Lifecycle Consumption Models and Dispersion in Consumption Lifecycle models base on the assumption that households want to maximize their consumption and at the same time insure themselves against income shocks.

However, there is a lot of (c.p.) consumption dispersion observed in the data. At lower ages, the literature argues that income processes are very persistent (and not insurable), and hence such consumption profiles are optimal.

However, at older ages, especially at retirement, most of the income uncertainty goes away. Yet, consumption keeps dispersing with age.

The only argument I heard that tries to justify the lifecycle models given this empirical observation is that at older age, "available income" is still a random walk, given that health expenses become potentially very high and persistent.

Are there other arguments that can reconcile the empirical observation of consumption dispersion at older ages with life-cycle consumption models? Are there any studies on that?

There are two major "qualifiers" to the life-cycle hypothesis (LCH). Both put forth by John Maynard Keynes in "The General Theory of Employment, Interest, and Money."

The first is the "precautionary effect," that people save more than the LCH would predict because they are uncertain about their future health, life span, medical bills, etc.

A second qualifier is that old people would want to leave a bequest, or legacy to their descendants, and therefore not spend all their savings.


Insignificant Interaction effect in applied econometrics (multinomial logit regression) I'm currently writing my master's thesis where I'm applying a multinomial logit regression with interactions effect.

The model I'm using is
$$
P(y=j) = exp(xb_j)/(1+∑exp(xb_h))
$$
where
$$
xb_j =x_1b_{ij} + x_2b_{2j} + x_1x_2b_{3j} + XB + \epsilon
$$
  $x_1$ and $x_2$ are dummy variables ($x_1$ is coded 0/1 for man/woman).

I use marginal (or incremental) effects to report my results, since I'm interested in the effect of a unit change on the probability of af given outcome of my y-variable.

My question is regarding my results, where I get an insignificant marginal effect of the interaction term, which I guess indicates that there is no difference between how men and women are affected by a unit change in $x_2$. But when I look separately at men and women, I get that the marginal effect of $x_2$ is significant for men ($\frac { \partial P(y=j) } {\partial x_2 }$ when $x_1=0$) and insignificant for women ($\frac { \partial P(y=j) } {\partial x_2 }$ when $x_1=1$). (I use three different significance levels (0.1, 0.05 and 0.01))

How do I interpret an insignificant effect from the interaction, but significant in a subgroup (men)?

It is not 100% clear what procedure you used to do the subgroup tests, so I will cover a few bases. Below, I suggest exploratory tests to better understand the results. The logic in this response is the same as you would follow in a linear model.

If you are taking the results from the single model and doing post hoc tests of the coefficents, be sure that you are interpreting the model correctly. Because men are the implicit reference category, the constant represents the men's average (ceteris paribus) and the constant plus b1 is the women's average. Similarly, b2 is men's "slope" with respect to x2, while b2 + b3 is women's "slope" with respect to x2. So, to test whether x2 matters at all for women, you would have to test b2 AND b3 together.

That said, it sounds to me like, after running the full model, you ran the model separately for men and women (minus the sex-related terms) to investigate further. Here's a few things to investigate:

How different are the other parameters for men and women in the separate models from the main model?

Are there similar point estimates?
Are the same variables significant?
Do the models have comparable model fit?

What kind of variation do you have in your data? You can do descriptive and exploratory analyses to understand this.

Present a descriptive table of the frequencies by sex, x2, and j. If every "cell" created in this way had exactly the same count, you would be rather unlikely to get a disparity between the subgroup models and the full model (unless the data generating processes for men and women are entirely different, and/or there is heteroskedasticity by sex).

That is, my hunch is that you have "micronumerosity" for some of the women in certain outcomes, and thus you cannot get a small enough standard error to distinguish the interaction term.
Another way of looking at this is figuring out the realm of common support for your model.

Run descriptive statistics (mean, SD) on the other variables separately for men and women and look for discrepancies. If there are substantial differences, it could mean something other than sex is driving the difference, and/or that collinearity between that other variable and sex is complicating the estimation.

If the covariates are normally distributed, you could run a series of t-tests in addition to eyeballing the means. (If you wanted to be more formal about this and take into account multiple hypothesis testing, you could do various kinds of balance checks, essentially using the x variables to predict sex.)

Graphs of the area of common support or distributions of other variables conditional on sex can help show what you find.

To write up the results, I'd suggest presenting a table with the coefficients for the full model without the interaction, the full model with the interaction (showing the interaction is not significant), and the models by gender.

For the discussion section, you can mention that you looked into the role of the interaction term. You can show your results about micronumerosity and covariate balance by sex (whether or not they seem to explain the problem). All of these are exploratory analyses, and so they can't stand on their own as definitive explanations. But they help you address what should be done in future analyses (e.g. that a larger N overall is needed or that oversampling people in small cells would help, or finding other ways to increase the precision of the model).


What is the significance of the Hansen-Jagannathan bound? The Wikipedia article on the Hansen-Jagannathan bound is short, giving only the following:

  Hansen–Jagannathan bound is a theorem in financial economics that says that the ratio of the standard deviation of a stochastic discount factor to its mean exceeds the Sharpe Ratio attained by any portfolio. This result is an application of the Cauchy–Schwarz inequality.

An important detail that is missing is a discussion of the theorem's significance. The Hansen-Jagannathan (H-J) bound gives us a type of mean-variance frontier. But what is importance/significance (in theoretical and empirical work) of the H-J bound over the classic mean-variance efficient frontier? (The two are different but related. What's the main insight from the H-J bound?)

Fundamental theorem of asset pricing tells us that if there is no arbitrage, there must exist a positive random variable $M$ (also called stochastic discount factor) such that for any return $R$, we must have $1 = E[M R]$. Thus to understand asset prices, in cross-section as well as across time, we need to understand which factors affect $M$.

Now, the most basic Hansen-Jagannathan bound can be stated as

$$
\left| \frac{E[R^e]}{\sigma[R^e]} \right| \leq \frac{\sigma[M]}{E[M]}
$$

where $R^e$ is an excess return. The main contribution is that it allows us to say something about moments of $M$ (right-hand side), which is unobservable, in terms of moments of returns (left-hand side), which can be (in principle) observed. Specifically, given the observed Sharpe ratio (say, around 0.4), the bound tells us that the SDF must be at least just as volatile.

The problem is that standard consumption-based models for $M$ have problems matching this volatility. For example, your basic Lucas-tree economy with CRRA utility leads to $M = \beta \exp(-\gamma \Delta c)$, where $\Delta c$ is log consumption growth. Since $E[M] \approx 1$, we have $\sigma[M] \approx \gamma \sigma[\Delta c]$. If $\sigma[\Delta c]$ is let's say around 1% yearly, we need to set $\gamma > 40$, an unrealistically high value of risk aversion.


Create simple graphs without functional forms I am looking for a suggestion of programs that can be used to illustrate effects like the income/substitution effect with regards to the consumption choice etc.

With many programs, need you to specify functional forms, but if you are just illustrating the effects it can be a hassle to conjure up some functional form to make a mere sketch of the effects.

And using paint is just... wrong... and ugly.

My main suggestions would be:

Adobe Illustrator, which will let you draw graphs freehand. This is a very powerful professional illustration tool that will allow you to produce crisp vector images (if you don't know the difference between a vector image and a raster image then you should look that up before starting).
PGF TikZ / PGFplots. This is my preferred way of producing figures. It requires that you code the figures (and therefore that you specify either a functional form or some points on the function), but produces extremely precise and crisp images. The main advantage of this approach, though, is that it is completely integrated with LaTeX. The fonts will match those in the rest of your document/presentation (assuming you are using LaTeX) and you don't need to mess around with any external files since the code can be included in your main LaTeX document.


How to get the percentage-change form of the quantity equation? In my book, the quantity equation is presented as
$$M\bar{V}=PY$$
And on the next page, the author presents its percentage-change form:
$$\% \ \text{Change in}\ M + \% \ \text{Change in}\ V = \% \ \text{Change in}\ P + \% \ \text{Change in}\ Y$$
My question is: where exactly does the second equation come from? I've tried manipulating the original equation to see if I could get the percentage-change form, but I didn't get anywhere. Any help will be appreciated. Thanks.

@edit

I've actually found an explanation in a footnote in another chapter of the book (see below), but there's still one thing I don't understand: what are we differentiating with respect to? If it's $Y$, then $d(PY)=P$; if it's $P$, then $d(PY)=Y$; unless we're actually differentiating with respect to some third variable of which $P$ and $Y$ are both functions. In this case, what would that variable be? (I may be misunderstanding it too). Thanks.

Here's the explanation (Mankiw Macroeconomics 7th edition, page 26):

  The proof that this trick works begins with the product rule from
  calculus: $$d(PY)=Y \ dP+P \ dY$$ Now divide both sides of this
  equation by PY to obtain: $$d(PY)/PY)=dP/P+dY/Y$$ Notice that all
  three terms in this equation are percentage changes.

I think the author is just taking logs of the original equation.  The natural log approximates percent changes for small changes.  More info here.  Using that and properties of the log, $\ln(ab)=\ln(a)+\ln(b)$, you get that formula.


Models of a Planned Economy Are there any mathematical models of a planned economy like in the soviet union? Specifically id like to know how we would model 5 year plans.

Do any exist?

