sustained as long as there is an economic incentive to do so. Whale oil as a lamp fuel would have been sustainable indefinitely. It's just that eventually enough whales were killed that it made the oil very costly to procure and made substitutes like kerosene far more appealing. The level of price at which whale oil would be sustained would be far too expensive for it to be useful. So it was not sustained-- but it could have been.
Likewise, Buffalo were hunted nearly to extinction in North America. But not totally. Why not? It simple: at some point, the scarcity made the value of buffalo much higher than the utility of killing more of them.
Absurd faith in carbon-scare computer models aside, there's no reason to think that fossil fuels will become any time soon more costly to use than they are worth. To the contrary, the world is "doubling down" with more fossil fuels being than ever before. Even as the world consumes more electricity than ever, electricity is overwhelmingly produced from fossil fuel sources.
There isn't any real debate. The issue is "settled" because people have voted globally with their feet. Fossil fuels are now and will remain for the foreseeable future the dominant and preferred energy source, and over time, this dominance has grown, not waned. They are getting cheaper, not more expensive. We are producing more, not less.
Let's stop pretending and just move on with optimizing the efficiency with which we consume those wonderful fossil fuels that are greening the planet and lifting millions out of poverty.
I used to think that the government should tax things uniformly to avoid distortions in the market. This was my economic-libertarian side, and I strongly prefer consumption taxes to taxes on income or property. I oppose property taxes on the grounds they undermine property rights. I oppose income taxes because they also undermine property rights by asserting a right to a person's income.
So when discussions in State politics turned to funding highway improvements and the two proposals were either raising a gas tax or raising a cigarette tax, I opposed both ideas on the ground that they were excise taxes. Excise taxes are taxes on particular items when purchased. They are a form of consumption tax, but narrowly tailored to a particular item.
I was particularly opposed to the cigarette tax because it would be born mostly by poor people. Smokers are lower median income than non-smokers by a significant amount. My thinking was that this particular excise tax would mostly harm the poor and, being an excise tax, introduce some market distortions.
I think a lot about the role of government and its relations to law and economics, because I think it is particularly consequential. Further reflection yielded a new insight: the rule of law gives government the right to allocate societal values, and it is therefore perfectly consistent for those value judgments to be reflected in the tax code.
Right now, the government has "distorted the market" for all kinds of items: illicit drugs, certain kinds of firearms, and a huge array of prohibited activities and items. By Prohibition, the government has done the act that bounds the most extreme end of the government punish/subsidize spectrum. (in a sense; once criminalized, one could say there is another spectrum of severity of criminal punishment and enforcement).
Why can it do this? It does this because we the people have decided there is a compelling state interest in discouraging illicit drug use (for example) and so it is prohibited, and Courts have ruled it is within the Constitutional scope of government to do so.
But I have some reservations. Here are some of the good and bad I see as likely outcomes:
An especially high rate of tobacco taxation is likely to invite a black market in tobacco products from states with lower rates of taxation. Other states with high tobacco taxes have already seen this, and it creates a major enforcement problem trying to determine the tax-origin of a particular cigarette. I don't like laws or policies that are difficult to enforce. All this can be avoided if the rate of taxation is set to a level comparable to neighboring states, such that smuggling or any other form of arbitrage is not cost-effective.
So the tax may fall most heavily on poorer people. Is this a bad thing? Higher cost is often cited as a reason to stop smoking. So one potential upshot is that people might be induced to stop smoking. Or not start. A poor person is likely receiving significant subsidy for medical expenses, so that person being healthier ends up saving taxpayer dollars on medical care. Or so goes the reasoning. This article suggests that common vices should actually be subsidized because people die faster and actually end up costing less to care for. Such is the sickeningly perverse incentives of a welfare state with socialized medicine like the NHS in the UK.
Since taxation yields money, and money is fungible, every dollar raised by one form of taxation is a dollar not needing to be taken from somewhere else. I'd like to think a little Pareto Principle here might be appropriate.
The problem with all excise taxes is that they invite lobbying as taxation and subsidy are the the ways that government picks winners and losers in the market.
So the takeaway is this: I think there is an argument to make for a "sin" tax on the consumption of certain goods that are luxuries or known to have public costs. A flat consumption tax, while facially neutral, ignores the fact that all consumption is NOT neutral in terms of personal and especially public cost.
For example, if local air quality becomes a problem, would it not be appropriate to tax consumption that most directly contributes to poor air quality? I would think so.