Jan Philipp Dapprich did something like this in his PhD-Thesis. For an introductory read, his blog might be a little more accessible, this article is a good start. His research, in short, is based on the assumption that the drastic increase in computational power during the last decades can be harnessed to fix some of the flaws of the planning performed in systems like the soviet union.
For a short description of the model I provide the following quote from the article:

An agent-based model that was developed for the purpose of the simulation is used to model the behaviour of consumers. In a real socialist economy this would not be necessary, as there would be actual consumers. In the simulation it is, however, necessary in order to determine the consumer demand for various product. Consumers are given a set amount of credits and will then semi-randomly decide how they will spend those credits. From this the overall demand for the various products is derived. What’s crucial about the consumer model is that demand is sensitive to credit prices. Consumers are less likely to buy a product if it has a higher price. Based on a comparison of demand and the supply of consumer products given by the optimized production plan “market clearing prices” can be approximated. This is done by successively adjusting the price of products depending on the difference between supply and demand until supply and demand match. The previous plan target is now adjusted through a comparison of prices and values (MDVs). The MDVs are scaled in order to make them comparable to prices. Should prices of a product exceed MDVs, this suggests that consumers are sufficiently interested in the product to justify further production. Therefore, the target entry for the product is increased. Should prices be below MDVs, this suggests that consumers are not interested enough in the product to justify the current emphasis on the product and the target entry is consequently lowered. By adjusting the plan target this way we make sure that in the next plan period the proportions at which products are produced more closely resemble what consumers actually need. This process also takes into account the cost of producing a product in the form of the MDVs. Should there be a significant limitation on the amount of greenhouse gas emissions that are allowed for, this would result in higher MDVs for products whose production involves higher amount of emissions. In this case the targets for those products will be lowered and a greater emphasis will be put on producing more environmentally friendly products. A central hypothesis that I wish to confirm using the simulation is that this shift towards environmentally friendly products distinguishes the effect of my model from the labour value model of Cockshott and Cottrell, as labour values don’t take emissions into account.

This excerpt is not sufficient to explain his whole methodology since it is quite complex. For the sake of readability I decided to just scratch the surface here and refer to the blog post series for deeper insight.


Textbook for industrial organization without calculus I'm planning to teach an undergraduate course in industrial organization. Calculus is not a prerequisite. I also want to avoid doing too much game theory in it so as to not be duplicative of a similar course offering in game theory. Any recommendations on textbooks?

Luis M Cabral : Introduction to Industrial Organization :- This book has a combo of game-theory and Cost curves. It explains strategic concepts using no calculus.
However, if you want more theoretical perspective , I would suggest reading the research papers (I have a course named Strategy , most of my readings are the journal papers on JSTOR or elsewhere).

If you could just expand on what your course comprises of , etc ;  or give a link to the university website , may be I can tell you more books I know of.


How monetary policy affects bank reserves In discussions of the "credit channel" in the transmission of monetary policy, I've read that increasing the policy rate reduces bank reserves. Can someone explain how this happens?

It's a direct result of how monetary policy is normally conducted. In normal-times monetary policy, the Fed will increase its target rate (the Fed Funds rate) by selling Treasuries to dealers, who will pay for them by withdrawing deposits they hold at banks. The cash that formed these deposits can therefore no longer be held as excess reserves at the Fed, which decreases the supply of funds in the Fed Funds market, increasing the price of borrowing funds (i.e., the Fed Funds rate). The reduction in bank reserves is actually the mechanism by which rates are increased; the sale of bonds is just a way of reducing reserves.

Here's a reference.


How can median wages be significantly different in two neighboring countries with open borders? As an example, take Germany and Czech Republic. Both are members of the EU and therefore trade barriers between the two countries are minimal. However the median salary in Germany is nearly 3 times as much as in Czech Republic. How can that be?

Are the Germans three times more productive than the Czechs? Do they produce products that cost three times as much on the global market? If so, does this mean that Switzerland (which has a median salary twice as big as Germany) is twice as productive as Germany and six times more productive than Czech Republic?

I simply don't understand where the monetary difference comes from, especially in neighboring countries with open borders. It's certainly easy to understand in case of oil-rich nations (e.g. Saudi Arabia vs. Jordan), but not in manufacturing-based economies.

The underlying reason are probably differences in labor productivity. Assuming decreasing marginal productivity of labor, the real question is why migration flows do not equate labor productivity and wages as would be predicted by simple GE models.

I can think of several factors:

Moving is costly
Czech people in Germany are not as productive as Germans in Germany, because they don't speak German.
People prefer to
live in their home country even if that means earning lower wages.

That being said, I guess that wages in the Czech Republic have been growing much faster than in Switzerland or Germany and the gap is indeed closing slowly.


Why is machine built this year but not sold this year included in GDP? GDP is the sum of market value of all final goods in the current year, so why is is machine built this year but not sold this year included? In addition why is machine not built this year but sold this year not included in GDP?

The comment provided by @Kitsune Cavalry:

  It counts as investment. Inventory, capital, and structures are part of investment. Unsold stuff counts as inventory.

is correct as to the accounting; let me provide a little more intuition to it.

GDP measures production (output), not sales— it is in the name: Gross Domestic Product. And for many firms, production into inventory, making a thing with the explicit intent of building inventories so that the thing is available for purchase when people want that thing, is a core part of the business.

Imagine a firm that makes snow throwers— they build these machines year round, then basically sell all of them during a period from late fall through mid-winter (the exact seasonal pattern doesn't matter, only the fact that there is one).

For the purpose of counting that firm's output in GDP, it should be clear why we want to recognize that they produced snow throwers all year, rather than having all that production only show up during the few months when the machines are actually sold.

This approach isn't without its downsides— in the event that large volumes of production into inventory occurs because demand for the product in question has fallen off permanently, then the market price of the units sold will have been applied to a bunch of units that would never have sold at that price, and the value of output will be biased. This bias is of smaller magnitude than the mis-measurement that would be introduced by normal inventory fluctuations if we were only to count output when products are finally sold, though, so we live with it.


Dif-in-Dif aggregating or not? If I have data that is on some level, say industry-county-year, and a certain treatment, say a regulation change in some states in certain years. Is it possible to run a Dif-in-Dif on the industry-county-year level and cluster by states? Or do I overestimate my statistical significance if I don't aggregate my data to the state level first? If it is not necessary, are there any advantages to aggregating?
And would clustering be a problem if only one state is treated? Thus, if there would only be 1 treatment cluster and ~50 control clusters.

Yes, you can run diff-in-diff with aggreggate treatment in this case.
Yes, standard errors should be clustered at the treatment (here=state) level.
No, the number of clusters is not an issue, as the relevant number is the total number of clusters and 50 should reasonably avoid the small cluster problem.
The way you can think about this is to forget states exits. You simply have some (US) observations (firms?) that are treated and others that are not. In principle, that gives you diff-in-diff if you can satisfy all the assumptions on the control group (similar behavior in absence of treatment).
However, three other general problems arise.

Ideally, in this case you'd also want to show results at the aggregated treatment level, which you can't do because of small sample bias, since that gives you only 50 observations in each time period, but that's fine.

Finding the right control units/states (satisfying assumptions, e.g. parallel trends) is hard. The fact that only one state is treated may mean that state is very unique in the context of your research question. Maybe you want to use propensity score matching or synthetic control in addition to diff-in-diff.

Treatments at state level can be very general and you need to ensure other diverging policies also aren't happening etc.

That being said, these types of analyses can be performed and are often done. I recommend finding a paper that does diff-in-diff with treatment at the state level and follow their steps to get you started.


Why doesn't capitalism run out of money? There's this question that doesn't let me sleep at night...

From what I hear, investing surplus money is always considered a good idea because it lets your money "work for you" and earn you more money, even while you do nothing (well, assuming you've made a thoughtful investment and don't just lose your money).

There are various ways to do this, but at the end of the day they are just a variation of the same theme: you buy some object O for X money and then rent it out to people for Y money per month (or week or year or whatever). Y is a fraction of X, but over time it amounts to way, way more than X. X can be anything from cars and boats to real estate and even money itself (aka lending). Companies are a special case of this where the owner rents the company to their employees. The rules are twisted here to be in favor of the employees (instead of a fixed Y they pay whatever surplus they produce; and if they don't produce a surplus then the owner has to pay THEM), but if a company is making a profit, that's essentially what happens anyway.

And the great deal about it is that you, the owner of the object, don't need to lift a single finger. You don't put in ANY work at all, you just rake in the cash. And then with the new cash you can then buy more O's and make money even faster and so on until infinity. Wealth this way expands exponentially, or at least it does until some real life misfortune happens to slow it down for a while.

But here's the problem with this that I cannot get over:

This means that the "owners" are like vacuums for money. There's a fixed amount of money in the system and they just keep getting more and more of it. Eventually, if nothing is done about it, you should end up with a few elite owners that own ALL the money, and vast masses of people who own absolutely nothing, because they've had to spend their last dollars to pay their rent payments.

The government can, of course, print more money, but that leads to inflation and we don't do that much anyway. You can tax the rich people and give a part of their income back to the poor, but that just slows things down.

So... why are capitalist economies thriving and average non-investing people keep getting better lives (and more purchasing power), when theoretically the opposite should happen?

First of all there are several incorrect statements in your text:

Investing money is not just raking in the cash. It involves a risk, actually non trivial part of the reason why you earn interest on money you invest is the compensation for you taking this risk. If you consistently take bad risks you eventually lose your money, so its not correct to say investing is just like "rake in the cash". Moreover, if you invest in your own business/projects you have to put entrepreneurial work in. The only portion of the return that you could consider as "raking in cash" is the portion that you get to compensate you for opportunity cost of not investing the cash but enjoying it.
There is no economic theory that says in capitalism lives should get worse. In fact in economics we dont even use term capitalism - but assuming you mean by capitalism mixed but predominantly market based economies there are no economic theories which says life should get worse in those. Note in science we usually use theory differently from common English. A theory must be some consistent and testable explanation of some mechanism, and often we reserve the word theory only to consistent explanations that were already successfully tested and supported by evidence.
Money in economy circulates. Unless the rich investor stuffs the money under a mattress, it is incorrect to compare her or him to vacuum cleaner. If you put your money in bank it will get lend out to people who need it. If you spend your money on high living then again the money is paid to producers of the stuff you enjoy.
You cannot invest any number of money you want and expect the same return. Ultimately you will experience diminishing marginal returns on your investment. As the supply of savings increases interest rate goes down as in a present situation when in some countries interest rate is even negative, so you get punished by saving and rewarded by borrowing money.

Now to answer your main question, why the economy does not run out of money (assuming no more printing of new money), it is because prices in economy adjust. Generally, the monetary part of macroeconomic can be described by classical monetarist equation which states that:

$$MV=PY$$

Where M is the money supply, V velocity of money, P price level (change in which will give you inflation/deflation), and Y is the real output - how much goods and services economy can produce.

Now lets assume that money supply is fixed (hence no more printing of additional money), and examine the effect of investment on this relation. First, real output actually depends on level of investment. The higher level of investment, the higher number of factories and capital workers can work with so also the higher output we get. So investment increases the real output Y. Real output is also actually what determines the well being of people within a country (at least as measured by lets say GDP per capita). However, to keep the equation balanced something else has to change. One possibility would be for velocity of money to increase- i.e. the speed in which money circulate, but velocity of money usually does not change much. So most likely what will actually happen will be that aggregate prices P decrease, meaning that for the same amount of money you will be able to buy more goods and services.

Lets also for a second suppose that rich would actually stuff all their money in the mattress and never used them in any way possible. This would just be equivalent of actually decreasing money supply M and again P or V would adjust. In the short run decline in money supply could also negatively affect Y, but in the long run output Y is fully determined by the production capabilities of economy not by number of dollar bills circulating.

So in either case sustained increase in material welfare is possible. Also, note redistribution is not actually about taking the money from rich and give them to poor its ultimately about transferring the resources from one to another. So redistribution also does not actually necessarily depend on availability of money in the economy (although thats a topic of its own that I wont go into).

So to sum it up, some of the premises in your question are not correct, and the answer why you can have a sustained increase in wellfare when money stock declines is that prices (or potentially also partially velocity) adjust. The total output and hence material welfare of country is (in the long run) independent on quantity of money.


Do stock markets price in existential risk (i.e. global nuclear war)? Question Moved from Money StackExchange:
  https://money.stackexchange.com/questions/74002/do-stock-markets-price-in-existential-risk-i-e-global-nuclear-war

Q: Do stock markets price in existential risk?

The Cuban Missile Crisis in November 1962 is an example of coming to the brink of global nuclear war, and while the markets were down until its resolution, the index price did not seem to reflect anything resembling "imminent end of the world".

More recently, Putin's declaration to escalate nuclear weapon systems development, Trump's comments on expanding nuclear capabilities, and Noam Chomsky declaring the exchange "very frightening" suggests that we're in a period of elevated nuclear war risk.

However, the US stock markets are at their historical highs. How can this be resolved? I can think of two possibilities:

Markets accurately reflect the risk. Cuban crisis was a real, but small risk, thus the market dip. Today, the risk is nearly zero, thus the markets are not down.
Markets ignore existential risk. Rational investors expect a return on investment, but if the investors believe an event would kill them and their estate, the loss due to that event is irrelevant. Thus the risk of existential, planet destroying events, does not correlate with the stock market prices.

Is this true, can stock market price be trusted to reliably reflect existential risk?

I don't have anything to say on the empirical side.

On the theoretical side, I think that is an interesting question, even in a standard framework with "rational" agents. The possibility of the end of the world reduces the expected stream of income from assets (e.g. a share of a firm) because maybe the firm will not be there any more to distribute dividends.

Maybe you will not be there (that's the existential risk). About this the prediction of the standard microeconomic theory is the same as in the science fiction movies : your preference for present should rise, the 'natural' interest rate and the actualization rate with it. (If you fear to be dead tomorrow, you will not lend your money today... unless for a very high interest rate.) That means the value of future income falls and you want to consume everything now.

So both effects go in the same direction and "standard" prediction would be that the stock prices should fall.


Is there a change over the business cycle in how much of economic activity is market-based? Out of the total production in an economy, a big part is market-based, as when you make something and sell it. But a big part is not, as when you make something and don't sell it, but exchange it or use it in some way. Examples of this are home production, cooking, washing, etc, as well as gifts to friends, social service, etc.

More over, the fraction of production that is market-based probably changes over the business cycle.  For example, suppose that, in a recession, I agree to cook for my neighbor every other day because we are both unemployed. He cooks for me every other day. In a boom, instead I get a newly created job which pays me 50 dollars a day, and I by a 10 dollar a day meal from my neighbor who now sells me the food he cooks. The economy is only producing 50 dollars more since we used to produce two meals a day and now we are producing the same two meals a day plus 50 dollars. However, because I pay for the 10 in food, it seems the economy is producing 60 dollars more instead of just 50.

Other things that happen in recessions that don't count as output: when you are unemployed, you fix your house, you make art, etc. Maybe businesses give away more goods and services because they have the installed capacity, but not the demand...

Is there a way to measure the size of this effect?

Yes, the two figures are different because that is how the economy works in a circular flow. To calculate the size of economy (GDP), we will find the total expenditure, total income or total production. In theory, all three calculations should converge at the same figure. However, in economies they may give different figures. It is a fault of whole concept of GDP that it does not take into account any transaction for which there is not a market price available. So, no matter how much gardening you do at your home, you are not adding up anything in the economy. So, whatever mutual understanding you have with you neighbor and whatever transactions you make, it won't be considered.
In the second part of your example, you are spending 10 dollars and your employer is spending 50 dollars so that add up to 60 dollars. This happens because of circular flow, your income is somebody else's expenditure.

Production for which there is no price will not be counted.


Would be there redistribution of wealth from actors with sticky prices to actors with more flexible prices under stagflation? Suppose there is inflation in our country, but we take a short term, thus some prices are sticky and there is non-positive "growth" of the real GDP. In other words, we experience stagflation.
If I understand correctly, non-positive "growth" of the real GDP means that our total wealth does not grow. BUT at the same time I know that stickness of prices allows some people/corporations to make some real short-term profits. They increase their personal wealth while total wealth of the economy does not increase. It seems logical to conclude that they must be increasing their wealth at cost of other actors. But who are these other actors? These people must be ones that experience losses. And who would experience losses under this situation? People who sell goods (including their own labor) and for some reasons (like contracts that take time to renegotiate, menu costs, etc) don't rise prices on their goods fast enough. So there would be redistribution of wealth from actors with sticky prices to actors with more flexible prices.
Is my reasoning correct? Is my conclusion true?

This would redistribute some resources from those who can't change their prices quickly enough, but studies (like this study here) show that the most important redistribution effect of inflation occurs along debtor/lender lines.


How can we know how PPF of our country looks like? PPF of our country is based on potential the most efficient use of our resources, NOT their actual use. The problem is, it's unclear for me how to estimate said potential. How to know where potentiality ends and impossibility begins? Not necessarily with clean-cut distinction, a rule of thumb can be good enough too.

Examples:

(An increase of productivity needs a cultural shift) Maybe we would be able to increase production by increasing participation of women in the workforce, but our culture CURRENTLY dictates that married women should NOT work.
So would our PPF include increased production caused by by cultural shift? Or would we experience an increase of PPF?
(An increase of productivity needs a political shift) Maybe our production would increase if we used these sweet new technologies. But alas, we can't have them due to sanctions and it would require radical change in our current political regime in order to have possibility for sanctions being lifted. If sanctions were lifted after revolution would it mean that our PPF increased or that we got closer to it?
Maybe our production would increase if we used industrial espionage to get new technologies, but such secret operations have very high possibility of a complete failrue. Would success mean that we increased our PPF?
Maybe our production would increase if there was industrialization in our society. But our society have never been industrialized in the first place. And it would take many years and lots of efforts to industrialize it.

You can estimate the PPF just by technically estimating what’s the production function for all goods and what’s the resource constraint.

For example, let’s for the sake of simplicity assume that the only factor of production is labor so the problem does not get too complicated. Also, just for a sake of simplicity let’s assume there only two goods apples and oranges.

An example of simple production function for apples would be $A=L^{1/2}$ and for oranges $O=2L^{1/2}$ and let’s assume that the maximum amount of labor hours country has available is 100 to make math simpler. This would give you PPF with corners at 10A and 20O and then some curve in between them. In real life the same calculation would be performed but production function would use more factors of production and would be estimated for all industries. That would be difficult to visualize because the graph would have dimensions equal to the number of industries and anything above 3D is very hard to plot but it would work the same way.

In this calculation you take production function and resource constraint as it is. There is always possibility that a country discovers new technology that increases efficiency or new resource, but you have to define PPF at some point of time otherwise it does not have any meaning - in long run the whole universe will became just cold lifeless void because of the entropy so for most of the time universe exists the PPF of any life form will be 0. But at present or different points of time it will be non-zero.

For example a new technology in above example for apples could change in time $t+1$ the apples production function to $A=4L^{1/2}$. Now the PPF would have corners at 40A and 20O at the time $t+1$ even though before it was smaller. However, at both periods of the time people could choose to produce not at the boundary of PPF but that would not be due to the technology or resources but due to preferences or perhaps some bad policies that prevent them to do that. Culture could affect preferences but also resource constraint - if you for example really have to observe shabbat and can’t work no matter what, but again at a point of time that’s fixed and implicitly included in PPF.