After all, even Uncle Miltie supported the idea of a tax if net externalities were truly negative. (which may not even be true for tobacco).
The Economic Policy Institute (EPI) is an advocacy group posing as a think tank. Before we get into the glaring sloppiness of this paper extolling the virtues of a unionized labor force, please watch this video explaining why so many "studies" like this are junk. Complete and total junk.
Poor data analysis techniques are alarmingly common in "hard" sciences like chemistry and physics. But it is truly an epidemic in social sciences where most of the "research" is done by advocacy groups setting out to prove a pre-existing conclusion. Recall that phrase about using science "as a drunk uses a lamppost: for support rather than enlightenment." (An example of how junk research is done is found in this post about guns and crime, where simply shifting the observed time period a couple years produced the opposite "proof" of the study).
So we know there is a lot of junk science out there. We know the EPI is an advocacy group, publishing studies in social science-- the discipline far most likely to contain some junk science. How rigorous is this study?
The Study
The executive summary of the study lists lots of claims that all generally go to support the claim that unions raise wages for both the unionized and non-unionized worker alike. The core findings of the study, however, concern the impact of unionization rates upon non-union wages.
On its face, this is a plausible claim. Indeed, higher wages and benefits are the raison d'être of organized labor. It is important to understand the mechanism by which this works: by introducing artificial scarcity, the existing demand will drive union wages higher by the reduction of supply. The union gets to be the monopoly provider of labor to the business that is unionized. It's pretty clear how union membership increases the wages of those union members.
It is also plausible that higher union wages might simultaneously raise non-union wages because the opportunity cost of union labor is higher, so the benefit of non-union labor is also higher. Putting numbers to it, if the free market labor rate is $20/hr and the union rate is $30/hr, a non-union worker could offer to work for $25 and still be cheaper than the union, while earning a premium relative to the free market rate. Remember that last phrase, as that is critical.
You can see that's it's reasonable to construct a situation where unionization raises the wages of both organized and non-union labor. But being able construct a scenario where this happens is not the same as saying that it actually has happened on a broad scale across a national labor market. And it is still another thing to claim that if it did happen on a broad scale at one period in time, that all of those factors would again combine the the same way to produce the same effects in a time period nearly 40 years removed.
While the authors do present some data for their claims, they also make some bald assertions that are completely unsubstantiated:
In the ongoing debates over wage stagnation, these indirect effects of unions have not received nearly the attention as the oft-cited accounts mentioned above. Partly this is due to the difficulty in disentangling the independent effect of unions on nonunion workers’ pay. Globalization, technological advances, and institutional shifts—most notably the dramatic decline of the U.S. labor movement, along with the falling real value of the minimum wage—have all affected average workers’ wages. These developments are intertwined in numerous ways. For example, union decline reduced resistance to offshoring, and offshoring, or the threat thereof, emboldened employers in union negotiations
There is no evidence to support the idea that the unions were able to keep companies from offshoring. And it's far from given that a union can both create the benefit of offshoring (via "union wage premium" and also dis-incentivize that same off-shoring. How is it that a union can prevent off-shoring? It cannot. Clearly other factors may explain the marginal utility of offshoring: increased productivity in the developing world, enhanced capital mobility, improvements in the rule of law and safety of intellectual property, etc. It seems for more plausible to me that union strength, not decline, drove much of the appeal to offshoring. The value of off-shoring derives substantially from the opportunity cost of on-shore labor.
Methodology
To really find out why the study is bogus, we have to dive into the methodology. Hang in there, this gets a little nerdy. The first thing to observe is what they include in their sample:
All of our analyses are limited to nonunion private-sector workers who report positive wages and report working 30 or more hours per week and are between the ages of 16 and 64. We exclude top-level managers along with the self-employed.
Samples are often a primary contributor to invalid results. In this case, the sample seems plausible. It is very large and encompasses a substantial portion of the nonunion labor force.
For our model-predicted wage series, referred to in the text as our “estimated” weekly wages, we regress weekly wages for private-sector workers who are not union members on the following set of covariates: industry-region unionization (described above), a oneyear lagged version of the industry-region employment rate (described above), four mutually exclusive race/ethnicity measures (non-Hispanic white, non-Hispanic AfricanAmerican, non-Hispanic other, and Hispanic), four mutually exclusive education measures (less than high school, high school diploma or equivalent, some college, four or more years of college), potential experience and potential experience squared, a set of four occupational measures (professional/managerial, production, service, farm/forestry/ fisheries), hours worked per week, a measure of whether the respondent lives in a metropolitan area, year dummies, and a dummy indicating whether the respondent works in the manufacturing sector. Wages are measured in constant 2013 dollars, and models are weighted to be representative of the active workforce. For our analyses of non–college degree and high school or less workers, we replicate the model described above but limit the sample first to those workers with less than a bachelor’s, and then to those workers with a high school diploma or less. We cluster our standard errors by industry-region in all models.