PPF is by its definition constructed by already taking technology (via production function) and amount of resources available (via the resource or budget constraint) at some time into the account so it’s not like you somehow have to add that to PPF. Of course, in real life people could argue that different econometric techniques might not capture these factors properly and might dispute your statistical methodology, but that’s whole different discussion


Long-term equilibrium price inelastic? I have doubts about some
economic interpretation:
Why is the long-term equilibrium price inelastic?
But in the short run, the equilibrium price is not necessarily inelastic. What is the economic interpretation of it?

For starters, elasticity is the percentage change in quantity (demanded or supplied, depending on what we're looking at) due to a one percent change in price. Or more vaguely: elasticity measures the sensitivity to price of behavior. From this definition it is clear that stating that a 'price is elastic' is nonsense. Trivially, the elasticity of price with respect to price is one.
That being said, I think I know what you mean. The long run supply curve is perfectly elastic. That is: the long run price is set in stone. It is insensitive to some changes. In a diagram as seen below this means a horizontal supply curve, with price being the same for all output levels. Why is this? Firms are often assumed to have some kind of efficient scale at which the average cost is at its minimum. That is: there is some particular quantity $q^*$ such that $AC(q^*)=\min\{AC(q)|q \geq0\}$. In the long run it seems reasonable to assume that every firm $i$ will produce exactly $q_i=q^*$. With number of firms operating such that in fact price will be equal to $P=AC(q^*)$. Define $P^*=AC(q^*)$,  formally the equilibrium would be such that: $nq^*=Q^d(P^*)$ (supply equals demand). So price will always be the same, namely $P^*$, with $n$ and thus $Q^S=nq^*$ changing in response to changes in demand. All this is long run.

Now  why does this theory make sense? Well, suppose at some moment the price in the market is below $P^*$, then every firm loses money, so it can't persist since firms can just stop operations. Suppose price is above $P^*$: a firm operating at the efficient scale can come in, undercut the market price by a small margin and sell at a profit.


Does credit really help an economy? I'm reading Ray Dalio's How the Economic Machine Works, and although I understand the role of credit in an economy, I don't understand whether credit actually adds any value to an economy.

Ray says that credit is what causes economic cycles (i.e. people borrow and invest when interest rates are low, and save when interest rates are high). This behavior leads to short term debt cycles in the economy.

However, Ray also says that these short-term cycles are centered around a long term productivity growth of around 2%.

So my question is as follows:
If productivity will always grow at around 2%, what is the point of credit? Aren't you simply borrowing from the future, and then subsequently paying off those debts? Would economic growth be the same without credit? Would the economy be as efficient?

Thanks

Edit: Here's the paragraph that I'm confused about. Is this paragraph saying that credit doesn't add anything to an economy (since long-term productivity growth is the same regardless?)

Credit or more broadly finance helps to allocate scarce resources to where they are most needed.

  Example. Suppose party A is sitting on a pile of resources from which he thinks he can make a return of 1% (per annum). Party B has no resources, but thinks she can use party A's resources to make a return of 3%. Then both parties can benefit if A loans his resources to B at a rate of 2%. (Society also benefits, at least insofar as the economic pie is larger than it would otherwise have been.)

(Of course, in the real world, A and B may be very much mistaken about their rates of return, which is partly how bad things like financial crises arise. But the above simplistic example illustrates what's "supposed" to happen, at least in a world of perfect information.)

P.S. Your presumption that "productivity will always grow at around 2%" is false. Finance/credit is at least part of the reason for non-trivial economic growth in the past 250 years. In a world where entrepreneurs are unable to gain credit, economic growth would be lower.


Intuitive explanation of $S(p,w)\cdot p=0$ Can anyone provide an intuitive explanation of why the Slutsky matrix right multiplied by the price vector yields a zero matrix?

I know this is true but I do not really understand why it is true. Can anyone help here?

This is a general mathematical property of the second derivative/Hessian matrix of multivariate functions that are homogeneous of degree one.

The Expenditure function $E$ is homogeneous of degree one in prices. Why?
If all prices change in the same proportion (which is how we check for the mathematical property of homogeneity), relative prices do not change. If relative prices do not change, the quantitative composition of the minimum-cost compensated consumption bundle in order to achieve a given utility does not change at all. Then, since all prices have increased by the same proportion, budget shares remain the same, and the Expenditure needed to achieve the same utility, rises in that same proportion: homogeneity of degree one.

By duality, the Hicksian demand vector is the gradient of the Expenditure function, $H = \nabla_p E$.

The Hicksian demand vector, gives us minimum-cost quantities demanded. Due to the homogeneity of degree one of the Expenditure function, the inner product of the Hicksian demand vector times the price vector equals the Expenditure function. This also should be intuitive: we just multiply each quantity demanded by the unit price that must be paid for it, and by summing these products we obtain the total Expenditure we must incur in order to acquire the minimum-cost bundle for given utility.

So we have  (simplifying differentiation notation) $ E = H\cdot p$ while also $\frac {\partial}{\partial p}E = H$. Therefore also

$$\frac {\partial}{\partial p}(H\cdot p) = H \implies H + \frac {\partial H}{\partial p}\cdot p = H$$

and it must be the case that

$$\frac {\partial H}{\partial p}\cdot p = 0$$

So the Hicksian demand vector is homogeneous of degree zero in prices (mathematically, this is a consequence of Euler's theorem for homogeneous functions, i.e. that if a function is homogeneous with degree of homogeneity $k$, its gradient has degree of homogeneity $k-1$).

But the 1st derivative (Jacobian) of Hicksian demand (which is the Hessian-matrix of second derivatives of the Expenditure function) is the Slutsky matrix, $\frac {\partial^2 E}{\partial p^2}=\frac {\partial H}{\partial p} = S(p,w)$. So $S(w,p) \cdot p = 0$.

So the result stems from the homogeneity of degree one of the Expenditure function. Is there an intuitive explanation, analogously with the intuition behind the homogeneity of degree one of the Expenditure function? Well, the former comes directly from the latter, so it is difficult to come up with a "separate" intuitive argument. One could informally say that compensated quantities demanded are "independent" of (not affected by) price variation when relative prices remain the same. Then in geometric terms, this means that the vectors of rates of change of compensated quantities demanded (which is what each row of the Slutsky matrix contains), are orthogonal to the price vector.


How do you utilize event studies in policy research? I apologize if this isn't exactly the right forum to pose this question. I am trying to use an event study to evaluate the adoption of a specific policy in several different states that happen at different times. I had a professor suggest using an event study but I am struggling to find resources on the use of event studies in policy research. I understand the mechanics of how to formulate the equation and structure the data and come up with a graph. However, I feel like I'm lacking knowledge that would allow me to fully interpret results. I feel like I don't know all the requirements of an event study or what it means to have a significant divergence from 0 following the treatment period.
Does anyone know of any resource that explains event studies in the policy context? I'm aware it's used more often in finance literature but those works don't follow the work I'm doing. This paper is an example of policy event studies but I'm looking for a more in-depth breakdown of when and how to use this technique.
A good example of a confusing result for me would be the following:
The treatment, in this case, is the implementation of a law that requires those under domestic violence restraining orders turn in their firearms and the outcome is the number of intimate partner homicides per 100,000 people. There are an additional set of policy and sociodemographic controls. I spoke to my advisor and he said to shift the graph down and center the year before treatment at 0 (but to do so transparently). I get the following when I do so: .
The problem is that I don't know how to fully interpret this or why it would be okay to make this transformation. If anyone could shed any light on why someone might do this, that would be very helpful. However, more broadly, I'd appreciate any resources that would help me help myself a bit more with this topic.

It sounds like you are really looking at differences in differences, not event studies.
Diff in diff compares outcomes before/after a policy intervention (as does an event study) (the first diff) and between treated and untreated units (the second diff).
The underlying assumption is that, in the absence of the policy, treated and untreated units (here: states) would have experienced the same changes over time.
A good reference is Ch 5 of Mastering Metrics (by Angrist and Pischke), but the previously suggested "Harmless Econometrics" by the same authors is also very good.


Does the Coase Theorem account for malicious externalities? The wikipedia page has a good summary:

  Coase developed his theorem when considering the regulation of radio frequencies. Competing radio stations could use the same frequencies and would therefore interfere with each other's broadcasts. The problem faced by regulators was how to eliminate interference and allocate frequencies to radio stations efficiently. What Coase proposed in 1959 was that as long as property rights in these frequencies were well defined, it ultimately did not matter if adjacent radio stations interfered with each other by broadcasting in the same frequency band. Furthermore, it did not matter to whom the property rights were granted. His reasoning was that the station able to reap the higher economic gain from broadcasting would have an incentive to pay the other station not to interfere. In the absence of transaction costs, both stations would strike a mutually advantageous deal. It would not matter which station had the initial right to broadcast; eventually, the right to broadcast would end up with the party that was able to put it to the most highly valued use.

This makes sense if you take the view that everyone broadcasting is only doing so to perform a legitimate service. However, if the practice of paying people not to interfere was prevalent, it would be very profitable to simply buy the most powerful transmitter available and set it up next to the most profitable radio station around and wait to get paid off. It's almost like saying that a gangster who demands protection money is simply operating under the Coase theorem and bargaining with shop owners who would like avoid the negative externalities of bricks thrown through their windows.

Does Coase somehow analyze this in his essay, and I'm missing it, or am I missing something else more fundamental that makes this irrelevant?

The problem you raise (you call it "malicious externalities") is perhaps better known as the extortion or blackmail problem. Medema (2014) reviews the history of this critique.

Those who raised the extortion critique.

Kneese (1964):

  an industrial enterprise may find it profitable to adopt processes which generate much waste in order to be able to accept payment for reducing discharge.

Wellisz (1964):

  any activity can be turned to profit as long as it is sufficiently annoying to someone else.

Kneese and Bower (1968):

  The administrative authority would have to stand ready to make payments to industrial plants which never do locate in the area but which would do so if a payment reflecting the costs their effluents would impose were not made. Payments on this basis would of course be an open invitation to extortion.

Rothenberg (1970):

  If external diseconomies against others can be expected to lead to bribes by victims to desist, then the production of negative externalities becomes a valid by-product of primary production. Profitability is enhanced whenever any firm can select from among its inputs and/or output alternatives those which cause substantial damage to third parties. Resource use will tend to become specialized toward much-augmented third-party interference. The new legal industry of selling protection against disturbance will be highly profitable.

Shoup (1971)

  Once it became apparent that polluters could extract payments for pollution reduction, there would be an incentive for entrepreneurs to threaten to undertake production activities with external pollution costs simply to be bribed not to undertake them.

Mumey (1971) makes the distinct point that contrary to some defenses of Coase against the extortion problem (below), in a frictionless world with costless communication, the problem of extortion or "malicious damages" is actually exacerbated.
Daly (1974) makes another distinct point:

  the simultaneous existence of competition and of small numbers of externally affected and effecting parties are mutually inconsistent phenomena. If the large numbers generally associated with competition are present there will exist the acknowledged prohibitive transactions costs. In the absence of such competition, however, market solutions are burdened with complex, game theoretic bargaining situations which involve a substantial likelihood of counterproductive (costly) activity and for which the achievement of efficient outcomes cannot be assured.

See also Daly and Giertz (1975, 1978).

Those who defended against the extortion critique.

Demsetz (1971):

  Under competition the price of a good, or of an agreement, is held to its cost. ... In the competitive regime, rivalry between “extortionists” would push to zero the price they receive not to undertake these excessive activity levels. Thus, competing “extortionists” would offer to refrain from extortionist activities for smaller, and ultimately for zero, payment.

Also:

  The concept of extortion is legal, not economic. All transactions are transfers of rights involving exactly what Shoup has called extortion with the possible exception of perfectly competitive ones, whatever they are. All transactions are preceded by attempts to gain information about the other party’s reservation price. A variety of techniques are used to do this. From the viewpoint of economic analysis, none of these differ. The use of the legal term “extortion” in economic analysis is likely to result in confusion and to hide implicit value judgments. There are some wealth transfer activities which, as citizens, we would seek to discourage through legal arrangements. But until we have formed a clearer notion of the criterion to be used to separate these from other “approved” wealth distributing activities, the use of terms such as “extortion” in economic discussions is laden with difficulties. Thus, if without any more valuable alternatives, Professor Shoup were to threaten to leave the University of Michigan unless he were paid more, thus threatening to impose real costs on that institution by depriving it of all the advantages of employing him, would that constitute an analytical use of the word “extortion”?

See also Demsetz (1972, 1978).

Zerbe (1971): extortion "does not occur ... because of high transactions costs, symmetrical possibilities of extortion [One man’s threat to raise chickens is countered by his neighbor’s threat to raise pigs], and the operation of the legal system in holding certain forms of extortion illegal [With costless bargaining, undoubtedly my neighbors would pay more for me not to grow pigs than I could make growing pigs. Hence, the law forbids my raising pigs in Chicago and in most cities]."

Zerbe also raises a separate point similar to Demsetz's second point:

  Extortion does occur, however, and it is not necessarily undesirable. The general economic rule governing extortion would seem to be that extortion rights should go to those who are willing to pay the most. This rule illumines the close relationship between extortion and market transaction. Indeed it is difficult to separate extortion from other production activities. Payments required to attract factors of production are also payments to ensure that these factors will not do something else. Consider Shoup’s case of the polluting oil company that threatens towns A, B, and C with location in them. This situation is similar to the case of a desirable company, say a computer company, also choosing among towns A, B, and C. The computer company chooses the town that offers the deal with the least amount of extortion money. In this way both end up in the right location. In this sense extortion is a method for achieving results and may or may not be desirable, depending on what other methods are available and whether or not extortion interferes with more desirable methods. In actuality, towns generally offer companies tax and other incentives and disincentives that reflect the town’s desire for the company. Here the cases become wholly symmetrical. Both the oil and computer companies choose the location for which the location price, ceteris paribus, is the least. At some price, the oil company becomes attractive to a town. It is not surprising then that there is a problem in distinguishing between legal and illegal extortion, and at this point an extensive legal-economic discussion of extortion is called for but is provided neither by this note, by Shoup, nor by my original article.

Jaffe (1975): With zero transaction costs, we will always arrive at the most efficient allocation:

  imagine that B can increase the destructive potential of his resource by physical investment. We further assume that the degree of destructive potential is an increasing function of physical investment. This function is known to both A and B.5 As B can now increase the potential of his resource, he may be able to exact an even greater bribe from A. However, applying the same rationality assumption used by Coase, no additional investment in the resource will actually be undertaken by B. Merely B's potential to create a more destructive resource should be enough to force A into a more generous bargain.

Coase (1988) himself:

  When I referred to the problem of blackmail in "The Problem of Social Cost," it was simply to show that, however rights were defined, there would always be opportunities for this type of blackmail. I pointed this out in the section of the article in which it was assumed that transactions were costless and in which therefore no resources were absorbed in bargaining. In such a world, it is not clear what the objection would be to this type of blackmail. The position is, however, very different if we make the realistic assumption that transaction costs are positive. It is obviously undesirable that resources should be devoted to bargaining which produces a situation no better than it was previously.

There is no such thing as the Coase Theorem.

Or as Coase stated repeatedly, including in this 2012 interview:

  "It's not about my work at all!"

Coase's goal was to illustrate that:

  institutions matter, and that understanding how and why they matter, and the implications of this for economic analysis and policy may require a re-orientation of the ways in which economists practiced their trade. Here, too, transaction costs occupy center stage. (Medema, 2014.)

Most of all, his goal was to direct economists' attention to the world of positive transaction costs.†

Ironically, economists became fixated with the simplistic world of zero transaction costs, where they could churn out a variety of clever "Coase Theorems" and avoid having to study the messier real world of positive transaction costs.

What I — and probably also Coase, Medema, and McCloskey — would say is this:

The extortion problem is worthy of our attention and analysis. However, we should study it not in the context of the economist's utopian world of zero transaction costs, but in the context of Coase's very real world of positive transaction costs.

We should study how and why, given the very real fact of transaction costs, institutions ameliorate (or fail to ameliorate) the extortion or blackmail problem. I think Coase (1988), Zerbe (1971), and Demsetz (1971) went some way towards initiating this discussion, but there is much more to be done.

† D. McCloskey has also repeatedly made the same point and stated the Coase Theorem (1985, p. 336) as:

  In the presence of transaction costs the location of a pollution tax or of other liability for damages does matter for efficiency.


Reconciling low wages with high technology and high education This is one example of many recent doom storied prognosticating that the "millennial" generation will be the first in (recent) history to experience lower incomes than their predecessors—at least in western societies like the UK.

How can we reconcile the idea that millennials will earn lower wages with the observations that

millennials work with better production technology than their parents did
millennials have enjoyed higher levels of education/training than their predecessors, and are therefore presumably more productive?

Possibly it is because of the better technology that they will receive lower income.

When there where fewer programmers/engineers/whatever, these individuals could typically expect high salaries due to their scarcity, compared to today, where you can hardly go outside without bumping into about two programmers and five web devs.

This means that highly-qualified individuals may not be able to find jobs relevant to their qualifications at all, even if they are willing to accept substantially lower salaries.

This may seem more focused on jobs requiring higher qualifications, but that may be one of the big factors here, especially when it comes to averages.

In summary, could it possibly be that we have an over-supply of well-qualified individuals? (This may not apply to all countries or industries)


The Cash Flow Sensitivity of Cash I am struggling with calculating the Cash Flow Sensitivity of Cash from the Almeida et al. (2004) working paper. (http://u.osu.edu/weisbach.2/files/2015/01/ACWJFinance-ui65te.pdf)

I am trying to apply his formula on one specific company case and additionally make some conclusions, whether the company has financially constraints or not.

I am trying to apply equation (8) on page 1787. However, I understand the main variables like  change of CashHoldings, CashFlow, Q and the Size but I do not understand the factors in front of them like a0, a1, etc. Therefore, I am not able to apply this equation.

On page 1793 the authors also provide a more simplified definition of the cash flow sensitivity of cash. "These estimates suggest that for each dollar of additional cash flow, a constrained firm will save around 5-6 cents"(p.1793).

If I stick to this definition I can not see the necessity to apply any equation. Following this definition I simply could calculate the cash flow sensitivity of cash with the data of changes in cash holdings and cash flows of the certain company. Is this right?

Moreover, does anybody know how to calculate the cash flow sensitivity of cash with the given equation (8)?

This is a standard regression equation, in the context of an econometric approach. The authors postulate a theoretical relationship between the variable "Change in Cash Holdings", which is treated as the "dependent variable", while "Cash Flow" and the rest are the explanatory variables/regressors.