Our counterfactual series replicates the model described above except we set the industry-region unionization rates at their 1979 levels. That is, we solve the regression equation by plugging in the observed values for every other covariate except for industryregion unionization. For example, for each individual in the dataset, we treat that individual as if their industry-union rate was equal to the 1979 rate (regardless of what the true rate is in that particular year), leave all other covariate values equal to their observed values, and compute their predicted weekly wage using the estimated model equation. Instead of modeling log weekly wages, we estimate generalized linear models (GLM) specifying a log link and gamma distribution family. 50 While estimating a log-linear model and then applying an appropriate smearing factor to retransform the dependent variable will improve the mean predicted wages relative to exponentiating log wages, this approach does not ensure that predictions for individual cases are particularly accurate, and there is little consensus regarding what smearing factor is preferable. Our approach does not require transformation of the dependent variable in the first place or retransformation post-estimation in order to predict values of the dependent variable in its raw-scale. We experimented with other common approaches, such as retransforming our predictions to the original scale following the estimation of log-linear ordinary least square (OLS) models using naive and Duan smearing estimators. Results are available upon request. In general, the GLM approach produces slightly larger counterfactual wage estimates than retransforming and using the Duan smearing estimators.
OK, what is that in English?
A regression is simply a comparison in how changes in one factor relate to changes in another. A good model with statistical power can predict the change in the dependent variable based on changes in the independent variable(s). It's like collecting a bunch of data relating how hard you throw a ball to how far it goes. With good analysis, you can predict (with high precision) the distance the ball will travel if you know how hard it was thrown. That is a simple two-factor regression. You can add several more factors and often get some fascinating results. For example, I once generated a multiple regression model that could predict within 7 degrees Fahrenheit the temperature of a section of a diesel engine piston based on fuel rate, injection timing, and a couple other factors.
But this study "regresses" the dependent variable against all those factors listed above relating to race, region, education, ethnicity, hours worked, etc. The thing with regression is that it must be done with continuous variables as independent variables. That means each factor must be capable of assuming the infinite range of values within its limits. It is "continuous" because there are no gaps in the range of values the variable can take. This is critical because regression is dealing with rates of change between variables. The difference between 3 and 4 is one. But what is the numerical difference between White and Hispanic? Regression must be able to quantify a change in an independent variable. It cannot do that for "occupational measures."
Which means that each of these variable must be made into "dummy" variables that are completely and utterly arbitrary. So perhaps you assign a value of -2 to not having finished high school, -1 to high school diploma, 0 to Bachelor's degree, 1 to Master's, and 2 to PhD. The problem with that approach should be pretty obvious; if you are attempting to examine the impact on wages, then the gap between variable positions must be the same. For example, if you have a variable like temperature to the nearest degree, it would be OK because the difference between 30 and 31 means the same as the difference between 50 and 51: namely, a single degree Fahrenheit. THIS IS NOT THE CASE WITH EDUCATION, you cannot consider the five levels of education I just outlined from the view that the differences between each increment are comparable with respect to wages. Moving from "1" to "2" is not twice as much education.
The only proper way to use a dummy variable in regression is as a binary yes/no indicator-- and even then it introduces much source of error. For example, a researcher studying life expectancy in Kansas might make a dummy variable of "is there a war happening" with yes/no options (usually represented by one or zero, like in computer programming). But that is irrelevant because you can't really define "war" in a context like this, and it would be far less of an indicator than the number of Kansans present in the military at any given time.
Returning the education example, one could create a dummy variable for "master's degree" that is yes or no (1, 0). But those with Master's degrees had to get Bachelor's degrees, too-- which means that every "1" for Master's should also have a "1" for Bachelor's. Meaning you have massive collinearity in the variables if you do it this way.
Because of these dummy variables, the numerical precision of the regression is certainly wrong.
Moreover, the authors make the absurd assumptions that everything (except unionization rates) were the same in 1979. This is where taking techniques from the hard sciences and employing them in social science leads to junk.