The "factors in front of them" are the regression coefficients, which are unknown, and which are to be estimated through a regression estimation approach, like Ordinary Least Squares or any other technique deemed appropriate, using a sample of observations, which could be either "cross-sectional" (one balance sheet for each company, many companies), "time series" (many consecutive balance sheets for one company), or "panel data" (many consecutive balance sheets for many companies).

Each coefficient represents the partial marginal effect of a change in the regressor on the dependent variable, holding the other regressors constant.

There is also a "stand alone" coefficient, $\alpha_0$: this is the "constant term of the regression". Attached to it there is an implied "explanatory variable" that takes the value $1$ throughout the sample. It captures the average value of the dependent variable after the average effect of the regressors has been subtracted. The $\varepsilon$ at the end is the regression error, and it is unknown -an estimation of it will be given by the series of residuals obtain as the difference between the estimated value of the dependent variable and the actual one.

In Table 2 p. 1794 "The Baseline Regression Model" in the paper contains the numerical estimates the authors obtained for the $\alpha$'s for the specific regression equation, based on a panel data set, it appears, and for a number of variants.

According to the authors, immediately below eq. $(8)$ they identify the "Cash Flow Sensitivity of Cash" with the unknown coefficient $\alpha_1$. As already mentioned in the question (and as can be seen in Table 2), they find that the estimated value of $\alpha_1$ is $0.05-0.06$.

As described immediately above eq. $(8)$, "Cash Flow" here is essentially Operating Profits plus Interest Costs minus Dividends. Then the interpretation is that "on average, for every dollar increase in thus defined "Cash Flow", the Cash Holdings of a company increase by 5-6 cents" (the authors use, perhaps misleadingly, the verb "save" instead of "increase". "Hoard" would also be accurate).

"Applying" a regression equation to a specific firm, means, use time-series data on that firm, run the regression as specified and obtain firm-specific estimates for the coefficients. You essentially "accept" the theoretical validity of the regression specification, and you attempt to find the values of the coefficient pertinent to a specific firm (remember, the obtained estimates by the authors are averages over data from thousands of firms). You can then compare these estimates with the estimates obtained by the authors and see whether the firm under examination is close to average or not.


Will SRAS curve definitely shift if LRAS curve shifts? From what I know, a shift in LRAS is generally caused by a change in maximum productive capacity of an economy, which affects the full-employment output level. Such change in maximum productive capacity would also cause a shift in SRAS.

So does this mean whenever LRAS curve shifts SRAS curve will also shift? If not, are there any examples against this?

The SRAS also shifts.

SRAS is normally used in models where the supply side does not adjust immediately to the new conditions in the market, i.e. models with nominal rigidities. Examples are: sticky wages, sticky prices.

Other instances where adjustments in prices or nominal money supply are not immediate and in which money has a real effect are the Lucas 'misperceptions' or 'islands' model or Cash in advance models.

However, and to the point of answering your question, all these models coincide that in the long term and when the agents in the economy have had the time to adjust their choice variables and/or information symmetry is restored, the AS is vertical again (LRAS). So, if there has been sufficient time for the long run AS to adjust, by definition of short run, i.e. shorter than the long run, there has been enough time for the SRAS to adjust as well.


GDP per Installed Computing Facilities I would like to quantify the importance of computing power for economic aggregate output in an economy in a "simple" GDP-to-installed-CPU ratio.

The idea is to calculate a measure similar to GDP-to-capital.

The challenge lies in quantifying installed CPU power in an economy. That is, to define an adequate (and feasible) measure of installed computational facilities and have an idea how to measure this.

There have been objections to a previous version of this question on the basis that this is difficult. It might, in fact, be.

Any suggestions on parts of this problem are highly welcome.

I have electrical engineering training, and the focus on CPUs creates a technical issue. Before the early 1990s (when I did my degree), CPUs would have largely been only embedded in what are recognisably digital computers. Electronics would otherwise be implemented with simpler digital/analog circuits. Currently, the path of least resistance is to just use a CPU for practically everything. For example, cars have plenty of embedded CPUs, whereas back in the 1970s, many might have been almost entirely analog circuits (the radio being the only possible exception).

So any measure of CPUs per unit of GDP should be exploding higher. This is just a reflection of electrical engineers’ design preferences.

If we just try to confine ourselves to CPUs in digital computers, we could try to compute some normalised computing capacity. (This raises problems: do we include phones? Game consoles? Tablets?)

The next problem is: so what? My computer CPUs have been getting faster over the decades, but at the same time, the bloated operating system chews up more of the resources. I end up doing roughly the same tasks in the same amount of time. Even without having any data, knowing what we know about computing power, my experiences are obviously be widely replicated. That is, real GDP is growing at around 2% per year in the developed countries, but installed CPU capacity has to be growing way faster than that, even if we take a narrow definition of “computer”.

Realistically, you might need to identify some industries that might show a correlation between their growth and CPU usage. It’s hard to see how adding server farms can increase the production of restaurants and barber shops.


How to estimate empirically a demand curve Suppose I have a small business that sell ice creams.
An Icecream cost 2$, and every day I have different values of sales.

Day 1: 50 sales
Day 2: 70 sales
Day 3: 30 sales
Day 4: 50 sales

How to I estimate the demand curve?

Should I change every day the price of the icecream and see how many sales I get? (the variation could be caused by many other factors)
Should I do a linear regression on the sales data?

I think You will have to account for various factors other than price. What is your purpose in estimating the demand curve?

This link will give you a good explanation on how to estimate demand curves if you are looking for simple exposition - assuming that sales and other factors have linear relationship.

If you are good at econometrics, you may want to consider simultaneous equations models. It is likely that your single equation model will suffer from simultaneity and omitted variable bias along with identification problems.


[Graduate Macro]Land in the Economic Growth Model: CES Function I am an undergrad student trying to study grad macro. I have been trying to solve exercise problem in Barro's economic growth.

The model is about adding land in the Ramsey Model. The model goes as follows:
$$Y=A\Bigl[a(K^\alpha L^{1-\alpha})^\psi+(1-a)\Lambda^\psi\Bigr]^{1/\psi}$$

Now, the questions asks what condition of $\psi$ can ensure that $Y/L$ is constant over time, and what condition will make $Y/L$ steadily decrease over time.

I have derived the per capita intensive form as follows:
$$y(t)=A\Bigl[a(k(t)^\alpha)^\psi+(1-a)\lambda^\psi\Bigr]^{1/\psi}$$

However, I do not grasp the meaning of how different value of $\psi$ can affect the outcome.

Suppose that the total amount of land ($\Lambda$) is fixed but the population measure increases exogenously ($\dot{L}>0$).

The only way the per capita product ($y(t)$) remains constant over time is when you can fully substitute capital against land. In formulas, you need to set $\psi=1$ to get:
$$ y(t) = A[a k(t)^\alpha + (1-a) \lambda(t)] $$
and, as you can see, you can always trade-off declining land per capita ($\lambda(t) =\frac{\Lambda}{L(t)}$, then $\dot{\lambda}<0$) with more investment in capital.

On the other hand, as soon as $\psi \neq 1$, then $Y/L$ will steadily decrease over time as a consequence of population growth in conjunction with decrease land per capita not being fully substituted by increases in capital.

This would be my intuition, hope it helps, I'm sure one can make the argument more rigorous, especially for the case of $\psi\neq 1$.


Why would a government allow currency speculation? I would like to know the reasons for any government to allow currency speculation. What is the benefit for a country and for it's people if the currency speculation is allowed?

My cousin's farm is producing vegetables. Providing some goods that the consumers need or desire and they are ready to pay for.
My fruit shop is selling fruits and vegetables. Providing a service that the consumers need or desire and they are ready to pay for.
My uncle's credit bank is helping the people who can save enough to buy a car today instead of waiting many years until they saved enough. Providing a service that the consumers need or desire and they are ready to pay for.
My aunt's currency trading company is helping the people based in the EU to buy products or services made in the USA. Providing a service that the consumers need or desire and they are ready to pay for.

If I, or my cousin or my uncle create monopolies or cartels and speculate and fix prices, artificially inflating the cost of the goods/services we provide, the government will put punish us for that, because we harm the consumers. That's why most countries have a Competition Council. But if my aunt creates a monopoly or cartel and buys large amounts of currency (becoming a speculator instead of being a trader) artificially increasing the price of the US dollar for the EU based consumers, reducing their buying power and therefore harming them, shouldn't the government react in the same manner?

Now, the currency speculators do not produce any good or service that consumers actually need or desire. Not even the international traders need to buy currency from speculators—they can buy the currency from the international traders based in the targeted country, or from the currency traders. So, nobody actually need the speculators "services".
By the contrary, their actions will make the target currency to be weaker (so the working people can buy less with their money - they have less buying power), and they will make profit from this.

At the end of the day, me, my cousin, my uncle and my aunt will be able to buy less food or clothes or computers with our income, even though we keep producing goods and services the consumer need, helping them to get what they need.
And in the same time, the speculators can buy a lot of food or clothes or computers even though they produced nothing that the consumers needed, all that at the expense of the consumers, harming them.

So we have a situation where money do not measure work anymore. Your money do not measure your capacity to produce some needed goods or services. Now the money measure your capacity to speculate and to harm other people by reducing their buying power, driving them poorer.

A very basic logic says that the money were created in order to help the people who produce goods and services to trade those goods and services. So the reason for using money (currency) is because it's supposed that the use of money will help them, instead of harming them. If using money hurts me more than helps me, I will think very seriously about not using money anymore.

It can't be hard at all for a government to detect if Brian Jones buys currency in order to actually buy goods with it or to simply speculate the currency value. They simply have to look at what Brian Jones is buying with the currency once it has it. The government can put a penalty (a fine) for speculating the currency so the consumers and the working people do not have to suffer from the speculation. And then it won't be profitable anymore to speculate a currency.

And then, what would be a government's arguments for allowing the speculation with it's own currency?

I provided all the data above in order to avoid superficial answers like "currency speculators help the people to buy foreign currency" - there are currency traders for that.

Speculators very rarely have large positions that come anywhere near what would count as a monopoly in other sectors. When Soros bet on the pound devaluing he only had a couple of billion dollar in his position.
The distinction between a currency trader and a currency speculator can be hard to make in practice. The difference between market making and market speculation is one reason why the Volker rule has been difficult to write and implement.
There is tremendous social value to informative prices. When prices are right, consumer and producers can rely on them to make better plans of economic activity. When prices are wrong (not reflecting the best information and most informed beliefs) those same agents are mislead by prices, leading to malinvestment and sub-optimal savings and consumption decisions.
It can be difficult to ban such speculative activity because it moves off-shore. In general, a country could ban speculation locally but not in other countries. The UK has little motive to help France ban speculation on the Euro in London. The only way to really do this effectively is with currency controls that make it difficult to bring currency out of the country. But currency controls can bring additional complications into currency markets, often a serious form of financial repression and results in exchange rates even less informative.

Faced with these issues, most well run countries, most of the time,  would take functioning currency markets with the warts and wrinkles of speculators, volatility, and the like, over the downsides of trying to control the price of their own currency.


Does the theory of contestable markets apply to industries where there is a natural monopoly? The theory contestable markets requires that there be no barriers to entry, no sunk costs, and access to the same level of technology. I know common industries that have natural monopolies (like utilities) often break these assumptions. Is there an example of an industry where there is a natural monopoly and it can still be considered a contestable market? Or can these two things not exist together?

Natural monopolies may exist because the cost structure is such that the market could not sustain two firms. This is usually explained by large sunk costs and relatively small demand - if there were two firms the average cost for at least one would have to be above the equilibrium price.
If there are no sunk costs at all - as set forth in the ideal case of a contestable market - then the long-run average cost function is likely not decreasing, hence this phenomenon cannot occur.


Why is the Marginal Cost (MC) of a monopoly horizontal I presume it's because they're price makers, but this doesn't really answer much. Furethermore, in a monopoly is it Marginal Cost or Long run marginal cost that's horizontal?

That is basically an assumption here. Often in monopoly problems we assume constant marginal costs (i.e. a linear cost function) to keep things simple. In that case the Marginal Cost Curve is horizontal in the graph.


Why is such a high US national debt not resulting in another great depression? The chart below shows that US national debt as a percentage of its GDP is at an all time high excluding the one during WW-II. While I'm not as well informed in economics as most of the people on this forum, I know that a national debt to GDP ratio of more than 100% means that the country is strongly headed in a direction where it cannot pay back its own debt. I have two questions.

Why is such a high US national debt, expressed as a percentage of its GDP, not resulting in another great depression?
What is a good mathematical measure(s) (if it is not national debt to GDP ratio) which will provide all the information needed to gauge the chances of US entering into a great depression/bad recession?

I am dying to know the answers to the above and I would greatly appreciate it if you can answer them for me.

You answered question (1) with your own chart. The United States had a “high” debt-to-GDP ratio in World War II - and there was no subsequent depression. (There was a sharp recession as a result of demobilisation, but that was short-lived.)

For a country that borrows in its own currency, there is no obvious reason how a high debt-to-GDP creates recession risk.

There is no easy answer to (2). We are currently headed into the greatest economic contraction since the Great Depression, and the cause was a pandemic, which would not show up in a standard mathematical economic model.


Within-Cluster Correlation I figured this would be more appropriate on this forum. I came across the following slides after a Google search of cluster-robust uncertainty estimators. On slide 10 the author attempts to demonstrate how the errors of students nested within the same classroom are correlated. That is, $\textrm{E}\left[u_{ig} u_{jg'}\right] = \sigma_{(ij)g}$ if $g \neq g'$.

Shouldn't this be: if $g = g'$? I assume $g \neq g'$ represents two students ($i$ and $j$) in different classrooms (groups). If so, wouldn't this be demonstrative of dependence across clusters?

Sorry if this question is a bit in the weeds. Can anyone provide further clarity?

Note that $u_i$ is a random residue. In the linear regression model, we assume the independence of the random residue (error term). We have two slides above for case i.i.d.: $\epsilon_i \sim (0,\sigma^2)$, thus $\forall_{i\neq j}E[\epsilon_i\epsilon_j|X]=0$ and $E[\epsilon_i\epsilon_i|X]=\sigma^2$. Later (slide 10) we assume i.n.i.d. and $\forall_{g\neq g'}E[\epsilon_{ig}\epsilon_{jg'}]=0$.

Slide 12 shows the example matrix $\Omega=diag(\Sigma_g)$ that dispels doubts.


Is there any mainstream criticism of the Benes-Kumhof conclusion that the Chicago Plan would improve the US economy nowadays? A 2012 IMF Working paper by Benes and Kumhof "The Chicago Plan Revisited
" has this abstract, finding in favor of the Chicago Plan an all counts basically:

  At the height of the Great Depression a number of leading U.S. economists advanced a
  proposal for monetary reform that became known as the Chicago Plan. It envisaged the
  separation of the monetary and credit functions of the banking system, by requiring 100%
  reserve backing for deposits. Irving Fisher (1936) claimed the following advantages for this
  plan: (1) Much better control of a major source of business cycle fluctuations, sudden
  increases and contractions of bank credit and of the supply of bank-created money.
  (2) Complete elimination of bank runs. (3) Dramatic reduction of the (net) public debt.
  (4) Dramatic reduction of private debt, as money creation no longer requires simultaneous
  debt creation. We study these claims by embedding a comprehensive and carefully calibrated
  model of the banking system in a DSGE model of the U.S. economy. We find support for all
  four of Fisher's claims. Furthermore, output gains approach 10 percent, and steady state
  inflation can drop to zero without posing problems for the conduct of monetary policy.

Interestingly, that paper even has a Wikipedia article but the only criticism mentioned there is from Austrians. I suspect however that the Chicago Plan couldn't be all that uncontroversial with mainstream either, as it would pretty much eliminate the fractional reserve etc. Has this DSGE model of Benes and Kumhof, validating the Chicago Plan with such rosy outlooks (10% improvement in output etc.) received any critical commentary in mainstream/orthodox sources?

I also found a paper by Dittmer (2015) about the topic (of full reserve banking) in Ecological Economics, but this journal is also rather heterodox. And another Sawyer and Fontana (2016) in the leading journal of heterodox economics...  (N.B.: the article in Ecological Economics is probably in part motivated by the fact that the Green Party of England and Wales has incorporated this full-reserve banking proposal in their platform; strangely enough, the former paper doesn't mention this fact, but the latter one does.)

On the Equivalence of Private and Public Money is a 2019 working paper by Brunnermeier and Niepelt that addresses this issue:

  We propose a generic model of money and liquidity.  We provide
  sufficient conditions under which a swap of private (inside) against
  public (outside) money leaves the equilibrium allocation and price
  system unchanged. We apply the results to Central Bank Digital
  Currency,  the “Chicago Plan,”  and the Indian de-monetization
  experiment.

This is their exercise:

  As a result of these developments, debates about the “right” monetary
  architecture resurface and classic questions about the nature of money
  return to the fore.  A key point of contention concerns the optimal
  balance between public and private money. Proponents of a strong
  government role fear that private money creation breeds instability
  and shifts seignorage rents from taxpayers to shareholders.  In the
  “Chicago Plan” of the 1930s and the  recently  rejected  Swiss
  constitutional  referendum  on  “Vollgeld”  (sovereign  money), they
  propose to severely restrict or even ban money creation by anyone
  except the central bank.  Less drastic proposals aim at electronic
  Central Bank Digital Currency (CBDC) for use by non-banks.  Monetary
  authorities in countries such as Canada, Singapore, and Sweden
  currently evaluate the introduction of such “Reserves for All;” the
  Banco Central del Uruguay has successfully tested the model; and other
  central banks consider it.

  Skeptics, on the other hand, warn of severe macroeconomic risks due to
  the replace- ment of private by public means of payment.  In their
  view, a reduction of bank issued inside money will hamper credit
  extension by banks to firms and households, with negative
  implications for growth;  and the introduction of CBDC will provide a
  safe haven asset for depositors to run into at the slightest hint of a
  crisis, rendering bank runs more likely and thereby threatening
  financial stability.

  To assess these arguments, we develop a generic model of money and
  liquidity.  Within this framework, we establish sufficient conditions
  under which it is irrelevant whether the public  or  the  private
  sector  issues  means  of  payment.

They summarize their results as follows:

  We  apply  our  results  to  two  proposals  for  monetary  reform:
  CBDC,  and  the  more drastic “Chicago Plan.”  We find that the
  introduction of CBDC need not change macroeconomic outcomes,
  independently of whether deposits are subject to bank runs or not.  If
  bank runs are a feature of the current system then the equivalent
  monetary regime with CBDC has state contingent transfers from the
  private sector to the central bank; if it does not, for instance due
  to a generous deposit insurance scheme, then no such transfers are
  needed.