Note also how we get certain factors to be lagged, squared, and such. There's no discussion of why lagging one year-- instead of three or not at all-- is the right amount. Nore is there any argument as to why certain factors should be squared.
In hard sciences, such lagging and squaring is very common, but it is completely justifiable. For example, science empirically proved long ago that aerodynamic drag is proportional to the cube of speed. So it would be appropriate to use "speed cubed" in a regression of factors that determine aerodynamic drag.
Not so with using "potential experience squared." What is "potential experience"? Why is it appropriate to square it?
To me, this suggests model "tuning" or "p-hacking" as the video above describes. The authors massaged their model until it supported (albeit very weakly) the headline case they wanted to argue: the unions help EVERYONE, even the non-union worker.
The recent media hype about “equality” with respect to bathrooms and gender has spurred a larger conversation about equality and the limits of discrimination. I’ve always wondered why it is that the politicians and business leaders like to couch this equality issue in economic terms. Consider this excerpt from an interview with Doug Burgum, candidate for Governor of North Dakota, on the impact of state legislation that would forbid discrimination based on gender identity as well as sexual orientation:
Currently, North Dakota has over 23,000 job openings. From an economic impact, filling these jobs would be like adding another large city to our state. Every policy the legislature is considering should be viewed through a lens of supporting workforce development in the state. After decades of watching the majority of our North Dakota University System graduates leave the state, we are now in new territory. We are not producing enough graduates to fill all the open positions in the state. We need population growth by both retention and net in-migration to fulfill our enviable growth needs. The aged 18-35 year old demographic is particularly attuned to the social climate of our cities and state. Any laws we have that discriminate against or limit the rights of any citizens based on gender orientation create a barrier for recruiting and retaining talent in our state.
Let’s put aside the disingenuity of using the impossible ideal of zero percent unemployment (an undesirable and impossible condition).
Let’s focus on the core fallacy Burgum uses—one shared by all the advocates of his position. Namely, the fallacy is that unless a law bans transgender discrimination, then the silence of the law assures 100% discrimination. Of course, Burgum then argues that there are economic advantages to preventing such discrimination. Stated differently, there is an economic disadvantage to allowing discrimination.
But if that is truly the case, then why would the law be necessary? If it is truly the case that economic advantage flows from extending special protections to the transgendered, then no business would indulge that discrimination without incurring an economic loss.
And what of those businesses that chose to incur that price? If you favor non-discrimination, wouldn’t you prefer that those who discriminate pay some kind of price to do so? Wouldn’t you prefer to gain a competitive advantage over your bigoted economic rivals? OF COURSE you would.
The only logically consistent position for the advocates of transgender (indeed all such LGBT) protections then is that they actually expect to have a disadvantage in the marketplace. Because they expect that people will prefer not to patronize a business with transgendered employees, their indulgence of the “equality” scam is really an effort to remove the competitive advantage a business rival may have from their “discrimination” against transgendered employees.
Consider the case of Hooters. What if you wanted to start a restaurant that also had the mediocre food I’m told Hooters has, but you resented the competitive advantage Hooters had in selling its food using attractive females? Why, let’s end discrimination against less attractive, less female, employees! With Hooters’ business model disrupted, you can sell you own mediocre food on a level playing field. Or something closer to level.
The point of the Hooters example is that a business can gain competitive advantage (or reduce the advantage of a rival) using the power of the government’s “equality” legislation.
Which means that the business lobby and its powerful interest groups really care about minimizing competition, and they want the government to protect them. Which is just about always the case in every circumstance.
The granting of special protections to the transgendered is therefore just time-honored rent-seeking in a newer, more fashionable guise. That, and elites wanting to force to people to adhere to a code that they themselves may not consider binding.
Lately I've been taking in more liberal media and 'mainstream' media than I'd normally. I feel like much of the media I'd been consuming had become nothing but an echo chamber, and frankly you can't really refine or develop an effective argument if its weak points are never challenged.
This morning, I was listening to NPR's coverage of the Republican proposal to amend the ACA to define "full-time' employment to 40 hours a week or more rather than the present definition of 30 hours per week or more. The Republicans are arguing that the ACA definition of 30 hours creates an incentive for employers to limit weekly hours for workers, thereby reducing the number of people they must offer health insurance to (under penalty of law). NPR interviews someone from the American Enterprise institute to represent this argument.
On the other side of the argument, NPR gives us a Dean of the School of Public Policy at NYU. They say she argues that the 30 hours definition is a good idea based on a couple points:
Very few people work about 30 hours. Only ~2.5% of all workers have schedules that are between 30 and 34 hours.
Vast majority of people are working about a 40-hour week, so by only reducing hours to 39, employers can move people below the threshold.
The is an argument that most 40-hour workers already have coverage, so they aren't affected by the change, but the 'research' iundicates that even if you count only those who have no coverage, there is "twice as many people at risk of losing hours" if you raise the full time definition to 40 hours from 30, and "the facts suggest that describing this legislation as an effort to protect workers is a smokescreen."
So which is the better argument? That depends on the answers to some other questions. What is the best measure of the impact? The total number of people affected? The total number of hours lost? This is why the Dean of the School of Public policy (like many academics) gets it wrong: she asks the wrong question.
The Dean argues that there are 'twice as many people at risk of losing hours' under the 40 hour definition, even after you exclude those who already have insurance and aren't an additional burden on the employer by working 'full time' hours. But the Dean is basically arguing that it's worse for a huge number of people to be taken down to 39 hours than it is for a smaller number of people to lose hours-- though they admittedly lose more hours (per person).
The Dean is arguing that more people losing a single hour per week that represents 2.5% of their total hours (from 40 to 39) is worse than a smaller number of people losing more than 25% of their weekly hours (from 40 down to 29). This is bad thinking. Moreover, the Dean is making the classic mistake of looking at how things are now in a static snapshot. "Hardly anybody works 30-34 hours per week." she says. Not only is this NOT an argument for the 30hour definition, it also ignores the probability that this number of people working 30-34 hours is likely to be impacted by this legislation. Indeed, the *facts* indicate that this is occurring and is precisely the motivation for re-defining the ACA limit.
Back in the 1970s, the spike in oil prices caused a general rise in prices that was at the time considered 'inflation.' Looking back, economist now know that a supply shock the spikes relative demand is NOT the same as inflation. Instead, inflation is always a monetary phenomenon.
Based on this erroneous read of prices, the Fed raised interest rates when it should have lowered them. The result of this error was the throttling of the economy that produced stagnation AND we still had high 'inflation.'
Fast forward to today. The crashing of oil prices is send the stock market downward, and creating a general downward trend on prices. The Fed is about to confuse this with deflation. Yes, the Fed believes that after printing 85 billion dollars a month for several months, we are about to experience deflation.
That wrongheaded reading will cause the Fed to forestall raising interest rates when it should, producing a very real risk of REAL INflation in the coming years because of super easy money for far, far too long.
Headlines today broadcast the news from the IMF: the US is no longer the world's largest economy, for the first time since the Grant (that's Ulysses S.) Administration. Is this news legitimate cause for concern, or just a blow to national pride?
My view is that it is a blow to national pride only. A more relevant measure of the prosperity a society has is not just nominal GDP, but rather GDP Per Capita. In other words, the per-person size of an economy. This directly translates into a standard of living. By way of contrasting GDP with the per-capita GDP, consider the case of India. (I will use Purchasing Power Parity figures, which is the convention rather than nominal figures).
In just a pure GDP basis, India falls just behind China and the US, at about $6.76T. India is the world's third largest economy.
But when we correct for the massive population of India, it drops to 126th place, just after Nigeria and slightly better than Viet Nam.
The first point here is that China should be a massive economy with such a massive population, and the fact that only now is it passing the United States when it has four times the population illustrates just how inefficient their economy really is.
The second point is that the Chinese economy really isn't what it is purported to be. Much of what counts in Chinese economic figures shouldn't. The money spent to build massive cities that produce nothing counts as economic production. The Chinese economy is also artificially inflated by depressed wages and artificially cheap currency (aided and abetted by American deficit spending).
In short, China is presently the largest economic bubble ever to exist. It cannot go on forever-- and because it cannot, it will not.
The drawbacks of America’s civil tort system are well known. What is new is the way that regulators and prosecutors are in effect conducting closed-door trials. For all the talk of public-spiritedness, the agencies that pocket the fines have become profit centres: Rhode Island’s bureaucrats have been on a spending spree courtesy of a $500m payout by Google, while New York’s governor and attorney-general have squabbled over a $613m settlement from JPMorgan. And their power far exceeds that of trial lawyers. Not only are regulators in effect judge and jury as well as plaintiff in the cases they bring; they can also use the threat of the criminal law.