  Contrary to the prevailing view that CBDC would make bank runs more
  likely, our analysis concludes that it might well make them less
  likely.  With pass-through funding, the central bank becomes a large
  depositor that internalizes run externalities, unlike small
  depositors.  This makes the financial system less fragile. Regarding
  the Chicago Plan, we also find that the conditions for equivalence are
  met provided that banks receive appropriate compensation for lost
  seignorage rents, or that the  ownership  structure  of  banks  is
  aligned  with  the  distributions  of  tax  burdens.   An important
  motivation for the “Vollgeld” proposal to outlaw banks from creating
  liquid assets  was  that  banks  should  be  forced  to  relinquish
  these  rents.   This  would  transfer seignorage from bank
  shareholders to taxpayers, undermining wealth-neutrality.

  We also apply our results to the recent Indian de-monetization
  experiment.  We find that it could not have been neutral because
  cash-based transactions at black-market prices could not have been
  replaced by deposit-based transactions.

In contrast, there is a Bank of England working paper that strongly disagrees.The macroeconomics of central bank issued digital currencies by Barrdear and Kumhof (2016):

  We study the macroeconomic consequences of issuing central bank
  digital currency (CBDC) -- a universally accessible and
  interest-bearing central bank liability, implemented via distributed
  ledgers, that competes with bank deposits as medium of exchange.  In a
  DSGE model calibrated to match the pre-crisis United States, we find
  that CBDC issuance of 30% of GDP, against government bonds, could
  permanently raise GDP by as much as 3%, due to reductions in real
  interest rates, distortionary taxes, and monetary transaction costs.
  Countercyclical CBDC price or quantity rules, as a second monetary
  policy instrument, could substantially improve the central bank’s
  ability to stabilise the business cycle.


Payoffs in an infinitely-repeated game with discounting Consider a game with the following payoff matrix:

3,5   0,0   0,0
0,0   5,3   0,0
0,0   0,0   0,0

Suppose the game is played infinitely many times, and both players have discount factor $\delta$.

The players want to create the outcome $4,4$ using only pure strategies. Intuitively, they should alternate between the (3,5) and the (5,3). This will give the row player:

$$(1-\delta)\sum_{t=0}^\infty \delta^{2t}*3 + \delta^{2t+1}*5 = \frac{3+5\delta}{1+\delta} $$

and the column player:
$$(1-\delta)\sum_{t=0}^\infty \delta^{2t}*5 + \delta^{2t+1}*3 = \frac{5+3\delta}{1+\delta} $$

The payoff vector goes to $(4,4)$ when $\delta \to 1$, but otherwise it is not exactly $(4,4)$.

I am looking for references about the following questions: in what conditions is it possible to attain exactly a desired payoff vector with only pure strategies (for an arbitrary game and arbitrary number of players)? And how this payoff vector can be constructed?

I looked at some papers about folk theorems for discounted infinitely-repeated games. The problem is that they usually assume that $\delta\to 1$, which is not always true in practice.

Disclaimer: I only have a slight clue about repeated games and I have virtually no clue about coding (except the compulsory stuff I had to do in grad school).
That being said, consider this stream of consciousness as how I would approach this problem (if I knew how to code). I am not sure this actually works, maybe someone could support this answer with a diagram (that may suggest, it does not work, see comment in the end). As you see, I cannot provide a reference. Take it for what it's worth and please tell me if my idea fails. I am interested. I am mainly replying such that this question gets attention again.

You are interested in a strategy that yields a ($\delta$-weighted) average payoff of 4 for both players in the infinitely repeated game. I construct a finite sequence of strategy profiles for the stage game that yields a payoff arbitrarily close to 4 for both players by alternating between the (3,5) and the (5,3). This sequence can then be repeated infinitely often.

Let $\Pi^i_t$ be player $i$'s ($\delta$-weighted) average payoff of the (to be constructed) sequence at step $t$. Let $NPV_t$ be the net present value of the sequence's payoff of player $i$ at iteration $t$ such that:
$$ \Pi_t^i = NPV_t^i \frac{1-\delta}{1-\delta^{t+1}}$$

Start with $\Pi^1_0 = NPV^1_0 =5$ and $\Pi^2_0=NPV^2_0 =3$ (so start with (5,3) outcome).
At each point in the iteration, we have $\Delta_t = |\Pi^1_t - 4| = |\Pi^2_t -4|$. Then there is some vector $s$ that tracks whether the (3,5) or the (5,3) outcome is played. It is a sequence of $i$s and $j$s indicating who got the 5 payoff. So start with $s_0 = 1$ (so by the later construction $s_1 = (1, 2)$)

Take some $\delta$ as given and take some $\varepsilon >0$.

Now comes the coding part that intuitively should work:

Start with $t=0$.

If $\Delta_t > \varepsilon$, continue.

If $\Pi^i_t > 4$: Set $NPV^i_{t+1} = NPV^i_{t} +3 \delta^t$ and $NPV^j_{t+1} = NPV^j_{t} +5 \delta^t$.
$s_{t+1} = s_t$ with an additional entry $i$.

Once $\Delta_t \leq \varepsilon$, stop and set $T$ equal to the current $t$.
Infinite repetition of this sequence yields of course average payoff $\Pi^i_T$ for all players, because the the (weighted) average is
$$\Pi^i = (1-\delta) NPV^i = (1-\delta) NPV_T \frac{1}{1-\delta^{T+1}}=\Pi^i_T.$$

Note that the payoffs $\Pi_t$ may NOT converge to 4. Since I do not program I did this manually for some iterations for $\delta =0.9$. $\Delta$ jumps around. But the jumps seem to become smaller.
Here I list the payoffs $\Delta$ following this procedure for $t\in \{1,\dots,12\}$. Every time $\Delta$ jumps up is boldface, see that the boldface sequence seems to decrease.

1: 0.05263

2: 0.26199

3: 0.00552

4: 0.15558

5: 0.00995

6: 0.09293

7: 0.00115

8: 0.0692

9: 0.00561

10: 0.04549

11: 0.00023

12: 0.03765

13: 0.00345


Are principal payments on government bonds included in normal budget spending? If I look, for example, at the UK government's Autumn 2017 budget document the spending piechart has a wedge which says 'net debt interest £41bn'. I notice the wording says 'interest' and not something like 'net debt servicing' or 'interest and principal'.  The government has to repay the principal amount of the bond when it matures.  My question is are these prinicpal payment included in normal budget spending?  If not where are they accounted for in government finances?  Thank you.

Normally, they aren’t, but there are exceptions. For example. The Japanese Ministry of Finance includes principal repayments in its definition of “debt service” - see budget summary for 2014, page 4.

(The inclusion of principal payments in debt service seems to be a convention for external debt: how much foreign currency debt that needs to be paid back can matter for credit risk. Floating currency sovereigns like the U.K. have negligible credit risk for their local currency borrowings.)

The reason why it is ignored principal repayment is a fairly meaningless number in government finance. If the government issues \$1 billion in short-term debt, in one year it will have:

(roughly) \$12 billion in principal repayments if it rolls over 1-month bills,
(roughly) \$4 billion in principal repayments if it rolls over 3-month bills, or
(roughly) \$1 billion in principal repayment if it issues a 12-month bill.
(Numbers are approximations since Treasury bills are discount instruments.)

The same principle works for longer-dated debt: you have twice the principal repayments if you roll over a 5-year bond instead of issuing a 10-year bond.

So all principal payments tell us about is the debt management strategy of the government.

If you want to know how government debt is out there, you just look at the debt stock.


Why is it called Bayesian Persuasion? I'm sure most of you are familiar with the paper titled "Bayesian Persuasion" by Kamenica and Gentzkow (2011), (you can find it here).

I was wondering what are your thoughts as to why it is called "Bayesian"? I understand that the receiver creates posteriors using Bayes rule, but this is true for any standard model of incomplete information.

A related question is what is the one minute explanation you would give to describe this framework to people who are not familiar with it? Say people who know the cheap-talk literature fairly well, for example.

I've tried a couple of things, with mixed results. I'm happy to discuss my ideas, but I would appreciate hearing some fresh perspective first.  Thank you.

In a standard cheap-talk setting, a sender (S) has better information on a state of the world and wants to communicate this information to a receiver (R) who then takes an action. However, S and R prefer different actions conditional on the state.
Importantly, S is free to send any message independent of what she knows. That is, she cannot commit to a signal given the true state. This is a complicated strategic setting, and communication may fully break down - in a babbling equilibrium any message is ignored, and R decides based on his prior.
Even equilibria with information transmission can be bad for S.

KG give S more commitment power in the sense that she can design a signal structure and the signal realization is truthfully communicated to R. This commitment assumption helps to see this as a sort of mechanism design problem. Instead of setting rules on transfer payments, the designer manipulates the informational environment. In this problem of information design, the commitment to the signal structure determines the meaning of a signal such that "babbling equilibria" are circumvented.

Can R be "persuaded" to do something S wants when R is a rational Bayesian and well aware that S wants to manipulate him? Perhaps surprisingly, the scope for manipulation is quite large. KG show how to optimally do this. The prosecutor-judge example illustrates the paper very nicely.
Essentially, KG interpret the signal structure as a distribution over posteriors and a signal realization then determines R's posterior. Can S just use any such distribution? No, S is restricted to a Bayes plausible distribution, the expected posterior must be equal to the prior! I guess that is why the "Bayesian" is important. Of course, R being a Bayesian is not a unique feature and quite standard, but in the same sense this word shows up all the time when analyzing standard settings, think of BNE, PBE, BCE...

However, I don't like the term persuasion so much. When I think of persuasion, misrepresenting and concealing information is part of it. This is explicitly excluded in KG! Depending on the application, I find the assumption on S's commitment power quite a stretch for a persuasion. If you just consider the theory without a persuasion application in mind, a smooth way around a discussion about commitment power is to consider a setting with symmetric information: S doesn't know the state herself but designs the information structure.